GEOVISUALIZING TERROR: THE GEOGRAPHY OF TERRORISM THREAT IN THE UNITED STATES

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Doctoral Degree of Philosophy in the Graduate

School of The Ohio State University

By

Jason Eugene VanHorn, M.S.

* * * * *

The Ohio State University 2007

Dissertation Committee:

Professor Mei-Po Kwan, Adviser Approved by: Professor Edward J. Malecki

Professor Ningchuan Xiao ______Professor Ola Ahlqvist Adviser Graduate Program in Geography

Copyright by

Jason Eugene VanHorn

2007

ABSTRACT

Terrorism is a world-wide multi-dimensional problem that appears at many scales.

Many aspects of terrorism have been studied extensively; however, the geographic and

spatial component of terrorism has received little attention by researchers. The aims of

this research are to evaluate definitions of terrorism and demonstrate how geographic scale within the definition of terrorism provides a clearer distinction between terrorism from other forms of violence, conduct a vulnerability analysis through the methods

presented by the hazard research paradigm, survey the general population and create a

perceived risk profile, and evaluate the effectiveness of the geographic scale at which

the Homeland Security Advisory System operates. The results demonstrate ways in

which geovisualization of terrorism using Geographic Information Systems (GIS) can

be achieved for both anti-terrorism and counter-terrorism activities. Realized (real)

and perception-based approaches are taken to understanding vulnerability and risk

issues in a regional study of Columbus, Ohio through geographic visualization.

ii

Dedicated to my Lord and Savior, Jesus Christ and to the love of my earthly life, Kellie

iii ACKNOWLEDGMENTS

I wish to thank my adviser, Mei-Po Kwan, for her stimulating intellectual challenges, advice and direction of my research, training to become a professional geographer, and most of all for her friendship. Without you, this would not have been accomplished. You are one of the best geographers I know and an inspiration to me.

I am grateful to my committee members, Dr. Ed Malecki, Dr. Ningchuan Xiao, and Dr. Ola Ahlqvist, whose incredible contribution to this work cannot be understated. Thank you for your excellent coursework and questions that helped form my thoughts, learning, and understanding. I will be forever grateful and thankful to pass on the knowledge I have learned from you. Thank you for the time you took to get to know me and your willingness to have an open door policy to be peppered with questions. I think well of all of you and hold you in high esteem. And in addition, I would like to thank Dr. Herbert Weisberg for his insightful questions in the PhD candidacy oral exam and for his instruction and excitement about surveys, which was very helpful to the completion of this work.

In addition, I would like to thank the Department of Geography at The Ohio

State University. The men and women whose classes I was privileged to partake gave me great inspiration. I am thankful to the chair, Dr. Morton O’Kelly, for his continuous support of this work and my education. I want to thank specifically Jim iv DeGrand and Jens Blegvad for all of your help with technical challenges and computer

hardware and software assistance. And I am so happy to thank from the bottom of my

heart, Diane Carducci, Linda Bryant, and Zanetta (Lynn) Lyons. NONE of this would

have been possible without you wonderful ladies. Your organization, support,

encouragement, excitement, and friendship to me have been such a tremendous part of my graduate education at OSU that I will be forever in your debt. You have taken care of me so well and will not be forgotten.

Furthermore, my devoted and encouraging classmates have made this work

possible. I have been intellectually stimulated by my classmates to do this work, to

strive to do excellent work always, and to do so with a smile, knowing what a privilege it is to be here at OSU Geography and to be in academics in general.

Specifically, I wish to thank Michael Niedzielski, Michael Ewers, Joseph Lewis,

Guoxiang Ding, Vyaskumar Krishnamurthy, Eric Boschmann , and David Wheeler.

Thank you so much for your friendship and steady encouragement to complete this work and to keep pushing forward into new boundaries of thought. You are my band of brothers.

This research has been supported in part by a grant from The Mershon Center

for International Security Studies. I would like to thank the Center for their

unwavering support of my research and continual encouragement of the value of this

work. Specifically, I would like to thank Linda Montano, Cathy Becker, and Dr. v Richard Herrmann for your attention to detail and tremendous helpfulness to

accomplish the goals.

I would like to acknowledge specific family members who have made this work possible. I would like to acknowledge my mother and father, Lee and Marsha,

who have provided not only a means for my education, but have felt an acute burden

to make it possible. I have learned so much from you in all aspects of life that I could

not possible tell you how much I am thankful to you. God has blessed me to have the

most supporting parents; I know none who have been so blessed as me. Thank you for

your guidance, love, prayers, and encouragement to make this mark in my life. I

would like to acknowledge my Aunt Karen and Uncle Jimmy Taylor for you both do

not know what an inspiration you are to me to pursue excellence in my research and

live life to the hilt. I would like to thank my Uncle Chuck for taking care of so many

logistics of life in graduate school that have made it possible to function here in

Columbus with owning a home. To my mother and father-in-law, Gary and Denise, I

give thanks for you have made this work possible in so many ways. Your words of

encouragement, prayers, support, insight to my research, and love have given me the

tools I have needed to keep going. Your help on the house, hospitality during visits,

inquiry of life, and pursuit of the path laid before you are all an inspiration to me and

is in part how this research came to be. Also, I acknowledge Matt Parker, my brother-

in-law, who helped me refine my ideas and encouraged me by reading portions of my vi work and commenting on this research to make it better. Finally, I acknowledge my

one-year-old son, Isaiah. You are a great inspiration to me to finish and to do well to provide for you. This has been a great year of joy getting to know you and I thank God for you in my life.

Of all people I am indebted to the most for this work, it is my wife Kellie. You

are the smartest person I know and my dearest friend. Your soft words to me and

willingness to help me through this challenging time in life have left a mark on my

soul so deep that it could never be spoken. The greatness of your humility and

kindness is truly like no other. Because of you, I have learned to strive for excellence; yet, I still have so much to learn from you. You are a refining fire, indeed like iron that

sharpens iron, and you make me a better man, a better father, a better teacher, and a

better researcher. Thank you for editing this entire work and for making suggestions

that provoked my thoughts. Thank you for making me smile at my mistakes and for

laughing with me instead of at me. Thank you for taking care of me and for carrying

such a tremendous burden while we have been in school. Thank you for working so

hard to make this pursuit possible. Thank you for being my best friend, for being for

me and never ever, not once against me, and for praying for me. I love you with all that I am and you have been so patient and kind to accept me as imperfect as I am. I could only hope that others find what we have for I am so thankful to God for you.

vii Finally, I acknowledge that this work is possible because of an inspiration from

God. The more I have put myself in the hands of God, the more I have found peace

and guidance. Believing with all my heart that He exists and that He is God, I asked

Jesus Christ into my heart at the age of nine, to come into my life and forgive me of my sins. He did and I experienced a change in my heart. Since then I have tried to give over all of myself to Him and acknowledge Him in all ways. He is the foremost inspiration to complete this research, to teach, and to learn and always has been so.

viii VITA

September 24, 1973……………………. Born – Fort Wayne, Indiana U.S.A

1996……………………………………. B.A. Geography and Political Science, Indiana University (Bloomington)

2003……………………………………. M.S. Geography, Texas A&M University

2003 – present.…………………………. Graduate Teaching and Research Associate, The Ohio State University

FIELDS OF STUDY

Major Field: Geography

ix

TABLE OF CONTENTS

Page Abstract…………………………………………………………………………... ii Dedication………………………………………………………………………... iii Acknowledgments………………………………………………………………. iv Vita……………………………………………………………………………….. ix List of Tables…………………………………………………………………….. xiii List of Figures……………………………………………………………………. xviii

Chapters:

Preface…………………………………………………………………………… 1

1. Introduction………………………………………………………………. 4 1.1 Terrorism…………………………………………………………….. 4 1.2 State of the Art of Geographic Contributions to Terrorism Research.. 10 1.3 Goals and Questions…………………………………………………. 17

2. Literature Review………………………………………………………... 21 2.1 Defining Terrorism…………………………………………………... 21 2.1.1 Introduction………………………………………………… 21 2.1.2 Definitional Disputes………………………………….…… 25 2.1.3 Geographic Scale and Terrorism…………………………... 34 2.2 Hazards, Vulnerability, and Risk…………………………………….. 44 2.2.1 Introduction………………………………………………… 44 2.2.2 Risk Assessment Paradigm………………………………… 46 2.2.3 Geographic Hazards Research….………………………….. 48 2.2.4 Geographic Hazards Research vs. Risk Assessment Paradigm…………………………………………………… 50 2.2.5 Terrorism as Hazards?……………………………………... 53 2.3 Geovisualization……………………………………………………... 58 2.3.1 Introduction………………………………………………… 58 2.3.2 Scientific vs. Information Visualization in Geography….… 61 2.3.3 Primary Geovisualization Techniques.…………………….. 64

x 2.4 Conceptual Framework for Visualizing Terrorism as a Hazard Through Geovisualization……………………………………………. 75

3. Methodology……………………………………………………………... 79 3.1 Hazards Modeling……………………………………………….…… 79 3.1.1 Introduction………………………………………………… 79 3.1.2 Strategic Threat Operation Program (STOP)………………. 81 3.1.3 STOP Steps………………….……………………………... 86 3.1.3.1 STEP 1…………………………………………… 86 3.1.3.2 STEP 2…………………………………………… 121 3.1.3.3 STEP 3…………………………………………… 127 3.2 Survey Methods……………………………………………………… 129 3.2.1 Introduction………………………………………………… 129 3.2.2 Survey Implementation and Design………………….…….. 129 3.2.3 Survey Sampling Scheme………………………………….. 131

4. Real Threat Analysis……………………………………………………... 137 4.1 Introduction…………………………………………………………... 137 4.2 STOP Math Scoring………………………………………………….. 138 4.3 Comparative VTI Scores…………………………………….………. 143 4.4 Model Validation…………………………………………………….. 145

5. Perceived Threat Analysis……………….………………………………. 155 5.1 Introduction…………………………………………………………... 155 5.2 Response Rates and Imputation……………………………………… 156 5.3 Columbus Perceived Terrorism Threat Profile………………………. 163

6. Geovisualizing Fear: An Illustration…………………………………….. 185 6.1 Introduction………………………………………………………….. 185 6.2 Visualizing Fear……………………………………………………… 186

7. Comparisons Between Spatial Physical and Spatial Psychological Threats From terrorism….……….…………………………………….… 195 7.1 Introduction…………………………………………………………... 195 7.2 Comparison of Actual and Perceived Threat……………………….... 196

8. Perceptions of the Homeland Security Advisory System.……………….. 205 8.1 Introduction…………………………………………………………... 205 8.2 Communication of Terrorism Alert Levels………………………….. 208 xi 9. Conclusions……………………………………………………………… 235 9.1 Introduction…………………………………………………………... 235 9.2 STOP Limitations……………………………………………………. 238 9.3 Spatial Physical and Spatial Psychological Approaches ……………. 241 9.4 Future Work………………………………………………………….. 243

Appendix A: Terrorism Definitions In United States Code……………………... 247

Appendix B: Cleaning the TKB Spanish Terrorism Incidents Data and Determining Incidents As Urban or Non-Urban…………………... 252

Appendix C: Critical Infrastructure and Parcel List for the STOP Model in Columbus, Ohio……………….…………………………………… 258

Appendix D: Columbus Terrorism Survey………………………………………. 269

Appendix E: Columbus Terrorism Survey Statistical Results…………………… 286

Bibliography……………………………………………………………………... 317

xii

LIST OF TABLES

Table Page 2.1 Explosion of terrorism research before and after September 2001 in peer-reviewed journals found through Academic Search Premier……………………………………………………………….. 23

2.2 Explosion of terrorism stories before and after September 2001 in the New York Times……………………………………………………... 23

2.3 Key elements from 109 different definitions of the term terrorism. After Schmid and Jongman (1984)…………………………………... 30

2.4 The characterization of the type of violence through the use of geographic scale in a definition of terrorism…………………………. 37

2.5 Five initially emergent themes from hazards research. After Cutter (2001)………………………………………………………………… 50

2.6 Comparison between risk assessment discipline and geographic hazards discipline. After Cutter (2001)………………………………. 52

3.1 Critical Infrastructure as identified by the United States government. After Office of the President (2003)……………..…………………… 87

3.2 Number of critical infrastructures in the U.S. National Asset Database. After Office of the Inspector General (2006).…………….. 89

3.3 Country-specific terrorism incidents missing the ‘City’ field in the TKB database, 1968-2006. Calculated from MIPT (2006)…………... 96

3.4 Urban and non-urban terrorism in the USA by target type, 1968- 2006. Calculated from MIPT (2006)…………………………………. 101

3.5 Urban and non-urban terrorism in Spain by target, 1968-2006. Calculated from MIPT (2006)…….………………………………….. 105

xiii 3.6 Percentage of terrorism incidents by type of target, U.S. & Spain. Calculated from MIPT (2006).……………………………………….. 106

3.7 Sensitivity analysis between U.S. and Spanish terrorism. Calculated from MIPT (2006)……………………………………………………. 108

3.8 U.S. and Spain terrorism magnitudes (sum of deaths and injures) from 1968-2006. Calculated from MIPT (2006)……………………... 111

3.9 General demographics of Columbus, Ohio. United States Census Bureau (2005).………………………………………………………... 113

3.10 Symbolic database sources for Columbus, Ohio places.……………... 125

3.11 Comparison of responses between postal mail and Internet surveys. After Evans and Mathur (2005)………………………………………. 130

4.1 Top indexed STOP VTI calculations in Columbus, Ohio……………. 140

4.2 Comparative threatened places based on STOP VTI calculations across the United States………………………………………………. 144

4.3 Validation results for the STOP model………………………………. 147

4.4 Actual terrorism weighted targets for four cities in the United States…………………………………………………………………. 151

5.1 Counts for CTS questions prior to Hot-Deck imputation…………….. 159

5.2 Comparison of survey respondent characteristics and general population characteristics. General population data from United States Census Bureau (2005)…………………………………………. 163

5.3 Questions used to gauge the perceptions of Columbus residents to terrorism vulnerability………………………………………………... 164

6.1 Testing for correlations between fear of terrorism at the airport and level of education…………………………………………………….. 192

xiv 7.1 Comparison of actual targets and perception of terrorism targets……. 199

C.1 Critical Infrastructure target codes…………………………………… 259

C.2 STOP parcel coding designation……………………………………... 260

D.1 The Columbus Terrorism Survey…………………………………….. 271

E.1 Do you consider terrorism to be an important issue today?………….. 287

E.2 Was terrorism an important issue to you before the events of September 11, 2001?…………………………………………………. 287

E.3 In your opinion, what is the likelihood that terrorists will attack the United States in the next year?……………………………………….. 288

E.4 Thinking about terrorism, what is the likelihood that a terrorist will attack the city of Columbus, Ohio in the future?…………………….. 288

E.5 The issue of terrorism in Columbus is something I talk about with my friends:……………………………………………………………. 289

E.6 What is the likelihood that a terrorist cell is operating in the Columbus, Ohio area?………………………………………………... 289

E.7 If terrorists attacked, which of the following do you feel are the most likely targets of terrorist interest in Columbus?……………………… 290

E.8 Are you personally concerned that a terrorist will attack somewhere in Columbus, Ohio in the next year?…………………………………. 291

E.9 How about within the next 5 years, will there be a terrorist attack in Columbus, Ohio?……………………………………………………... 291

E.10 Are you apprehensive about going to any of the following places because of potential terrorism:…………………………………….…. 292

E.11 Do you believe terrorism is more likely to happen anywhere in the United States or only in specific geographic locations?……………… 293 xv E.12 Do you think terrorism is more likely to occur in the United States:… 294

E.13 Rank the cities below for their likelihood of terrorist attack (in the next couple of years):………………………………………………… 294

E.14 Rank the following Ohio cities in their likelihood of terrorist attacks:………………………………………………………………... 297

E.15 Rank the level of responsibility you expect from the government/law enforcement to protect you from terrorism in Columbus, Ohio……… 299

E.16 For whatever reason, is the government/law enforcement protecting you adequately from terrorism in Columbus, Ohio?…………………. 300

E.17 Have you ever seen the Homeland Security Advisory System that looks like this?………………………………………………………... 301

E.18 In your opinion, is the Homeland Security Advisory System helpful to you?………………………………………………………... 302

E.19 In your opinion, the purpose of the Homeland Security Advisory System is to:………………………………………………………….. 303

E.20 How effective is the Homeland Security Advisory System to notify you of a terrorist threat to the United States?………………………… 304

E.21 How effective is the Homeland Security Advisory System to notify you of a terrorist threat to Columbus, Ohio?…………………………. 304

E.22 Would you avoid any of the following locations because of the threat level rising to High Risk (Level Orange) or Severe Risk (Level Red) on the Homeland Advisory Security System?………………………... 305

E.23 In your opinion, are you reasonably aware of what each color means?………………………………………………………………... 307

E.24 Have you done any of the recommended activities that are associated with the Homeland Security Advisory System color scheme?………. 307

xvi E.25 In your opinion, when the national threat level has changed to level High Risk (Level Orange) did it impact you enough so that you did some of the recommended activities suggested by the government?… 308

E.26 Now knowing details about the 2 highest risk colors, will you now conduct some of the recommended activities?……………………….. 309

E.27 Now knowing details about the 2 highest risk colors, would you avoid any of the following locations because of the national threat level rising to High Risk or Severe Risk on the Homeland Advisory Security System?……………………………………………………... 310

E.28 Knowing the details about the 2 highest risk colors, how effective is the Homeland Security Advisory System to notify you of a terrorist threat to the United States?…………………………………………… 311

E.29 Knowing the details about the 2 highest risk colors, how effective is the Homeland Security Advisory System to notify you of a terrorist threat in Columbus, Ohio?……………………………………………. 312

E.30 Have you ever visited the site http://www.ready.gov/?………………. 313

E.31 Have you ever made a terrorism disaster kit in case of a terrorist attack?..……………………………………………………………….. 314

E.32 Do you still have the kit and is it current with supplies?…………….. 315

E.33 Have you ever discussed a plan of action in the event of a terrorist attack with your immediate family?………………………………….. 315

E.34 After you have taken this survey, will you discuss a plan of action with your immediate family in the event of a terrorist attack?………. 316

xvii

LIST OF FIGURES

Figures Page 2.1 Parallel coordinate plot results with Pearson correlation…………... 68

2.2 A visual representation of the conceptual framework for the geographic visualization of terrorism………………………………. 77

3.1 Top ten terrorism incident countries, 1968-2006. Calculated from MIPT (2006)………………………………………………………... 102

3.2 Map of Columbus, Ohio within Franklin County. By author……… 113

3.3 Association of frequencies of terrorism incidents in the United States and Spain with tax auditor codes in Franklin County, Ohio… 115

3.4 Potential terrorism targets by frequency in Columbus, Ohio. By author.………………………………………………………………. 116

3.5 Frequency and magnitude standardization. Calculated from MIPT (2006)………………………………………………………………. 119

3.6 Weighted potential terrorism targets in Columbus, Ohio. By author.………………………………………………………………. 120

3.7 Three-dimensional geovisualization of downtown Columbus, Ohio. By author…………………………………………………………… 126

3.8 Vulnerability Threat Index of Columbus, Ohio. By author………... 128

3.9 Postcard mailed to potential participants…………………………... 135

4.1 STOP model weights compared with four U.S. cities……………… 148

5.1 Household population density of Columbus, Ohio by zip code area. By author…………………………………………………………… 165

xviii 5.2 Do you consider terrorism to be an important issue today?………... 166

5.3 Was terrorism an important issue to you before the events of September 11, 2001?……………………………………………….. 167

5.4 Proportional change in the importance of terrorism pre 9/11 and five years later. By author………………………………………….. 168

5.5 In your opinion, what is the likelihood that terrorists will attack the United States in the next year?……………………………………... 169

5.6 Thinking about terrorism, what is the likelihood that a terrorist will attack the city of Columbus, Ohio in the future?…………………... 170

5.7 The issue of terrorism in Columbus is something I talk about with my friends:………………………………………………………….. 171

5.8 What is the likelihood that a terrorist cell is operating in the Columbus, Ohio area?……………………………………………… 172

5.9 Likelihood of a terrorist cell operating in Columbus, Ohio. By author……………………………………………………………….. 173

5.10 If terrorists attacked, which of the following do you feel are the most likely targets of terrorist interest in Columbus?……………… 174

5.11 Perceived likelihood of terrorism targets in Columbus, Ohio. By author……………………………………………………………….. 175

5.12 Are you personally concerned that a terrorist will attack somewhere in Columbus, Ohio in the next year?…………………... 176

5.13 How about within the next 5 years, will there be a terrorist attack in Columbus, Ohio?…………………………………………………… 177

5.14 Concern about a terrorist attack within the next five years. By author………………………………………………………………. 178

xix 5.15 Do you believe terrorism is more likely to happen anywhere in the United States or only in specific geographic locations?…………… 179

5.16 Do you think terrorism is more likely to occur in the United States:………………………………………………………………. 180

5.17 Rank the cities below for their likelihood of terrorist attack (in the next couple of years):………………………………………………. 181

5.18 Perceived likelihood of terrorist attack throughout the United States. By author…………………………………………………… 183

5.19 Rank the following Ohio cities in their likelihood of terrorist attacks:……………………………………………………………… 182

6.1 Fear of terrorism in Columbus, Ohio………………………………. 188

6.2 Animated geovisualization of fear in Columbus, Ohio. By author… 191

7.1 Rank the level of responsibility you expect from the government/law enforcement to protect you from terrorism in Columbus, Ohio……………………………………………………. 201

7.2 For whatever reason, is the government/law enforcement protecting you adequately from terrorism in Columbus, Ohio?……………………………………………………………….. 203

8.1 The Homeland Security Advisory System…………………………. 206

8.2 Citizen guidance on the HSAS. Ready.gov (2004)………………… 207

8.3 Have you ever seen the Homeland Security Advisory System that looks like this?……………………………………………………… 210

8.4 In your opinion, is the Homeland Security Advisory System helpful to you?……………………………………………………… 211

8.5 In your opinion, the purpose of the Homeland Security Advisory System is to:………………………………………………………... 212 xx 8.6 How effective is the Homeland Security Advisory System to notify you of a terrorist threat to the United States?………………………. 213

8.7 How effective is the Homeland Security Advisory System to notify you of a terrorist threat to Columbus, Ohio?……………………….. 214

8.8 Would you avoid any of the following locations because of the threat level rising to High Risk (Level Orange) or Severe Risk (Level Red) on the Homeland Advisory Security System?………... 215

8.9 In your opinion, are you reasonably aware of what each color means?……………………………………………………………… 217

8.10 Have you done any of the recommended activities that are associated with the Homeland Security Advisory System color scheme?…………………………………………………………….. 218

8.11 In your opinion, when the national threat level has changed to level High Risk (Level Orange) did it impact you enough so that you did some of the recommended activities suggested by the government?………………………………………………………... 219

8.12 Now knowing details about the 2 highest risk colors, will you now conduct some of the recommended activities?……………………... 220

8.13 Now knowing details about the 2 highest risk colors, would you avoid any of the following locations because of the national threat level rising to High Risk or Severe Risk on the Homeland Advisory Security System?…………………………………………………… 221

8.14 Knowing the details about the 2 highest risk colors, how effective is the Homeland Security Advisory System to notify you of a terrorist threat to the United States?………………………………... 222

8.15 Knowing the details about the 2 highest risk colors, how effective is the Homeland Security Advisory System to notify you of a terrorist threat in Columbus, Ohio?………………………………… 223

8.16 Have you ever visited the site http://www.ready.gov/?……………. 224 xxi 8.17 Have you ever made a terrorism disaster kit in case of a terrorist attack?..……………………………………………………………... 225

8.18 Do you still have the kit and is it current with supplies?…………... 226

8.19 Have you ever discussed a plan of action in the event of a terrorist attack with your immediate family?………………………………... 227

8.20 After you have taken this survey, will you discuss a plan of action with your immediate family in the event of a terrorist attack?…….. 228

B.1 Methodological procedure for analysis of urban terrorism………… 257

D.1 Screen capture of Internet-based Columbus Terrorism Survey……. 270

xxii

PREFACE

The geography of terrorism is a story of scale. From the grandest stage set forth across the globe to the personal landscape within the heart of mankind, terrorism finds a foothold to flourish. It penetrates space and time, minds and hearts, even to the very core of our being.

It operates in two dimensions at multiple scales – the physical world and the world of the mind. Terrorism has a very real physical geography and an extraordinary multi-scaled psychological geography. The power of its reality can be felt both by body and by mind. This scalar multi-dimensional effect makes the problem of understanding, even that much greater. The inherent spatial component of terrorism and its permeation across political boundaries makes it ripe for a spatial perspective.

In these pages you will see the debate on the nature of defining terrorism.

Terrorism has taken multiple forms and been an issue since Old Testament days. Basked in the pages of history, countless recall moments of tyranny – the birthplace of terrorism.

Unethical, unrighteous, immoral rulers of old committing atrocities on people and places, so as to hold them in terror and fear throughout times around the world. Fast-forward to

1 eighteenth-century France and the revolution. There, in that place and at that time, the

word “terrorism” was coined. But how has it changed and morphed since then as

‘propaganda by the deed’ to its internationalization and its current dominant form as

being highly religious and characteristically Middle Eastern?1 You will also bear witness to the application of scale to the definition of terrorism, which results quite remarkably in the establishment of a critical element in any good definition of terrorism, being separate and distinguished from other types of conflict and violence. Scale is the key to unraveling the juxtaposition of terrorisms’ definition with spatiality.

What then can be said of the physical geographical dimensions of terrorism? We

will explore the ways in which researchers in the discipline of geography have come to

recognize their unique ability to understand the subject matter and ways understanding

has fallen short. Then a multi-scaled spatial model will be used in the geographic hazards

tradition to present a realized approach to urban vulnerabilities in the United States – a

spatial model of real terrorism targets weighted by their level of susceptibility to terrorist

action. Built from actual terrorism incidents, the model you will see incorporates

frequencies and magnitudes of attacks as well as levels of symbology in distinguishing

between one target and another.

To address the multi-scaled psychological geographic dimension of terrorism, an

investigation into the perceptions of terrorism and vulnerability is explored. A

probability-based survey reveals the results of questions geared to address levels of fear

1 The author sincerely acknowledges the use of the term “Middle East” as a historically dominant Euro- centric term in describing the area of Southwest Asia. The term has been chosen by the author because of its generally ‘popular’ and common usefulness to describe the area and is not to mean any slight to people living in or from this region of the world. 2 from terrorism, expectations from government and police for protection, and

effectiveness of the Homeland Security Advisory System to communicate levels of terror

alert to the United States population. Scale becomes the dominant centerpiece in the

inquiry; communication and fear are analyzed at multiple scales for the purpose of anti-

and counterterrorism efforts, as well as the efficient allocation of resources.

The point of the pursuit is to learn in what manner a spatial perspective of geovisualization can give insight into how to fight and prevent terrorism. Of course this is only the beginning. Other researchers will join the debate and give insight from a

spatial perspective I have failed to address. I welcome their knowledge and wisdom. In

this journey one might start with asking why terrorism happens in the first place. I believe

the answer to the question starts in no small part in the corruptness of the heart. The

fallible and impressionable heart of mankind yearns to be filled with something, whether

evil or good. Mostly it is filled with both, such as the person who is capable of great

goodness and equally capable of great destruction. The final decision always lies with

conviction, the highest level of belief, in the righteousness of the cause. Justification

comes through faith – whether faith in laws of mankind or laws of a higher source. The

degree of corruptness in the law and the manner in which laws fail the test of consistency

is up for debate, legal or theological. In the final analysis, however, violence will always

occur as long as mankind’s heart remains corrupt and this violence will occur in multiple

forms, including terrorism. Thus as terrorism continues, the great need exists to research

this topic for the continuance of well-being and peace to all people.

J.E.V. Columbus

3

CHAPTER 1

INTRODUCTION

“Far and away the best prize that life has to offer is the chance to work hard at work

worth doing.” – President Theodore Roosevelt (Anderson 1990, pg. 7)

1.1 Terrorism

Some events in world history leave such an indelible impact on the human psyche

that everyday life is never the same. The events of September 11, 2001 have left such an

impact. The life-altering results have been felt worldwide. Nations, governments, states, and citizens have changed the way they conduct business, the way they see the world, and the way in which terrorism is thought and fought. Before 9/11, few US citizens gave a second thought to the issues of international terrorism. Surveys conducted by ABC

News and the Washington Post in 1996 and 1997 showed that only 26 percent of

Americans on average were worried greatly about the possibility of a terrorist attack as

4 opposed to 49 percent after 9/112 (Polling Report 2006). Consider the words of Bellow

(2001),

“This morning’s paper [1976] reports that nine men were found dead in an Argentine ditch, blindfolded and shot through the head; the South Moluccans seized a Dutch train and murdered some of the passengers. Scores of people are killed in the streets of Beirut every day; terrorists take hostages in London and explode bombs in Belfast. As an American, I can decide on any given day whether or not I wish to think of these abominations. I need not consider them. I can simply refuse to open the morning paper” (119).

So far removed both by geography and time from most terrorist activities around the

world, US citizens gave little attention to the issue of terrorism. Today, however, the

public discourse on the issues of terrorism are so much in the forefront of United States

public consciousness that even refusing to open the paper is problematic to avoid the

debates and reality of terrorism threat in the United States.

Modern terrorism in the United States developed after World War II. The 1950s

were considerably quiet; however, from the 1960s through the 1980s, attacks consisted mostly of aviation terrorism through highjacking of airplanes and through radical student group bombings, such as threats conducted by the Weather Underground (a.k.a.

Weathermen). During the 1960s terrorism of an international flavor emerged.

Hoffman (1998) establishes the birth of the internationalization of terrorism in

1968. The author points to the events unfolding in July of that year when the Popular

Front for the Liberation of Palestine (PFLP) hijacked the Israeli El Al commercial airliner. As Hoffman argues, this incident had four distinct differences from those of the

2 The question was “How concerned are you about the possibility there will be more major terrorist attacks in this country? Is that something that worries you a great deal, somewhat, not too much, or not at all?" 8/96 – 31% Great Deal | 6/97 – 21% Great Deal | 9/01 - 49%. N=608 American Adults nationwide. MoE ± 4 5 past. First, the incident was done to make a political statement rather than a means to travel to a destination. Past incidents, such as those in North and South America, were often perpetrated for the sole purpose of reaching Cuba (cf. Arey 1972; St. John 1991;

Taillon 2002). Second, the plane was picked for its value as a symbol of Israel. Pillar

(2004) reminds us that incidents tend to have a greater psychological impact in comparison to the physical harm done. The power of the symbol, in this case Israel, allowed a limited-force group to have a voice against an entire state because El Al was the national airline of Israel. As a result of this event, it became more apparent to small terrorist groups that they could be a political force through the use of a symbolic target.

McKinnon (1986) contends that regular warfare is too costly for terrorists, so groups and small countries act to create or export terrorism to engage in their cause for ideas. The low cost associated with this hijacking made it not only highly effective as a psychological symbol, but also laid the groundwork for similar types of terrorist acts through the late 1960s and 1970s. Horgan (2005) describes terrorism targets as being partial chosen simply because of their symbolic nature. Third, the consequences of inaction by the Israeli government forced their hand to deal directly with the terrorists.

Finally, terrorists realized the power of the media by the worldwide coverage given to the incident. The media acted as the “amplification effect,” giving even greater psychological power to the act of terrorism (Combs 1997). The psychological amplification from the media allowed for the extension beyond political boundaries reaching literarily into the minds of citizens in all corners of the earth who gave any consideration to the matter. The

6 decidedly Middle Eastern and religious flair of terrorism developed during this time

period also made its way across the Atlantic to the Untied States.

By the 1990s, new forms of terrorist acts were perpetrated on US soil. One of the

first major international terrorist attacks in the United States occurred on February 26,

1993 at the World Trade Center (WTC) in New York City. In a failed attempt to destroy the twin towers, five people and an 8-month unborn child were killed and many injured from the detonation of a truck full of explosives beneath the WTC (Kleinfield 1993).

Two years later the tragic Oklahoma City bombing on April 19, 1995, where 168 people lost their lives, hit the geographic heartland of the US (Thomas 1997). Initial reports

suspected international terrorists, but through investigation and pursuit, Timothy J.

McVeigh, a United States citizen, was eventually convicted of the terrorist act along with

Terry L. Nichols on charges of conspiracy and Michael J. Fortier on charges of misprison

of felony, which is failure to report knowledge of a felony (Thomas 1998). By the next

year, another domestic terrorism act occurred at the Olympic games in Atlanta, Georgia.

On July 27, 1996, a bomb planted by US citizen Eric Rudolph hit the Centennial Olympic

Park, the location used for outdoor concerts. Two died from the attack and over 100

reported injuries (Gurganus 2003). The atrocities of September 11, 2001 in New York,

Washington DC, and western Pennsylvania mark the final major modern terrorist event in

the United States since 1993.

But what really is terrorism? Who decides what is terrorism and who is a terrorist?

For example, is a group such as the Weathermen really a terrorist organization? What

makes them different from other groups? As a student movement group born out of the

7 Civil Liberties era of the 1960s, why are they considered terrorists and not instead

considered revolutionaries? Is that the same thing? There is much debate on the issues

and these fundamental questions have yet to be fully answered by scholars and the media

alike, although progress has been made in some arenas. As a result of this debate,

definitional disputes have direct ramifications on the focus of this study – spatial insight

by geovisualizing terrorism.

Rich with understanding, literature on terrorism has been developed by academics

for many decades. However, only recently has anti-terrorism and counter-terrorism

literature been developed. Anti-terrorism refers to the activity of developing defensive

measures to lessen the vulnerability of populations and infrastructure (either critical/vital

or stable infrastructure) to terrorist attacks. Counter-terrorism refers to the development

of offensive measures for the prevention of terrorism and for the response to terrorism

should an attack occur. An example of anti-terrorism efforts would be the implementation

of protective non-shattering glass on public transportation vehicles, such as buses. Should

a terrorist bomb a bus, the result of glass fragments exploding outward from the bus would be mitigated, preventing further injury to by-standers. In this example, counter-

terrorism efforts would include the preparedness of bus riders to recognize a terrorist act

and if possible, to prevent a terrorist from attacking.

Scholars have begun the challenging task of answering fundamental questions

about how to prevent the many facets of terrorism, such as chemical, biological,

radiological and nuclear (CBRN) terrorism, and how to thwart terrorist if they do attack

with Weapons of Mass Destruction (WMD) (c.f. Arquilla et al. 1999, Stern 1999, Chyba

8 2002, Pilch 2004, Ellis 2004a, Howard 2004, Salama and Hansell 2005). Researchers have taken different approaches, but the largest gap apparent in the literature is the

paucity of spatial approaches to anti-/counter-terrorism – especially in terms of

geovisualization or scientific visualization. Most research on terrorism tends to be non-

spatial in nature and misses the inherent spatial dependency of terrorism (Mustafa 2005).

Although the geographic hazard research tradition by the academic community has

provided essential knowledge (understanding, prediction, mitigation) for geophysical

extreme events and even some technological disasters (e.g. Chernobyl), it has yet to

provide adequate spatial models and assessments for terrorism. In addition, risk and

vulnerability research focus mainly on the psychological aspects of fear – something that

terrorists seek to instill. Visualization of fear and understanding how issues of place and

space play into that fear is critical in anti-terrorism efforts. However, research to show fear and to visualize the perception of fear from terrorism is few and far between. A spatial perspective in terrorism analyses is rarely present.

One way to think about how to fight and counter terrorism is through the application and development of spatial analysis. The spatial research areas of geographic terrorism can be broken down into four distinct areas – 1) terrorists and their networks, 2) vulnerability and security modeling, 3) emergency response, and 4) damage mitigation.

Each of these classes has a distinct focus and plays a particular role in anti- and counter- terrorism efforts, yet few geographers have applied their expertise in these areas. Spatial analysis can include both quantitative efforts and qualitative methods. Potential research on terrorists and their networks asks questions regarding the spatial dimensions of

9 organizations – how do terrorists organize themselves, conduct business, how do they

move, and where are they? Using spatial analysis for vulnerability and security modeling, potential research avenues could include the geography (human and physical) of people and infrastructure to terrorist attacks. Emergency response research conducted by spatial experts could be used to develop models for enhancing transportation, navigation, and the effectiveness of first responders in an attack. Finally, research into the spatial dimensions of damage mitigation would look at how, in the event of an attack, the damage of property, information, and loss of life can be mitigated by spatial analysis. It is only recently, however, that geographers have begun to address the spatial dimensions of terrorism; thus, a rich field of exploration in anti- and counter-terrorism research awaits

contributions from spatial experts.

1.2 State of the Art of Geographic Contributions to Terrorism Research

Only within the last few years, since 9/11, have geographers begun to engage in

the study of terrorism. Geographers have engaged the subject of war and peace in the past

(Mamadouh 2005); however, there is a clear paucity of work on subject of terrorism by

spatial experts and we are only in the beginning stages of exploring the subject from a

geographic perspective. Although the topic of terrorism has been discussed among

geographers in publication and conferences, little research by geographers is present in

the vast terrorism literature or geographic literature. Mustafa (2005) argues contrary to

this position by stating “the wider geographic community has been quite attentive to the

issue of terrorism” (74). Even Mustafa himself cites only one article before 2001 by

10 Sidaway (1994) to support his statement, which hardly constitutes attentiveness to the subject. In fact, those in the discipline of geography have only begun to use their spatial knowledge to address the issues of terrorism in any abundant way and are only now realizing the massively inherent spatial quality of terrorism and its connection to place and space.

Although it is widely accepted that “terrorism” is a pejorative term, it is only recently that geographers have begun to use their spatial knowledge to bring further understanding to the term terrorism. Mustafa (2005) is the first in the geographic literature to suggest that spatial understanding can add greatly to an adequate and widely accepted definition of terrorism. Defining terrorism with space in mind, he has initiated a pathway that has yet to be explored in any great detail. For example, he suggests scale is important in a definition of terrorism, but does not expand this notion. He also argues persuasively that the geographic hazards tradition is best suited within geography from which to build a conceptual framework to increase knowledge on terrorism because of the confluence of understanding about the interplays of the environment and society.

Terrorism as a geographic hazard was acknowledged earlier in the literature by Montz et al. (2003). Mustafa builds his arguments as a complement to the conceptual framework of

Flint (2003) presented below. Although being the first to suggest these avenues conceptually for development, little of substance has been presented in the literature. No hazards-based model of terrorism has been presented and therefore, will be explored.

The book The Geographical Dimensions of Terrorism (Cutter et al. 2003) brought together many geographers to address ways in which terrorism research can be

11 addressed through the lens of geography. The goal of the publication was to add to the ongoing literature by academic disciplines on the topic of terrorism and to put forth a research agenda to study and understand the relevant issues of the topic from a spatial perspective. The main themes can be summarized into research that looks at terrorism sources, causes, and networks; vulnerability and security modeling; emergency response; and damage mitigation. Not comprehensive by any imagination, it might act as an inspiration for further research efforts on the subject by geographers (Buttenfield 2004,

Cutter et al. 2004). However, it is hardly unnoticed that the book has received substantial critique in the literature as well as at academic conferences (de Blij 2004, Johnston 2004,

Griffith 2004). Indeed, it should come as no surprise that geographers have not in the past wrestled with the notions of terrorism to any large extent when reading this first significant work on the subject of terrorism by spatial experts. One of the main critiques of the text, as identified by the cited authors above, is that there is little on the origins of terrorism and that the term terrorism is not defined adequately.

In a subsequent work, one of the contributing authors to the highly criticized

book, Colin Flint, recognizes four ways that geographers can uniquely contribute to the

area of terrorism research (Flint 2003). He argues that geographers are well positioned to

provide expertise on regional affairs, geopolitics of territory and borders, spatial

organization, and scale. Within these four categories a multitude of research questions

exist which can be examined by spatial professionals as suggested below.

First, geographers are uniquely trained in regions of the world, which allows them

to contribute uniquely to the study of terrorism. During the 1940s, geographers were

12 hired in WWII by the United States and Great Britain to provide knowledge about regions of the world to battle the Axis powers (Smith 2003, Mamadouh 2005). Their unique knowledge and devoted study brought with it important details about peoples and places, making their contributions to the war effort indispensable. Recently, Beck

(2003), an expert in the specific landscape and people of Afghanistan in the Zhawar Kili region, provides an example of the unique knowledge geographers possess to contribute to the study of terrorism.

Geographers are trained, starting as undergraduates to understand the world and its different geographies: physical, social, political, and economic, and how each of these geographies fit in the broader context of relations within regions and the global community. This is the approach by de Blij (2005). In an argument of “Why geography matters”, he explains that to understand subjects like terrorism and conflict a very broad regional geographic comprehension is necessary. The broad knowledge gained in introductory courses of world regional geography will often precipitate interest to study a specific region in more detail; thus, in many geography departments it is common to see regional studies such as Latin America, North Africa, Near East, or even country specific courses such as, China or India. Students of geography are equipped to address issues of place and are taught accordingly. De Blij (2005) concentrates on the Afghanistan region and explains the regional interactions between countries over time to provide a context to global terrorism.

The second way that Flint believes the geographical perspective contributes uniquely to the study of terrorism is through knowledge regarding issues of territory and

13 border relations or more broadly, geopolitics. Terrorism is highly political (Schmid and

Jongman 1984) and is inherently spatial – it has to happen in a place under some

condition. Therefore, to understand terrorism, one has to have a good understanding of

the contexts regarding politics and culture within space. Geographers have this

understanding. Terrorism topics in general can include such things as religion, exclusion

and inclusion (whether in terms of race and/or money) issues, politics, identity and power

(Tuathail 1998, Tuathail 2000, Flint 2003b, Tuathail 2003, Graham 2006, Hannah 2006,

MacPherson et al. 2006). However, when combining these topics with the spatial

dimension, a deeper understanding can be gained, which is what differentiates geographers’ approaches from that of other researchers like Hoffman (1998), Pillar

(2004), Stern (2003), and Scheuer (2004).

Hannah (2006) responds to the call of Flint (2003) to “help uncover the multiple

roots of all contemporary geopolitical acts”(100). Hannah positions his research to

address the issue of legitimacy of torture as a means for ending terrorism. After

presenting the ethical dilemmas associated with torture, he argues that terrorism can be

couched as a biopolitical threat. The term “biopolitics” comes from the French

philosopher Michael Foucault, who coined the term in a lecture on March 17, 1976

(Foucault 2003, 243). Biopolitics refers to the focus on the protection of life through the

power over the human body. This includes various approaches to deal with problems

such as, “infectious disease, chronic poverty (especially in cities), ‘racial’ threat brought

on by immigration, and other forces thought to endanger the health and productivity of

national populations that were at the same time national labor forces” (Hannah 2006,

14 627). The identification of threats to life focused primarily on documentation and statistics, such as disease rates or fertility statistics. It is within this contextual philosophical setting that Hannah argues that terrorism is a threat to the protection of life.

The third way that Flint argues how spatial experts can contribute uniquely to the study of terrorism is through the idea of spatial organization. Geographers study spatial relations and consequently how places, peoples, and networks work together. Whether trying to understand and rethink relationships and networks (Miller 2003, Ettlinger and

Bosco 2004, Flint 2005), attacks on critical infrastructure (Kelmelis and Loomer 2003,

Tiat 2003, Wilbanks 2003, O’Kelly et al. 2006, Murray and Grubesic 2007), emergency response networks (Bruzewicz 2003, Goodchild 2003, Kwan 2003), or in disaster hazard mitigation (Galloway 2003, Wright et al. 2003, Mustafa 2005), geographers are uniquely contributing to the study of terrorism by providing an explanation of complicated notions of space. It is in this complex understanding of space where geography stands to add greatly to the literature.

The final way that Flint identifies the uniqueness of geography’s contribution to the study of terrorism is through geographic scale. Geographic scale at the local, state, regional, national, and global scales are all levels geographers conduct research but unlike other scholars, often the issues of scale are the key focus of research. Through an investigation of the effects local settings have on a sense of place and relating that within a global scale, for instance, is an example of one type of research focus geographers concentrate on. Relating back, the issues of terrorism occur at all levels of geographic scale. In bringing scale to the forefront of scholarly conversations about terrorism,

15 geographers are positioned to add to the growing body of knowledge about terrorism

through a discussion of scale.

One area that Flint (2003) and Mustafa (2005) failed to recognize how

geographers can uniquely contribute to the area of terrorism research in their proposed

conceptual frameworks is cartographic and geovisualization. The use of mapping

terrorism and visual presentation may be one of the primary strengths in which

geography can add significantly to issues of terrorism. Borchert (1987) says it well:

In short, maps and other graphics comprise one of three major modes of communication, together with words and numbers. Because of the distinctive subject matter of geography, the language of maps is the distinctive language of geography” (388).

To what degree geovisualization can add to knowledge of terrorism has yet to be

addressed, organized, or realized.

However, even though geographers are well poised to contribute to terrorism

research through their specific expertise, not all geographers are keen to advance thought on terrorism through peer-reviewed work. Past work surrounding questions of academic norms in published work related to securities issues are hotly contested current discourse.

O’Loughlin (2005) raises several issues in response to Beck (2003), expressing concern about problems of replication, classified research, and violating value neutrality in research dealing with terrorism. Both articles were highly publicized through the premier journal, The Professional Geographer (PG), as were subsequent related articles by Beck

(2005) in response to O’Loughlin (2005) and Shroder (2005) who provided additional

commentary surrounding the issue of replication of classified research and violations of value neutrality. It is an issue that remains unresolved and has serious ramifications for

16 the future research by geographers on the subject. These papers above constitute the

major work by geographers on the issues of terrorism from which much of the following

goals and questions are developed.

1.3 Goals and Questions

This research attempts to address many fundamental questions around the issues

of terrorism from a spatial perspective. First, how does geographic scale allow us to

challenge conceptual definitions of terrorism? Indeed, how does geographic scale play a key role in a clear definition of terrorism – differentiating terrorism from other types of

conflict? Considerations will be examined which test a multitude of different scales and

conflict..

Second, an examination of hazard science in the geographic tradition will be

undertaken to address the question of whether a vulnerability index for terrorism can be

developed at an urban scale to capture the spatial physical aspect of terrorism. Although

Mitchell (2003) and Mustafa (2005) are clear to point out the value of the hazards

approach as a good avenue for modeling terrorism, neither provides such a model. A

quantitative approach will be taken in this section and will build upon the conceptual

framework of hazards and vulnerability science. The approach will incorporate data

associated with infrastructure and people. Although models in the hazards tradition vary

considerably, four areas of assessment can be examined that act as an evaluation measure

for modeling hazards (Cutter 2001). It is from these areas of assessment, in conjunction

with two different environmental hazards models, that a terrorism vulnerability

17 assessment model, named Strategic Threat Operation Program (STOP), is built. The

STOP model provides a terrorism vulnerability index that spatially identifies higher and

lower vulnerability areas in an urban setting and tries to capture the spatial physical

dimension of terrorism, which is the real threat to people and property posed by

terrorism. Geographic reasoning will be discussed in terms of scale, distance, areal space, and ground-truthing.

Finally, the last question addressed by this research is how fear and the perception of fear can be mapped and visualized to capture the spatial psychological aspect of terrorism. The spatial psychological aspect of terrorism refers to the threat posed to the mind from terrorism and is linked to perception. I will be incorporating a perception-based approach through the use of a survey. The goal is the development of a

‘perception of terrorism’ profile from the residents in the United States city of Columbus,

Ohio. The profile will include which locations residents perceive as most vulnerable to terrorist attacks, levels of fear that residents have that their area will be attacked by terrorists, and amount of change residents make to their daily lives when the national threat level increases. One purpose of the survey will be to evaluate the local effectiveness of the Homeland Security Advisory System, which is disseminated at the national geographic scale and seeks to provide the United States residents with a daily threat level to anticipate terrorist attacks. Geographic reasoning will include concepts of relative versus absolute space, spatial proximities, and scale.

Therefore, there are three main goals of this research:

18 1. To define the term terrorism from a geographic perspective and to show

the challenges and ramifications of mapping terrorism.

2. To develop a Strategic Threat Operation Program (STOP) based on the

geographic hazards paradigm to provide a terrorism vulnerability index

for infrastructure in an urban area using GIS.

3. To survey the general population of Columbus, Ohio to develop a

perceived threat profile of the residents.

Building upon literature and by meeting the main goals above, the key questions addressed by this research are as follows:

1. How does the geographic concept of scale change the definition of

terrorism and what impact does that have conceptually?

2. Can a vulnerability index for terrorism be developed at an urban scale?

3. What are the perceptions of Columbus, Ohio residents to terrorism

vulnerabilities in the region?

4. How do we map and visualize fear in a digital environment using GIS

techniques?

5. What are the associations between quantified risk and perceived risk

from the threat of terrorism - does the terrorism vulnerability index

coincide with the perception of fear profiles?

6. Is the national scale at which the Homeland Security Advisory System

operates, which seeks to provide United States residents with a daily

threat level in hopes of anticipating terrorist attacks, an effective scale

19 level for dissemination of threat to the public audience of Columbus,

Ohio?

First, to address the initial goal of this research, the development of terrorism definitions will be examined and then the application of geographic scale to define the phenomena will be shown. Second, the geographic hazards literature will be reviewed to form a foundation for the development of the STOP model. The Methodology and

Analysis section will then follow the literature review. The STOP model methodology will be described and implemented, followed by a section on the survey methodology and the analysis of results from the perception-based portion of this research. The conclusions will review the work conducted in this research and will compare and contrast the realized and perception-based approaches in an attempt to gain a more holistic understanding of modern terrorism threat in the United States of America.

20

CHAPTER 2

LITERATURE REVIEW

“I don’t know, but I have a hunch that what you're gonna find won't be categorized or

easily referenced.” – Special Agent Fox Mulder in the movie The X-Files: Fight the

Future (Ten Thirteen Productions 1998)

2.1 Defining Terrorism

2.1.1 Introduction

The term terrorism has been a hot topic since 9/11 – indeed the word is rampant almost everywhere one travels. The term itself has been used so much that it conjures up a plethora of mental imagery and emotion. Academics in many disciplines have been heavily involved in research that pertains to the subject. Just to get an idea of the explosion of literature on terrorism, one only needs to take a cursory glance at Academic

Search Premier (ASP). Conducting a search on the term terrorism in peer-reviewed

21 academic journals from January 1996 to September 2001 results in 685 different articles

on the subject. However, from September 2001 to May 2007 there were a total of 7591

articles published related to the subject of terrorism. Putting that into perspective, there

were 11 times more articles published in the span of 5 years and 8 months after 9/11 than

in 5 years and 8 months prior3. Table 2.1 details the search results on Academic Search

Premier. Not only has the academic community jumped on the terrorism bandwagon, but a media explosion has also occurred.

Using ASP to search The New York Times newspaper for the word terrorism results in 1489 articles associated with the term from January 1996 to September 2001.

Compare that in Table 2.2 to the 7269 articles published from September 2001 to May

2007 – nearly 5 times more articles.

But what is terrorism? No one knows, yet everyone knows. If you ask someone if a particular event is terrorism, you would get a myriad of answers. Likewise, if you ask who is a terrorist you might run the gamut from a convicted criminal or radical personality to the leader of a sovereign nation, such as a dictator or a prime minister. As a result of these varying views, it would seem that defining terrorism is considerably relative. Incidents of terrorism might be described as a bomb threat or a hijacking. Or it could be the deliberate poisoning of food at a local buffet by a group intent on making a point. Acts might be called terrorism if a group trying to overthrow a government

3 The choice of searching based on 5 years and 8 months is simply the time frame from 9/11 to the present. Also it is indicative of the fact that Academic Search Premier peer-review journals broadly only date back to the early 1990s. Before then, few journals have online archives. By limiting the search back to the mid 1990s ensures, for the most part, that the journals searched after September 2001 were also the same journals searched prior to that date.

22 commits them. Indeed, a kidnapper or an assassin might be called a terrorist. Some would consider extortionists, militant protestors, and crime bosses as terrorists. In fact, one might even call the President of the United States a terrorist4.

Search Criteria Dates* Number published 01/96 to 09/01 (5 yrs 8 mn) 685 09/01 to 05/07 (5 yrs 8 mn) 7591 *The search criteria was for all peer-reviewed journals that mentioned the term “terrorism”

Table 2.1 Explosion of terrorism research before and after September 2001 in peer- reviewed journals found through Academic Search Premier.

Search Criteria Dates* Number published 01/96 to 09/01 (5 yrs 8 mn) 1489 09/01 to 05/07 (5 yrs 8 mn) 7269 *The search criteria was for all articles in the NYT that mentioned the term “terrorism”

Table 2.2 Explosion of terrorism stories before and after September 2001 in the New

York Times.

The truth of the matter is that defining terrorism can be a difficult and complicated thing. Respected terrorism expert Walter Laqueur (1987) claims, “No

4 Cindy Sheehan, mother of a combat solider who died in the war in Iraq – Enduring Freedom, has accused President George W. Bush of numerous things including being the world’s foremost terrorist. She is opposed to the war effort and sees the President as committing illegal war in Iraq. (Young 2005) 23 definition of terrorism can possibly cover all the varieties of terrorism that have appeared

throughout history”(11). Part of the problem is that the term terrorism is tossed around to

mean so many different things that a clear understanding of terrorism is lost. The term is also dependent on perspective. Whittaker (2004) challenges the reader to consider definitions of terrorism from four different perspectives – authoritative (governmental), onlookers of a terrorism event, victims of an incident, and the perpetrators of the act of terrorism. Based on any of the given perspectives, different definitions emerge.

So why is it important to have a clear definition of terrorism? There are three primary reasons. The first one is in regards to the legal implications, whether terrorism is considered to be separate from other forms of violence in any given judicial system.

Pillar (2004) points out that a hard and fast definition of terrorism can be beneficial to the scholar and the lawyer. In the court of law, before an act can be considered terrorism, one must first have a clear definition of the term. If then the act is determined to be terrorism, specific consequences will follow. Correct interpretation of the law is bound by clear definitions.

The second reason to have a clear definition of terrorism is to allow advancement of research on the subject. After all, how are scholars to study the subject if there is no definition of what terrorism is or is not? Coinciding is the issue of database development. Quantitative work on terrorism is dependent on database development of terrorism incidents (Stern 1999). Countless texts have included arrays of catalogued terrorism incidents but comparison of these lists is dependent on a similar definition of terrorism. If researchers are to take seriously the cataloging and referencing of terrorism

24 incidents then a clear working definition of terrorism is essential. Therefore, when

designing and building terrorism incident databases, a clear definition of the term has to

be recognized for inclusion or exclusion of daily acts of violence.

The third reason to have a clear definition is based the financial liabilities

associated with the phenomena. Without a clear definition, insurance agencies, which are

aggressively implementing insurance protocols based on terrorism, are hard-pressed to

know what is or is not a target of terrorism and thus constitutes higher premiums. In the

United States, much of this liability is based on modeling terrorism before it happens because so few examples exist from the past. Having a clear working definition of

terrorism allows insurance companies to know what terrorism is and what targets might

be of greater interests to attacks from terrorism for liability purposes.

2.1.2 Definitional Disputes

Therefore, since we are interested in a clear definition, the logical choice is to

begin with the dictionary. Although many dictionaries exist, the etymological authority

for the English language is widely recognized as the Oxford English Dictionary. It

defines terrorism as:

Terrorism: A system of terror. 1. Government by intimidation as directed and carried out by the party in power in France during the Revolution of 1789-94; the system of the ‘Terror’ (1793-4). 2. gen. A policy intended to strike with terror those against whom it is adopted; the employment of methods of intimidation; the fact of terrorizing or condition of being terrorized (2005).

Does this definition satisfactorily define terrorism clearly? It provides a contextual

historical account of what was considered terrorism in the eighteenth century; however, it

25 is unable to provide a present-day definition of terrorism. True, the second definition

provides a general description of terrorization, but not modern terrorism. Historically, the

French made the term terrorisme popular after the French Revolution under their régime de la terreur (Hoffman 1998). As the definition above says, the term came about as a means for the government to establish itself and squelch any additional revolutions or dissent (Whittaker 2004). Oddly, Hoffman reminds the reader not only that the term had a positive connotation, but also that it was even closely associated with the idea of democracy, opposite of present-day. Indeed, Rapoport (2004) shows how Russian terrorism of the nineteenth century was considered noble and sublime, because it dealt with issues of freedom and fighting the tyranny of oppression. Stern (1999) calls it

“heroic” terrorism (16). In geographic scope, Hoffman and Rapoport clearly point out that the nation-state was the focus of activity (Flint 2005). Although the Oxford definition is vague for present-day terrorism, the two good attributes of this definition are its provision of a historical context and the mention of intimidation, which is important to understanding terrorism.

Another dictionary, Merriam-Webster (2005), provides a slightly better definition.

It defines the word as:

“Terrorism: the systematic use of terror especially as a means of coercion”

The dictionary goes on to give a definition of terror as:

“Terror: violence (as bombing) committed by groups in order to intimidate a population or government into granting their demands”

Is this a clear definition or is it missing something? Is violence a key aspect of terrorism?

Are demand requests required? In fact, this definition also falls short of giving clear

26 comprehension into what is present-day terrorism. However, it does bring us closer to

what we might understand as modern terrorism by introducing the concept of a group.

The United States Code (USC), which is the legal code for the US and contains all

legal definitions, has a variety of different definitions of terrorism. Martin (2007) reports

on a sample of what she was able to find in the USC, as organized in Appendix A. She

explains that there are actually only a few statutory definitions that are repeated

throughout the USC. However, Hoffman (1998) is able to show definitional disparities

among United States government entities that are bound by US Code. He first references

22 USC 2656f, which covers the law code for the Annual Country Reports on Terrorism

(ACRT) put out each year by the government. 22 USC 2656f says terrorism is,

“premeditated, politically motivated violence perpetrated against noncombatant targets by subnational groups or clandestine agents” (Hoffman 1998, p38)

Hoffman then gives the United States Federal Bureau of Investigation (FBI) definition of terrorism as,

“the unlawful use of force or violence against persons or property to intimidate or coerce a Government, the civilian population, or any segment thereof, in furtherance of political or social objectives” (Hoffman 1998, p38).

He then compares this with the United States Department of Defense (DoD) definition of terrorism as,

“the unlawful use of – or threatened use of – force or violence against individuals or property to coerce or intimidate governments or societies, often to achieve political, religious, or ideological objectives” (Hoffman 1998, p38).

When comparing these three definitions it is clear that the priorities and interests of the governmental agencies are the driving factors behind how the definition of terrorism is determined. The DoD and FBI definitions both indicate the unlawfulness of terrorism,

27 while only the DoD definition includes threat as a viable component of terrorism. Also the DoD definition is broader, including the, “political, religious, or ideological objectives”, whereas the FBI definition only mentions political and social objectives.

Looking at the ACRT definition, the focus is on politically motivated violence where the target of the act is against non-combatants. The ACRT would not count attacks or bombings against military combatants as terrorism, unlike the DoD and FBI definitions.

This simple comparison confirms what Whittaker (2004) described as one of four types of perspective approaches that can be taken when defining the term – the authoritative position when definitions are chosen that coincide with their objectives. Given this decidedly different approach to defining terrorism, what are common elements in a generally accepted definition of terrorism?

As Pillar (2004) points out, a reasonable definition of terrorism must include certain key elements. His list includes five such elements: premeditation, political motivation, noncombatants (civilians and unarmed/off duty military personnel), subnational groups or clandestine agents, and threat of terrorism. For Pillar, incidents that have these components mark a contrasted difference with other forms of violence, distinguishing terrorism as a specific type of violence. Like him, others have developed key components in their attempts to define terrorism, such as Stern (1999), who argues that terrorism has only two distinctions, attack on non-combatants and that it is done for dramatic purposes. Crenshaw (1998) believes historical construction is the best approach to explore definition. In contrast, Laqueur (1999) thinks that, “the only characteristic generally agreed upon is that terrorism always involves violence or threat of violence”(6).

28 With a bevy of different opinions on what constitutes a good definition, researchers have conducted analyses on the many definitions of terrorism.

Schmid and Jongman (1984) have compiled many definitions of terrorism through searches in the terrorism literature. They sampled 109 different definitions of the term terrorism and measured the frequency of key concepts. Table 2.3 shows the results of their survey, which includes expert definitions as early as 1936 continuing through to

1981. The authors acknowledge that a majority of the authors of the definitions are

Anglo-American and that Anglo-American authors were the majority of contributors to the development of terrorism literature in the work they surveyed.

From Table 2.3, notice that a majority of the 109 definitions analyzed indicate the key elements of violence or force, the inherent political nature of terrorism, and the fear of terror, followed closely by the focus on threat, and by psychological effects. Thus, the top five most frequent key elements in a definition of terrorism are: violence, politics, fear, threat, and psychological effects. However, since Schmid and Jongman agree that the attempt to precisely define terrorism may not be attainable because of the comprehensiveness of the term terrorism itself (they indicate that the term may have at least 22 distinct elements from their survey), I contend that it is important to build upon the top five key elements they have identified and develop them further. After all, violence may be a key component of terrorism, but it is important to distinguish between types of violence, such as the difference between war and terrorism.

In terms of distinguishing between different types of violence, Mueller (2004) shows how war is primarily conducted by a conventional, large group using disciplined

29 violence, while terrorism features small group disciplined violence. The distinguishing difference for Mueller between the two is the size of the group. Unclear about whether terrorists and guerrillas are the same, Mueller writes that both use hit and run tactics, a form of unconventional warring. Unconventional warfare, sometimes termed

Rank Key Elements Frequency (%) 1 Violence, Force 83.5 2 Political 65 3 Fear, Terror emphasized 51 4 Threat 47 5 (Psychological) effects and (anticipated reactions) 41.5 6 Victim-target differentiation 37.5 7 Purposive, planned systematic, organized action 32 8 Method of combat, strategy, tactic 30.5 9 Extra-normality, in breach of accepted rules, with 30 humanitarian constraints 10 Coercion, extortion, induction of compliance 28 11 Publicity aspect 21.5 12 Arbitrariness, impersonal, random character, 21 indiscriminateness 13 Civilians, non-combatants, non-resisting, neutrals, 17.5 outsiders as victims 14 Intimidation 17 15 Innocence of victims emphasized 15.5 16 Group, movement, organization as perpetrator 14 17 Symbolic aspect, demonstration to others 13.5 18 Incalculability, unpredictability, unexpectedness of 9 occurrence of violence 19 Clandestine, covert nature 9 20 Repetitiveness, serial or campaign character of 7 violence 21 Criminal 6 22 Demands made on third parties 4

Table 2.3 Key elements from 109 different definitions of the term terrorism. After

Schmid and Jongman (1984).

30 asymmetrical warfare, is the type of warfare that attacks opponents at their weak points and avoids an opponent’s strength (Dunlap 1998). An example of this is best illustrated by discussing the antonym to asymmetric warfare – symmetric warfare. World War II featured symmetrical warfare where like forces were pitted against each other in fairly orthodox tactics. Planes bombed battlefields, artillery was used to advance troops, and tanks defeated divisions by overmatching strengths. Strength was pitted against strength.

In opposition, the sniper shows a type of asymmetrical warfare, where one individual is assigned to attack high-ranking officers with a high-power rifle, attempting to avoid a matching of “gun against gun” and instead, in an unorthodox manner, remains hidden throughout the attack. As a result of terrorists using asymmetrical tactics, the fine line between war and terrorism can often be confused – we do not know if we should call an act war or terrorism.

Mueller (2004) points out the confusion between war and terrorism. He writes,

“It is common to define terrorism by its target and to consider only attacks on civilians to be acts of terror. However, this means that strategic bombing of cities or the application of economic sanctions would have to be considered to be terrorism, thus distorting the meaning of the term as it is usually applied. To deal with this problem, some definitions require that terrorism must be carried out by substate actors. But this means that at least one side in almost any civil war – including the one in Vietnam – would have to be considered terrorists, particularly in the early stages. What the Vietnamese Communists were doing to the civilian population in the early and middle 1960s – assassination, ambush, harassment, sabotage, assault – has generally been considered to be war, not terrorism, because it was so sustained and was accomplished by a sizeable, well- organized group” (20).

Although Mueller (2004) argues that the substate actor condition in a clear definition of terrorism is problematic by espousing that in a civil war one side has to be considered

31 terrorists but are never called that, Bruce Hoffman argues to the contrary. Hoffman

(1998) points out:

“Terrorism is often confused or equated with, or treated as synonymous with, guerrilla warfare. This is not entirely surprising, since guerillas often employ the same tactics (assassination, kidnapping, bombings of public gathering-place, hostage-taking, etc.) for the same purposes (to intimidate or coerce, thereby affecting behavior through the arousal of fear) as terrorists” (41).

He continues further that the guerrilla is usually characteristic of a large group of people,

who are armed, function as a military unit against military forces, and usually hold a

geographic territory (which accurately describe Mueller’s Vietnamese Communists). In

contrast, a terrorist operates in secret away from the open, does not usually hold or seize

geographic areas, and circumvents enemy military forces, avoiding them if possible.

Therefore, by making this distinction between the guerrilla and the terrorist, we can

understand why, as Mueller points out, the Vietnamese Communists were not considered

terrorists but actors of war; it is because they held geographic territory. By this account,

Mueller’s problem that a definition of terrorism require the perpetrators to be substate

actors becomes no problem at all. A definition of terrorism can include the notion of

substate actors and is unproblematic in being able to distinguish between violence that is civil war and violence that is terrorism.

The arguments and distinctions put forth by Mueller and Hoffman help in better

understanding what terrorism is and is not. Interestingly, Hoffman’s (1998) definition of

terrorism incorporates the top five key constructs identified by Schmid and Jongman:

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence in the pursuit of political change” (43).

32 The Hoffman definition indicates the idea of deliberate acts done by terrorists, an element included in construct key element number seven in Table 2.3, purposive and organized – that is, terrorism is calculated, organized, and planned. It is not something that is random or unordered. Farrell (1982) reminds the reader of this important point:

“Terrorism is frequently described as mindless, senseless, and irrational violence. However, none of these terms is appropriate. It is not mindless…[and] should be viewed as a means to an end and not an end unto itself” (6).

The distinction of violence conducted by a terrorist as being mindful is particularly

important for discussions on anti/counter-terrorism options. Schmid and Jongman (1984)

offer over twenty different purposes and functions terrorists utilize. Included are the

concepts of terrorizing, seizing political power, maintaining power, disrupting and

discrediting government processes, and desire to impose domination through

intimidation, to name just a few. If chaos is brought to order, at least somewhat, by the

actions and plans of a terrorist, then strategies by security and government can be equally

effective at combating terrorist tactics, since they eliminate randomness to some degree.

Terrorists do play by some rules. They need money. They need weapons. They follow a

chain of command. These things and others are to be exploited for combating the unseen

enemy. These issues can contribute to a clear definition of terrorism.

Therefore, in a pursuit that seeks to clearly define terrorism, it seems one should

include not only the issues of violence, political motivation, fear, threat, and

psychological effects, but also include the idea of terrorism as ordered and not random.

Looking at the definition of a terrorist might be helpful in understanding a definition of

33 terrorism. Hoffman (1998) helps in looking for the definition of a terrorist. He contends that terrorism is:

• “Conducted by an organization with an identifiable chain of command or conspiratorial cell structure (whose members wear no uniform or identifying insignia). • Perpetrated by a sub-national group or non-state entity” (43).

As a result of these characteristics, one can move beyond the simple definition of a terrorist as being someone who conducts terrorism, to the particulars of who a terrorist is.

Terrorists follow a chains-of-command and have demonstrated clear leadership chains.

Stern (2003) shares gripping tale after tale of uses of religious terrorism. In each account she relates through personal interviews, how chains-of-command functioned, how people communicated and interacted and how, in some cases, subjects were held in virtual psychosis by their leaders. In the same manner, Gunarantna (2002) details the terrifying global network of actors in the al Qaeda organization who have operated covertly over a decade. In addition, one only need reflect on the details of September 11, 2001 as outlined in the 9/11 Commission Report (2004), to understand the actions and leadership decisions of Osama bin Ladin’s preparation of the attacks against the United States and how he developed the al Qaeda network into a global organization.

2.1.3 Geographic Scale and Terrorism

Referring to Table 2.3, there is one concept that is missing from the 22 key elements presented by Schmid and Jongman: there is no mention of geography. Of the

109 definitions, the concept of place is never considered, not once – either it is not mentioned because it is thought unimportant, it is implied, or it has been forgotten. I

34 contend, however, that the spatial component that is most integral to a clear definition of

terrorism and must be specified is the issue of geographic scale. For example, if we take the definition of terrorism put forth by Hoffman (1998),

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence in the pursuit of political change (43)”,

the question that follows then is at what geographic scale does this happen? It is neither

apparent nor addressed. Adopting Hoffman’s definition, one could argue that terrorism

might be conducted by a nation trying to invoke fear by violence around the world in

hopes of changing the political system of the world, or it might be a local organized militia trying to change its country. The result is that we know neither who the actors are nor at what geographic scale they act.

But what exactly is scale? It is a term that can mean many different things in many different contexts. For example, small-scale paper printed maps refer to maps that show a vast area, such as the whole earth, on a two-dimensional paper surface (Dent

1999). It seems counter-intuitive to say something is small, when the area actually is quite big. Geographic scale of a region might mean the grouping of states, such as the northwest United States (although may or may not be the same states agreed upon by everyone), or it could mean the geographic extent of an American Indian tribe within one or multiple states. However, geography is not a requirement of scale, especially in usage of concepts like large-scale projects. A large-scale project can refer to work that encompasses a vast amount of people, regardless of geography.

The term scale and the term geographic scale are both elastic concepts that have multiple meanings. Jurisdiction, community, greater city area, and Mid-West are all scale

35 concepts that might have generally agreed upon bounds but by themselves only give vague impressions of geographic extent. In the same way, local, national, and global refer to geographic scale but only have vague notions of what exactly is meant by each. As the

Preface introduced, terrorism operates at many geographic scales. One purpose of this research is to see to what extent geographic scale plays a part in defining terrorism.

Does geographic scale really matter to a clear definition of terrorism? The answer

is yes. Let us consider the question by applying geographic scale into the Hoffman

definition:

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence [insert scale] in the pursuit of political change [insert scale].

By changing the geographic scale we change the meaning of the definition and therefore

make the issue of scale paramount for definitional clarity. Yet geographic scale remains

absent in all definitions of terrorism. The inherent spatiality of terrorism makes it

essential that the definition include spatiality; for how can a spatially dependent subject

have an a-spatial definition and yet be clearly defined? It cannot and therefore, I offer the

possibilities for inclusion of geographic scale into a definition of terrorism below.

At this juncture, it is important, before each of the nine cases are discussed, to

define what is meant by global, national, and local, the three new terms inserted into the

Hoffman definition. Global scale means the entire earth. National scale is refined to the

political nation-state or state sponsor. Finally, local means within a political state and is

typically to be understood to be at the city level, for this research.

The nine possibilities of the use of scale and its application in definitions of

terrorism are presented in Table 2.4. Notice first that the three scales of global, national,

36

1.The deliberate creation and exploitation of fear through violence or the threat of violence ... Locally Nationally Globally

2. for the pursuit of political change ...

Dictatorship, Empire The mob, crimi- Warlord, or To- committing world nal syndicate or/ Locally talitarian recognized acts of and terrorism State war

Empire State of Civil committing world Terrorism Nationally War recognized acts of war

State exercising Empire pursuit to become committing world Terrorism Globally a conquering recognized acts of empire war

Table 2.4 The characterization of the type of violence through the use of geographic scale in a definition of terrorism.

37 and local are present along both the x- and y-axis and each is dependent on its particular part of the Hoffman terrorism definition. Starting with the x-axis, the use of Hoffman’s first portion of his definition of terrorism in the context of geographic scale. Following that, the second part of his definition is present with all available scales along the y-axis.

What results is the conceptual typological framework for different notions of violence.

Table 2.4 differentiates types of violence based on scale. Only in two cases is the type of violence categorically terrorism. The following is a detailed description of the nine possibilities as shown in Table 2.4.

Global to Global, National, & Local

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence globally in the pursuit of political change globally, nationally, and/or locally.

The result is that terrorism is a threat to the entire globe both in terms of violence and in terms of political change at all scales by an empire. For this exercise, empire can be interchanged with political nation-state or state-sponsor (for example the United

States, China, USSR, etc…or any conquering empire – the Roman Empire, the Byzantine

Empire, the Third Reich). Global domination by way of the first part of the definition would require a state-sponsored coordination and military campaign. The only way that a terrorism-related group could gain such a global dominance and be able to threaten the global stage for political change at any and all scales would require such an immense amount of resources and power (particularly geographic space of land and sea to house weapons of destruction from which the world recognizes such power), that they would

38 not be considered a group perpetrating terrorism, but instead they would be an power hungry empire determined to force their way of life on the global community. If this situation arose, it is highly likely the condition would precipitate a response from the global community before such an attack or threat of attack could occur. The difficulty of

clandestinely making this come about in the present age of information and remote

sensing is unlikely, but if somehow it were kept a secret, the immense level of

coordination and resources to acquire these weapons of mass destruction (WMDs) would require a state-sponsor in the present political global climate. The only possible contestation to this line of argument would be to break down the specific types of global destruction weapons (chemical, biological, radiological, nuclear (CBRN) and cyber

(Cy)), yet in the end proves fruitless because although biological agents could be globally introduced, the ability to do so without being a state would be impossible5. Therefore, the

5 The five options for weapons of global destruction commonly are referred to in the literature as CBRN (chemical, biological, radiological, nuclear) and Cy (cyber terrorism). In the case of a global attack just from CRN (no B), the resources necessarily in the present age would require a state-sponsor and therefore would be an empire committing world recognized acts of war. In the case of Cy, the threat does not presently exist or will not exist because of the high level of proprietary hardware and software that controls security systems. In the case of the Internet there is tremendous resiliency and redundancy, which provides global protection from such an attack (O’Kelly et al. 2006). The biological in CBRN is the only other option.

The case of biological terrorism would be the only situation that might render the global actor as a terrorism-related group and not an empire. In the case of biological terrorism, the choice of attack is made from a pathogen of some sort in order to sicken or kill by disease a person(s). Driving around a van in the streets of Tokyo, the terrorism group Aum Shinrikyo attempted to use sarin gas sprayer to distribute the toxic air-borne poison to the general population, but failed. Instead they opted to use a “closed-system” in the subway and killed many and let over 5000 injured (Juergensmeyer 2000). Like Aum Shinrikyo, others have used biological agents against innocent people. The Rajneeshee cult attempted to change local elections in the US state of Oregon by using salmonella poisoning by putting it on food items on a local restaurant salad bar (Pilch 2004). In both of these cases, the scale of attack was local, Tokyo and The Dalles (a small city of 10,000 in Oregon), but if a biological attack were able to be disseminated globally or shown to be able to be disseminated globally, then potentially a terrorism group/person might be able to replace the empire under in Table 4. Theoretically speaking, for this to occur it would require the biological agent to have a sophisticated dissemination system that would allow global dissemination (maybe an airplane) to make the terrorism group actually able to hold the world in suspense, or it would have to be 39 only agent possible of deliberately creating and exploiting fear through violence or the threat of violence globally for the pursuit of political change at all scales is an empire. To indict these empires as being perpetrators of terrorism is incorrect; instead they would be criminal acts of war by nation-states and would be tried on the global stage as such.

National to Local

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence nationally in the pursuit of political change locally.”

In this modification of the Hoffman definition we have a dictatorship, warlord state, or totalitarian group suppressing the local polity. Not exactly the model of terrorism, but more a form of suppression by a national entity (either government or military dictator) to squash rebellion or a substate group. Violence on a national level involves large, organized groups. The real world manifestation of this adaptation of the

Hoffman definition aptly could describe Saddam Hussein’s national violence and killings against the Kurds in northern Iraq. Not an act of terrorism, but an act of dictatorship and by many accounts, an act of genocide (Bennis 2003), punishable on the world stage by a tribunal court.

National to National

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence nationally in the pursuit of political change nationally.”

distributed globally and activated locally or the agent would have to be highly infectious globally (which would mean a low incubation period as opposed to high, where death from initial infection occurs within a couple of days such as with Ebola (Preston 1994), and would have to have no remedy. If this situation were to ever arise, then the empire concept could be replaced with a terrorism group. 40 In this case of the scale application to the Hoffman definition, the violence can be described as civil war. The violent struggles are held at the national scale with the purpose of influencing national political change. Civil war is the fight for the way of life between indigenous populations within a country or territory to gain power or control in the nation-state or territory. Terrorism-associated tactics can be taken by either side in civil war (Stern 1999, Mueller 2004), but the application of geographic scale clarifies the differences between violence considered terrorism and that of civil war. Using terrorism tactics does not constitute labeling violence as terrorism as Hoffman points out with the differences between guerilla warfare and terrorism (1998). As a result in the application of scale to the definition we have violence being described as civil war, not terrorism.

National to Global

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence nationally in the pursuit of political change globally.”

Here the application of scale to a definition of terrorism results not in what would be considered terrorism but instead in the type of violence that would be indicative of an empire or nation state attempting to influence the political global climate through threat or violent action. In essence, the situation above describes a nation that has aspirations of becoming a conquering empire. Some examples of this exist today to a degree, such as the North Korean threat of nuclear weapons on nation-state neighbors and on the United

States. As the state pursues political change globally through violence or threat of violence, their actions become more and more like a conquering empire and less and less like terrorism.

41 Local to Local

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence locally in the pursuit of political change locally.”

Of all the nine cases, this is the one that can be argued effectively both ways as being terrorism or not being terrorism. The local component is certainly something that perpetrators of terrorism want to control (cf. Hamas struggle for local independence through tactics of terrorism). However, I argue that instead of being terrorism, a better terminology should be used to differentiate this scale of violence or threat of violence.

This type of violence can be best described as being mob or crime syndicate in origin.

Local crime bosses or mob bosses use violence or the threat of violence for political change locally.

Local to National, Global

“Terrorism: the deliberate creation and exploitation of fear through violence or the threat of violence locally in the pursuit of political change nationally and globally.”

Finally, the scale changes to the Hoffman definition give us terrorism. In the case above, the type of violence or threat of violence that is locally produced for the purpose of political change on a national or global scale is terrorism. This adaptation results in a description much more closely related to modern-day terrorism. In this passage, the local level is where the fear is invoked through violence or threat of violence with direct ramifications to national politics. Certainly this modified definition covers the terrorism created by Hamas by fear-inducing suicide bombing attacks to change the political situation in the West Bank and Gaza Strip. Changing the second inserted word from

42 ‘nationally’ to ‘globally’ results in a description of al Qaeda activity, and thus the definition fits the events of 9/11 – al Qaeda’s eventual mission to kill Americans anywhere they are found; to destroy the West and repulse it from Arab lands (bin Ladin et al. 1998). The only essential thing missing from Hoffman’s definition is scale.

Geographic scale is the key for a clear definition of terrorism as being distinctly different than other types of violence. Using geographic scale as the essential ‘Rosetta Stone’ unlocks the confusion between types of political violence that help determine whether an incident is something that can be termed terrorism.

Following that train of thought and utilizing the top five key elements of the term terrorism from the 109 definitions surveyed by Schmid and Jongman (1984), the comprehensive definition that will I use for this research is:

Terrorism: planned and calculated use of fear in politically motivated, criminal violence or threat of violence against non-combatants by a sub- national group at a local scale for the purpose of influencing broadly, nationally and globally, for political, religious, social, and/or ideological reasons.

Seeing the effects of scale on the definition of terrorism and now having a clear definition of terrorism, we will next look at the conceptual framework of hazards, which forms the foundation in the development of a model for measuring and visualizing vulnerability. Risk assessment, hazards research, and general vulnerabilities will be explored.

43 2.2 Hazards, Vulnerability, and Risk

2.2.1 Introduction

Laqueur (1996) comments, “the most advanced societies are the most vulnerable”

(24). Agreeing with Laqueur, I believe there are three specific reasons that support this position. First, advanced societies offer the highest profile and most valuable targets as opposed to less advanced places. These societies represent the world’s highest value infrastructures, banking and finance systems, and wealthiest populations. Second, many of the most advanced societies work under the concept of civil liberties where exploitation of infrastructure and population targets within the system is easier. The more freedom of movement, livelihood, and technology use, the greater propensity of risk, thus the greater number of vulnerabilities. Finally, advanced societies are dependent on technology as a way of life. The more dependent the society becomes on these systems, the greater the damage, especially psychologically, will be from a terrorism attack.

Disruption to the system can act as a symbol and promote the idea that attacks from terrorism can come at anytime and anywhere – disruption of the financial markets and banking (9/11 attacks on the World Trade Center), disruption of air transportation system

(thwarted attacks in London using chemical vials as bombs on airlines in August 2006), and the disruption of government leadership (April 2007 bombing of Iraqi Parliament) are vivid examples of how terrorism continues to win out over anti-terrorism measures.

Terrorism wins over freedom and civil liberties as the inconveniences and fear perceived by advanced society grow due to the greater number of attacks and the proportional size of disruptions to normal daily activity.

44 In the title to his book, Matthews (1998) asks, “Can America be defeated?” For he and his colleagues the answer is: maybe. An asymmetrical attack on the United States might be the best way to bring down America. Speaking from a purely militaristic point of view, Matthews and contributing scholars point out that attacking the economic sector, the critical infrastructure, the networks that bind the United States to the global system, and installing fear in the hearts of Americans, terrorists can use unorthodox tactics to penetrate the more vulnerable segments of the American society to defeat it. Referring specifically to the United States, Sloan (1998) points out, “there are unfortunately many vital targets in a complex, technologically interdependent society” (185). One only need to look at the direct economic impacts from the 9/11 atrocities, which amounted to billions of dollars and immediately crippled some parts of the American economy, but more importantly showed the power of destabilization on a much larger scale than was ever seen before. Billions were needed to rebuild and sustain lower Manhattan and the

Pentagon, support the survival of US-based airline corporations, and repair the damage inflicted from loss of critical information. So what are the United States’ areas of vulnerability? Who and what is really at risk? Are Americans really at risk from terrorist attacks? If so, how much of a risk is there?

Risk, Mitchell (2003) says, is “the potential to inflict harm,” while vulnerability refers to “degrees to which risk-affected populations are likely to suffer loss” (17). Risk refers to the probability of a hazardous situation occurring, whereas vulnerability is a function of risk to the population. Thinking about this in terms of terrorism, risk is the probability that a terrorist attack will occur, whereas, vulnerability is the degree to which

45 terrorism will affect the target. For example, if there is a large chance that a certain area that is densely populated will be attacked by terrorism (its risk), the degree to which the area will be affected (its vulnerability) most likely will be high – the area is said to be highly vulnerable. Two academic paradigms have emerged to address the issues surrounding risk and vulnerability – risk assessment and geographic hazards research.

Both traditions come from different origins and have different methods for answering very similar kinds of questions. I explore these different origins and show how they are different. The risk assessment tradition will be considered first, followed by the hazard tradition. Then, a comparison of both will be presented to help understand how they are similar yet different. The methodology section, which follows this literature review, is based on the geographic hazard tradition; therefore, the hazard research paradigm is explored in more detail than its counterpart, the risk paradigm.

2.2.2 Risk Assessment Paradigm

Surveying the literature reveals that the risk assessment paradigm is a fairly recent tradition, less than 40 years old (Anderson 2002). Covello and Mumpower (1985) cite that humans have been conducting risk analysis for around 5000 years and that modern risk analysis can be traced as far back as the ancient Greeks and Romans. Kates and

Kasperson (1983) demonstrate that technological hazards have been of interest since the early portion of the twentieth century. According to Cutter (2001), many researchers attribute Starr (1969) as the beginning of modern quantitative risk analysis. Indeed, one of the primary psychometric researchers involved in risk analysis, Slovic (1992), details

46 that Starr’s paper “set us on a course that my colleagues and I continue to explore today”

(118).

Eleven years after the influential paper by Starr, the founding of the first professional society based on the subject formally institutionalized risk assessment research. Golding (1992) details the development of risk analysis in academia and the creation of the professional Society for Risk Analysis, founded in 1980. Approximately twenty of the major players in risk research came together in the society, as Golding retells, to define “risk as a probabilistic concept and clarifying the confusion between risk and hazard”(35). In addition, the flagship journal, Risk Analysis, accepts submissions from a variety of social, physical, and life science disciplines that focus on various physical and socially hazardous phenomena (Anderson 2002). Many of the articles have focused on technological or industrial risk and less on physical extreme risks (Cutter

2001). The journal dedicated its Volume 3, 2002 issue to address specifically the subject of risk assessment and terrorism. The covered topics reviewed how risk analysis could be used regarding the issue of terrorism. Infrastructure (Haimes and Longstaff 2002), risk communication (Deisler 2002), uncertainty (Kunreuther 2002), and intelligence gathering

(Pate-Cornel 2002) were discussed as key points for risk analysts to enter the debates surrounding terrorism.

The risk research paradigm is based primarily in mathematical probability to calculate both risk and vulnerability. Papers like those referenced above in the risk analysis tradition focus on producing answers that are oriented in likelihood and probabilities, such as the statement: the probability of the chemical being dispersed to the

47 population is 1 chance in 1 million. It is not a research tradition that uses mapping per se in the analysis, even though often statements of risk and vulnerability refer to human or animal populations, which inherently have a geographic location. This is different from the geographic hazards tradition, as we will see, which uses the geographic techniques of mapping in the analysis. The distinctions between the two approaches have been described by Cutter (2001) and will be discussed in detail below. This research uses the geographic hazards tradition for risk and vulnerability analysis because, of the two paradigms that address issues of risk, it is more closely associated with geovisualization.

Therefore, we will now look at how hazards research addresses these same types of issues, but from a different perspective.

2.2.3 Geographic Hazards Research

Geographic hazards research refers to the academic study of natural and societal extreme events, such as flood hazards, blizzards, tsunamis and earthquakes and the calculation of risk and vulnerability (Montz et al. 2003). Mitchell (2003) cites hazards as including geophysical, biological and technological risks to populations. Cutter (2001) defines hazards broadly as “…a threat to people and the things they value” (2). Until recently however, research on environmental hazards has lacked a comprehensive theory

(Hewitt 1997). Montz et al. (2003) have commented that this is due to the applied nature of hazards research historically to solve practical problems - underpinning theory was not a focus. They suggest that some hazards researchers within the last ten years have sought to provide a theoretical structure to their work. According to the authors, major

48 conceptual development has occurred in 1) understanding physical forces, 2) addressing the critical nature of their work, 3) conceptualizing how nature, society, and technology interact to create hazards, and 4) vulnerability analysis. As such, what we see now in hazards research is not just an individualized approach to studying a hazard, but instead, a holistic approach where hazards are placed within a continuum of interactions between nature, technology, and society – on one end are extreme environmental events (like hurricanes) and on the other extreme social events (like terrorism).

There has been a rich hazards studies tradition in the discipline of geography.

Hazards research developed from the work of Gilbert White. Burton and Kates (1986) attribute White’s work on flood plains in the 1940s and 1950s as the beginning of hazards research; White was trained by Barrows, whose work was in human ecology (Barrows

1923). According to Gregory (2000), White’s 1958 piece, Changes in Urban Occupance of Flood Plains in the United States, launched the hazards paradigm in the literature.

Cutter (2001) summarizes the initial areas investigated by hazards researchers as seen in

Table 2.5. In addition, the table includes a short summary of the themes, which have direct correlation with the present-day themes seen in hazards research. As a result of the work by White, the development of hazards research over the last fifty years has grown steadily. In regards to the discipline of geography one might call hazards research pure geographic inquiry, since it incorporates both elements of human and physical geography and utilizes both quantitative and qualitative techniques. The battle lines of division seen in much of the discourse on the existence of geography’s divide (Johnston 1997, Gregory

2000, Sheppard 2001, Goodchild 2004, Kwan 2004), may find solace in

49

Theme Description Theme Summary 1. Identification and mapping of the human Human hazard mapping occupancy of the hazard zone. 2. Identification of the full range of human Human adjustments adjustments to the hazard. 3. Study of how people perceive and estimate Human perception the occurrence of hazards. 4. Description of the processes whereby mitigation measures are adopted, including the Processes evaluation social contest within which that adoption takes place. 5. Identification of the optimal set of Optimal solution and adjustments to hazards and their social consequences consequences.

Table 2.5 Five initially emergent themes from hazards research. After Cutter (2001).

the field of hazard study. Hazards research is profoundly human and physical geography in one and it comes as no surprise that the author of the foreword to the compendium

Geography in America at the Dawn of the 21st Century (Gaile and Willmott eds 2003), would be no other than Gilbert White, a scholar who has exemplified excellence in scholarship in both human and physical geography.

2.2.4 Geographic Hazards Research vs. Risk Assessment Paradigm

Cutter (2001) describes how questions of where hazards occur and the patterns they exhibit, as well as the what questions of hazards, which look at the physical/social interactions over time, require the distinctive perspective of a geographer. Although similar in research, there are two distinguishing features that separate risk analysis

50 research from hazards research – context and answers. First, White (1988) asserts that hazards research considers the social context in which the event takes place, whereas risk analysis does not, such as in his studies of social ramifications of home ownership in floodplains. Second, the types of answers are significantly different between the two.

Cutter (2001) breaks down the types of questions and answers one would see in risk research versus hazards research by considering four elements: 1) hazard identification,

2) dose-response assessment, 3) exposure assessment, and 4) risk characterization, which will be discussed in detail later. A comparison of the two research tracks and the types of questions asked in each discipline is in Table 2.6. Notice that the hazards discipline deals with identification of the hazards through mapping procedures and incorporates the geographic element into the paradigm. Conversely, the risk assessment paradigm is concerned primarily with an output of a statistical measure or probability. Interestingly, the insurance industry has historically developed more along the lines of the geographic hazards tradition. Old Sanborn fire maps provided a visual guide to urban areas for fire insurance liability. They gave a geographic location, knowledge about structure and gas lines, and proximities to fire stations; now the industry uses the strengths of digital GIS liability maps to perform insurance calculations (Oswald 1997).

51

Element Risk Assessment Hazard Approach Approach 1. Hazard Question What is the threat and Does the agent cause an Identification Type when and where did it adverse affect? occur? Response Chemical X has a 1 in Output 1000 chance of causing Map the specific hazard(s). Y in humans. 2. Dose- Question What is the relationship What is the magnitude, response Type between the dose and duration, and frequency of Assessment the incidence in the hazard? humans? Response Exposure of X parts per The potential human Output million of chemical Y consequences of the hazard for a period of Z days if it occurs is X, Y, and Z. causes heart damage. 3. Exposure Question What exposures are What is the pattern of Assessment Type currently experienced human occupancy in and/or anticipated under hazard zones? different conditions? Response X amount of the agent The vulnerability of people Output will reach Y amounts of and places to hazards is X people. and mapped as Y. 4. Risk Question What is the estimated What accounts for different Characterization Type incidence of the adverse adjustments and effect in a given adaptations to hazards? population? Response Societies prepare for, The likelihood of agent Output mitigate, and should X will be seen in Y respond to risk and hazards number of people. with X criteria.

Table 2.6 Comparison between risk assessment discipline and geographic hazards discipline. After Cutter (2001).

52 2.2.5 Terrorism as Hazards?

Montz et al. (2003) describe hazards research along a continuum. Historically, social hazards have been grouped into the term technological hazards (Cutter 1993). However,

Montz et al. (2003), recognize now that social hazards encompass “failures to social disruptions to terrorism” (480) and keep technological hazards in a separate category.

What then is useful or directly transferable to the context of terrorism from the hazards research paradigm and in what ways does terrorism present new challenges for which prior research is not directly applicable?

Cutter (2001) identifies four key elements in hazards research given in Table 2.6 as: hazard identification, dose-response assessment, exposure assessment, and risk characterization. Each will be discussed in detail, drawing links between terrorism and hazards, as well as points of departure.

Hazard identification is exactly what it sounds like - it deals with identifying either the environmental, technological, or social hazard. In the identification process, the threat(s) are pinpointed and mapped out, revealing the specific hazard or hazard zone.

Hazard identification is somewhat transferable to the concept of terrorism, but not completely. The hazards paradigm identifies natural or technological hazards such as hurricanes, floods, volcanic activity, earthquakes, chemical waste, air pollutants, etc....

Also, the hazard is based on a temporal scale; they are identified before, during, or after the event. However, terrorism is slightly different. Terrorism is often rarely identified before an event, not identified as a hazard during an event because it often happens too quickly, and identified after an event has occurred. What results is that the actual

53 identification of terrorism as a hazard is challenging. A new definitional framework needs to be developed to deal with identifying terrorism as a hazard.

In this case it is the nature of the threat that is the defining characteristic that makes terrorism difficult to transfer to the hazard paradigm. Threats from natural or technological hazards have specific characteristics. First, the threat is usually based on physical laws with minor if any interactions from mankind. For example, in the case of hurricanes, the hazard operates on physical laws and occurs, naturally, without much regard for mankind. Earthquakes, volcanoes, rain, avalanches, and fire generally work and operate on physical laws. Sometimes humans can intervene, such as directing a fire with burn paths, but fire still operates on physical laws. In contrast, terrorism operates based on human laws and is socially based. Terrorists as human actors make decisions and plans based on non-physical laws. They operate within society and choose as rational actors to perpetrate, promote, and use terrorism for their goals (Schmid and Jongman

1984, Crenshaw 1998, Laqueur 1987, Hoffman 1998, Cordesman 2002). Therefore, when modeling this social hazard using the geographical hazards tradition, the research must take into account new forms of variables, such as symbolic values and intent. For example, environmental and technological hazards are unintentional from a human perspective. It is easy to see how environmental events are unintentional (meaning, they do not attack humans intentionally). Technological hazards are equally unintentional (for example, companies do not dump chemical waste to intentionally to attack certain humans or produce pollutants to get back at certain people). But unlike hazards, terrorism

54 is intentional (Crenshaw 1998). Terrorists are purposeful and carry out violence against people and places, with motives and with plans.

Cutter’s second key element, the dose-response assessment, tries to determine what the magnitude, frequency, and duration of the hazard will be based on the identified hazard. It deals with potential human vulnerability and consequences. In terms of terrorism, it can be said that dose-response assessment is a very transferable concept from the hazards paradigm. Since terrorism events can have certain magnitudes, frequencies, and durational effects, which can be measured based on past attacks, measuring the potential human consequences can be achieved. However, databases on terrorism incidents are usually found only in the back of terrorism-related books and have not been widely available until recently. With the newly available data, tallying past attacks, magnitudes of people affected (killed and injured), frequencies of attacks in specific geographic areas, and trends over time, calculations can be made of populations in a specific places, establishing terrorism trends.

The third element of hazards research is exposure assessment. Exposure assessment deals directly with what vulnerabilities exist for people and places. In this regard, it is highly transferable to terrorism analysis. Since measures can be developed to assess vulnerability to people and places from terrorism attacks, one can use the ideas of exposure assessment for terrorism.

The differing factor, however, between hazards research and terrorism vulnerability lies in the analysis of vulnerability. In the example of hazards, we could adopt from the earthquake literature (Reiter 1990) a simple four-step plan of vulnerability to

55 infrastructure. First, we would analyze an area and make a simple prediction plan (i.e. does it have a fault line? If yes = go to second step, if no = stop). Second, we would suggest steps for how to reduce risk and make buildings (and thus people) less vulnerable. Third, we would adopt some mitigation strategy in case of an earthquake.

Lastly, following the mitigation after an earthquake, a time for assessment/reassessment would be conducted and the cycle would start again. In the first step, the prediction plan is based on an identifiable characteristic related to the hazard (earthquakes = fault lines; fires = dry brush; avalanches = heavy snow on mountains; floods = land below sea level; tornados = merging air systems; etc…). It is different for terrorism. Terrorism has no such characteristic, thus the difficulty for insurance liability (Coaffee 2003, FEMA 2003).

There is no defining element for terrorism other than it appears in cities more often than rural areas (Savitch and Ardashev 2001) and that it is often related to the central city

(Savitch 2005). Terrorism does not appear in the same manner as physical hazards. If there is heavy snow on a mountain we can expect there will be an avalanche or that it may occur. If there is land below sea level, we may expect flooding to occur. Of course humans can intervene and try to prevent it, certainly. But for terrorism we cannot say that a dense city is necessarily a high predictor of terrorism. What we can say is that based on threats received, attacks made in the city or other cities, and methods and strategies terrorists have used before, we can identify targets within cities that are more likely to be attacked than others. Therefore, although terrorism and hazards can be assessed in terms of exposure, the method of the analysis has to be different.

56 Finally, risk characterization deals with how society prepares for and responds to hazards. This concept is directly transferable to terrorism. Cities and regions can use risk characterization to develop plans of preparation in anticipation of terrorist attacks, as well as emergency response and disaster mitigation plans.

From these four key elements it can be seen that terrorism analysis might be conducted within a general hazards framework. However, hazards and terrorism research diverge at specific points. General concepts of hazards modeling can be applied to terrorism modeling. But when the specifics of models are operationalized they might look very different. Therefore, different modeling strategies are needed to compensate for the lack of hazards models to completely capture terrorism.

With terrorism recognized as applicable under the geographic hazards paradigm,

Cutter (2003) calls researchers and their graduate students of geography to lead the charge in vulnerability science. To further understand vulnerability, as it is associated with terrorism, she suggests the development of models of risk vulnerability for forecasting impact, providing links between physical and social indicators, development of new methods for visualization and representation, and to offer decision-makers ways for implementation and effectiveness. Mustafa (2005) suggests that the geographic hazards tradition is recognizably the best geographic approach to modeling terrorism because of the physical and social modeling capabilities. However, the social aspect, on the continuum from physical to social hazards, is what makes geographic hazards modeling most challenging because of the random nature of humans (Cutter 2003, Montz et al. 2003, Mustafa 2005). Cutter and Mustafa both call for such modeling, but little has

57 yet emerged. New methods for visualization and representation can call upon the recent geovisualization tradition through the modeling process.

2.3 Geovisualization

2.3.1 Introduction

Visualization is a form of communication (Borchert 1987, DeFanti et al. 1989). Like words written on a piece of paper or words spoken in a speech, visualization acts to stimulate the reception of the eyes as means of communication. It is a powerful tool to understand a great deal of information in a quick manner (Tufte 2000). One of the best examples of this is a road map. Road maps contain a large amount of information; yet can quickly give you information that you need, such as relative distance or spatial organization of cities. Visualization deals with representation. In the case of maps, they try to re-present reality with a certain level of abstraction (Wood 1992).

Ware (2004) identifies five features of computer-aided visualization:

• Visualization provides an ability to comprehend huge amounts of data

• Visualization allows the perception of emergent properties that were not

anticipated

• Visualization often enables problems with the data itself to become immediately

apparent

• Visualization facilitates understanding of both large-scale and small-scale features

of the data

• Visualization facilitates hypothesis formation

58 The detail of this list has come about from the development of the field called visualization science.

Visualization science is a relatively new field of inquiry, developed in the late

1980s coinciding with advances in computing power and supercomputers. Computer graphics engineers played a large role in the emergence of the field when trying to find suitable funding for NSF supercomputers that lacked graphics tools (Frenkel 1988). As a result, the computer graphics engineers came up with the benefits of using data visualizations for the NSF supercomputers and the new field of visualization science emerged. Two main sub-fields exist within the field of visualization of data, scientific and information visualization. Both are similar, yet have fundamental differences.

Scientific visualization (ViSC) developed from the work of McCormick, DeFanti, and Brown (1987). Their NSF paper, Visualization in scientific computing, established a framework for scientific visualization and was released during the July 1987 SIGGRAPH meeting of experts in computing and graphics (Frenkel 1988). The importance of the paper was significant enough to be made into a special issue of the leading journal

Computer Graphics in November of 1987 (Slocum et al. 2005). The paper states that “the goal of visualization is to leverage existing scientific methods by providing new scientific insight through visual methods” (3). ViSC includes such topics as molecular modeling, medical imaging, and brain structure visualization (McCormick et al. 1987). The visualization is based data from the physical or real world phenomena that can be seen or is orthogonal in 2D or 3D (Keim 2001). Tufte (2000) produces imaginative visuals and demonstrates, perhaps like no other, the power of producing scientific visualizations for

59 new thought-provoking understanding. He challenges readers to consider visual thinking and provides many various visuals considered just as full of phenomena explanation, as is artistic mastery. His work and others, highlight how even the most complicated datasets can be visualized using advanced computing methods to produce images that help advance issues of science and understanding based on data (Yoo and Kwan 2004).

Like ViSC, Information visualization (ViIN) acts to visualize data, but does so with data that lacks real 2D or 3D qualities (Keim 2001). It is the visualization of abstract concepts and notions separate from the physical world. Graphical user interfaces used on many computers as operating systems are a form of abstract visualization. Card (1996) recognizes that ViIN has the goal of information comprehension. By explaining ViIN in this manner, aspects of communication that are non-visual, such as, audio and tactile perceptions can be incorporated. Card however, limits his discussion to textual visualization. Fabrikant (2000) discusses the role of Information visualization for retrieval of textual information from the Alexandria Digital Library. By using spatial concepts of distance, arrangement, and scale, she develops an experimental interface for searching the library. Building on that research, Fabrikant and Buttenfield (2001) explore the use of Information visualization for the GEOREF textual geologic database. By building elevation surfaces from the first level keywords in 100 documents, the authors display how many documents in GEOREF have that same keyword at any other level than the first. The result is elevated surfaces of peaks where gray-tones represent how many documents have the keyword, organized by discipline.

60 With the possibility of advanced visualization of large amounts of data, how does geographic visualization take advantage of these capabilities? And what differences exist between what is termed geographic visualization with that of historically developed cartographic visualization? Next we will explore the emergence of geovisualization within geography.

2.3.2 Scientific vs. Information Visualization in Geography

Scientific and Information visualization techniques can be used by a wide array of academic disciplines. The geographic sub-field of cartography is one that holds closely to capabilities and concepts of visual representation used in ViSC and ViIN. MacEachren and Kaack (1997) establish that the term visualization can be seen in the cartographic literature as early as 1953. Significant work in cartographic visualization includes work on symbology (Bertin 1967), automation (Macinlay 1986), visualization through touch

(Vasconcellos 1992), sound visualization (Krygier 1994), and the conceptualization of the above visualization approaches by MacEachren (1995).

MacEachren and Kaack (1997) challenge the geoscientific community to expand on the concepts of visualization. In a later paper, MacEachren et al. (1999) define geographic visualization as:

“From a base in cartography, geographical information systems, image analysis, and spatial analysis and has strong ties to related efforts in scientific and information visualization more generally, and to exploratory data analysis efforts in statistics” (313).

This definition of MacEachren et al. (1999) had its inception from DiBiase (1990) who established the notion of visual thinking and visual communication across a spectrum that

61 included concepts of exploration and confirmation (visual thinking), as well as, synthesis and presentation (visual communication) in cartography. Building on that conceptual model, MacEachren (1994) built a cartographic cube to try and distinguish the characteristics of geovisualization as different from cartography. He argued that geographic visualization deals with the concepts of private activity (as opposed to public in cartography) and exploration of unknowns (as opposed to presentation of things known as presented in a final cartographic output) in a highly interactive map-human environment (as opposed to a low level interactive environment in a static cartographic display). The interactive, privately conducted exploration that distinguishes geovisualization from cartography is commonly done using GIS. Through the use of GIS for the purposes of visualization that were identified by Ware (2004) presented earlier, geographic visualization can be conceptualized as including spatial, image, and data exploration analysis. However, is geographic visualization more associated with scientific visualization or information visualization?

It seems that geographic visualization (GViz) includes both ViSC and ViIN and is not closer conceptually to one over the other. MacEachren et al. (1999) used within their definition of GViz, both concepts of ViSC and ViIN. Present-day definitions in some of the recent textbooks of GIS and cartography include geovisualization. According to

DeMers (2005), GViz is defined as:

“A spatial approach to scientific visualization where the cartographic output is designed to elicit a response from the map reader that results in the formulation of new scientific hypotheses” (447).

62 Using the DeMers (2005) definition with a ViSC example, we might choose to display quantitative variables to build an elevation surface. DeMers states that GViz is within the sub-domain of ViSC, but should that be the case? Based on the work by Fabrikant (2000) and Fabrikant and Buttenfield (2001), we can choose to display a cartographic output of a non-numerical textual database like GEOREF to elicit a response from the map-reader that results in the formulation of new scientific hypotheses. By this evidence, the DeMers

(2005) definition appears to partially true but at the same time incomplete. On the other hand, Worboys and Duckham (2004) defines geovisualization as,

“the process of using computer systems to gain insight into and understanding of geospatial information” (305).

And Slocum et al. (2005) define it as,

“a private activity in which previously unknown spatial information is revealed in a highly interactive computer graphics environment” (475).

Both definitions above draw from the MacEachren (1999) definition of GViz and neither is relegated specifically to ViSC or ViIN data types only to spatial information.

So according to the above GViz definitions, the Fabrikant and Buttenfield (2001) research can be included in the realm of GViz because although they use ViIN types of data, it becomes spatial information in their GEOREF system. Clearly then, the most important concept drawn from this investigation is that geovisualization is more generally about using spatial constructs and metaphors for the process of exploring data, whether numerical or non-numerical for the purpose of scientific exploration using computers.

To summarize, ViSC and ViIN have their foundation in visualization science.

ViSC capitalizes on displaying large amounts of numerical data information for the

63 purposes of scientific pursuits and learning. ViIN is based in non-numerical data and using visualization to display large amounts textual information to develop new hypotheses and learning. GViz is the process of visualizing geographic data, whether numerical or non-numerical, for the purpose of scientific pursuits. It has roots in both

ViSC and ViIN and has its foundation in cartography and GIS, as well as image, spatial, and exploratory data analysis (MacEachren et al. 1999).

2.3.3 Primary Geovisualization Techniques

Many different mapping techniques exist. Arthur H. Robinson’s dissertation at The

Ohio State University (1947) set the stage for cartography and map-making as a science rather than merely an art, as he expounds on the book that developed from his dissertation, The Look of Maps (Robinson 1952). That is not to say that others in the past had not tried to produce accurate representations of phenomena or landscape using maps or that cartography was not being taught in an organized manner (Raisz 1938), but rather to say that Robinson’s dissertation examined which methods had gone before and attempted to standardize the design elements of mapping. His dissertation examined the foundation of cartographic methods, details regarding map lettering, map structure, and map color methods (Robinson 1947). From this significant contribution, others have built new methods of cartography, showing the science behind the methods as well as the elements of subjectivity and art that must go into the map-making process. Understanding the differences between geovisualization and cartography as described previously, five of the primary techniques will be presented which represent the primary areas for mapping

64 investigation that widely available in GIS software and can be directly applicable to terrorism-related visualization.

Isopleths

Slocum et al. (2005) identify isarithmic maps dating back to at least the eighteenth century. Wallis and Robinson (1987) cite that the earliest known use is water-depth isarithms in the sixteenth century. The term isarithm is derived from the Greek is (os), equal, and arithm (os), number (Wallis and Robinson 1987). The most common isarithmic map is certainly the contour map, generally depicting equal elevation value lines, such as on the United States Geological Survey topographic maps. Lines of same elevation are created from known reference or control points to create contours of elevation; therefore, what is most consistent about isarithmic maps is that they represent continuous data, like elevation, temperatures, or atmospheric pressure. By creating contours over a specific area, a “surface” is created.

The main problem with isarithmic maps is that only a limited amount of data are used to represent the entire region. The values for reference points are known but everything in between the points is unknown. This problem is one of interpolation – what methods exist to accurately build a surface of an area based on known reference points? Six primary methods used in cartographic analysis for creation of isosurface interpolation maps, whether 2D or 3D, are linear interpolation, averaging, triangulation, inverse-distance weighting, trend surfaces, and kriging. Spatial analysis textbooks cover

65 thorough discussions on each of these interpolation methods and will not be discussed in detail here.

Parallel Coordinate Plot

Parallel coordinate plots (PCPs) are a visualization technique for displaying and comparing large amounts of multidimensional data in two dimensions. The technique was developed from mathematician Alfred Inselberg. During his work in geometry as a student, he became frustrated that he was working on many different algebraic expressions without being able to visually see the results. His fascination with visualization lead his students to challenge him; “ In 1977 while giving a Linear Algebra course I was challenged by my students to “show” them some multi-dimensional spaces”

(Inselberg 2007). The results of his work (Inselberg 1985) are what researchers now build upon.

PCP is a method that allows the user to explore the data and see relationships among variables visually. By example, I will use the Pearson’s correlation coefficient (r), which is a statistical measure that allows variables to be compared with each other. Since r ranges from +1 to –1, the PCP takes advantage of this range in a visual manner. For attributes that are highly correlated and close to +1, they will appear in a bin as observations being parallel to one another as in Figure 2.1. For those attributes that are inversely correlated to each other, more near to –1, they will appear to merge together in the center of the bin. Finally, for those attributes that are highly uncorrelated with each

66 other, near to zero, they will appear as being at a diagonal and crisscrossing other variables along the top to bottom spectrum of the bin.

The power of the visualization that can be achieved using PCP has applications for many areas of research. Utilizing the method to display large amounts of multidimensional data to draw conclusions and build hypotheses has aided research from the field of health to geographic remote sensing (Edsall 2003, Lucieer and Kraak 2004).

Edsall (2003) demonstrates the power of PCP in visualization through the use of case studies. He used two studies to show how PCP can be used in the knowledge discovery process, as well as in the health field. The Apoala Project at Penn State

University uses a dynamic PCP as an exploratory data analysis tool (GeoVista 2000).

Dykes (1997) and Andrienko and Andrienko (1999) also have written on the process of using PCP for exploratory visualization purposes. Xiao and Armstrong (2006) have developed a PCP interface within their software package for choropleth classification.

67

r = 1 r =-1 r = 0

ns io

68 Observat

Attribute 1 Attribute 2 Attribute 3 Attribute 4

Figure 2.1 Parallel coordinate plot results with Pearson correlation.

Choropleth mapping

The term choropleth, taken from the Greek choro, area, and pleth, value, represents an additional technique available for GViz. Choropleth mapping has its roots with Baron Charles Dupin, who holds the distinction in 1826 of producing the first choropleth map (without class intervals), showing the number of persons per male child in school for each department in the Conservatoire des Arts et Métiers in Paris, France

(Wallis and Robinson 1987). Each enumeration unit was gray-shaded to fit each value.

The choropleth map is in high use today. It could easily be argued that it is the most used mapping technique, for it is simple to create and easily communicates statistical information. Many mapping sites online use the technique, including the US

Census Bureau and the Center for Disease Control (CDC).

The power of the choropleth map comes from standardization of data, a requirement that Robinson et al. (1995), Dent (1999), and Slocum et al. (2005) discuss.

Standardizing values by the enumeration unit is a technique that allows for cross comparison of enumeration units by taking into account their geographic size and provides a more accurate visualization - using raw counts is an inappropriate procedure in choropleth mapping because the size of the enumeration unit is not taken into consideration. Raw counts are best represented by proportional symbols (Brewer 2006).

The main discussion in choropleth mapping, which has challenged cartographers for years, is the issue of classes. With choropleth mapping, often there is an important decision to be made about how many classes to break data into for display purposes.

Tobler (1973) reintroduces the concept of choropleth mapping without classes (remember

69 that the first choropleth maps were without class intervals). He comments on the ability of automated cartographic line plotting which could circumvent the problem of class intervals by plotting different shades of gray exactly proportional to the data intensity.

Dobson (1973) responds to Tobler’s report by showing as the number of shades of gray increase, and hence the number of enumeration units grows, the more difficult it becomes for the reader to comprehend the map, since maps are meant to be generalizations. This debate continued into the 1990s (e.g. Peterson 1979, MacEachren 1982, and Peterson

1992). The final issue really comes down to whether or not the numerical intensity should be maintained and whether the map is meant to be for presentation or exploration

(Slocum et al. 2005).

One of the major research interests in choropleth mapping is the selection of classes, involving the grouping of data. There are six primary methods from which to choose: equal intervals, quantiles, mean-standard deviation, maximum breaks, natural breaks, and optimal. George Jenks has been a tremendous influence on the development of class intervals. His work has focused on optimal data classification, minimizing the sum of absolute deviations about the class means (Jenks and Caspall 1971), on optimal classification using mathematical modeling that guarantees an optimal solution (Jenks

1977). Coulson (1987) reviews of Jenks’ large contribution to classification methods.

Others have contributed as well. Armstrong et al. (2003) developed the selection of class intervals using genetic algorithms, not on the basis of data distributions, but instead considering the geographic characteristics of the data. Also, Xiao and Armstrong (2006)

70 have released ChoroWare software for the selection and evaluation of multiobjective choropleth class intervals based on the cartographer’s specific application.

Another major research area has been in the selection of color schemes for choropleth mapping. Robinson’s Elements of Cartography (1995) discusses at length issues of color. Recently Cynthia Brewer has been instrumental in this area. Early last decade (1994) she introduced color scheme determination based on the kind of data, with a focus on sequential schemes. Brewer (1996) discussed color issues for diverging schemes. Using the color spectrum of the magnetic spectrum, she tested the sufficient use of the spectrum to make suggestions about how to use a spectral scheme (1997). Taking this knowledge, Harrower and Brewer (2003) developed an excellent color scheme selection tool available online at colorbrewer.org.

Cartograms

Cartograms are a unique form of mapping. They utilize numerical data to emphasize the size of enumeration units. They distort the size and shape of normally recognizable geographic areas based on the numerical data for a given variable to reflect a theme. Cartograms have their beginnings with the proportional circles and squares of

August Freidrich Wilhelm Crome in 1785 and William Playfair in1786 (Wallis and

Robinson 1987). Tobler (2004) cites the earliest use of the modern term ‘cartogrammes a foyer diagraphics’ (maps with diagrams) from 1851.

The term cartogram is not standardized around the world. Tobler (1986) reports that

71 “The term ‘value-by-area-map’ is used by Raisz (1938). The Soviets call them ‘varivalent projections,’ the French ‘anamorphoses,’ a mathematician uses ‘uniformizing maps. Mass distributing (pyconomrastic) map projections is proposed for this class and is consistent with recognized usage” (49).

In addition, cartograms can be contiguous or non-contiguous. Contiguous cartograms maintain their neighbors and are not separated (usually some form of blocks are used in this case), whereas non-contiguous cartograms break from each other and often are in the shape of circles.

Tobler (1963) demonstrated how cartograms could be defined by partial differential equations, as well as how cartograms are a form of map projection. Wallis and Robinson (1987) credit Tobler as the first to suggest that cartograms are a form of map projection. Tobler (1986) explained that pseudo-cartograms can be developed from projections, but they need to be as conformal as possible. Tobler (2004b) reviews the last

35 years of cartogram research, under what we might call ‘his watch’.

Many researchers have devoted careers to the study and development of cartograms. Dougenik et al. (1985) present an algorithm that tries to preserve shape of geographic units while remaining contiguous. Gusein-Zade and Tikunov (1993) also developed an algorithm that preserves shape and contiguity and produces better images than previous work. House and Kocmoud (1998) build an algorithm that preserves shape of regions and is contiguous. Some of the work done by Rittschof et al. (1996) tested cartograms on undergraduates and he and his colleagues found that long-term familiarity with regional geographic shape played a significant role in understanding cartograms of that same region.

72 A very nice series of cartograms can be found in atlases. Kidron and Segal (1991) produced The New State of the World Atlas. The cartograms found in the atlas are contiguous and show different themes for the countries of the world. Their interesting themes include: territorial percentage claims, share of the world exports, purchasing power compared with income, manufacturing output, articles published in scientific journals, manufactured cigarette output, government purchases, refugee contributing states, military themes, and emissions of greenhouse gases. Additionally, Dorling (1995) produced an excellent atlas based on his algorithm that uses non-contiguous cartogram circles in the book A New Social Atlas of Britain. The atlas is a significant piece of work.

One final note is that the GeoDa program developed by Anselin (2004) produces non- contiguous cartograms similar to those seen in Dorling (1995). In addition to atlases, some of the best of cartogram creation can be found from the New York Times coverage of the 2004 United States presidential elections (New York Times 2004, view map by

Electoral College), the fabulous display of economic monitoring of the New York Stock

Exchange at Smartmoney.com (Smart Money 2007), and of course, from the cartogram website, worldmapper.org (Worldmapper 2007).

Animation

Animation concludes the last of the five geovisualization methods explored here.

Animations have been of interest to cartographers since the 1950s. Norman Thrower

(1959) discussed the topic of animated cartography in The Professional Geographer. He suggested that animating such themes as population and transportation movement, urban

73 expansion, removal of forests, climatic changes, and political boundary shifts could capture continuous change.

Computer animations in geography started in the late 1960s and early 1970s.

Highly gifted and with access to computers at the University of Michigan, Tobler (1970) showed population growth in Detroit in what he called a computer movie, and introduced Tobler’s almost off-the-cuff “First Law of Geography (TFL)”,

“Everything is related to everything else, but near things are more related than distant things” (236).

The TFL has been discussed in open forum at the AAG meeting in New Orleans 2003 and subsequently produced as special journal section (cf. Barnes 2004, Goodchild 2004b,

Miller 2004, Sui 2004b, Phillips 2004, Smith 2004, and Tobler 2004). Hal Moellering, from the Michigan school influenced by Tobler, explored animation for application to geographic traffic crashes (Moellering 1976). Moellering (1980) was also able to animate population growth in the United States from the mid-1800s and farm tractor diffusion during the twentieth century. Fortunately today, much of the early work of these cartographers is possible now due to the power of personal computing. As a result, many software packages can be used for animation and the list of growing data standards, such as Geographic Virtual Reality Markup Language (GVRML), continue to grow.

Animation techniques can be broken down into different types as described by

Worboys and Duckham (2004). Generally, the animation on a computer is built from a series of static images, which forms the scene. The scene usually depicts change over time or/and a fly-by through the scene. Visual variables associated with maps can be manipulated in various ways. Visual variables include position, size, orientation, shape,

74 color, and pattern (304). The manipulation of the variables occurs in the interactive environment of the scene and can include the moment of change, frequency changes, duration of the manipulation, magnitude of the change, the order of changes, and finally, the synchronization of variables (309). Other cartographic textbooks discuss animations in a similar manner as Worboys and Duckham, including Peterson (1995), Robinson et al.

(1995) and Slocum et al. (2005).

Another way to categorize animations is to term them non-interactive and interactive (Slocum et al. 2005). The authors discuss a variety of non-interactive animations by Wilhelmson et al. (1990) on thunderstorms, Treihish (1992) on ozone depletion, Weber and Buttenfield (1993) on temperature changes, and urban change by

Acevedo and Masuoka (1997). Interactive animations used for exploration include

Dorling (1992), Monmonier (1992), Harrower (2001) and Andrienko et al. (2000).

Personally, I have found that making the static maps and then visualizing them in

Windows Movie Maker by Microsoft has been an easy way to make animations. Also,

Tracking Analyst in ArcGIS is a powerful tool for map animation of data over time.

2.4 Conceptual Framework for Visualizing Terrorism as a Hazard Through

Geovisualization

I have introduced the influence of spatial ideas on the definition of terrorism, showing how the geographic concept of scale changes the definition of terrorism and what impact that has conceptually on how we understand different types of conflict, achieving the first of my six objectives in this research. Also, I have introduced the

75 geographic hazards tradition and shown the intricate ties it has with terrorism research. In addition, geovisualization has been discussed, which aids in the visual exploration process for hypothesis development, as well as the various types of geovisualization techniques available. With the necessary background we can now begin to build a conceptual framework to study and geovisualize terrorism.

A visual representation of the conceptual framework for the geographic visualization of terrorism is presented in Figure 2.2. Referring to the figure, the Venn diagram establishes the merging of geography and violence as subjects. Within the overlap, terrorism related research emerges. Looking deeper at terrorism itself, theory, concepts, and definitions are either developed a priori or a posteriori, and some coherence is decided upon regarding what is or is not terrorism. Because terrorism is so highly spatially dependent, geographers’ contributions can be made at this level, as I have done by explaining the role of geographic scale in a good definition of terrorism. From here we have four primary areas in which terrorism can be addressed from a spatial perspective. The four ‘mappables’ are comprised of terrorism subject matter that can be mapped.

76 GEOGRAPHY Terrorism VIOLENCE

Terrorism Theory & Definition

Terrorism Groups Vulnerability & Emergency Damage & Networks Security Modeling Management Mitigation

MAPPABLES

Figure 2.2 A visual representation of the conceptual framework for the geo- graphic visualization of terrorism.

77 Within the four mappable areas where terrorism can be mapped, a multitude of geovisualization techniques can be used. Five specific techniques have been discussed above, however there are strengths and weaknesses of each method as it pertains to mapping terrorism. Isopleth geovisualization can provide enumerable surface maps for the exploration of spatial hypotheses, but require data that is captured or aggregated as point data for interpolation. PCP analysis can provide visual exploration of various variables, but require accurate data, which is currently problematic among many terrorism incident databases, as addressed later. Choropleth geovisualization has great possibilities for terrorism related geovisualization, however is dependent on enumeration units and spatially captured terrorism data. Such data is rarely available at a scale finer than the political state level in incident databases. Cartograms serve well to show glaring differences among variables and offer great possibility. However, fine scale continuous cartograms such as those that might be at the zip code scale are fraught with problems because people typically don’t know how to understand the visual garble that results from these operations. Finally, animations might play a larger and larger role in exploratory geovisualization for understanding terrorism, yet are complicated to make and take large computational power to handle multiple variables.

The techniques of isopleths, choropleths, and animations are explored for both the spatial physical dimension of and the spatial psychological dimension of terrorism. Each dimension is explored more deeply as the mixed-methodology approach is now introduced.

78

CHAPTER 3

METHODOLOGY

“Thus far, no human technological innovations have helped humanity solve problems without creating new ones.” – Daniel Z. Sui (2004, pg. 65)

3.1 Hazards Modeling

3.1.1. Introduction

This chapter describes in detail the methods used in this research. Since this work includes methods that attempt to address both real and perceived threats from terrorism, each will be examined in relation to the objectives of this research. First, hazards modeling and the geovisualization of terrorism will be explored. Second, the perception of the terrorism threat to the urban environment will be introduced, as well as different methods for exploring the perception of terrorism geographically.

79 Geographic hazards modeling is an effective way to deal with the myriad of environmental hazards that face mankind. Often many different types of talents are required when modeling hazards, including the abilities to deal with both quantitative and qualitative aspects of a project. Reiter (1990) reminds the researcher working on earthquake hazards that quantitative knowledge of geophysics and mathematics, as well as qualitative social understanding of people and public policy is often required.

It can be argued that there is no standard approach to modeling hazards and many different methods exist. Indeed, little comprehensive theory exists as was discussed in

Chapter 2. Cutter’s (2001) comparison of the risk assessment paradigm with that of the geographic hazards tradition perhaps is the best framework for developing a comprehensive modeling theory shown in Table 2.6.

Similarities can often be seen among different models even without a standard theoretical approach. Models show commonalities even though the actual hazards are different (Hill and Cutter 2001). Combinations of topography, the built environment, and the location of people or populations are included in geographic hazards modeling. Also, hazards modeling has similarity in the utilization of mathematical or statistical calculations and incorporating mapping or cartographic/animated forms in the analysis.

Researchers create hazards models with an end goal of developing some type of overarching measure or index that allows comparison between different areas. Finally, hazards are always modeled at a certain geographic scale. For example, the choice of scale can be local, like at the city level (Davidson 1997) or regional and countywide

(Thomas and Mitchell 2001). Davidson (1997) uses an earthquake disaster index to

80 compare ten different cities and their disaster potential. He combines the natural and built environment with people to form the index in his geographic inquiry. Furthermore,

Thomas and Mitchell (2001) present a series of maps that attempts to answer the question of which US state is the most hazardous. They account for several different hazards and show geographic patterns over time. Their approach combines statistical calculations and visualization through cartographic maps of the United States; thus, a majority of their data are geographic (although at times using raw counts in choropleth maps!). So despite the lack of an overall theory guiding geographic hazards modeling, commonalities do exist between models even with differences in hazards, scale, and geographic location.

3.1.2. Strategic Threat Operation Program (STOP)

One of the overarching goals of this research is to develop a geographic hazards model that will calculate a terrorism vulnerability index for United States cities. Although

Mustafa (2005) argues well that the geographic hazards tradition approach to modeling terrorism perhaps is the best way geographers might model terrorism, no specific model in the geographic hazards tradition has been presented in the literature. Therefore, I will now introduce a model that captures real terrorism vulnerabilities to the urban U.S. environment in the geographic hazards tradition. The model is called the Strategic Threat

Operation Program (STOP), which calculates a vulnerability index of terrorism threat at the local city level and is comparable among U.S. cities, nationwide.

STOP incorporates the framework of Cutter (2001) by trying to capture the various elements of general geographic hazards modeling seen in Table 2.6. The

81 identified hazard is terrorism. The dose-response assessment is the capture of the frequencies, duration, and magnitudes of terrorism incidents. Exposure assessment is built into the STOP model by examining the size and distribution of the urban environment. Finally, risk characterization is a resulting by-product from the model and addresses simply the areas that are most vulnerable so that decision-makers can begin to address urban vulnerabilities identified by the STOP index. The STOP model is based on the WHIMS and the HERO geographic hazards models.

The Wildfire Hazard Identification and Mitigation System (WHIMS), is a system for identifying and educating local populations of how vulnerable they are to the hazard of wildfires (Boulder County Wildfire Mitigation Group 2001). Evaluating their surrounding area, local homeowners can see what level of risk their property (parcel) has based on a range from zero to ten, with zero being no hazard and ten being severe hazard.

A system of weighting the different variables was implemented based on survey information from local experts in the fire response industry. Parcel infrastructure was evaluated in the area based on criteria of topography, infrastructure design as it related directly to the spread of fire, landscape, water supply locations, transportation accessibility, and weather patterns.

The Human-Environment Regional Observatory (HERO) gives a protocol and index for assessing vulnerability to environmental hazards (Wu 2002). The protocol uses a four-step process to develop a vulnerability index at the census block group level. The four stages include identification and mapping of 1) natural hazards, 2) technological hazards, and 3) coping ability of the population. The last step 4) is to overlay the maps

82 created in the first three stages and incorporate them together to form a total hazards map and a coping population map, which can then be combined to produce a final total vulnerability map. Weights are used in the model process and are calculated from magnitudes and frequencies of the hazards. Standardization of the population is conducted based on percentages of the total population in a given county.

The two models, WHIMS and HERO, were chosen primarily for three reasons.

First, they both serve as examples of how GIS can be used to model environmental hazards. Second, both of the models use a ranking measurement system for final evaluation but go about it in an opposite manner. The WHIMS model calculates weights based on expert knowledge and then uses a ranking system to determine if a parcel is vulnerable to wildfire. The HERO model uses a ranking system to order magnitudes and frequencies and then calculates the weights by multiplying them together. Finally, the models were chosen because of their geographic scale and simplicity. The WHIMS model uses parcel data, which is used in the STOP model. The HERO protocol offers a generalized methodology for implementing various circumstances of hazards and evaluates them in ways similar to how statistical terrorism research is conducted through frequencies and magnitudes and is operationalized in the STOP model.

The STOP model is separated into three specific steps. Step 1 involves the identification of terrorism incidents in the past and mapping their spatial distribution. In this stage, weights are assigned to differentiate between levels of probability between terrorism targets. Step 2 refines the level of detail to evaluate why one target in an urban area might be selected over another based on the symbology, or the psychologically

83 attributed value (Horgan 2005), associated with of a target and the size of the population

(which allows a between-cities comparison approach). Lastly, Step 3 uses the results of the calculations in the first two steps to build a vulnerability threat index (VTI). The model has been designed for spatial but not for spatio-temporal analysis.

Strategic Threat Operation Program (STOP) Procedure

STOP is based on the geographic hazards paradigm and seeks to provide a terrorism vulnerability index for infrastructure and people using GIS.

Step 1: Identify the infrastructures attacked by terrorists in the past and map their spatial distribution.

1. List the terrorist attacks that have occurred in the region. The list should include, but not necessarily be limited to: • Critical Infrastructure (CI) list. • Common Target list (anything not on the CI list, such as buses, night clubs, or shopping areas).

2. For each attacked Infrastructure and Common Target, find data on: • Area affected (spatially referenced) • Frequency (the temporal scale) • Magnitude (sum of casualties (deaths) and injured)

3. Identify how often (percent) the CI were attacked.

4. Equate, for mapping purposes, by taking the CI list and associating it with the parcel coding within an urban area. Recoding parcel may be required to accurately equate CI with parcels.

5. Divide all frequencies equally into 5 categories, and assign each of the attacks a frequency score (F) from 1 (low) to 5 (high). Similarly divide all magnitudes into 5 equal interval categories, and assign each of the attacks a magnitude score (M) from 1 (low) to 5 (high).

6. Calculate the weight (w) for each of the locales:

weight (w) = F x M (1)

84 7. Map the area affected for each of the attacks, either as point or polygon layer by weight.

Step 2: Evaluate and map One-Target-Over-Another (OTOA). 1. Identify all OTOA places. The indicators should include, but not necessarily be limited to:

• Region Population (P) • Symbology (S)

2. Deal with each OTOA separately.

A. Region Population (P): Metropolitan Statistical Area – If the MSA is: <500,000 = a value of 1 500,000 to 1,000,000 = value of 2 1,000,000 to 4,000,000 = value of 3 4,000,000 to 8,000,000 = value of 4 >8,000,000 = 5 or National Capital = value of 6

B. Symbology (S): If a structure is: a national monument or historic landmark or major event site (e.g. stadium) or important government facility (city hall) or a building over 12 stories tall (» 150 ft) or tallest 3 buildings or capital building/president or governor home = value of 2; otherwise = value of 1 (Results can be cumulative)

3. Derive a value for One-Target-Over-Another as:

OTOA = P x S (2)

Step 3. Create the Vulnerability Threat Index (VTI).

1. Take the weighted values completed in Step 1 and multiply them by the OTOA values found in Step 2 for each point or polygon as:

85 w x OTOA = VTI (3)

3.1.3 STOP Steps

3.1.3.1 STEP 1

1. List the terrorist attacks that have occurred in your region. The list should include, but not necessarily be limited to: • Get Critical Infrastructure (CI) list. • Get Common Target list (anything not on the CI list, such as buses, night clubs, or shopping areas).

2. For each attacked Infrastructure and Common Target, find data on: • Area affected (spatially referenced) • Frequency (the temporal scale) • Magnitude (sum of casualties (deaths) and injured)

3. Identify the how often (percent) the CI were attacked.

What is critical infrastructure and what is a common target? This straightforward question is fraught with difficulty. The United States government has come up with a general list of critical infrastructure in the document Physical Protection of Critical

Infrastructures and Key Assets (Office of the President 2003b), which provides in overall plan for the protection of key assets (infrastructure that allow the country to run normally) within the United States. A detailed list of critical infrastructure categories as identified by the U.S. government is given in Table 3.1. Translating this list to a modeling environment, however, proves challenging.

The National Asset Database (NAD) has been assembled by the Department of

Homeland Security (DHS) as an inventory of the critical infrastructure of the nation.

However, when the DHS built the NAD from the critical infrastructure assets provided by

86 the 50 United States, the results were less than stellar (Lipton 2006, Office of the

Inspector General 2006). As the Inspector General of the DHS has revealed, the disparity within the data are large, leaving the NAD quite suspect as to its usefulness. The list of

Critical Infrastructure Agriculture and Food Water Public Health Emergency Services Defense Industrial Base Telecommunications Energy Transportation Banking and Finance Chemical Industry and Hazardous Materials Postal and Shipping

Table 3.1 Critical Infrastructure as identified by the United States government. After

Office of the President (2003).

critical infrastructure as gathered in the NAD is given in Table 3.2. Notice that Indiana leads the nation in the number of critical infrastructure assets, but of course that is absurd because we could expect the largest number of critical assets to correspond with the largest populations and the largest geographic states. In that case, Texas and California serve best. Texas ranks second among the largest size of states and has the fifth largest population and California ranks third in size and first in population. According to the

NAD, Indiana has 2.6 times more critical infrastructure assets than Texas and 2.7 times more than California. How could this be? The state of Indiana, just like all the other states and territories, was asked to provide the DHS with a list of critical infrastructure within 87 its bounds (McGarrel, personal communication6). The U.S. states and territories were given a list of what is critical, but were left to decide on the particulars of what was or was not to be included. As a result, a state like North Carolina, which ranks 11th in population and has one of the highest technologically advanced areas on the eastern half of the United States, ranks 30th in the number of critical assets. The discrepancies present in the NAD point out the massive difficulty of building such a database – no standard for what is actually critical infrastructure exists. Without a critical infrastructure standard, assets considered critical in one place are not in another. As Lipton (2006) points out, the

“Old MacDonald’s Petting Zoo, the Amish Country Popcorn factory, the Mule Day

Parade, [and] the Sweetwater Flea Market” were examples of the critical infrastructure of the United States. Are these really critical and targets of terrorism? What really is a target of terrorism?

6 McGarrel 2007, personal communication. This reflection is based on the conversation I had with Dr. Edmund McGarrell of Michigan State University, the chair of one of the foremost Criminal Justice departments in the nation, who informed me of this DHS request. 88 Assets in the Database Pop Assets in the Database Pop. Rank State Number Rank Rank State Number Rank 26 Massachusetts 764 13

1 Indiana 8,591 15 27 North Dakota 763 49

2 Wisconsin 7,146 20 28 Idaho 747 40

3 New York 5,687 3 29 Louisiana 746 24 4 Virginia 4,231 4 30 N. Carolina 720 11 5 Texas 3,804 5 31 Alabama 710 23 6 Washington 3,650 14 32 Missouri 684 18 7 Nebraska 3,457 39 33 Arizona 675 17 8 California 3,212 1 34 Alaska 635 48 9 Pennsylvania 2,873 6 35 Nevada 607 36 10 Illinois 2,059 5 36 Minnesota 577 21 11 Florida 2,014 4 37 Utah 558 35 12 Ohio 1,887 7 38 W. Virginia 521 38 13 Maryland 1,692 19 39 Arkansas 496 33 14 Georgia 1,514 9 40 Delaware 455 46 15 Michigan 1,467 8 Iowa 455 31 16 Montana 1,385 45 42 Wash. D.C. 416 42 17 New Mexico 1,348 37 43 Wyoming 396 52 18 Colorado 1,293 22 44 S. Dakota 360 47 19 Kentucky 1,123 26 45 S. Carolina 308 25 20 Mississippi 1,026 32 46 Oklahoma 305 29 21 Kansas 983 34 47 Maine 271 41 22 Tennessee 975 16 48 Hawaii 202 43 23 Connecticut 930 30 49 Puerto Rico 130 49 24 New Jersey 904 10 50 Guam 116 53 25 Oregon 840 28 51 Rhode Island 97 44 52 Virgin Islands 86 54 53 N. Hampshire 77 42

54 Vermont 70 50 Table 3.2 Number of critical infrastructures in the U.S. National Asset Database. After

Office of the Inspector General (2006).

89 Terrorism Incident Data

To answer this question from a geographic hazards approach, the locations, frequencies, and magnitudes of past terrorism incidents should be examined. These data are available in terrorism incident databases. Although terrorism existed prior to 1968, it was not until the internationalization of terrorism that researchers began to collect incident data, as discussed in Chapter 2. Many of these incident datasets exist only in pages of text. One of the most frequently encountered detailed lists is that of hijackings, which have been compiled by multiple scholars (e.g. Phillips 1973, Evans and Murphy

1978, and the United States Department of Transportation Federal Aviation

Administration 1981). Incident lists on specific terrorist groups also exist, including, for example, tables and data on exploits of the Basque group ETA, from Spain (Shabad and

Ramo 1995). Some, such as Pape (2005), have focused on cataloging modes of attack, such as suicide terrorist incidents, providing such fields as the date, perpetrating group, weapon used, target of attack, and the number of people killed. However, there are more comprehensive incident datasets available for the analysis of terrorism.

Fowler (1981) describes the design and development of terrorism databases and provides details on eight specific incident datasets. Of these, two are commonly mentioned in the literature, ITERATE and the RAND7-St. Andrews Terrorism

Chronology. The ITERATE (an acronym for International TERrorism: Attributes of

Terrorism Events) database was developed by Mickolus (1980). Four different versions have been created, expanding each time to include more years with each new edition, the

7 RAND is a non-profit corporation investing in improving policy and decision-making through rigorous analysis. RAND is a contraction for research and development. (RAND 2007). 90 latest covering incidents from 1968-2004 (ITERATE 4). The various versions have been used in numerous research efforts over subsequent years (Heyman and Mickolus 1980;

Mickolus 1981; Mickolus 1987; Sandler and Scott 1987; Weimann and Brosius 1988;

Ross 1994; Weinberg and Eubank 1999; Eubank and Weinberg 2001; and Robinson et al.

2006). The database includes a large number of variables for analysis of trends in terrorism and has been considered significant for understanding terrorism (Hoffman and

Hoffman 1995).

An equally comprehensive database, the RAND Terrorism Chronology 1968-

1997, was first developed by Brian Jenkins and his colleagues in 1972 to focus on international terrorism where terrorists attack abroad for the sake of creating an international event (Schmid and Jongman 1984). In 1994, the Center for the Study of

Terrorism and Political Violence at St. Andrews University began administering the database and its name was changed to the RAND-St. Andrews Chronology (Hoffman

1999). The database is referred to in the literature as both the RAND and RAND-St.

Andrews dataset, interchangeably. Numerous publications by RAND use the Chronology for the analysis of terrorism trends and terrorist group characteristics, as well as in other research efforts (Cordes et al. 1985; Hoffman and Hoffman 1995; Hoffman and Hoffman

1996; Hoffman and Claridge 1998; Merari 1999; Silke 2001; and Dugan et al. 2005).

One of the most thorough incident databases available online is from the

Memorial Institute for the Prevention of Terrorism (MIPT), Terrorism Knowledge Base

(MIPT 2006). The database consists of over 26,000 terrorism incidents since 1968, compiled by the RAND Corporation and MIPT (Ellis 2004b). The data made available

91 are merged from two primary sources, the RAND-St. Andrews Chronology and the

RAND-MIPT Terrorism Incident database, which includes both domestic and international terrorism incidents worldwide from 1998-2006. The RAND-MIPT

Terrorism Incident database was a continuation of the RAND-St. Andrews Chronology, except for the inclusion of domestic events, increasing the amount of data several times over starting in 1998. The combination of the datasets will be referred to hereafter as the

TKB (Terrorism Knowledge Base).

Although the database is accessible to the public online and the use of the data are highly encouraged by MIPT, it is NOT available for download as a complete dataset. As a result, research is quite limited and highly restricted to the analysis tools available from

MIPT and the limited search options. To solve this limitation, I was able to develop a procedure for collecting the data from the Internet by spidering each incident page (over

26,000), grabbing the data on the page, and dumping it into a database. Internet search engines, like Google, use the spidering method to help web users find content from searches. Collecting the entire database on a local computer allows research to be conducted using advanced statistical and geographic analysis, which is not possible online with the limited MIPT tools.

The TKB incident data are archived based on a definition of terrorism, which is provided by MIPT. The categories seen in Table 2.3 form the basis for a broad definition of terrorism and all 22 categories identified by Schmid and Jongman (1984) are either alluded to or present in the TKB defintion:

“Terrorism: For the purposes of this database, terrorism is defined by the nature of the act, not by the identity of the perpetrators or the

92 nature of the cause. Terrorism is violence, or the threat of violence, calculated to create an atmosphere of fear and alarm. These acts are designed to coerce others into actions they would not otherwise undertake, or refrain from actions they desired to take. All terrorist acts are crimes. Many would also be violation of the rules of war if a state of war existed. This violence or threat of violence is generally directed against civilian targets. The motives of all terrorists are political, and terrorist actions are generally carried out in a way that will achieve maximum publicity. Unlike other criminal acts, terrorists often claim credit for their acts. Finally, terrorist acts are intended to produce effects beyond the immediate physical damage of the cause, having long-term psychological repercussions on a particular target audience. The fear created by terrorists may be intended to cause people to exaggerate the strengths of the terrorist and the importance of the cause, to provoke governmental overreaction, to discourage dissent, or simply to intimidate and thereby enforce compliance with their demands.” (TKB Definition 2006)

The TKB authors use this definition as the criteria for inclusion into the database and it is not in conflict with my definition of terrorism presented previously in Chapter 2; it is broader to include the idea of maximum publicity.

One natural goal of the TKB is to catalog all terrorism incidents worldwide.

Because incident inclusion in the TKB relies on a definition of terrorism, although a comprehensive one, it is inherently subjective in interpretation and may not report all incidents properly (Laqueur 1987). This is what Bogen and Jones (2006) point out when they indicate that some level of spuriousness or data collection artifacts may be present in the TKB data. The authors establish that for some reporting of deaths and casualties in the TKB data, there may be overestimation based on initial reports made by media

(although corrections and updates are consistently made). Although some limitations exist with the data, the authors were able to conclude that trends of morbidity worldwide could be established and also aid as a predictive measure. In addition, since the database

93 is actively updated, it is important to note that my analysis is based on 26,505 terrorism incident records updated on March 15, 2006.

Conveniently, two geographic fields exist that are pertinent to this research: a city and a country field. Guo et al. (2007) demonstrate through the development of different visualization techniques the power of understanding trends over time for this dataset primarily using the country field. However, the TKB database does not follow the normal convention of country designation, as evidenced by the assignment of Kashmir and the

Persian Gulf under the country category. The country field not only has non-country units within it, but also there exist multiple country names for the same area, such as the Czech

Republic and Czechoslovakia. Political units that no longer exist are still within the TKB.

Other fields include: the date, the terrorism group who perpetrated the incident (if known), the target and tactic taken, the number of deaths and injuries, and a description field that allows the investigator to read brief details about each incident. In many cases, the incidents are completely filled with data for all fields. However, in some cases data are absent, including the city field, as shown in Table 3.3.

An examination of the historical account often can help to impute the missing or incorrect location for each incident. The review of historical documents can alleviate many of the questions surrounding geographic location in the TKB database. Some of the most valuable resources for historical retrieval are newspaper and news wire accounts.

News wire accounts are snippets of information sent through press sources to report on unfolding events. Both newspaper and news wire accounts were used to fill in missing fields, primarily found through the Lexis-Nexis archive system and from the archives of

94 the New York Times and the London Times. Though tedious and difficult at times due to the challenge of searching archives, the process can result in clarification of the description for an incident and allow for the imputation of location.

Using a combination of the location field and the description field, many of the exact locations of the terrorism incidents can be identified. As a result, the locations of terrorism incidents can be determined to be either urban or non-urban, even though the category is not present in this dataset. Since the goal of this research is to build a vulnerability index for the urban environment, the differentiation has been made to separate urban incidents from non-urban incidents as part of the identification of locations, frequencies and magnitudes of the incident – the goal of point 1, 2, and 3 of

Step 1 in the STOP model.

95

Countries* Number Number of missing ‘City’ Records Field 193 26, 505 4,459** Countries* with the highest number missing ‘City’ Field 1. Colombia 559 2. Iraq 439 3. West Bank/Gaza 368 4. Turkey 263 5. Thailand 262 6. Israel 181 7. Lebanon 171 8. France 145 9. Kashmir 126 10. India 99 11. Philippines 94 12. Federal Republic of Germany 93 13. Peru 84 14. Spain 82 15. Greece 73 *The TKB country category designated such places as Kashmir and the Persian Gulf as countries and have separate categories for places like the Czech Republic and Czechoslovakia, which has to be accounted for in analysis. **148 of the 193 countries had at least 1 missing ‘City’ field.

Table 3.3 Country-specific terrorism incidents missing the ‘City’ field in the TKB database, 1968-2006. Calculated from MIPT (2006).

96 Urban from Non-Urban

While it has long been assumed that terrorism is urban in nature, Savitch and

Ardashev (2001) are the first to empirically show that terrorism and the urban environment are linked through analysis of terrorist incidents. They contend that terrorism is an urban phenomenon and argue that terrorism has a distinct urban address.

In their empirical analysis, they utilize the data available from the document Patterns of

Global Terrorism (POGT) (U.S. Department of State 1985-2004). It is a collection of terrorism incidents organized by year in the form of paragraphs and charts. Savitch and

Ardashev use the POGT from 1993 – 2000 as the basis for their analysis, from which they conclude that terrorism is more prevalent in urban areas than in rural areas and that terrorism has increased in number of incidents and damage from 1993 to 2000. Although using a clear definition of terrorism from the POGT document, which attempts to provide a comprehensive incident report for terrorism, the authors are vague about their definition of urban, which is highly problematic from a modeling perspective.

It is important here to point out briefly that there is great debate among scholars and governments over definitions of what constitutes urban and rural. Slack (1990) shows that confusion exists between definitions of urban and rural among different municipalities. Berry et al. (2000) cite that many scholars reject notions of simple definitions of the urban/rural divide. When definitions are chosen, little consideration is given to that of other geographic places (Cohen 2004); therefore, what is considered urban in one place may be non-urban in another. Showing a considerable range from one country to the next, Zlotnik (2004) reports that urban definitions span from 200 persons

97 (in Sweden) to those of 50,000 persons or more in the U.S. for urbanized areas (cf.

United States Census Bureau 2006). Criteria for classification of rural and urban can incorporate both administrative and demographic criteria, such as the urban definition in

India. Bhagat (2005) points out that in 1961, urban India was defined as: All places with urban characteristics and having a population of 5000 or more, a population density of

400 persons per square kilometer, and 75 percent of the workforce employed in the non- agricultural sector. By 1981, the decision was made that only males be considered as members of the workforce, introducing a demographic gender variable into the Indian urban definition. As a result of the debate about the varying definitions of what is urban and not urban, it can be seen that no simple definition exists for all places at all times.

Therefore, when attempting to answer empirically whether or not terrorism is urban or rural a definition of urban is needed to guide the research. Before a tally can be made of the frequencies and magnitudes of terrorism incidents, a distinction between urban and non-urban incidents should be made. However, what is presently lacking is a methodological approach for distinguishing urban areas and combining them with terrorism incidents for analysis. Determining a methodology is important not only for the sake of scientific replication, but also for the efficient allocation of resources for the protection of people and property from terrorism. Presently, it appears impossible to replicate the results of Savitch and Ardashev (2001) because it is unclear how they determined whether a terrorism incident was urban or non-urban.

To construct a simple methodology for distinguishing terrorism incidents in the

TKB as being urban or non-urban, context is the key. For example, what is considered

98 urban in Spain is not considered urban in Indonesia; a specific uniqueness of place exists because urban structure, design, and growth are dependent on societal conventions.

Social definitions of urban often are dependent on census counts, since urban environments are usually determined for places reflecting higher densities of people.

Other scholars, such as Cohen (2004) and Zlotnik (2004), have recognized this fact and used country-specific definitions for the differentiation of urban from non-urban.

Therefore, to address the issue of urban terrorism in this study, the context of country and how individual countries determine their own definition of urban is considered as the agent for what is or is not urban. Then, terrorism incidents will be catalogued as being either urban or non-urban based on the country specific definition. It should be noted that non-urban has been used as a term for anything other than urban, which includes incidents in rural areas and incidents not on land, such as in the sky or sea.

The United States is the country of interest for the development of the vulnerability index. Therefore, all U.S. incidents in the TKB were selected and combined with census data, since the Census Bureau determines statistically the definition of urban in the United States. The TKB cover approximately 4 decades from 1968 to 2006 (1968-

1980, 1981-1990, 1991-2000, 2001-2006). Comparing the TKB locations by decade for

U.S. incidents with that of the decennial definition of urban results in no significant changes from 1968 to 2006. This means if a location in the TKB dataset was urban in

1968, then it was statistically considered urban to 2006. Likewise, locations that were considered non-urban in 1968 were also still non-urban in 2006, even with small refinements and changes to the urban definition as given by the Census Bureau over the

99 four-decade time frame. Therefore, each U.S. incident in the TKB dataset was determined to be either urban or non-urban and all incidents for which location could not be identified were deleted.

With the urban and non-urban distinction made for the U.S. incidents, the frequencies and magnitudes can be determined. To capture the frequencies of the events, each incident was tallied based on target type as shown in Table 3.4. In the table, the types of targets are along the top and have been determined by the TKB dataset creators.

Notice that diplomatic, business, and airports/airline attacks have occurred the most since

1968 in the United States. In total, of the 479 incidents, 440 of them were in the urban environment. However, what appears somewhat problematic from a hazards modeling perspective is the lack of current incidents. Notice that although there have been 440 urban terrorism incidents more than half of them are older than 25 years. From 1968 to

1980, the highest number of urban incidents, 260, appear which coincide with the radicalism and rebellion of the turbulent late 1960s and 1970s. After these decades, the urban terrorism incidents significantly decrease, leaving then a disproportionately small number of terrorism incidents from which to build an accurate current model of terrorism vulnerability in the United States.

Therefore, in hopes to increase the accuracy of the vulnerability modeling effort a surrogate should be used to supplement the U.S. TKB data to account for more current trends in terrorism incidents. Where can such a surrogate come from? Examining the top ten TKB terrorism incident countries might provide a suitable nation to supplement the older terrorism incident U.S. data with current trends in terrorism. The top ten are

100

rty Urban & Non- stitutions

Urban ted n Supply Terrorism in a USA by Target 1968 to 2006 aritime ilitary irports/Airlines bortion Rel iplomatic GO tilities ther usiness eligious Person/Place ducational I elecommunication errorists ourists ransportation ood /Water olice ournalists & Media A A B D E F J N P Private Citizen & Prope M M R T T T T U O Total Count Government 1968 Non-Urbn 1 14 1 16 1980 Urban 37 55 113 2 13 1 1 2 1 1 4 2 1 27 260 1981 Non-Urbn 0 1990 Urban 6 24 28 1 1 5 1 2 4 1 12 85

101 1991 Non-Urbn 4 4 5 13 2000 Urban 3 5 3 3 10 1 1 1 1 3 31 2001 Non-Urbn 8 1 1 10 2006 Urban 1 1 24 1 3 12 5 14 1 2 64 Non-Urbn 0 0 12 0 0 0 5 0 0 0 6 14 0 0 0 0 0 0 0 2 39 Total Urban 44 4 108 145 3 3 29 21 0 1 18 1 2 6 0 5 1 4 1 44 440

Table 3.4 Urban and non-urban terrorism in the USA by target type, 1968-2006. Calculated from MIPT (2006). presented in Figure 3.1. The two most important aspects in relation to supplementing the

U.S. data with that of another country are target types and current attacks.

. Terrorism Incidents 1968 to 2006 4500 Top 10 Countries 4000 3500 3000 2500 2000

1500 1000 500

0

q a a a n y e l n ) a z i i i e c e a K I r b d a k n ra t Ga In p r a s is (U / m S u r I k k lo T F a d n o P n a C la B e . Ir W rn e h 4139

t 696 2001 1683 1579 1290 1164 937 748 r 1097 o N

Figure 3.1 Top ten terrorism incident countries, 1968-2006. Calculated from MIPT

(2006).

Obviously the geopolitical context in the United States is different than every other country in the world. Therefore, one way to get around the differences in geopolitics is to examine countries based on similarities of target types. This type of

102 approach can in some ways circumnavigate the geopolitical climate differences and yet still capture the results of terrorism. Although the motives of terrorism are probably different based on the geopolitics of specific countries, the idea is that targets will still be the same or similar, when generalized in a database. For example, there will always be a need for people to travel. What form or fashion travel systems develop throughout the world will certainly be different and in some cases dramatically different, noting the uniqueness of geographic place. In the end, however, travel systems will still be catalogued as such, regardless of uniqueness when generalized. Terrorism incidents that attack parts of travel systems are generalized as being transportation related incidents in a database like the TKB. In the example above, a high level of generalization results in terrorism targets being categorized as transportation in the TKB. So regardless of the motive, transportation terrorism targets like trains, cars, gas stations, and subways get grouped into a transportation category. Individual categories are created only when specific modes of transportation are attacked in high numbers, such as with the airport/airline category in the TKB database. Then, which of the top ten countries exhibit similarities of targets with that of the United States and also have current trends in terrorism incidents (not dominate in terrorism incidents in the 1960s and 1970s)?

Spain, ranking as the fifth highest terrorism incident country from 1968 to 2006, shares similar trends to United States terrorism incidents. By conducting the same decade determination of urban from non-urban incidents in Spain using the country’s census data and urban definition as was done for the United States, the results confirm that terrorism incidents in Spain are similar to the U.S. and that they demonstrate current trends as

103 shown in Table 3.5 (See Appendix B for a thorough discussion of the methodological procedure for cleaning the Spanish data and determining Spanish terrorism as being urban or non-urban8). Notice first that the types of targets are identical to the U.S. types of targets in Table 3.4, except for three categories. Spanish terrorism does not feature attacks on abortion clinics, food and water supply targets, and religious persons and places. These three categories account for only 13 total attacks, which constitute very few of the overall percentage of attacks in the United States, further supporting the similarity between target types of the two countries. Furthermore, the total number of attacks in

Spain tally to 1240, of which 1010 are urban. Of those urban terrorism incidents, almost

951 of the 1010 incidents have occurred within the last 25 years. Since the types of targets are very similar to U.S. targets and Spanish terrorism incidents are fairly recent giving a good approximation of current trends, Spain arguably is an excellent choice for supplementing the United States TKB terrorism incident data.

Combining the United States and Spain TKB data, the calculation of terrorism incident frequencies is shown in Table 3.6 and includes all urban non-modern and urban modern terrorism incidents in both countries since 1968. However, to know exactly how the Spanish-supplemented data is different from the U.S. data, a sensitivity analysis was conducted. Table 3.7. gives the results of the analysis. First, the complete totals for the

U.S from Table 3.5 are included as the USA Urban column. The percent from the total are given in the Percent column. Second, the modern incidents were isolated in the U.S. urban terrorism data in the column USA Modern. Modern is considered after 1980 and its

8 Cleaning the United States data was identical to the procedure explained in Appendix C for the Spanish data. Both errors of commission and omission were corrected for in the same manner for the U.S. as was done for Spain in Appendix C. 104

Urban & Non-

Urban ication lines Terrorism in n

/Air

y Spain by Target s ry

1968 to 2006 ert p nknown

ducational rivate Citizen & ro nstitutions Maritime Milita Telecommu Terrorists Tourists Transportation Utilitie Other Airports Business Diplomatic E I P P U Total Count Government Journalists & Media NGO Police 1968 Non-Urban 2 1 3 1980 Urban 17 15 23 1 1 1 59 1981 Non-Urban 16 1 2 3 1 23 1990 Urban 5 84 27 1 5 2 4 2 9 1 140 1991 Non-Urban 26 24 1 10 2 8 2 5 3 5 2 88 2000 Urban 2 114 9 10 134 20 1 43 4 11 2 25 3 25 11 417 2001 Non-Urban 29 31 1 15 5 7 4 8 3 6 7 116

1 128 11 0 2006 Urban 5 2 2 89 14 46 13 1 10 8 40 5 20 394 5 Non-Urban 0 73 0 0 55 2 0 25 7 2 2 15 0 6 16 6 12 9 230 Total Urban 29 341 61 13 223 34 1 89 17 0 10 21 2 15 67 8 55 24 1010

Table 3.5 Urban and non-urban terrorism in Spain by target, 1968-2006. Calculated from MIPT (2006).

Target Percent Airports/Airlines 5.03 Abortion Related 0.28 Business 30.97 Diplomatic 14.21 Educational Institutions 1.10 Food/Water Supply 0.21

Government 17.38 Journalists & Media 3.79 NGO 0.07 Police 6.21 Private Citizen & Property 2.41 Maritime 0.07 Military 0.83 Religious Person/Place 0.41 Telecommunication 1.45 Terrorists 0.48 Tourists 1.10

Transportation 4.90 Utilities 0.62 Other 6.83 Unknown 1.66 Total Count 100.00

Table 3.6 Percentage of terrorism incidents by type of target, U.S. & Spain. Calculated from MIPT (2006).

106

importance has been argued above. All incidents totaled 180 and the Percent2 column shows the calculation of the percentage from this total. Finally, the last column, US &

Spain %, shows the results of combining the two data sets, which has been given in Table

3.6, and is referred to interchangeably below as the ‘combo set’ and US & Spain %.

Comparing the overall, USA Urban, with the combined data, US & Spain %, gives differences. First, diplomatic targets come in at a frequency of 32.95 percent (USA

Urban), 17.78 percent (USA Modern), and 14.21 percent (US & Spain %). The modern and combo set reflect more similar numbers than USA Urban, demonstrating for this target that the use of the combo set reflects much more accurately the current modern trend of terrorism. The 1960s and 1970s incidents of terrorism against diplomats are significantly higher, where nowadays this type of terrorism is highly infrequent. Second, business targets come in at a frequency of 24.55 (USA Urban), 29.44 percent (USA

Modern), and 30.97 percent (US & Spain %). Again, the modern and the combo set reflect more similar numbers than USA Urban and reflect current terrorism trends.

Importance here is that business percentages are more frequent for the more modern incidents and that the USA Urban numbers reflects older target trends. Finally, government targets come in at a frequency of 6.59 percent (USA Urban), 8.89 percent

(USA Modern), and 17.38 percent (US & Spain %). The modern and the combo set here are of largest difference, yet what is true is the rise in incident frequency for USA Modern at almost 9 percent. This trend is obviously rising and the TKB confirms this. Table 3.4

107 shows that just within the years 2001 to 2006, 12 government targets were attacked in the

U.S. Extrapolating that out to a decade worth of data gives approximately 23 to 24

USA Target USA Urban Percent Modern Percent2 US & Spain % Airports/Airlines 44 10.00 7 3.89 5.03 Abortion Related 4 0.91 4 2.22 0.28 Business 108 24.55 53 29.44 30.97 Diplomatic 145 32.95 32 17.78 14.21 Educational Institutions 3 0.68 3 1.67 1.1 Food /Water Supply 3 0.68 1 0.56 0.21 Government 29 6.59 16 8.89 17.38 Journalists & Media 21 4.77 20 11.11 3.79 NGO 0 0.00 0 0.00 0.07 Police 1 0.23 0 0.00 6.21 Private Citizen & Property 18 4.09 16 8.89 2.41 Maritime 1 0.23 0 0.00 0.07 Military 2 0.45 2 1.11 0.83 Religious Person/Place 6 1.36 5 2.78 0.41 Telecommunication 0 0.00 0 0.00 1.45 Terrorists 5 1.14 1 0.56 0.48 Tourists 1 0.23 1 0.56 1.1 Transportation 4 0.91 2 1.11 4.9 Utilities 1 0.23 0 0.00 0.62 Other 44 10.00 17 9.44 6.83 Unknown 0 0.00 0 0.00 1.66 TOTAL 440 100.00 180 100.00 100.01

Table 3.7. Sensitivity analysis between U.S. and Spanish terrorism. Calculated from

MIPT (2006).

government target attacks (remember that the dataset is current to March 2006). This number would be significantly higher than any other decade and therefore, it seems more trustworthy to use the combo set data at 17.38 percent, which more accurately reflects current trends in terrorism. 108 So if USA Modern is similar to the US & Spain %, why use the combo set? The reason to use the US & Spain %, reported in Table 3.6, instead of just the USA Modern is primarily due to the fact by using just the 180 USA Modern incidents does not account well for all of the potential targets of terrorism. Five viable targets (excluding the

Unknown category) have zero percentage in USA Modern, which would result in no

STOP model calculations for targets such as telecommunications, utilities, NGOs, maritime targets, and terrorism attacks on police. Leaving these categories out of the

STOP model would be foolish from the immense evidence of these categories being targets of terrorism in the past globally (the TKB data has for the globe from 1968 to

2006: telecommunications (136 incidents), utilities (917 incidents), NGOs (314 incidents), maritime targets (132 incidents), and terrorism attacks on police (2246 incidents)). Therefore, the best way to gauge current trends in terrorism for incorporation into the STOP model is to combine the Spanish data as a supplement to U.S. terrorism incidents.

In the same manner, magnitudes of the terrorism incidents have been calculated by joining the U.S. and Spain TKB data. Magnitude of terrorism incidents means the summation of deaths and injuries from an incident. Combining the U.S. and Spain data, the effect of terrorism incidents on people can be determined, as shown in Table 3.8.

Notice that business-related targets have the largest number of deaths and injuries, a result from the overwhelming magnitude of the 9/11 events. This one event, although certainly skewing the overall distribution to a degree, points out the tremendous number of deaths and injuries that can occur in cities with many tall buildings and the use of a

109 weapon of mass destruction, in this case an airplane. Typical model practice usually excludes outliers like the 9/11 events, however, this “outlier” event is important to include in the STOP model because it typifies a goal of terrorism to maximize terror.

Including this event within the model captures present-day pursuits of terrorists and makes including it an important step in modeling potential modern terrorism. Also notice that the 9/11 attack on the Pentagon (TKB 7759 Incident 2007), is categorized as government and not military. In total, there were 189 deaths and 76 injuries at the

Pentagon from the attacks of 9/11. An argument might be made that in the case of the

Pentagon, there are significant numbers of both government civilians and military personnel, but that the role of the Pentagon does not function like a typical military base.

All military targets are in Spain and form incidents that are direct attacks on military personnel or/and military bases. The final point of interest is the large number of Spanish terrorism causalities and injuries from attacks on transportation targets. Although this is unique to Spain when compared to the United States, the incorporation of this incident data supports the predictive notions of the US government to secure transportation as critical infrastructure in the “Public Transportation Terrorism Prevention and Response

Act of 2004” (United States Congress 2004). The frequency list by target type and calculated magnitude form the basis for parts 1, 2, and 3 of STEP 1 in the STOP model for all United States cities. The next section introduces the case study area to address part

4 of STEP 1 in the STOP model.

110

USA SPAIN USA & SPAIN Target Mag. Target Mag. Target Mag. Abortion Related 3 Abortion Related 0 Abortion Related 3 Airports & Airlines 114 Airports & Airlines 118 Airports & Airlines 232 Business 6066 Business 92 Business 6158 Diplomatic 61 Diplomatic 32 Diplomatic 93 Educational Institutions 2 Educational Institutions 3 Educational Institutions 5 Food or Water Supply 0 Food or Water Supply 0 Food or Water Supply 0 Government 1014 Government 259 Government 1273 Journalists & Media 19 Journalists & Media 9 Journalists & Media 28 Military 0 +Military 146 =Military 146 NGO 0 NGO 0 NGO 0 Other 39 Other 28 Other 67 Police 0 Police 36 Police 36

111 Private Citizens & Proper 7 Private Citizens & Propert 43 Private Citizens & Prop 50 Religious Figures/Institut 3 Religious Figures/Institutio 0 Religious Figures/Instit 3 Telecommunication 0 Telecommunication 17 Telecommunication 17 Terrorists 2 Terrorists 3 Terrorists 5 Tourists 6 Tourists 29 Tourists 35 Transportation 8 Transportation 799 Transportation 807 Utilities 0 Utilities 0 Utilities 0

Table 3.8 U.S. and Spain terrorism magnitudes (sum of deaths and injures), 1968-2006. Calculate from MIPT (2006). STOP Case Study

4. Equate, for mapping purposes, by taking the CI list and associating it with the parcel coding within an urban area. Recoding parcels may be required to accurately equate CI with parcels.

The case study urban area that has been chosen for illustrating the geographic hazards model is Columbus, Ohio, shown in Figure 3.2. The general demographics of the city are given in Table 3.9. Columbus has been chosen because it is a very good representative city or ‘proto-typical’ U.S. city and is highly accessible for research conducted at The Ohio State University. The city is predominantly within one county

(Franklin) and it has a racial distribution and Internet penetration rate that allows it to be consistently used as a test-bed city for new restaurants and for market research, although data shows a decline (Stammen 2004) . It is not the most likely city for terrorism targets in the United States and, arguably, nor is it the least likely, considering al Qaeda terrorists were discovered operating in Columbus and plotting terrorism in the city and elsewhere

(Riskind and Torry 2003, Mayhood 2007). Therefore, the STOP model results can be applied to most U.S. cities with a decent level of reliability. Specific differences among cities are discussed in the evaluation of the model.

112

2005 (US. Census Bureau) Population 1,090,771 Racial Distribution • Caucasian – 72.7% • African-American – 19.8% • Asian – 3.7% • Hispanic – 3.3% • Other – 0.5%

Gender Distribution • Males – 49.1% • Females – 50.9% Median Household $45,410 Income th Internet Penetration 70.1% (11 Nationally Ranked) (Neilson Net Ratings 2003)

Table 3.9 General demographics of Columbus, Ohio. United States Census Bureau

(2005).

Figure 3.2 Map of Columbus, Ohio within Franklin County. By author. 113 With Columbus, Ohio selected as the case study, the STOP model methodology requires an association be made between the general frequencies and magnitudes of the

U.S. terrorism incidents and the urban parcels of the city. To complete point 4 of STEP 1 in the STOP model, the parcel codes have been collected for Columbus, OH from the

Franklin County Tax Auditor office (2006). The parcel codes are not a perfect coding system that can be directly associated with the frequencies and magnitude calculations; however, the parcel codes can be determined to fit within specific categories in the frequencies and magnitudes scheme, as seen in Figure 3.3. The figure demonstrates the coding options from parcel designation to the critical target framework. Notice that places like gas stations can be coded as Transportation targets and single-family homes as being designated in the Private Citizen & Property category. In some cases, parcels are clearly apart of one specific target category but may also be fall into one of two categories. For example, gas stations are part of transportation, but also act as a business.

Gas stations are not categorized as oil and gas nor as transportation in the NAD and have been put in Transportation because the oil and gas category deals with oil refining, not small-scale distribution (Office of Inspector General 2006) and because the TKB records over 150 terrorism attacks on gas stations as categorized as Transportation. The target type of category chosen from the parcel is dependent on researcher choice, yet has been guided by the NAD criteria (Office of Inspector General 2006). To be clear how each parcel was coded in the STOP model, the details are provided in Appendix C. Mapping the results of the frequencies by parcel is shown in Figure 3.4.

114

Target Percent Airports/Airlines 5.03 Abortion Related 0.28 Business 30.97 Diplomatic 14.21 LU Description

Educational 457 PARKING LOT/STRUCTURE

Institutions 1.10 458 GAS STATION/CONVENIENCE FOOD STO 459 GAS STATION/CAR WASH Food/Water Supply 0.21 460 THEATRES Government 17.38 461 DRIVE-IN THEATRES Journalists & Media 3.79 462 GOLF DRIVING RANGES AND MINIATURE NGO 0.07 463 GOLF COURSES P olice 6.21 464 BOWLING ALLEYS Private Citizen & 465 LODGE HALLS AND AMUSEMENT PARKS Property 2.41 466 TRUCK/FARM EQUIPMENT SALES & SER Maritime 0.07 467 USED CAR SALES LOT 468 OPEN CODE OR OTHER INDUSTRIAL Military 0.83 469 OPEN CODE OR OTHER INDUSTRIAL

Religious 470 SINGLE FAMILY DWELLING

Person/Place 0.41 471 SINGLE FAMILY DWELLING, CONVERTED Telecommunication 1.45 475 RETAIL CONDO Terrorists 0.48 479 DOG/CAT KENNELS

Tourists 1.10

Transportation 4.90 Tax Auditors Parcel Coding C.I. & Common Target List Utilities 0.62 Other 6.83 Unknown 1.66 Total Count 100.00 Frequencies Target List

Figure 3.3. Association of frequencies of terrorism incidents in the Untied States and

Spain with tax auditor codes in Franklin County, Ohio.

115

Figure 3.4 Potential terrorism targets by frequency in Columbus, Ohio. By author. 116 5. Divide all frequencies equally into 5 categories, and assign each of the attacks a frequency score (F) from 1 (low) to 5 (high). Similarly divide all magnitudes into 5 equal interval categories, and assign each of the attacks a magnitude score (M) from 1 (low) to 5 (high).

With parcels coded into the CI framework, the frequencies and magnitudes for each parcel in the county can be standardized providing an avenue of weighting for the final determination of the VTI. Point five of Step 1 above is the method for standardizing.

Although somewhat arbitrary, the weighting system has been developed in a similar manner as an equal interval method with choropleth map categorization. The data range was considered and then an equal interval categorization was chosen for the frequencies.

The magnitudes interval categorization was done in a similar manner as Savitch and

Ardashev (2001) with similar magnitude breakpoints. However, it is important to note that although break points here are crisp and that 100 deaths, for example, should be given a rank of 3 and 101 deaths a rank of 4, there is no accepted standardized level. In the case of modeling these tragedies of death and injury, a decision was made based on standard numerical break points (10, 50,100, and 250) and in no way should be interpreted that 50 deaths are not as important as 100 or 250 deaths. The STOP model does not make a moral statement regarding the loss of life but attempts to provide a measure to help protect human life. For the purposes of simplification, five categories were chosen as seen in Figure 3.5. Each of the critical infrastructures was given a categorical rank.

117 6. Calculate the weight (w) for each of the parcels:

weight (w) = F x M

At point 6 of Step 1, the standardization of the frequencies and magnitudes are multiplied to form weights, which will be combined with Step 2 results to form the VTI. By assigning weights to the frequencies and magnitudes a more accurate picture of the potential strength of a terrorism attack can be realized because both the frequency of occurrence and the magnitude strength of previous attacks are taken into consideration.

7. Map the area affected for each of the attacks, either as point or polygon layer by weight.

The resulting weights can then be mapped as seen in Figure 3.6.

118

USA & SPAIN USA & SPAIN Frequency Target Freq Cat. Target Mag. Cat. Category 1 0 – 5.0 NGO 0.07 1 Food or Water Supply 0 1 Category 2 5.01 – 10.0 Maritime 0.07 1 Maritime 0 1 Food/Water Supply 0.21 1 NGO 0 1 Category 3 10.01 – 15.0 Abortion Related 0.28 1 Utilities 0 1 Religious Person/Place 0.41 1 Abortion Related 3 1 Category 4 15.01 – 20.0 Terrorists 0.48 1 Religious Figures/Institution 31 Category 5 +20 Utilities 0.62 1 Educational Institutions 5 1 Military 0.83 1 Terrorists 5 1 Educational Institutions 1.1 1 Telecommunication 17 2 Magnitude Tourists 1.11 1 Journalists & Media 28 2 Telecommunication 1.45 1 Tourists 35 2 Private Citizen & Property 2.41 1 Police 36 2 Category 1 0 – 10 Journalists & Media 3.79 1 Private Citizens & Property 50 2 1 1 Category 2 11 – 50 Transportation 4.9 1 Other 67 3 9

Airports/Airlines 5.03 2 Diplomatic 93 3 Category 3 51 – 100 Police 6.21 2 Military 146 4 Category 4 101 – 250 Other 6.83 2 Airports & Airlines 232 4 Diplomatic 14.21 3 Transportation 807 5 Category 5 +251 Government 17.38 4 Government 1273 5 Business 30.97 5 Business 6158 5

Figure 3.5 Frequency and magnitude standardization. Calculated from MIPT (2006).

Figure 3.6 Weighted potential terrorism targets in Columbus, Ohio. By author. 120 3.1.3.2 STEP 2

With the weights determined by the seven points of Step 1, now the analysis of why a particular target might be chosen over another can be determined. Step 2 incorporates multiple levels of scale to determine One Target Over Another (OTOA).

1. Identify all OTOA places. The indicators should include, but not necessarily be limited to:

• Region Population (P) • Symbology (S)

2. Determine each OTOA separately: (P) then (S).

A. Region Population (P): Metropolitan Statistical Area – If the MSA is: <500,000 = a value of 1 500,000 to 1,000,000 = value of 2 1,000,000 to 4,000,000 = value of 3 4,000,000 to 8,000,000 = value of 4 >8,000,000 = value of 5 or National Capital = value of 6

First, the regional population is taken into consideration. The Metropolitan Statistical

Area (MSA) determined by the Census Bureau is used to determine a ranking system that will be incorporated to determine the VTI. The MSA rank value in the STOP model allows for a comparison between cities in the United States. For example, if all things are considered equal between two places in regards to STEP 1 of the STOP model, yet one city is larger than another, the MSA rank can help determine if a place is more likely to be attacked by terrorism. The simple logic here formulated by Schmid and Jongman

(1984) is that larger, more populated places are more attractive to terrorism attack than

121 less populated places in the United States. The data in Tables 3.4 and 3.5 confirm that urban areas are attacked much more often than rural areas and analysis of the U.S./Spain

TKB data show that larger cities are more often attacked than smaller cities. I have extended their ideas to build this part of the STOP model, arguing that when comparing one city over another, the larger one is more likely to harbor terrorism than the smaller.

The only exception to the MSA size is the national capital of Washington D.C., which receives the highest rank because it represents a more attractive target than the other cities due to its status as national capital and its prominent role as the United States seat of government.

B. Symbology (S): If a structure is: a national monument or historic landmark or major event site (e.g. stadium) or important government facility (city hall) or a building over 12 stories tall (» 150 ft) or tallest 3 buildings or capital/president or governor home = value of 2; otherwise = value of 1 (Results can be cumulative)

Symbology remains one of the important aspects of terrorism and why one target is chosen over another. Pape (2005) argues that suicide bombers pick public targets like outdoor markets for reasons of damage, fear, and publicity. Hoffman (1998) points out that symbology played an important aspect in the internationalization of terrorism during the 1960s as was discussed in the Introduction section. Finally, Rapoport (2004) argues that four distinct waves of terrorism can be understood through history and that in the

122 fourth wave, religious-sourced terrorism starting from the 1960s used media and publicity by selecting symbolic places for attack.

To distinguish places as being symbolic, a scheme was developed to try and incorporate important symbolic aspects of U.S. life – history, government, public gatherings, and business. There are certainly other ways to try and organize what is symbolic or important to U.S. citizens and this is only one way to categorize symbology.

Under this rubric of categories the following represent what will be considered symbolic in the STOP model: national and historic landmarks (rooted in PLO attacks, Hoffman

(1998, 20 TKB incidents)); major event sites where thousands of people gather such as sporting events, concerts, or rallies (rooted in U.S. Olympics attack of 1996 and WMD threat portrayed in popular print and movies such as those by Clancy (2002), 26 TKB incidents); important government facilities such as legislative, judicial, or executive branch high-level facilities (rooted in Russian eighteenth century terrorism and the

Weathermen attacks of the 1960s and 1970s (Combs 1997), 69 TKB incidents); buildings over 150 feet in height and the three tallest buildings in a city (rooted in Hezbollah and al

Qaeda attacks (Byman 2003), 379 TKB incidents (however, search was inconclusive about building heights, so this is total of skyscrapers and hotels attacked); and a final category if a place is a state or national capital building or if it is the place of residence of the governor or president of the United States (rooted in Russian eighteenth century terrorism and Basque terror attacks (Shabad and Ramo 1995), 68 TKB incidents).

If a place is determined to fit any of these categories then it is awarded a value of two in the STOP model, otherwise it is given a value of one. Often it is the case that

123 places can be a combination of symbolic categories, such as the U.S. Capital. It is historic, taller than 150 feet, a major government facility, the national capital building, and a major event site. As a result, a value of two is assigned for each of those symbolic categories and the results are cumulative. The U.S. Capital building has a symbolic value of 10 in the STOP model.

From a modeling standpoint, databases were developed for each of the symbolic categories. As a result, several different databases were used to determine which places in

Columbus were symbolic, which is shown in Table 3.10. From these sources, the specific polygon building footprints were established by manual digitizing using a combination of areal photography and building footprint files, both available from the Franklin County

Tax Auditors office. A 3D model was built using the footprints and the symbolic categories to visualize the downtown area of Columbus as seen in Figure 3.7. Notice that the same rubric was applied to these buildings and as a result four buildings are modeled as the most symbolic in the downtown Columbus area – the Ohio Statehouse, the Rhodes

State Office Tower, LeVeque Tower, and the William Green Building.

3. Derive a value for One-Target-Over-Another as:

OTOA = P x S

The cumulative symbolic values (S) in Figure 3.7, are multiplied by the regional population MSA to derive a value to determine One-Target-Over-Another. In the case of

Columbus, Ohio, the population MSA is a little over a million and is given a rank of 3.

Figure 3.7 shows the cumulative symbolic values as multiplied by 3. Once this was

124 complete for the city of Columbus throughout Franklin County, the Vulnerability Threat

Index was completed, as shown below.

Type Source National National Historic Landmarks Program (National Parks Monument Service 2004) State Ohio Historical Society (Ohio Historical Center 2004) Monuments Major Event Franklin County Tax Auditor Office 2006 Site (…FranklinCounty\2006_0411_xtra_group\ P_VENUES & …\ P_SHOPPING Important Compiled list to include only: Rhodes State Office Tower, Government Franklin County Sheriff Office, Franklin County Court Facilities House, Ohio Supreme Court, Ohio Statehouse, Police HQ, and City Hall Building over Buildings of the City Columbus Ohio (Emporis 2007) 150 feet Tallest Three Compiled list includes: Rhodes Office Tower (629 ft), LeVeque Tower (555 ft), and William Green Building (532 ft) (Emporis 2007). Ohio Statehouse Franklin County Tax Auditor Office 2006 and Governor’s (…FranklinCounty\2006_0411_ P_NOTABLE Home

Table 3.10 Symbolic database sources for Columbus, Ohio places.

125

Figure 3.7 Three-dimensional geovisualization of downtown Columbus, Ohio. By author.

126 3.1.3.3. STEP 3

1. Take the weighted values completed in Step 1 and multiply them by the OTOA values found in Step 2 for each point or polygon as:

w x OTOA = VTI

The VTI is calculated by taking the weights derived for each polygon as completed in Step 1 and the One-Target-Over-Another footprint in Step 2. Overlay GIS analysis was used to make this determination in Columbus, Ohio. Figure 3.8 shows the results of the operation.

Before the analysis can be discussed the survey methodology is introduced and discussed in the following section. Analysis of the realized threat and the perceived threat will be given afterwards.

127

Figure 3.8 Vulnerability Threat Index of Columbus, Ohio. By author.

128 3.2 Survey Methods

3.2.1 Introduction

One of the goals of this work is to survey the population in the Columbus study area to develop a terrorism threat profile for an urban area to understand people’s perceptions of terrorism threat. Survey methodology is a science unto itself, therefore, short explanations and justifications of choices in survey design, implementation, and analysis are provided where necessary. The grant money to conduct the survey was provided through the Mershon Center for International Security Studies at The Ohio State

University. A peer-reviewed proposal was accepted and funded for the full amount requested, $6950. The Ohio State University IRB approved an expedited review and allowed the survey to be conducted under protocol number 2006E0683. The name of the survey is the Columbus Terrorism Survey (CTS) and all of the questions are available in

Appendix D.

3.2.2 Survey Implementation and Design

A probability-based Internet survey has been shown to be one of the more versatile methods available in surveying (Evans and Mathur 2005). It has several advantages when compared to other designs, such as postal mail surveys, including implementation speed, simplicity of data acquisition, and correspondence (Archer 2003).

Comparing postal mail and Internet surveying, Evans and Mathur (2005) show the power of a web-based design in Table 3.11. The Internet survey provides greater control, instant

129 results, and behind-the-scene skip logic present in most paper surveys for greater ease to the respondent. Chang (2001) found that Internet surveys offer reliable means

Postal Mail Survey Internet Survey No sequencing Sequential precision controls Possibly inaccurate Accurate filtering filtering Possible Completeness of the incompleteness of answers Full answers Response Respondent not Full Response to Respondent not under to the under time pressure the survey time pressure to survey to answer includes: answer includes: No controls on time Time controlled and spent on surveys monitored Some analysis of Full analysis of respondent behavior respondent behavior possible possible Answers linked to Answers linked to respondent attributes respondent attributes

Table 3.11 Comparison of responses between postal mail and Internet surveys. After

Evans and Mathur (2005).

for data collection and offer more data quality than phone surveys. The Internet survey medium also costs less than face-to-face surveys (Scholl et al. 2002), phone surveys

(Roster et al. 2004), and postal mail surveys (Dillman 1978). Keeter et al. (2000) spent

$6000 alone on advanced letters, incentives, and follow-up letters for their telephone survey with 1000 respondents. The $6000 did not include the wages for interviewers, the programming and use of the computer assisted telephone interview (CATI) software, the supervising wage, or the data processing costs.

130 However, even though versatile, there are methodological challenges associated with this medium. Orr (2005) provides a list of considerations for web survey implementation. She cites cost, security, correspondence, design, flexibility, and data issues as the major components that should be considered when choosing the online host to use when the web survey is the medium of survey choice. After reviewing several

Internet web survey hosting options, it became apparent that Survey Monkey

(Surveymonkey.com 2006) would serve as the best choice in meeting with Orr’s considerations for the CTS. Surveymonkey.com minimized the cost, allowed for additional security features such as https:// 128-bit protocol protection for the data, allowed for flexible construction of the design to mimic successful surveys of postal mail/paper-pencil/phone surveys, offered logical connections that were easy to understand for the survey participant, and allowed for: a progress indicator (Crawford et al. 2001, Couper et al. 2001), radio buttons or drop-down menus (Heerwegh and

Loosveldt 2002a, Couper et al. 2004), multiple color schemes (Whitcomb and Porter

2004), XML for portability (Banks 2000), and flexible design options of single page

(Dillman 2000) and multiple pages (Evans and Mathur 2005), all important aspects as discussed by the various authors above.9

3.2.3 Survey Sampling Scheme

A sample survey is a study involving a subset of individuals selected from a larger population, where the members of the sample are interviewed and characteristics of

9 Some sites reviewed include: zapsurvey.com, inquisite.com, bigsurveys.com, and all-in-one sites like Harris Interactive, which provide not only the survey implementation options but also the sample of respondents. 131 interest, or variables, are measured on each observation. A sample survey is different from a census, where all individuals in a population are measured. The motivation for taking a sample instead of a census is primarily the prohibitive expense, in terms of time, capital, and human resources, of enumerating a population of interest. In some small studies where relatively few subjects are to be measured, it is possible and worthwhile to perform a complete census of the target population.

Weisberg et al. (1996) describe two types of sampling schemes, probability and non-probability sampling. Both types of schemes can be used with online approaches

(Anderson and Gansneder 1995; James et al. 1995; Swoboda et al. 1997; Kaye and

Johnson 1999). Both approaches were used in this research, but only the probability- based approach will be explained for the CTS. The non-probability results will be developed in a future paper as a comparative approach to test reliability differences between the methods. The primary reason to use a probability-based approach is that the statistical rigor is greater than non-probability approaches and can result in levels of confidence providing a measure of reliability. The reliability and validity of the summary statistics, or population parameter estimates, are interrelated with the design of the sample. Reliability is associated with the size of the standard error of an estimate and validity is measured by the bias, or deviance from the true population value, of an estimate. Reliability of an estimate can only be assessed if a probability-based sample is taken, so that the probability of selecting any individual for the sample is known. Levy and Lemeshow (1999) demonstrate the differences between probability-based and non- probability-based sampling schemes. The distinguishing characteristic is that in

132 probability-based schemes, each element or individual selected in the sample has a non- zero probability of being included in the sampling frame, which is an actual list of the population from which the sample can be obtained (e.g. households lists, phonebook lists, etc...). Quota sampling or “open-to-the-public” Internet polling is considered non- probability-based because there is no way to achieve a non-zero probability of selection for individuals who do not take the survey (Weisberg et al. 1996). Weights then can be assigned that correspond to how many households in the population are represented by one household in the sample. The finite population correction (FPC) can be used that impacts the standard error (SE) for a variable estimate under investigation. Using the FPC to lower the SE results in greater reliability or how reproducible the estimator is over repetitions of the process yielding the estimate. Non-probability sampling does not offer any of these reliability options.

Since this research uses the strengths of an online survey, there inherently will be people who are either unable or unfamiliar with the Internet and may automatically be excluded even among those randomly chosen and asked to participate, which may produce a unrepresentative bias. To address the possibility of misrepresentation, Witte et al. (2000) and Best et al. (2001) have found that although the digital divide exists, there are positive results showing Internet samples and general population sample responses to be similar. Furthermore, in consideration of the specific city under investigation,

Columbus, Ohio, the level of Internet savvy may be used. Columbus ranks 11th nationally among the most wired cities with an Internet penetration (the percent of people who use the Internet often) of 70.1 percent (Neilson Net Ratings 2003). As a result, even though

133 bias is surely to be present, in which people without access to the internet are excluded because of the digital divide, it is assumed to have no affect on the results and analysis.

A simple random sample was selected from the population. The sampling frame came from the Franklin County Tax Auditor and the household level represents the enumeration unit. One person per household was allowed to take the survey. Once the

14,000 simple random sampled households were selected from the frame, each was mailed a post card announcing the survey and requesting their participation in the survey.

Figure 3.9 shows the postcard that was mailed to potential participants. To increase participation, the CTS was held open for 2 weeks from November 28, 2006 to December

15, 2006 on the website www.columbusterrorismsurvey.org (the domain no longer is available). A discussion on response rates and results of the survey questions will be presented in the Analysis section.

Bosnjak and Tuten (2003) examined the issue of prize drawings versus prepaid monetary incentives as a way to increase rates for online surveys. They found the prize drawings to be a more effective method for increasing rates in their study. Tuten et al.

(2004) followed up this research to test if immediate notification of winning the prize upon completion of the survey was a significant contributor to rate measures and

134

Figure 3.9 Postcard mailed to potential participants.

concluded immediate notification was much better than a delayed notification for increasing response rates. Sills and Song (2002) commented that their survey to Arizona

State students should have included some type of incentive to effectively bolster response rates. The CTS had Ohio State University Men’s Basketball and Hockey tickets as incentives. Additionally, other prizes such as DVD players and restaurant gift certificates were used to induce participation. Once the CTS was complete, the prizes were distributed from a random selection of participants by staff members of The Mershon

Center for International Security Studies.

135 Correspondence refers to both the developmental procedure answering, “who will be contacted to participate in the survey” and verification techniques from the survey process (Weisberg et al 1996). To handle verification, one of the requirements of the respondents was to provide their zip code, which also serves to identify them geographically. In addition, it was optional in the CTS to include their name and home address information, which may be an issue of privacy concern. Cho and LaRose (1999) investigate the issue of privacy protection to increase response rates. The researchers offer a variety of different approaches and practices for increased response rates including consent forms, disclosure protocols, privacy certification, and incentives. The

IRB required clear consent at the beginning of the CTS survey in the introduction, also seen in Appendix D.

With both the realized and perceived threat model methodologies presented above, the next section will discuss the analysis of each. First, the mathematical rationale of realized threat STOP model will be discussed followed by the explanation of the procedure and analysis of validation. Second, the survey analysis, which captures the perceived threat of terrorism in Columbus, will be discussed in detail providing a full disclosure of results from the Internet-based survey.

136

CHAPTER 4

REAL THREAT ANALYSIS

“Every good citizen makes his country’s honor his own and cherishes it not only as precious but as sacred. He is wiling to risk his life in its defense and is conscious that he gains protection while he gives it”. President Andrew Jackson (Anderson 1990, pg. 79)

4.1 Introduction

Terrorism has a logical order (Crenshaw 1998). It is planned and strategic.

Modern terrorism simply does not find all places to be useful targets. The STOP model tries to establish which places are targets and identifies locations that are most susceptible to attack – the realized threat or spatial physical aspect of terrorism. The STOP model results in the creation of a Vulnerability Threat Index by parcel across geographic space in an urban area, as was shown for Columbus Ohio in Figure 3.8. The scale of analysis is considered local where the bounds of the research observations fall within the county

137 boundaries. However, between-city analysis can also be conducted, which represents a different scale where city-to-city comparison can be made. The geographic scale of analysis is representative of one level at which funding and resource allocation for homeland security is given – the city level. For the city of Columbus, one would expect that the overall real threat level to be low throughout most of Franklin County according to the logic of terrorism – not all places are really targets of terrorism. STOP tries to model reality and in doing so becomes a realized threat spatial analysis tool that can be used as a device to help increase protection for people and property from terrorism in places most likely to be attacked.

First in this analysis, the specific mathematical scoring in the model, which differentiates targets throughout the urban environment, will be discussed. Particular attention is given to specific urban targets and interpretation. Second, a comparative analysis is done to show how the STOP model performs nationally when comparing between cities, demonstrating the functionality of the model across scales. Last, the validation analysis is given where arguments center around the procedure for confirming how reliable STOP is to model reality and results in a discussion of the strengths of the model as a tool of generality for cities in the United States as well as how adjustments can be made for geographic specificity.

4.2 STOP Math Scoring

As can be seen in Columbus, the most likely targets of terrorism according to the

STOP model occur at specific locations in and around the urban area. Throughout the

138 spatial context of the urban area, a shaded scale results in low to high index scores. A correct interpretation from the STOP model VTI is that the greater the VTI index number, the greater the likelihood that the place will be attacked from terrorism based on frequency and severity of past attacks and symbolic level. The VTI is calculated based on a procedural mathematical operation and was conducted throughout the county for each parcel. With this approach it is possible using the STOP model to determine an index ranking for virtually every place in the area. To understand the level of real threat as calculated from the STOP model, it can be helpful to see the specific details of the index creation itself. For example, there were 8 specific places in Franklin County that had a

VTI rank of 240 or higher. Table 4.1 shows the results of the mathematical operations. As was explained previously, the first step in the STOP model is to determine the level of weight to be assigned to the various targets that have been attacked by terrorism in the past from a standardization of previous frequencies and magnitudes. The second step of

STOP refines the analysis scale to look between cities and within cities as to why terrorist would choose one target over another target. One-Target-Over-Another is determined from the categorization of the Metropolitan Statistical Area population and the cumulative symbology of targets. The VTI is the product of answers from Step 1 and 2 for each polygon.

139

STOP Vulnerability Threat Calculations VTI = OTOA x weight OTOA = MSA Population x cumulative Symbology weight = Frequency x Magnitude per target category 1 LeVeque Tower = (Historic (2) + Tall (2) + Top Three (2)) x Business (25) x MSA (3) = 450 Ohio Stadium = (Historic (2) + Tall (2) + Event (2)) x Business (25) x MSA (3) = 450 2 Ohio Statehouse = (Historic (2) + Major Gov. (2) + Capital (2)) x Gov. (20) x MSA (3) = 360 Rhodes Tower = (Major Gov. (2) + Tall (2) + Top Three (2)) x Gov. (20) x MSA (3) = 360 William Green =(Major Gov. (2) + Tall (2) + Top Three (2)) x Gov. (20) x MSA (3) = 360 3 Riffe Tower = (Major Gov. (2) + Tall (2)) x Gov. (20) x MSA (3) = 240 State Judiciary = (Historic (2) + Major Gov. (2)) x Gov. (20) x MSA (3) = 240 County Courthouse = (Major Gov. (2) + Tall (2)) x Gov. (20) x MSA (3) = 240

140

Table 4.1 Top indexed STOP VTI calculations in Columbus, Ohio.

The top two targets of likely terrorism attack in Columbus, Ohio are Ohio

Stadium and LeVeque Tower, shown in Table 4.1, both having an index score of 450.

Each was weighted in the top category of Business from Step 1 in the STOP model. Also, each place has a similar cumulative symbology, the only difference being that Ohio

Stadium is an event symbol whereas LeVeque functions as one of the top three skyscrapers in downtown Columbus. The MSA category standardization is consistent for all within city calculations and in the case of Columbus is a value of 3. Each of these locations has been highlighted in Figure 3.8. The results of the STOP model then suggest that Ohio Stadium on the campus of The Ohio State University and LeVeque Tower are the two most likely targets of terrorism in the city.

The second tier of places with a high level of realized threat based on the model is focused more centrally in the downtown area. The Ohio Statehouse, the Rhodes State

Office Tower, and the William Green Building were calculated to have a VTI index score of 360. Consistent among the second tier were their weights, calculated as government critical infrastructure targets from STEP 1. Also, there are no calculated differences between Rhodes and William Green. However, for the Ohio Statehouse, the symbology ranking is somewhat different with a cumulative score based on its historic nature, major government facility status, and the seat as the capital of Ohio.

The third tier of locations indexed highest from the VTI scores were all government targets. The Vern Riffe building, where the governor of Ohio works, the

State of Ohio Judiciary and Supreme Court of Ohio, and the Franklin County Courthouse

141 each had a VTI index score of 240. The Riffe building and courthouse had identical symbology in the model, whereas the Judiciary had a different symbolization value as historic. Despite the differences in symbology equations, each had a cumulative score of

4 before further calculations.

The scoring of each parcel in the area makes it possible to know what level of realized threat, according to the STOP model, might be expected and is transferable to any urban city in the United States through the parcel-coding process. From this modeling procedure, plans for greater protection of people and property can be implemented. Applying these results for places like Ohio Stadium and LeVeque Tower, greater anti-terrorism measures should be implemented to protect people from terrorism.

As these two have been identified from the STOP model as being the most likely targets of terrorism in the city, specific measures by local law enforcement should be directed first to these places and then to the second and third tier.

The VTI should be interpreted only as an interval level result. The designation of interval is in the context of nominal, ordinal, interval, and ratio level data. This means that indices created are not ratio numbers where the score of 450 represents twice the threat of places around the 225 VTI score. The index scores are relative to the point of being interpreted to mean on a scale that 450 is simply more than 225 or in the case of the second and third tier scores, greater than 240 and 360. For example, the centigrade scale is an interval scale where 50 degrees is only hotter than 25 degrees. It is incorrect to state that 50 degrees is twice as warm as 25 degrees. For this to be true, an absolute zero must be present, making the scale ratio (like the Kelvin scale). A correct interpretation of the

142 VTI is to understand it as interval level data and treat all protection measures accordingly.

4.3. Comparative VTI Scores

The eight buildings in Table 4.1 rank as the highest on the VTI in Columbus,

Ohio. However, how do these VTI index scores compare with those of other places?

Five places were selected for comparative purposes, seen in Table 4.2. The U.S. Capital and White House in Washington D.C., The Empire State Building and the former World

Trade Center buildings in New York City, and the fourth tallest building in the world and tallest standing in the United States at the time of this writing on June 15, 2007, the Sears

Tower in Chicago.

Each of these buildings has a relatively higher VTI score than any in Columbus,

Ohio. There are two reasons for this. First, each of the buildings has a larger cumulative score than any place in Columbus, which aids when the multiplicative factor is used.

Secondly, the MSA population rank is also higher. This difference in ranking shows how the VTI math can show differences between cities. Since each of the MSA ranks is either

5 or 6 in the case of the Washington D.C. MSA, the multiplicative factor increases the overall VTI score. So places in Washington D.C. or New York City can now be compared with places in Columbus.

The results of the VTI calculations seen in Table 4.2 show some of the largest

VTI scores in the country. Rarely will any other place have as many cumulative symbolic

143

STOP Vulnerability Threat Calculations VTI = OTOA x weight OTOA = MSA Population x cumulative Symbology weight = Frequency x Magnitude per target category U.S. Capital = (Historic (2) + Tall (2) + Major Gov. (2) + Capital (2) + Event (2)) x Gov. (20) x MSA (6) = 1200 The Empire State Building = (Historic (2) + Tall (2) + Top Three (2)+ Event (2)) x Business (25) x MSA (5) = 1000 World Trade Center = (Historic (2) + Tall (2) + Top Three (2)+ Event (2)) x Business (25) x MSA (5) = 1000 The White House = (Historic (2) + Major Gov. (2) + Home (2) + Event (2)) x Gov. (20) x MSA (6) = 960 Sears Tower = (Historic (2) + Tall (2) + Top Three (2)+ Event (2)) x Business (25) x MSA (5) = 1000

144

Table 4.2 Comparative threatened places based on STOP VTI calculations across the United States.

categories as the U.S. Capital, which is historic, taller than 150 feet, a major government facility, a capital building, and a facilitator of major events. When using the STOP model to calculate the VTI for the U.S. Capital the result is an index score of 1200!

Comparatively, the highest score in Columbus was 450. The VTI score then means that according to the STOP model when comparing the two places, the U.S. Capital is the more likely target of terrorism.

4.4 Model Validation

Validation is a normal part of the modeling procedure. Since models are approximations of reality, the normal course of action is to use or develop a method to test the reliability of the model in its approximation of reality. Modeling future terrorism and target likelihood presents a challenging arena to produce validated results. Therefore, one of the best courses to take in the STOP model validation process is to test how good the results are against previous acts of terrorism.

The STOP model was compared to the past incidents of New York City, Miami,

Washington D.C. and Los Angeles to see how well it would have predicted terrorism in those cities. By analyzing how well the STOP model performs in predicting the past events, the model can be evaluated for its accuracy to show terrorism target attack likelihood. By conducting the analysis in this manner the ability of the STOP model to bring out the geographic uniqueness of place in relation to modeling terrorism is demonstrated.

145 The results of the validation analysis can be seen in Table 4.3 for each of the cities. First, each of the cities was examined in the TKB to gather the number of incidents of terrorism by target. Then the actual percentage was calculated to show what the frequency of attack by target was for each of the cities in the past. The actual frequency was then compared to the STOP model overall frequency. The difference between the actual city frequencies recorded from real terrorism incidents with that of what the STOP model predicted is recorded in the Error column of Table 4.3. Positive numbers indicate in the column that the model over-predicted the actual percentage seen from terrorism in each category for each city, while a negative value indicates an under-prediction by the

STOP model. The same rubric was adopted as was originally done in the model to standardize the frequencies into five categories as shown in Figure 3.5 previously. The magnitudes of each of the targets were then calculated by city, summing the total number of deaths and injuries from each target. The magnitudes were then standardized into five categories in the same manner as done previously. Then the weights were calculated as the product of the standardized frequency and magnitude categories (FreqCat x MagCat in Table 4.3).

How well did the STOP model weight terrorism targets? As shown in Figure 4.1, the STOP model weights are compared with four U.S. cities. The overall STOP model ranks the top six weighted categories as the following (with weight in parenthesis):

Business (25), Government (20), Diplomatic (9), Airports/Airlines (8), Other Target

146 Frequency Magnitude LA Incident Actual % Model % M_US% Error FreqCat CityMag MagCat Weights Airports & Airlines 6 15.79 5.03 -10.76 4 6 1 4 Business 6 15.79 30.97 15.18 4 0 1 4 Diplomatic 11 28.95 14.21 -14.74 5 5 1 5 Government 3 7.89 17.38 9.49 2 0 1 2 Journalists & Media 1 2.63 3.79 1.16 1 0 1 1 Maritime 1 2.63 0.07 -2.56 1 0 1 1 Other 9 23.68 6.83 -16.85 5 4 1 5 Utilities 1 2.63 0.62 -2.01 1 0 1 1 Grand Total 38 100.00 78.9 -21.10 15

MIAMI Incident Actual % Model % Error FreqCat CityMag MagCat Weights Airports & Airlines 10 16.95 5.03 -11.92 4 0 1 4 Business 19 32.20 30.97 -1.23 5 2 1 5 Diplomatic 9 15.25 14.21 -1.04 4 0 1 4 Government 6 10.17 17.38 7.21 3 0 1 3 Journalists & Media 2 3.39 3.79 0.40 1 2 1 1 Maritime 4 6.78 0.07 -6.71 2 0 1 2 Other 8 13.56 6.83 -6.73 3 3 1 3 Police 1 1.69 6.21 4.52 1 0 1 1 Grand Total 59 100.00 84.49 -15.51 7

NEW YORK Incident Actual % Model % Error FreqCat CityMag MagCat Weights Airports & Airlines 18 10.71 5.03 -5.68 3 95 3 9 Business 35 20.83 30.97 10.14 5 6064 5 25 Diplomatic 81 48.21 14.21 -34.00 5 36 2 10 Educational Institutions 1 0.60 1.1 0.50 1 0 1 1 Government 3 1.79 17.38 15.59 1 0 1 1 Journalists & Media 9 5.36 3.79 -1.57 2 7 1 2 Other 12 7.14 6.83 -0.31 2 22 2 4 Private Citizens & Property 2 1.19 2.41 1.22 1 1 1 1 Terrorists 3 1.79 0.48 -1.31 1 2 1 1 Tourists 1 0.60 1.1 0.50 1 6 1 1 Transportation 3 1.79 4.9 3.11 1 8 1 1 Grand Total 168 100.00 88.2 -11.80 6241

WASHINGTON DC Incident Actual % Model % Error FreqCat CityMag MagCat Weights Airports & Airlines 3 6.67 5.03 -1.64 2 0 1 2 Diplomatic 27 60.00 14.21 -45.79 5 8 1 5 Government 7 15.56 17.38 1.82 4 297 5 20 Journalists & Media 5 11.11 3.79 -7.32 3 0 1 3 Military 1 2.22 0.83 -1.39 1 0 1 1 Other 1 2.22 6.83 4.61 1 3 1 1 Terrorists 1 2.22 0.48 -1.74 1 0 1 1 Grand Total 45 100.00 48.55 -51.45 308

Table 4.3 Validation results for the STOP model.

147

Overall

Model

Top Weight Categories 1. Business (25) 2. Government (20) 3. Diplomatic (9) 4. Airports/Airlines (8) 5. Other (6) 6. Transportation (5)

New York Miami Top Weight Categories Top Weight Categories 1. Business (25) 1. Business (5) 2. Diplomatic (10) 2. Airports/Airlines (4) 3. Airports/Airlines (9) . Diplomatic (4) 4. Other (4) 4. Government (3) 5. Journalists & Media (2) . Other (3)

Washington DC Los Angeles Top Weight Categories Top Weight Categories 1. Government (20) 1. Diplomatic (5) 2. Diplomatic (5) . Other (5) 3. Journalists & Media (3) 3. Airports/Airlines (4) 4. Airports/Airlines (2) . Business (4) 5. Various (1) 5. Government (2)

Figure 4.1 STOP model weights compared with four U.S. cities.

148 Types 10(6) and Transportation (5). However, when we compare that to four major U.S. cities actual localized frequencies based on actual past events, the same weighting does not exist. The top five categories for each city have been tallied and show some similarities with the overall STOP model weighting. Notice that geographic uniqueness, or city core function, is highlighted from the weighting. New York City (NYC), for instance, is a city best characterized as a business city. It is no surprise that business- related targets dominate the weighting scheme of New York. The STOP model weighting highlights the core function of the city and is adjustable to account for the differing functions of particular cities.

The uniqueness of place in relation to terrorism is brought to the forefront even more significantly than if simple descriptive statistics were used. Following the STOP model weighting reveals which targets are dominant in a city and highlights the core function of the city. It is within this uniqueness of geographic place where weighted adjustments can be made to the STOP based on specific geographic place, yet because of the close similarities that the model shares in predicting top categories for all four of these major U.S. cities, it serves well as a generalized urban terrorism likelihood threat model for cities in the nation. It is particularly useful to practitioners within cities across the country that wish to implement the STOP model without adjusting weights for minor differences in geographic place, that should still account for an accurate and quick assessment of targets in their (the practitioners) place of interest.

10 Other Target Types as designated in the TKB are primarily citizen targets or property of citizens. Many terrorism attacks from nature-loving groups like the ELF on cars and property as well as the Anthrax scare by postal mail in late 2001 were catalogued in the TKB as Other. I believe they could have just as easily been categorized in the private citizen/property category, but yet remained unchanged in the analysis. 149 Comparing all the weighted categories of the four cities with the overall STOP model provides a simple relative measure for the goodness of the weighting. Notice first in Table 4.4, that the largest difference is between the STOP weighting for Business and the averaged weighting of the four cities. Should the STOP model weights be adjusted according to this obvious difference? There are two reasons why the STOP model should remain unchanged even with this large difference. First, other cities have not experienced the level of business-related terrorism in NYC but have the same potential magnitude.

Skyscrapers dot the urban landscape in most major urban areas in the United States and support the employment of thousands of workers during the day. As a result, the events of terrorism in NYC in 1993 and 2001 serve as excellent examples (as sad and tragic as the loss of life and resulting injuries were), of the potential threat terrorism poses to U.S. urban areas. Second, the events in NYC that caused Business to be so heavily weighted also have occurred more recently than older types of terrorism such as that of diplomatic attacks or skyjacking terrorism. The overall STOP modeling procedure weighting should be used because terrorism comparably has a similar potential magnitude among many cities in the United States and because the events are modern and reflect current trends in terrorism. Weighting Business targets the heaviest makes sense based on past attacks and the potential for loss of human life.

150

151 151 Di Ove Los Was M New i ffe am r A h Y al Avg re i i ngel n

l o n

gt ST r c k . o e

C e

of n OP s

D. i t c y i M C t

i e . odel

Table 4.4Actualterrorism we s

1 8 25 2 5 4 6

. 5 5 .5

Business

13 6 20 2 3 1 2 . 0 5 .5

Government

10 4 5 5 6 9 3.

0 Diplomatic

4 3 . 4 2 9 4 8 . 7 2

5

5 Airports/Airlines

3 2 . 3 1 4 5 6 . 2 7

5

5 Other

0 ighted targetsforfourcities intheUnitedS 4 . 1 5 .

2

7

5

5 Transportation

0 3 . 1 4 . 2

7

5

5 Police

0 3 . 1 4 .

2

7

5

5 Military

1 0 . 1 3 2 1 2 . 7 2

5

5 Journalists/Media

0 1 . 1 2 .

2

7

5

5 Private Citizens

2. 0 2

0

0 Telecom

0 1. . 1 2

2

75

5 Tourists

1. 0 1

00

Abortion Related

0 0. . 1 1

2

75

5 Education Inst. t 1. ates. 0 1

00

Food or Water

0 0. . 2 1 1

7 25

5 Maritime

1. 0 1

00

NGO

1. 1 0

00

Religious Target

0. 0 1 1 1

. 50 5 Other Terrorists

0 0. . 1 1

2 75

5 Utilities The next largest difference between the overall STOP model categories and the four largest cities is the Government category. Table 4.4 shows the difference between the average of the four cities and the STOP model as being 13.5 points difference. So why keep the weighting for the STOP model at (20) instead of adjusting it down? There are also two reasons, like Business, why the weight for Government has not been further adjusted. First, the motivation of terrorism is highly political. As my specific definition presented in Section 2.1.3. points out, as do the results of the survey by Schmid and

Jongman (1984) shown in Table 2.3, the goal of terrorism is often political influence or change. This does not necessarily equate to an attack on Government targets because any targets can be attacked for the sake of political means. However, what it does mean is that the government is the main agent that terrorism is intended to affect. Bin Ladin et al.

(1998), indicate that,

“The ruling to kill the Americans and their allies – civilians and military – is an individual duty for every Muslim who can do it in any country in which it is possible to do it, in order to liberate the al-Aqsa mosque [Jerusalem] and the holy mosque [Mecca] from their grip, and in order for their armies to move out of all the lands of Islam, defeated and unable to threaten any Muslim” (p150).

The point of terrorism here is to remove the U.S. government influence in Israel and

Saudi Arabia and in effect to convince Americans to persuade their government to have nothing to do with the Israelis or Saudis. Bin Ladin (1996) makes it clear that the point of his attack is to remove non-Islamic or western influence out of Muslim society,

“To push the enemy – the greatest kufr [unbeliever] – out of the country is a prime duty. No other duty after Belief is more important than [this] duty. Utmost effort should be made to prepare and instigate the umma [whole Muslim nation] against the enemy, the American-Israeli alliance – occupying the country of the two Holy Places” (p139).

152

The entire process of attacking the government through the means of its civilians and military to achieve the political agenda of bin Ladin and his associates confirms the need to keep Government weighted in the STOP model where it currently is – it should not be decreased but remain a highly significant weight. Second, evidence indicates that

Government remains a highly sought overall target of terrorism in the United States. The

9/11 plot intended the fourth plane, which crashed in Pennsylvania, to either attack the

U.S. Capital or the White House (The 9/11 Commission Report 2003). Also, the Anthrax scare through the postal service after the attacks of 9/11 was directed at media persons and Senators Tom Daschle and Patrick Leahy. However, what is unique about the attacks is that the media received a dark brown substance Anthrax (cutaneous or skin-infecting), while the senators received a light-white powdered form. The Armed Forces Institute of

Pathology confirmed after examining the substance with help of Fort Detrick in Maryland that the powdered form of Anthrax sent to the Senators contained silica, which acted as a weaponizing agent that prevented the Anthrax spores from aggregating, thereby making them easier to aerolize (Kelly 2002). Since the letters were sent to the Hart Office

Building, the home of Senator offices in Washington D.C., it is conceivable that the attacks were meant to extend beyond the two Senators and enter the ventalation system to infect the occupants of the entire building. The TKB data confirm that 28 Senate staffers were infected with inhalation Anthrax. Twelve additional attacks on Government targets occurred between the Anthrax attacks of 2001 and the start of 2006. Since terrorism is highly political and there is evidence of numerous attacks on Government targets in the

153 U.S. the weight for Government (20) remains unchanged. The rest of the overall STOP categories are equivalent to the averages of the four cities.

The validation procedure has brought to the forefront the differences in weighting between places based on a comparison of the STOP to predict past events in four major

U.S. cities. The actual incidents in each city were examined and then compared to prediction of the STOP model. First, the weighting indicates on the one hand that certain targets of terrorism should be weighted high overall even though within some cities the weights for some targets are low. The reason for this is due to the potential magnitude that exists as a result of a terrorism attack and is rooted in previous terrorism incidents.

Second, on the other hand, the uniqueness of place is brought to the forefront when individual cities are examined through the STOP model procedure when comparing its ability to predict past events in individual cities. Local effects at the city level are highlighted by the model and general core function of the city as it is related to terrorism can be identified. New York appeals to terrorists because of business and diplomatic targets. Washington D.C. appeals to terrorists because of the government targets. Los

Angeles and Miami appeal because of the broad array of targets available, with certain subtle targets more attractive than others. The STOP model weights in such a way to bring out the known function of each of these cities and has the capability for weight adjustment, yet remains a good method for predicting the vulnerabilities to terrorism in general across American urban landscapes. The generalized model acts as a quick, easy, and past terrorism trends-based model for implementation in cities throughout the nation to create a more realistic picture of potential threat.

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CHAPTER 5

PERCEIVED THREAT ANALYSIS

“Eternal vigilance is the price of liberty” – President Thomas Jefferson (Anderson 1990, pg 35).

5.1 Introduction

A survey was conducted to capture the perceived threat from terrorism in the urban environment of Columbus, Ohio. Section 3.2 explained the process of choosing, designing, and implementing the Columbus Terrorism Survey (CTS). The geographic scale of the survey extends to the bounds of Franklin County, but not beyond. One reason for this is to combine the results of the CTS to compare them to the STOP model results, which were gathered at the same scale extent and will be discussed later in Chapter 7.

The CTS results address the spatial psychological aspect of terrorism. This chapter will describe the analysis of the results from the survey of perceived threat.

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5.2 Response Rates and Imputation

Miller et al. (2002) have shown response rates might be low for Internet surveys.

Response rates are dependent on a number of factors, including completion of a survey once begun, correct mailing addresses, and refusal rates. The Association for Public

Opinion Research (AAPOR) has developed standards for calculating response rates based on others’ research (Association for Public Opinion Research 2004). The AAPOR offers six different methods for the calculation of response rates, which is essentially done by dividing the number of completed surveys by the total number in the sampling frame. Of the six options, the RR2 option was chosen because it provides the most conservative

(minimum) response rate that includes both complete and partial interviews. RR2 is calculated as follows:

I + P RR2 = (4) (I + P) + (R + NC + O) + (UH + UO)

Where: I = Complete interview P = Partial interview R = Refusal and break-off NC = Non-contact O = Other UH = Unknown if household/occupied HU UO = Unknown, other

However, before the RR2 can be calculated, the total number of people who received a postcard must be counted, resulting in an accurate estimate of how many people were actually eligible for the survey. There are two options to calculate this: 1)

156 count the number of returned postcards that never reached their destination, or 2) if the number of postcards that returned were unknown, an estimate procedure to calculate the number must be done. Since the printing service did not put a return address on the postcard, the second choice of estimation must be done.

There are many ways to estimate the number of postcards that did not arrive. The easiest way is to use United State Postal Service (USPS) address matching software to verify each address in the sampling frame. However, the cost of such software is very expensive and was not available for this research. A second option is to do a statistical sampling of the addresses in the sampling frame and check them on the USPS website at no cost. Though more work and tedious, the second option was used to estimate the error term in RR2.

To estimate the number of legitimate postal addresses from the sampling frame a systematic sampling was taken from the frame. The total sampling frame, 14,000 addresses, was randomly chosen from a list of all households, 305,608, in Franklin

County, OH. Of these, every 100th address was checked for validity, or 140 in total. Of the 140 addresses, 4 were discovered to be undeliverable. Extrapolating that out, an estimate of 400 addresses were undeliverable in the sampling frame, essentially changing the frame from 14,000 to 13,600.

In addition, since the methodology of sending a post card by mail to induce participation through an Internet-based survey was different than traditional methods, the

Internet penetration rate for Columbus, Ohio is also used to estimate eligibility. Since the best estimate of penetration is 70.1 percent (Neilson Net Ratings 2003), an assumption is

157 made in conjunction with the sampling frame to reduce the frame to the level represented by the penetration rate. This means that with a sampling frame of 13,600, adjusting it to reflect the Internet penetration rate results in an estimated frame of 9534.

Of those 9534 households eligible for the study, 293 responded. With that sampling frame and respondent total, the RR2 for the CTS is 0.0307, or around 3.1 percent. However, of the 293, only 263 provided a geographic identifier, either a zip code or a home address. Therefore, for the analysis, the 263 responses will be used. The total counts for each question, prior to the imputation procedure described later, are given in

Table 5.1.

Typically, the mode used to induce potential respondents to participate in an online survey is to contact them through email and track them for a probability-based analysis (Dillman 2000). No email list was available to contact potential participants for the CTS, but a postal address list was obtained and used to encourage participation as described in Chapter 3. Since postcards were used to induce participants, it was hypothesized that many thousand postcards would need to be mailed to get an adequate sample with a reasonable precision. The formula used to estimate the desired sample size was:

(N p )()p (1− p) Ns = 2 (5) ()N p −1 ()b /Con + (p)(1− p)

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Counts Q8_Stadiums 260 Q11_Boston 251 Q15d_OSU 19 Q16a 245 Q25_Race 263 PARTICIPATE 263 Q8_Electrical 257 Q11_Vegas 254 Q15d_Capital 41 Q16b 261 Q31_education 263 CONSENT 263 Q8_Water 257 Q11_Philly 254 Q15d_Stadiums 71 Q17 129 Q27_Born 258 Q1a_terrorism 263 Q8_Schools 256 Q11_SanFran 254 Q15d_Electrical 21 Q18_Malls 67 Q28_livedCol 262 Q1b_tbefore911 262 Q8_TallBuild 258 Q11_Denver 249 Q15d_Water 27 Q18_Airport 114 Q29_zip 263 Q2_USA 262 Q8_Highways 256 Q11_Columbus 257 Q15d_Schools 9 Q18_OSU 27 Q29b_own 251 Q3_Columbus 262 Q8_Home 255 Q12_Coumbus 263 Q15d_TallBuild 49 Q18_Capital 49 Q29c_income 230 Q3b_friends 262 Q8b_Malls 7 Q12_Cincy 259 Q15d_Highways 7 Q18_Stadiums 92 Q30_marital 259 Q4_cell_Colum 260 Q8b_Airport 13 Q12_Cleveland 260 Q15d_None 108 Q18_Electrical 33 Q32_drawing 262 Q5_Malls 135 Q8b_OSU 3 Q12_Akron 254 Q15d_StayHome 19 Q18_Water 35 Q33_Name 241 Q5_Airport 166 Q8b_Capital 8 Q12_Toledo 255 Q15d_Other 11 Q18_Schools 7 Q34_Address 241 Q5_OSU 104 Q8b_Stadiums 12 Q12_Dayton 259 Q15d_ORespons 0 Q18_TallBuild 72 Q35_City 241 Q5_Capital 82 Q8b_Electrical 6 Q13_Local 262 Q15c_effHSASC 244 Q18_Highways 17 Q36_State 240 Q5_Stadiums 196 Q8b_Water 6 Q13_State 262 Q15e_Malls 62 Q18_None 75 Q36_Zip 234 Q5_Electrical 56 Q8b_Schools 1 Q13_National 262 Q15e_Airport 103 Q18_StayHome 39 159 Q5_Water 110 Q8b_TallBuild 13 Q14_loc_protect 249 Q15e_OSU 31 Q18_Other 15 Q5_Schools 41 Q8b_Highways 2 Q14_state_protect 250 Q15e_Capital 44 Q18_OResponse 0 Q5_TallBuild 109 Q8b_Other 20 Q14_national_pro 251 Q15e_Stadiums 84 Q19 261 Q5_Highways 23 Q9_Anywhere 262 Q15_seenHSAS 257 Q15e_Electrical 28 Q19b 258 Q5_Home 2 Q10_city_rural 262 Q15a_helpfulHSAS 242 Q15e_Water 30 Q20 263 Q5_None 1 Q11_NYC 261 Q15aa_threat_loc 85 Q15e_Schools 10 Q21 260 Q5_Other 29 Q11_DC 261 Q15aa_threat_state 89 Q15e_TallBuild 66 Q22_Y_kit 15 Q6_Concered 263 Q11_Chicago 258 Q15aa_threat_natio 223 Q15e_Highways 8 Q22_N_kit 7 Q7_Concered5 263 Q11_LA 260 Q15aa_none 9 Q15e_None 63 Q22_Y_current 8 Q8_Malls 261 Q11_Atlanta 253 Q15aa_other 23 Q15e_StayHome 55 Q22_N_current 11 Q8_Airport 260 Q11_Miami 254 Q15b_effectiveHS 246 Q15e_Other 9 Q23 263 Q8_OSU 258 Q11_Seattle 251 Q15d_Malls 47 Q15e_ORespons 0 Q23b 169 Q8_Capital 258 Q11_Dallas_Ft 251 Q15d_Airport 71 Q16 247 Q24_Gender 263

Table 5.1. Counts for CTS questions prior to Hot-Deck imputation.

Where:

Ns = The sample size needed for level of precision

N p = The size of the population p = The expected proportion to choose the response categories (at 80/20 split) b = acceptable amount of sampling error from the true population value Con = The z statistic level of confidence. (1.96 corresponds to the 95% confidence level)

The completed formula yields:

()305608 (.8)(.2) N = = 245.67 or approximately 246 people. s ()305608 −1 (.05/1.96)2 + (.8)(.2)

As a result of the methodology, nearly 300 people were induced through the mailing and prize incentive to log online and participate in the CTS and over 260 people responded with their geographic identity. However, was the low cost of the survey and the unorthodox methodology to sample broadly throughout Columbus worth the price of a low response rate?

Low response rates often are associated with non-response bias, which is the situation when a large number of participants do not respond and they have different characteristics than those that do respond, which is relevant to the study (Dillman 2000).

It is very difficult to know if the characteristics are different for those that do respond than from those that do not. Groves et al. (2004) point out that high or low response rates do not act as an indicator for the quality of responses, but instead should be thought of as a way to reduce non-response bias. Thus, in a survey that has a high response rate, there is potentially less likelihood of having non-response bias, even though it is not

160 guaranteed. One way to try and address this potential is to test if the sample is representative of the population by comparing the respondent characteristics to that of the general population. The judgment of the survey executor is needed to determine if the sample is very different from the actual population when response rates are low. If characteristics are too different, often adjustment weights are used to compensate for any questionable non-response bias through post-stratification.

In the case of the CTS, since the general population is of interest in this study and were randomly selected for their opinions on terrorism, the respondents’ statistical characteristics were examined to see if they matched the general population statistics given by the Census Bureau. In doing so, the low response rate can be evaluated to see if the distribution has similar characteristics between the sample and the population (or that those who did not respond are properly accounted for by those that did respond). The first step is to test the likelihood for non-response bias by comparison as shown in Table 5.2.

Notice that the gender distribution is fairly close to the actual county distribution. This is a positive sign affirming the methodology used for the CTS was adequate to capture respondent attitudes even for people who did not respond. When broken down by ethnicity there are also similar differences. First, in the CTS the sample distribution for

Caucasians is somewhat over-represented compared with the county population distribution. Second, slight under-representation is present for African-Americans,

Asians, and Hispanics for the CTS when compared with the actual population distribution. However, despite these differences, the analysis below makes the assumption that the general characteristics of the respondents in the sample represent

161 those that did not respond and therefore suffers from little non-response bias because the percentages are close to one an other and also because, in an attempt to build a general perceived threat profile for Columbus, the questions in the CTS are analyzed from the basis of overall perceptions rather than opinions sub-divided into various strata.

Methodologically speaking, there are only two options for survey item non-response.

Either the researcher has to eliminate the entire observation from analysis or has to provide an answer for the non-response, which is called imputation. Many imputation procedures exist, including mean, regression, and hot-deck. The imputation procedure used in the CTS for an item non-response was a hot-deck imputation. This imputation procedure is probably the most frequently used method and shares commonalities with regression imputation (Groves et al. 2004). The procedure involves borrowing a prediction for item non-response from an observation that shares similar characteristics.

Weisberg et al. (1996), Dillman (2000), and Groves et al. (2004) all provide textbook explanations for hot-deck imputation. The CTS was hot-decked where item non-response was present based on three sorted categories: gender, ethnicity, and education. Once the sorting was done, the hot-deck imputation was done by question to fill in the missing data with similar data, where necessary.

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Survey Respondents General Population Gender Males – 46.8% Males – 49.1% Females – 53.2% Females – 50.9% Ethnicity Caucasian – 87.8% Caucasian – 72.7% African-American – 5.7% African-American – 19.8% Asian – 0.8% Asian – 3.7% Hispanic – 0.4% Hispanic – 3.3% Other – 5.3 % Other – 0.5%

Table 5.2 Comparison of survey respondent characteristics and general population characteristics. General population data from United States Census Bureau (2005).

5.3 Columbus Perceived Terrorism Threat Profile

The CTS was designed to gain insight into the opinions of area residents on their perceptions of terrorism vulnerability. Several questions were designed to gather their responses and are presented in Table 5.3. Each question in Table 5.3 will be examined below. The analysis was run using STATA SE 9.2 (2006) for its robustness and ability to handle the FPC statistic, described in the Section 3.2.3, which allows a higher reliability with lower standard errors. The number of respondents was 263 unless otherwise stated.

The profile will take on a format similar to report style where graphs and maps are placed within the text itself. For complete statistical numerical tables, please refer to Appendix

E. As a reference, Figure 5.1 has been provided to give a visual geographic reference of 163 Columbus, Ohio. The figure shows the zip codes throughout the county as a measure of household density. Several questions of about the geographic scale are considered and many of the responses from questions have been geovisualized.

Q1a: Do you consider terrorism to be an important issue today? Q1b: Was terrorism an important issue to you before the events of September 11, 2001?

Q2: In your opinion, what is the likelihood that terrorists will attack the United States in the next year? Q3: Thinking about terrorism, what is the likelihood that a terrorist will attack the city of Columbus, Ohio in the future?

Q3b: The issue of terrorism in Columbus is something I talk about with my friends: Q4: What is the likelihood that a terrorist cell is operating in the Columbus, Ohio area?

Q5: If terrorists attacked, which of the following do you feel are the most likely targets of terrorist interest in Columbus? (Check All That Apply)

Q6: Are you personally concerned that a terrorist will attack somewhere in Columbus, Ohio in the next year? Q7: How about within the next 5 years, will there be a terrorist attack in Columbus, Ohio? Q9: Do you believe terrorism is more likely to happen anywhere in the United States or in only in specific geographic locations?

Q10: Do you think terrorism is more likely to occur in the United States:

Q11: Rank the cities below for their likelihood of terrorist attack (in the next couple of years): Q12: Rank the following Ohio cities in their likelihood of terrorist attacks:

Table 5.3 Questions used to gauge the perceptions of Columbus residents to terrorism vulnerability.

164

Figure 5.1 Household population density of Columbus, Ohio, by zip code area. By author.

165 Q1a: Do you consider terrorism to be an important issue today?

0.7

0.6

0.5 n 0.4 tio

opor 0.3 Pr

0.2

0.1

0 Very Important Somewhat Average Somewhat Not Important Important Importance Unimportant

Figure 5.2 Do you consider terrorism to be an important issue today?

Results in Figure 5.2 show that 92.4 percent of Columbus households believe that terrorism is an important issue today (Very Important + Somewhat Important). Notice also, that not one household responded in the sample saying that terrorism was not an important issue today.

166 Q1b: Was terrorism an important issue to you before the events of September 11, 2001?

0.35

0.3

0.25

0.2

0.15 Proportion

0.1

0.05

0 Very Somew hat Average Somew hat Not Important Important Important Importance Unimportant

Figure 5.3 Was terrorism an important issue to you before the events of September 11,

2001?

Results in Figure 5.3 show that 38.8 percent of Columbus households believe that terrorism was an important issue to them prior to the 9/11 attacks (Very Important +

Somewhat Important). This key question points out the large magnitude of which the

9/11 attacks have had on the consciousness of Columbus residents. The percent difference before and after for the category ‘Very Important’ is given in Figure 5.4, showing the spatial distribution of the responses. People on the west and northwest side of Columbus appear to have had the largest proportional differences.

167

Figure 5.4 Proportional change in the importance of terrorism pre 9/11 and five years later. By author.

168 Q2: In your opinion, what is the likelihood that terrorists will attack the United States in the next year?

0.45 0.4 0.35 0.3 n

tio 0.25

opor 0.2

Pr 0.15 0.1 0.05 0 Very High High Average Low Very Low

Figure 5.5 In your opinion, what is the likelihood that terrorists will attack the United

States in the next year?

Results in Figure 5.5 show that 59.3 percent of Columbus households believe that there is a high likelihood that terrorists will attack the United States in the next year

(Very High + High). This indicates that a majority of citizens in Columbus believe that a terrorist-related attack is likely to occur sometime in the United States during 2007.

169 Q3: Thinking about terrorism, what is the likelihood that a terrorist will attack the city of

Columbus, Ohio in the future?

0.45 0.4 0.35 0.3 0.25 0.2

Proportion 0.15 0.1 0.05 0 Very High High Average Low Very Low

Figure 5.6 Thinking about terrorism, what is the likelihood that a terrorist will attack the city of Columbus, Ohio in the future?

Results in Figure 5.6 show that 43.7 percent of Columbus households believe that there is a low likelihood that terrorists will attack the city of Columbus, Ohio in the future

(Very Low + Low). In fact, according to these results, Columbus residents have a very low expectation that the city will be attacked in the future. Only 0.76 percent thinks there is a very high likelihood.

170 Q3b: The issue of terrorism in Columbus is something I talk about with my friends:

0.45

0.4

0.35

0.3 n

tio 0.25

opor 0.2 Pr 0.15

0.1

0.05

0 Everyday Often Sometimes Not Often Never

Figure 5.7 The issue of terrorism in Columbus is something I talk about with my friends:

Results in Figure 5.7 show that 55.5 percent of Columbus households discuss issues of terrorism with their friends infrequently (Not Often + Never). Although people who live in Columbus have overwhelmingly considered terrorism to be an important issue today, it is not a preoccupation that is discussed among friends too often.

171 Q4: What is the likelihood that a terrorist cell is operating in the Columbus, Ohio area?

0.5 0.45 0.4 0.35 0.3 0.25 0.2 Proportion 0.15 0.1 0.05 0 Very High High Average Low Very Low

Figure 5.8 What is the likelihood that a terrorist cell is operating in the Columbus, Ohio area?

Results in Figure 5.8 show that 19.7 percent of Columbus households believe that the city has a low likelihood that a terrorist cell is operating in the area (Very Low +

Low), while 35.3 percent believe there is a high or very high likelihood. The average likelihood response of 44.9 percent indicates that many households believe Columbus generally has the same likelihood as other places. The geographic distribution is shown in

Figure 5.9. There appears to be a concentration of households in the south of Columbus that believe more strongly than the rest of the city inhabitants that there is a high likelihood of a terrorist cell operating in Columbus.

172

Figure 5.9 Likelihood of a terrorist cell operating in Columbus, Ohio. By author.

173 Q5: If terrorists attacked, which of the following do you feel are the most likely targets of terrorist interest in Columbus? (Check All That Apply)

0.8

0.7 0.6

0.5

0.4 0.3 Proportion 0.2

0.1 0

s s c s s s l rts tal i gs y e er al o i ter ol in a ove OSU um ctr d M rp ap di e Wa il hw om ab Oth Ai C El Scho ig H Sta ll Bu H of a e T on N

Figure 5.10 If terrorists attacked, which of the following do you feel are the most likely targets of terrorist interest in Columbus?

Results in Figure 5.10 show that the highest proportion of Columbus households believe that sports stadiums/big events represent the most likely target of terrorist interest in Columbus (74.5 percent), followed by the Port Columbus airport (63.1 percent), the area shopping malls (51.3 percent), the local water supply (41.9 percent), downtown tall buildings (41.4 percent), The Ohio State University (39.5 percent), the Ohio Statehouse building (31.2 percent), the electrical supply (21.3 percent), schools (15.6 percent), other types of targets (11 percent), highway system (8.7 percent), and finally both individual homes and no targets attacked (0.76 percent). The geographic distribution is shown in

Figure 5.11. 174

Figure 5.11 Perceived likelihood of terrorism targets in Columbus, Ohio. By author.

175 Q6: Are you personally concerned that a terrorist will attack somewhere in Columbus,

Ohio in the next year?

0.4

0.35

0.3

n 0.25 tio 0.2 opor

Pr 0.15

0.1

0.05

0 Very Somewhat Average Somewhat Not Concerned Concerned Concern Not Concerned Concerned

Figure 5.12 Are you personally concerned that a terrorist will attack somewhere in

Columbus, Ohio in the next year?

Results in Figure 5.12 show that 24.8 percent of Columbus households are personally concerned that a terrorist will attack somewhere in Columbus, Ohio in the next year (Very Concerned + Somewhat Concerned), while 40.7 are not concerned (Somewhat

Not Concerned + Not Concerned).

176 Q7: How about within the next 5 years, will there be a terrorist attack in Columbus,

Ohio?

0.35

0.3

0.25 n

tio 0.2

opor 0.15 Pr 0.1

0.05

0 Very Somewhat Average Somewhat Not Concerned Concerned Concern Not Concerned Concerned

Figure 5.13 How about within the next 5 years, will there be a terrorist attack in

Columbus, Ohio?

Results in Figure 5.13 show that 33.9 percent of Columbus households are personally concerned that a terrorist will attack somewhere in Columbus, Ohio in the next

5 years (Very Concerned + Somewhat Concerned), while slight more, 35.4 percent, are not concerned (Somewhat Not Concerned + Not Concerned). The geographic distribution is shown in Figure 5.14. Notice that for most of the city, there is little concern, but for two hotspots in south central Columbus, the issue is more serious.

177

Figure 5.14 Concern about a terrorist attack within the next five years. By author.

178 Q9: Do you believe terrorism is more likely to happen anywhere in the United States or only in specific geographic locations?

0.8 0.7 0.6 0.5 0.4 0.3 Proportion 0.2 0.1 0 Anyw here Specific Locations

Figure 5.15 Do you believe terrorism is more likely to happen anywhere in the United

States or only in specific geographic locations?

Results in Figure 5.15 show that 54 percent of Columbus households believe terrorism is more likely to happen only in specific geographic locations as opposed to anywhere in the United States.

179 Q10: Do you think terrorism is more likely to occur in the United States:

0.6

0.5

0.4 n tio 0.3 opor

Pr 0.2

0.1

0 Cities Rural Areas Equal Chance Only Certain Cities

Figure 5.16 Do you think terrorism is more likely to occur in the United States:

Results in Figure 5.16 show that 51.7 percent of Columbus households think terrorism is more likely to occur in the United States in cities, while 38.8 percent believe terrorism is likely to occur only in certain cities, 9.5 percent believe there is an equal chance of terrorism happening in both cities and rural areas, and no one thinks terrorism is most likely to happen in rural areas.

180 Q11: Rank the cities below for their likelihood of terrorist attack (in the next couple of years):

0.7

0.6

0.5 High 0.4 Likely Average 0.3 Not Likely Proportion No Chance 0.2

0.1

0

o a e s n . n s C C A t mi tl a o as d a er D L a t ll g la r v NY cag lan F t Mi ea Da ost hi en sh. hi A S B P an D lumbu C s Ve S o Wa La C

Figure 5.17 Rank the cities below for their likelihood of terrorist attack (in the next couple of years):

Results in Figure 5.17 show that when Columbus households rank the cities above for their likelihood of terrorist attack (in the next couple of years), Washington D.C. is most likely (61.2 percent) followed closely by New York City (58.2 percent). The cities that were on the lowest end with no chance were Columbus (6.5 percent) and both San

Francisco and Boston (3.8 percent), which was somewhat surprising. Columbus residents believed that Washington D.C., New York City, Los Angeles, Chicago, and Miami had at 181 least some chance of being attacked in the next couple of years (No Chance = 0). The geographic results are given in Figure 5.18. Notice that households in Columbus ranked most of the cities as having an average chance.

Q12: Rank the following Ohio cities in their likelihood of terrorist attacks:

0.6

0.5 High 0.4 Likely 0.3 Average Not Likely 0.2 No Chance 0.1

0

s i n o n at nd ro d n a k le yto in el mbu c v A o lu n e T Da Co Ci Cl

Figure 5.19 Rank the following Ohio cities in their likelihood of terrorist attacks:

Results in Figure 5.19 show that when Columbus households ranked the Ohio cities above for their likelihood of terrorist attack, Cleveland was most likely (11.4 percent) followed closely by Columbus (9.9 percent). The cities that were perceived to have the lowest end with no chance were Akron (19.8 percent), Toledo (19.0 percent), and Dayton (14.1 percent).

182

Figure 5.18 Perceived likelihood of terrorist attack throughout the United States. By author. 183 Combined, these thirteen questions make up the Columbus Perceived Terrorism

Threat Profile (Columbus–PTTP). From these responses, it appears that terrorism is an important issue for people in Columbus, but that there is a low degree to which they believe the city will be attacked. According to the results, the people of Columbus believe certain cities are most at risk of attack in the United States. The local countywide perception of terrorism is that Washington D.C. and New York City as the most likely targets of terrorism. In the state of Ohio, Columbus residents believe that Cleveland is the most likely target of terrorism, followed by the capital city of Columbus and then

Cincinnati. There is a fairly even distribution of about one-third who are concerned about an attack in the next five years in Columbus and one-third who view an attack on

Columbus as likely as that of other places. Within the city, however, the CTS results confirm that sports/event stadiums have the highest likelihood of being most vulnerable to a terrorism attack. The local geographic perception is that terrorists, either within the city or from outside the city are most likely to target local sports/event stadiums than other places within the county. Columbus residents view the city as an unlikely target of terrorism and terrorism-cell operations when compared with other U.S. cities. From the results of the CTS, it can be reasonably argued that residents view terrorism as an important issue today but most think that the city has a low threat level from terrorism attack.

184

CHAPTER 6

GEOVISUALIZING FEAR: AN ILLUSTRATION

“It is just as important to study the proper and effective use of various forms of graphic presentation, as it is to study the values of different methods, treatments, grades, and forms of verbal presentation” – William Morris Davis (Robinson 1952, pg. v.)

6.1 Introduction

Many techniques are available in research to better visualize phenomena. Keim

(2001) describes how techniques help aid understanding of the physical word. Fabrikant and Buttenfield (2000) show ways in which metaphors and semantics can be used to provide a basis for abstract concepts. Geovisualization has the power to address both types of phenomena from a variety of angles. Section 3.3 describes some of the different types of techniques available. However, one of the most difficult types of phenomena that have yet to be adequately geovisualized is emotion.

185

6.2 Visualizing Fear

Mapping emotion is a challenging task. Searching for the keywords, fear or emotion in major cartography journals yields no results11. The work by Kwan (2007) indicates potential for using geotechnologies for emotional articulation through mapping.

Her 3D travel visualization of fear of places in the urban environment has ushered in an age into new methodologies to capture real emotion geospatially. However, although certain authors have expounded on the use of maps to combine art and maps (Wood

2006), fear as a real emotion mapped across space remains highly un-researched by cartographers.

Since terrorism is highly associated with fear, an attempt has been made in this research to explore the potential for using GIS to map fear of terrorism. At the outset, the

CTS was considered the guiding protocol from which people would communicate their level of fear of terrorism. Since the participants in the survey were kind enough to provide their spatial location, each person can be spatially referenced within the county boundaries and mapped based on response. Two specific questions have been used in the

CTS to gather the necessary data to guide the visualization. Question 8 asks, “We have found some to be apprehensive about traveling, while others are not. Are you apprehensive about going to any of the following places because of potential terrorism?”

[Several locations throughout Columbus are available for selection, as shown in

Appendix D]. The second question asks, “Do you avoid any of the following because of

11 The journals searched included: Cartography and Geographic Information Science, Cartography & Geographic Information Systems, Cartographic Journal, Cartographic Perspectives, and Cartographica. 186 the potential for terrorism? [Several locations throughout Columbus are available for selection, as shown in Appendix D]. The results of Q8 are given in Figure 6.1 and will be the focus of the geovisualization to below.

First, notice that the categorical variables have been calculated using the mean.

Normally, categorical ordinal data analysis precludes the calculation of the mean.

However, Jaccard and Wan (1996) were able to demonstrate that Likert-type scales, which assume interval-ness, have been shown even in severe departures from interval- ness to exhibit little affect on Type I and Type II errors. This means that as long as the

Likert-type scale used for responses to a question is interpreted as being a part of a continuum, then it can be treated like interval data (and even in cases where it is poorly interpreted as being interval, little error may exist). In fact, this is the usage by many universities across the United States when evaluating instruction. The mean is assessed on a Likert-type scale for how good an instructor is on a variety of categories (such as

“Rank how well the instructor was on a scale from 1 to 5”). In the CTS, Likert-type scales were used and in Q8 shown in Figure 6.1, the scale was a five category Likert-type scale, which was coded in the analysis as 1 = Very Apprehensive, 2 = Somewhat

Apprehensive, 3 = Average Apprehension, 4 = Little Apprehension, and 5 = Not

187 Q8: Are you apprehensive about going to any of the following places because of potential terrorism?

5 4.5 4 3.5 3 2.5 Mean 2 1.5 1 0.5 0

t l s r a s c er ls s ll t m t o g ys me po pi ctri o in o Ma r OSU a iu e d wa Ai C Wa il H El Sch igh Stad Bu H ll Ta

Figure 6.1 Fear of terrorism in Columbus, Ohio.

Apprehensive. The results from the analysis show that Port Columbus Airport has the highest terrorism fear associated with it (at 3.77), followed by stadiums/events (at 3.78), downtown tall buildings at (4.07), and shopping malls (4.09). Next came, the Ohio

Statehouse building (4.22), The Ohio State University (4.24), water facilities (4.26), electrical facilities (4.37), highways (4.55), and finally, private homes (at 4.78) have the least amount of terrorism related fear.

To map fear across geographic space, the location of highest fear indicated by respondents has been selected as the foundation for the geovisualization. Incorporating the geo-coded information provided by each respondent to the CTS and then examining their individual response to the Port Columbus Airport category can form the basis of a

188 geographic visualization of fear. First, each person’s household is identified geographically within the county. Next their individual answer to the question of fear apprehension of the airport serves as the basis for the creation of a fear surface map.

Using the response as the variable of interest, an interpolated geographic surface is created showing a possible surface of fear. In the case of this analysis, the Inverse

Distance Weighting method was used to create the interpolation. The resulting visualization is provided in Figure 6.2. In the lower right inset box, the geographic distribution of respondents in the CTS can be seen. For this example, the number of respondents limits the interpretation of the interpolation because such large gaps are present between observations. Often the more data observation points the better, even though a law of diminishing returns exists for computational time versus surface improvement. In the map, the spikes indicate geographic positions of people who were most fearful of going to the airport. The animated map also provides a view of the lower segments in the interpolation that indicate areas of least apprehension about fear of terrorism at the airport. This type of interpolation is one way that emotion and fear can be mapped across geographic space and sets a precedent for further analysis. In fact, since this surface is simply an interpolation based on a weighting system (IDW specifically in this geovisualization), this type of geovisualization should be used as a spatial exploratory data analysis (SEDA) tool. However, Figure 6.2 shows more the potential of fear geovisualization than it does as a good SEDA visual.

When the geovisualization in Figure 6.2 is used as a SEDA tool, new hypotheses can be created. In the case of this particular geovisualization, the researcher should note

189 that there is an exceedingly large geographic area of low concentration in the interpolation centered on the part of Columbus near The Ohio State University. A test might to used see the level of association by comparing between the levels of education to that of level of fear of the airport on the hypothesis that the two are related. Using contingency tables for these categorical variables to test various explanatory levels of education on the response variable of fear results in several test statistics presented in

Table 6.1. Notice that the results of categorical contingency table analysis give four different statistics. The Pearson chi-square test statistic results in being statistically significant at the .05 level, while the likelihood-ratio test is significant at the .1 level.

Both of these statistics then lead to rejection of the null hypothesis (H0) and acceptance of the alternative hypothesis (HA). It can be statistically concluded that fear of going to the airport because of potential terrorism is related to level of education. In the analysis, the gamma statistic and Kendall’s tau-b test statistic provide the level of correspondence between the two variables to know the strength and direction of the dependency. Both gamma and Kendall tau-b give a weak to moderate negative correspondence between the variables (negative because order matters here and notice the categories are reversed numerically), which indicates additional explanatory variables are likely to exist. The statistical results are opposite of the initial thought from the exploratory geovisualization and might have resulted from selection bias. In either case, whether trust worthy or not the example demonstrates how statistical hypotheses can be generated from exploratory geovisualization.

190 Port Ohio State Columbus University Airport

Spatial Distribution

N Downtown

Figure 6.2 Animated geovisualization of fear in Columbus, Ohio. By author.

191 H0 = Fear about going to the airport because of potential terrorism is not statistically associated with education level

H A = Fear about going to the airport because of potential terrorism is statistically associated with education level

| Q31_Education Q8_Airport | 1 2 3 4 5 6 | Total ------+------+------1 | 1 3 7 1 5 2 | 19 2 | 0 3 12 4 7 5 | 31 3 | 0 14 6 2 17 9 | 48 4 | 0 7 15 3 16 17 | 58 5 | 1 9 26 10 37 24 | 107 ------+------+------Total | 2 36 66 20 82 57 | 263 Pearson chi2(20) = 31.6795 Pr = 0.047 likelihood-ratio chi2(20) = 28.5758 Pr = 0.096 gamma = 0.1452 ASE = 0.063 Kendall's tau-b = 0.1098 ASE = 0.048

Variables Q8_Airport Q31_Education Question: Are you apprehensive about Statement: The highest education level I have going to any of the following because of achieved is: 192 potential terrorism: (airport)? 1 = Very Apprehensive 1 = Did not graduate from High School 2 = Somewhat Apprehensive 2 = High School graduate/GED 3 = Average Apprehension 3 = Some college but no degree (yet) 4 = Little Apprehension 4 = 2 year college degree 5 = Not Apprehensive 5 = 4 year college degree 6 = Postgraduate degree (MA, MBA, MD, PhD, JD, etc.)

Table 6.1 Testing for correlations between fear of terrorism at the airport and level of education.

What results then from the geovisualization is a new avenue into mapping fear geographically. By using the interpolation methods available to geographers, surfaces can be created that provide geovisualizations for exploration of survey data that captures emotion. However, it is not without its limitations. Since interpolated surfaces present continuous data, interpretations of created surfaces cannot explain completely nor should they be used to completely explain results from nominal or truly ordinal data. First, in the case of the visualization in Figure 6.2, the data are ordinal to a degree but designed to be perceived as on a continuum or continuous-like. As has been shown, often this type of survey data are treated as interval data and assumed to be continuous (Jaccard and Wan

1996). So geovisualizations have to consider the “interval-ness” of the questions being mapped. Second, interpolations are essentially computer-assisted best guesses. The gaps existent between observations may or may not accurately reflect emotions spatially of other households in the CTS. More respondents should mean better and more accurate geovisualized interpolated surfaces, given the general assumption based on general notion derived from “Tobler’s First Law of Geography” that nearer things are more related to each other than to farther things.

Geographic scale also plays a pivotal role in interpolation. The illustration above uses actual point data, and the more point data available the more reliable the interpretation is likely to be. However, when using aggregated data (such as the centroid of a parcel as representing the data in a US state or county), caution should be exercised when conducting spatial analysis and interpolation. Openshaw (1984) describes the

193 modifiable areal unit problem (MAUP) that shows how when spatial scales change the aggregate data captured at one scale should not be analyzed or compared to another geographic level. Therefore, care should be exercised to avoid MAUP situations when creating and comparing between geographic scales in the case of fear mapping.

The illustration in this chapter shows briefly a new avenue into fear mapping potential. The data gathered are link to emotion and represent actual opinions from individuals, who have been geographically identified at a local level through their home address. This type of geovisualization approach is best done with as many data points as possible to ensure the best possible surface differences. Whether to gauge where the most fear exists for strategic education plans or to draw links between various demographic variables, the most likely use of this technique is as an SEDA tool for hypothesis creation, which can aid in spatial decision-making situations.

194

CHAPTER 7

COMPARISONS BETWEEN SPATIAL PHYISCAL AND SPATIAL

PSYCHOLOGICAL THREATS FROM TERRORISM

“Never tell people how to do things. Tell them what to do and they will surprise you with their ingenuity.” – George S. Patton Jr. (Anderson 1990, pg. 123)

7.1 Introduction

This research has provided two types of analyses to understand terrorism threat in the United States. The STOP model was designed to capture the actual threat posed by terrorism in urban environments. The model was designed to capture the real or the spatial physical dimension of terrorism. The CTS was used to create the profile,

Columbus–PTTP, to capture the perception of terrorism threat to the city of Columbus,

Ohio. The survey instrument attempted to capture the spatial psychological dimension of

195 terrorism. A comparison of the two methods attempts to provide a holistic view of the threat of terrorism in Columbus.

7.2 Comparison of Actual and Perceived Threat

The STOP model was designed to provide an index for the level of threat vulnerability to urban places in the United States. However, do people believe that cities are the actual targets of terrorism? Although it is well regarded that cities are the general focus of terrorism (Schmid and Jongman 1984), the CTS asked the people of Columbus what they thought. Q9 asks, “Do you believe terrorism is more likely to happen anywhere in the United States or only in specific geographic locations?” Fifty-four percent of households in Columbus believe that terrorism is most likely to occur in specific locations rather than just anywhere, while forty-six percent think terrorism can happen anywhere. Of those that responded to terrorism occurring most likely in specific locations, 30 percent indicated that New York City and Washington D.C. were the most likely cities for terrorism attacks when compared with all the cities in Q11. Also, Q10 asks, “Do you think terrorism is more likely to occur in the United States: (In cities, in general; In rural areas, in general; Equal chance in both cities and rural areas; and Only in certain cities). Here, the respondents believe that terrorism in the U.S. is most likely to occur in cities in general (52 percent). The next highest proportion is that of ‘Only in certain cities’ at 39 percent. Only 9.5 percent believed an equal chance in both cities and rural areas and no one responded stating that terrorism would most likely occur in rural areas. Examining the results of these questions, the respondents clearly believe that

196 terrorism is most likely to occur in cities rather than in rural areas. That is not to say that terrorism cannot happen in rural areas, but the perception is that terrorism is most likely to occur in urban areas in the United States as indicated by respondents of the CTS.

Given this perception, the STOP focus on modeling the urban environment coincides well with that the Columbus-PTTP.

The STOP model indicates based on the use of past terrorism incidents that, in general, business and government targets are the most likely targets for terrorism in the urban environment. Is this the perception of people in Columbus, OH? Q5 asks, “If terrorists attacked, which of the following do you feel are the most likely targets of terrorist interest in Columbus? (Check All That Apply)”. The perception results from the survey indicate that the top five targets as perceived by the Columbus public are sports stadiums/big events (74.5 percent), Port Columbus Airport (63.1 percent), the area shopping malls (51.3 percent), the local water supply (41.9 percent), and downtown tall buildings (41.4 percent). Here we see a different perception from that of the STOP model. Remember that the calculated weights of the model put the most emphasis on business (weight 25), followed by targets of government (20), diplomatic (9), airports/airlines (8), and transportation (5). Comparatively, the CTS respondents put sports stadiums/big events first (business-type target), followed by Port Columbus

Airport (airport/airline-type target), shopping malls (business-type target), local water supply (utilities-type target), and downtown tall buildings (business or government-type target), shown in Table 7.1. In the table, all of the target types identified in the STOP model are presented with a corresponding question category in the CTS. The weight is

197 given and the rank indicates how people ranked the specific targets by percentage in Q5

(for the actual percentages see the Columbu s-PTTP in section 4.2.4).

The top-weighted category in the STOP model was business. The CTS respondents clearly indicated that they perceive business targets as being the most likely targets of attack by ranking business-type targets first, third, fifth, and tenth highest ranked when comparing between the two methodologies. This would seem to indicate that the STOP model business weight corre sponds with the perception of likely terrorism targets in Columbus. The general public in the urban area of Columbus has chosen targets that historically have been associated with the highest magnitudes and frequencies. The two targets highest on the VTI, according to the STOP model were Ohio Stadium and

LeVeque Tower. Both of these targets were considered business targets in the STOP mode l. Two places on The Ohio State University campus were separated into the business-weighted category, Ohio Stadium and th e Value City Arena at the Jerome

Schottenstein Center as opposed to inclusion in the Education category. LeVeque Tower is also home to several business offices and is home to the Palace Theater. Interestingly, the CTS respondents placed the highest likelihood of terrorism targets as being stadiums/event locations, which corresponds well comparatively with one of the two highe st Vulnerability Threat Indexed target s. In this case, the STOP model and the perception of Columbus people are nearly the same.

198

Weight STOP Targets Rank Perceived Targets Sports stadiums/Big events 1 (Business) 3 Shopping Malls (Business) 25 Business Downtown Tall Buildings 5 (tie) (Business) Other (Business) [Batelle, Ross 10 (tie) Labs] Downtown Tall Buildings 5(tie) (Government) The Ohio Statehouse Building 20 Government 7 (Government) Other (Government [Federal 10 (tie) Offices]) 9 Diplomatic None Port Columbus Airport 2 (Airport/Airline) 8 Airports/Airlines Other (Airport/Airline) 10 (tie) [Rickenbacker Airport] Other (Transportation) [Petroleum 10 (tie) 5 Transportation Depot, Railways] 11 Highway System (Transportation) Other (Military) [Defense Supply 4 Military & Police 10 (tie) Center] Journalists, Media, Citizen Property, 2 12 Citizen Homes (Citizen Property) Telecomm, Tourist Places Water Supply (Food & Water 4 Supply) Abortion Related, The Ohio State University Education, Food & 6 (Education) Water Supply, 1 8 Electric Supply (Utilities) Maritime, NGOs, 9 Schools (Education) Religious, Terrorists, and Utilities 10 (tie) Other (Religious) [Churches] Other (Food & Water Supply) 10 (tie) [Food Supply]

Table 7.1 Comparison of actual targets and perception of terrorism targets.

199

However, after business it becomes fuzzy when trying to draw comparisons.

Notice, respondents of the CTS ranked Port Columbus Airport as the second most likely target. One additional airport in the Columbus area was mentioned in the Other category

(Ranked 10th), that being Rickenbacker Airport. Although the airport/airline category has one top five rank when compared with the STOP model weights, the Government category has one as well. The Government weighted category shown in Table 7.1 has three corresponding question response categories that result from Q5. Downtown tall buildings (ranked 5th), the Ohio Statehouse building (ranked 7th), and the Other category

(ranked 10th) appear within the Government category. Comparing between these two to see if there is a related correspondence to the level of weight in the STOP model is difficult. What can be concluded however, is that the perception of targets in the

Columbus urban area is that the airport is a very likely target and that government targets are very likely targets.

Probably the most surprising difference between the STOP model weighting and what the people of Columbus perceive as being a likely target is the water supply. The

STOP model does not have water supply weighted highly based on past terrorism incidents. In fact, there is very little evidence based on past terrorism indicating that it is a highly sought target. The perception, however, does not correspond with past incidents.

For whatever reason, Columbus residents believe that the water supply is a very likely target for terrorism. Nearly 42 percent of the respondents indicated that the water supply was a likely target. The STOP model gave the corresponding weighted category of Food

200 & Water, a weight of one. In the case of the water supply category, there is a very low correspondence between the STOP model and the perception of terrorism. Perhaps it will be a likely target of terrorism in the future, but past events suggest that this will not be the case.

When asked to “Rank the level of responsibility you expect from the government/law enforcement to protect you from terrorism in Columbus, Ohio, respondents answered as the results show in Figure 7.1.

Q13: Rank the level of responsibility you expect from the government/law enforcement to protect you from terrorism in Columbus, Ohio:

0.5

0.45

0.4

0.35

n 0.3 Local tio 0.25 State

opor National

Pr 0.2

0.15

0.1

0.05

0 Completely Somewhat Average Little Not Responsible Responsible Responsibility Responsibility Responsible

Figure 7.1 Rank the level of responsibility you expect from the government/law enforcement to protect you from terrorism in Columbus, Ohio:

201 According to Figure 7.1, most people feel a higher sense of responsibility from national government and law enforcement (42.6 percent completely responsible) than from local

(23.6 percent) or state (29.3 percent). However, people perceive that local, state, and national government and law enforcement are all somewhat responsible for protecting them. Combining the Completely Responsible and Somewhat Responsible shows the expectation from the public that people perceive that local authorities are 62.0 percent responsible, the state is 75.3 percent responsible, and the national government and law enforcement is 85.9 percent responsible for protecting them from terrorism. Therefore, since it is now known what level of responsibility people are expecting from the various entities for protection, one of two things are required. Either, the government and law enforcement provide the level of responsibility people are expecting from them, or they educate the public to help them understand who exactly is responsible and at what level.

So is the perception of level of responsibility people are expecting from the various levels of government and law enforcement the same as what they feel they are getting? Q14 asks, “For whatever reason, is the government/law enforcement protecting you adequately from terrorism in Columbus, Ohio?” The response is in Figure 7.2.

202 Q14: For whatever reason, is the government/law enforcement protecting you adequately from terrorism in Columbus, Ohio?

0.45

0.4

0.35

0.3 n Local tio 0.25 State

opor 0.2 National Pr 0.15

0.1

0.05

0 Very Adequate Somewhat Moderate Somewhat Not Not Adequate Adequate Adequacy Adequate -

Figure 7.2 For whatever reason, is the government/law enforcement protecting you adequately from terrorism in Columbus, Ohio?

According to Figure 7.2, most people feel that they are getting moderate protection from local and state government and local and state law enforcement and somewhat adequate protection from the national entities. Interestingly, 15.5 percent of people feel they are getting less than adequate protection from local authorities, 13.7 percent less than adequate from state authorities, and 12.2 percent feel they are getting less than adequate protection from national government and law enforcement. Perhaps from this analysis, local campaigns promoted to increase awareness of existing protection

203 will alleviate fear from terrorism threat and provide a better measure for people to feel their local, state, and national authorities are doing a good of a job protecting them.

When comparing the mixed-method results from the spatial physical threat and the spatial psychological threat, an increased understanding of terrorism threat can be gained. The actualized model can help to alleviate gaps in local security of important places, especially the highest targets indicated from the STOP VTI. In examining the perceptions of terrorism threat from the population, education plans can be formulated by decision makers to help citizens in their specific geographic location to understand the real threats that exist and to help eliminate unnecessary fear. By approaching research that uses a mixed-method approach, both elements of terrorism, the real threat and the psychological threat can be addressed.

204

CHAPTER 8

PERCEPTIONS OF THE HOMELAND SECURITY ADVISORY SYSTEM

“I have one yardstick by which I test every major problem – and that yardstick is: Is it good for America?” – President Dwight D. Eisenhower (Anderson 1990, pg 131)

8.1 Introduction

Thinking about how people perceive local, state, and national protection from terrorism can provide a measure to decision makers about how to adjust levels of protection, awareness, or both. One way that the national government has been able to provide a communication device to the public to alert them of possible terrorist threat is through the Homeland Security Advisory System (HSAS), shown as Figure 8.1. The

HSAS is designed to alert the public of terrorism threat through a five-tiered color-coded scheme. Each level is associated with a distinct color so that the public can easily

205 recognize the current level of threat. The hope, from the standpoint of the Department of

Homeland Security (DHS), is that the public will respond with a certain level of responsibility depending on the given level of threat. Figure 8.2 provides the detailed list of items the DHS wants citizens to perform.

Figure 8.1 The Homeland Security Advisory System.

206

Figure 8.2 Citizen guidance on the HSAS. (Ready.gov 2004).

207 8.2 Communication of Terrorism Alert Levels

The HSAS was created from Presidential Directive 3 under the George W. Bush administration in March of 2002 (Office of the President 2002). The plan was to create a framework for communicating to all levels of government, to private industry, and to public audiences a measure of terrorist threat to the United States. The plan calls for the system to be under the jurisdiction of the Attorney General and then the DHS as outlined by Presidential Directive 5 (Office of the President 2003).

Purportedly there are several geographic scales at which the HSAS can operate.

Presidential Directive 3 indicates that the threat level can be applied nationally, regionally, by sector, or to a potential target. However, what remains unclear is: 1) how the public is to know how accurate the threat level really is (all discussions about when to raise or lower the threat level are secret and may also provide a mechanism for possible abuse for political ends (Stannard 2006)), and 2) how is the public to know what scale the

HSAS is operating at and how to understand it? Also, what remains unclear is what is meant by region and sector? Possibly it means whatever the decision makers want to be; if several states are threatened then that is the region, if only one state or city is threatened then that is the region, etc… What seems to be problematic is that most or all of the country residents are simply tuned to the national scale (a threat to the United

States as a whole) as a means to understand their own local (city) threat level. The DHS has almost never12 declared any region in the United States as being at the Blue or Green

12 Apparently, however, the state of Hawaii was at the Blue level for a short portion of time in 2003 (Staff and News Reports 2003).

208 level – effectively the HSAS is only a three-tiered system13. Since the HSAS is perceived as the daily guide to terrorism threat to the United States, the question arise: is the national scale at which the Homeland Security Advisory System operates, which seeks to provide the United States residents with a daily threat level in hopes of anticipating ter ro rist attacks, an appropriate scale level for dissemination of threat to the public audience of Columbus, Ohio? Is the HSAS at all effective to help the residents of this midwestern city to understand the threat of terrorism the national government says exists by virtue of the HSAS?

The CTS was used to answer these questions. The following is the analysis of the

CTS in regards to HSAS type questions to find out if the geographic scale at which the

HSAS operates is effective for citizens in Columbus, Ohio. Level of effectiveness is considered in the context of percent of people who pay attention to the threat system and who respond to it. As with all survey questions, a certain level of assumption must be made that people will perceive the asked question the way it was intended. Of course, care was given to the creation of each question but poorly worded questions can always crop up and give unanticipated results. In the case of the CTS, questions geared at distinguishing between ways people might conceive of different geographic scales made assumptions that in general, local is considered where people live or in general ‘the city’, state refers to the political bounds of the state, such as Ohio, and national is considered the entire United States. The question numbers follow the same number system as shown

13 For a chronological list of the rise and fall of HSAS alerts see U.S. Department of Homeland Security (2007). 209 in the complete CTS given in Appendix D and the statistical tables are available in

Appendix E, and correspond directly with the question number.

Q15: Have you ever seen the Homeland Security Advisory System that looks like this?

1 0.9 0.8 0.7 n 0.6 tio 0.5

opor 0.4 Pr 0.3 0.2 0.1 0 Yes No

Figure 8.3 Have you ever seen the Homeland Security Advisory System that looks like this?

210 According to Figure 8.3, almost 94 percent of the people of Columbus have seen the

HSAS logo.

Q15a: In your opinion, is the Homeland Security Advisory System helpful to you?

0.6

0.5

0.4

0.3

Proportion 0.2

0.1

0 Yes No

Figure 8.4 In your opinion, is the Homeland Security Advisory System helpful to you?

In Columbus, 56.7 percent of the people who have seen the HSAS find it helpful to understand the terrorism threat, while 43.3 percent do not, as shown in Figure 8.4.

211 Q15aa: In your opinion, the purpose of the Homeland Security Advisory System is to inform you of a: (Please Check All that Apply)

0.9

0.8

0.7

0.6 n

tio 0.5

opor 0.4 Pr 0.3

0.2

0.1

0 Threat to Threat to State Threat to USA None of these Other Purpose Community

Figure 8.5 In your opinion, the purpose of the Homeland Security Advisory System is to inform you of a:

According to Figure 8.5, in Columbus, 32.3 percent of households believe that the

HSAS has the purpose of informing them of threats to the local community, 33.8 percent believe its purpose is to inform them of a threat to the state, and 84.8 believe the purpose of the HSAS is to inform them of a terrorist threat to the United States.

212 Q15b: How effective is the Homeland Security Advisory System to notify you of a terrorist threat to the United States?

0.35

0.3

0.25 n 0.2 tio

opor 0.15 Pr

0.1

0.05

0 Very Effective Somewhat Average Somewhat Not Not Effective Effective Effectiveness Effective

Figure 8.6 How effective is the Homeland Security Advisory System to notify you of a terrorist threat to the United States?

From the results shown in Figure 8.6, 31.6 percent of residents in Columbus believe that the HSAS is somewhat effective to notify them of a terrorist threat to the

United, while only 18.3 percent feel it is very effective. Over 25.6 percent of people feel it is less then average effectiveness.

213 Q15c: How effective is the Homeland Security Advisory System to notify you of a terrorist threat to Columbus, Ohio?

0.3

0.25

0.2 n tio 0.15 opor Pr 0.1

0.05

0 Very Effective Somewhat Average Somewhat Not Not Effective Effective Effectiveness Effective

Figure 8.7 How effective is the Homeland Security Advisory System to notify you of a terrorist threat to Columbus, Ohio?

Changing the scale to ask about Columbus, only 28.1 percent of people felt that the HSAS was effective (Very Effective + Somewhat Effective) to notify them of a terrorist threat to Columbus, as shown in Figure 8.7. Not surprisingly, 48 percent of people felt the HSAS was somewhat not effective to notify them of a threat to the city specifically.

214 Q15d: Would you avoid any of the following locations because of the threat level rising to High Risk (Level Orange) on the Homeland Advisory Security System? (Check all that apply) &

Q15e: Would you avoid any of the following locations because of the threat level rising to Severe Risk (Level Red) on the Homeland Advisory Security System? (Check all that apply)

0.45

0.4

0.35

0.3 n

tio 0.25 Orange Red opor 0.2 Pr 0.15

0.1

0.05

0

rt s o tal ter alls p SU ums ctric a ings ay one M ir O api i e hools d w N A C l W c il tad E S u igh S at Home ll B H tay Ta S

Figure 8.8 Would you avoid any of the following locations because of the threat level rising to High Risk (Level Orange) or Severe Risk (Level Red) on the Homeland

Advisory Security System?

215 According to Figure 8.8, in every category, except Other, people in Columbus would have a higher tendency to avoid places around the city if the HSAS were Red instead of Orange. The airport is the place of highest avoidance by people in the city when the threat level is raised to Orange (27 percent would avoid) and to Red (39.2 percent). Almost the same thing exists for places that are stadiums or big event locations.

About 27 percent of people avoid those places when the level is Orange, while nearly

31.9 percent avoid them when the HSAS is Red. It is interesting to note that daily commute may or travel on highways may not be affected much at all. Only 2.7 percent said they would avoid highways when the threat level was Orange and not many more would avoid the highways if it were Red (only 3 percent). Associated with this are the proportions of people who would stay home during Orange alert (7.2 percent) and Red alert (20.9 percent).

216 Q16: We have found some people who understand what each color means and others who do not, such as the difference between High Risk (Level Orange) and Severe Risk (Level

Red). In your opinion, are you reasonably aware of what each color means?

0.9 0.8 0.7 0.6 0.5 0.4

Proportion 0.3 0.2 0.1 0 Yes No

Figure 8.9 In your opinion, are you reasonably aware of what each color means?

In the case of Columbus, 85.2 percent of people believe they know what Orange and Red

Level means as shown in Figure 8.9.

217 Q16a: Have you done any of the recommended activities that are associated with the

Homeland Security Advisory System color scheme?

0.5 0.45 0.4 0.35 0.3 0.25 0.2 Proportion 0.15 0.1 0.05 0 Yes No Not Sure

Figure 8.10 Have you done any of the recommended activities that are associated with the Homeland Security Advisory System color scheme?

According to the results shown in Figure 8.10, of those that indicated that they knew what the colors mean in the HSAS, almost 79 percent have not done or were not sure if they had done the recommended activities associated with the HSAS (the recommendations can be seen in Figure 5.9, previously shown).

218 Q16b: Here are the 2 highest risk levels and their meanings, please read them…

We have found that regarding the Homeland Security Advisory System, some people have learned more about it, while others ignore it. In your opinion, when the national threat level has changed to level High Risk (Level Orange) did it impact you enough so that you did some of the recommended activities suggested by the government?

0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 Proportion 0.2 0.15 0.1 0.05 0 Yes No

Figure 8.11 In your opinion, when the national threat level has changed to level High

Risk (Level Orange) did it impact you enough so that you did some of the recommended activities suggested by the government?

According to Figure 8.11, in the case of Columbus, when trying to confirm that each respondent knows exactly the recommendations suggested by the DHS, only 51.3

219 percent of the population actually did any of the steps when the HSAS went to Orange.

Nearly half of the people of Columbus did none of the activities recommended by DHS.

Q17: Now knowing details about the 2 highest risk colors, will you now conduct some of the recommended activities?

0.9

0.8

0.7

0.6

0.5

0.4 Proportion 0.3

0.2

0.1

0 Yes No

Figure 8.12 Now knowing details about the 2 highest risk colors, will you now conduct some of the recommended activities?

After knowing the details about the HSAS Orange and Red colors, 80.6 percent of the population in Columbus does not believe the HSAS color codes mean anything to them and do not feel it is worth their time or effort to do the activities, according to results shown in Figure 8.12.

220 Q18: Now knowing details about the 2 highest risk colors, would you avoid any of the following locations because of the national threat level rising to High Risk or Severe Risk on the Homeland Advisory Security System? (Check all that apply)

0.5 0.45 0.4 0.35 0.3 0.25 0.2 Proportion 0.15 0.1 0.05 0

t ic s tal y ne pi tr ter o rpor OSU a dings ome Malls ec W hools il N Other Ai Ca El Sc at H Stadiums ll Bu Highwa Ta Stay

Figure 8.13 Now knowing details about the 2 highest risk colors, would you avoid any of the following locations because of the national threat level rising to High Risk or Severe

Risk on the Homeland Advisory Security System?

According to Figure 8.13, with the knowledge of what is suggested as recommendations for action regarding Orange and Red, in most cases there were comparable numbers before (Q15d,e) and after impartation of knowledge (Q18). Only slightly larger proportions exist for shopping malls, Port Columbus Airport, the Ohio

Statehouse building, stadiums/big events, electrical and water supplies, tall buildings, and

221 highways. The only category to decrease in avoidance if the threat level were to raise to

Orange or Red on the HSAS were schools. The most notable difference between responses of Q15d,e and Q18 was the highway category, which rose more then others from Q15d,e (Orange – 2.6 percent would avoid, Red – 3.0 percent would avoid) to Q18

(6.5 percent would avoid).

Q19: Knowing the details about the 2 highest risk colors, how effective is the Homeland

Security Advisory System to notify you of a terrorist threat to the United States?

0.4 0.35 0.3

n 0.25 tio 0.2 opor

Pr 0.15 0.1 0.05 0 Very Effective Somewhat Average Somewhat Not Not Effective Effective Effectiveness Effective

Figure 8.14 Knowing the details about the 2 highest risk colors, how effective is the

Homeland Security Advisory System to notify you of a terrorist threat to the United

States?

222 As a result of the knowledge about the color HSAS, there was a slight increase in the number of people who felt an above average level of effectiveness compared prior in

Q15b, as shown in Figure 8.14.

Q19b: Knowing the details about the 2 highest risk colors, how effective is the Homeland

Security Advisory System to notify you of a terrorist threat in Columbus, Ohio?

0.3

0.25

n 0.2 tio 0.15 opor

Pr 0.1

0.05

0 Very Effective Somewhat Average Somewhat Not Not Effective Effective Effectiveness Effective

Figure 8.15 Knowing the details about the 2 highest risk colors, how effective is the

Homeland Security Advisory System to notify you of a terrorist threat in Columbus,

Ohio?

As a result of the knowledge about the color HSAS, there was a moderate increase in the number of people who felt an above average level of effectiveness compared prior in Q15c, up from 28.1 percent to 36.8 percent, as shown in Figure 8.15.

223 There was also a decrease in the proportion of households that felt the HSAS was not effective to notify them of a terrorist threat in Columbus, Ohio.

Q20: The national government has made a website about being prepared in case of a terrorist attack, it is http://www.ready.gov/. Have you ever visited the site http://www.ready.gov/?

1 0.9 0.8 0.7 0.6 0.5 0.4 Proportion 0.3 0.2 0.1 0 Yes No Not Sure

Figure 8.16 Have you ever visited the site http://www.ready.gov/?

Nearly 86.7 percent of households in Columbus have not visited ready.gov as shown from the results presented in Figure 8.16.

224 Q21: Have you ever made a terrorism disaster kit in case of a terrorist attack?

1 0.9 0.8 0.7 0.6 0.5 0.4 Proportion 0.3 0.2 0.1 0 Yes No

Figure 8.17 Have you ever made a terrorism disaster kit in case of a terrorist attack?

Only 11 percent of households in Columbus have made a terrorism disaster kit, according to Figure 8.17. Two types of people exist that made a terrorism disaster kit, those that made one and visited ready.gov and those that made one but did not visit ready.gov. Of the people who said that they had been to ready.gov in Q20, only 3 percent

(8 households of 263) made a terrorism disaster kit.

225 Q22: Do you still have the kit and is it current with supplies? (Check all that apply)

0.1 0.09 0.08 0.07 0.06 0.05 0.04 Proportion 0.03 0.02 0.01 0 Yes, still have Yes, current No, not No, don't current have

Figure 8.18 Do you still have the kit and is it current with supplies?

According to the results shown in Figure 8.18, around 6.1 percent of people that made a terrorism disaster kit in Columbus, still have the kit, and only half of them indicated that it is current.

226 Q23: Have you ever discussed a plan of action in the event of a terrorist attack with your immediate family?

0.7

0.6

0.5

0.4

0.3 Proportion 0.2

0.1

0 Yes No

Figure 8.19 Have you ever discussed a plan of action in the event of a terrorist attack with your immediate family?

Only 34.6 percent of Columbus households have ever discussed a plan of action in the event of a terrorist attack, as shown in Figure 8.19.

227 Q23b: After you have taken this survey, will you discuss a plan of action with your immediate family in the event of a terrorist attack?

172 said ‘No’ to Q23

82 said ‘Yes’ 90 said ‘No’ to Q23b to Q23b

Figure 8.20 After you have taken this survey, will you discuss a plan of action with your immediate family in the event of a terrorist attack?

After have taken this survey, 47.7 percent of the people that indicated that they had never discussed a plan of action in the event of a terrorist attack, said they now would discuss a plan of action with their family, according to the results shown in Figure 8.20.

So is the national scale at which the Homeland Security Advisory System operates, which seeks to provide the United States residents with a daily threat level in hopes of anticipating terrorist attacks, an appropriate scale level for dissemination of threat to the public audience of Columbus, Ohio? Most households in the city have seen

228 the HSAS, but only a little more than half the people in Columbus find the HSAS to be helpful to them. Around half of the households feel that the HSAS purpose is to inform them of threats to the United States. Only around one-third of people felt it had its purpose to inform them of a threat to the state of Ohio or a threat to the city of Columbus.

That means that when we consider the local or city effect the HSAS has on people, they typically are not considering the threat to pertain realistically to the Columbus area. So how helpful is the HSAS in its present form? According to these numbers, not very helpful at all. Although Presidential Directive 3 outlines that the HSAS can function at a regional level, it appears that households in Columbus perceive it as being primarily only a national threat level system. What may be needed is a new type of geographically disseminated system that people recognize as their own indicator of threat. For example, if the DHS were to map the country into regions and have the HSAS broken down by those regions, then with a quick glance people in those regions could more easily identify the reasonable threat level in which they are being asked to operate. The current national scale is too simplified and is not a helpful system for people in Columbus.

Is the HSAS at all effective to help the residents of Columbus to understand the threat of terrorism the national government says exists? To test the effectiveness of the

HSAS, one of the results of a CTS question pointed out that a little less than half of the people felt it was effective to notify them of a threat to the United States, and even fewer, only around 28 percent felt it was effective to notify them of a threat to Columbus. To see what effect the threat levels have on the daily lives of people, it seems that even at a high alert (Orange) nearly two-thirds of the cities citizens would not avoid any of the places

229 indicated in the survey. Since only a little more than half the people feel the HSAS is helpful to them and only around 28 percent feel it is effective to notify them of a threat to

Columbus, Ohio, it seems reasonable to assume that many people in the city do not feel very threatened personally when the threat level increase to Orange, and only slightly more th reatened when it raises to Red. This could be an indication that the HSAS is simply not an effective communication system since it is run primarily as a national system instead of a regional or local one.

So why not break into regions? It can be argued that one reason the DHS may not want to geographically separate the HSAS is that it could tip off potential terrorists that

Intelligence experts might know about their plans. However, this would seem to be a mute point if the caretakers of the HSAS were serious about communicating accurate and reliable information. Since they are mandated to provide updates of threat to regions, sectors, and even to specific targets, then the reasoning that increased levels might tip off terrorists is not really strong because, after all, the DHS is supposed to inform the public when they know about a threat to a specific place.

What can be gained by breaking the HSAS into geographic regions is a better system of communication and a better agent of accountability to the DHS. People in regions like Ohio or, more specifically, Columbus can better understand the threat if the

HSAS is geographically informed for their normal place of operation. It does not help that local Columbus households fear levels rise unnecessarily induced by raising the threat levels for the whole nation. Nacos et al. (2007) have shown effectively that when the HSAS level goes up, there is an increased public fear of terrorism. Geographically

230 informing the HSAS will reduce fear and eliminate any possible political motivation for abusing the system; as Nacos and her colleges have discussed, when President George W.

Bush indicates that the threat level has gone up, public opinion polls go up for the

President. By separating out the HSAS into geographic regions, fear and anxiety will be lessened, potential abuse for political gain will be checked (at least nationally), and it should result in a more sound accountability between the public and the DHS. If encouraged to break the HSAS into regions, the DHS will be at liberty to investigate the intelligence reports and give a threat level to regions. It might be possible that once regions are established, the DHS might still have each region at the same threat level, but at least there is the potential for a more geographically informed communication system through regions.

Is the current HSAS system understood generally by people to communicate terrorism threat? It is possible that people may think they know what the threat level means but actually do not, so questions were designed to test their level of knowledge about the color-coding itself. Nearly 85.2 percent of people indicated that they knew what each of the colors in the HSAS meant. But well over one-third of these people were not sure if they had ever done the activities associated with the color levels (activities seen in

Figure 8.2). That means that possibly more than 95,000 households in Franklin County may be unaware of what the HSAS color codes mean and consequently not comprehend clearly an actual terrorist threat should it occur in the city.

Just in case survey respondents had never seen the activities associated with the two highest levels of Orange and Red, participants were asked to read the

231 recommendations and then respond to questions with that specific knowledge. Equipped with the knowledge of what each color means, only a little over half indicated that they actually had done some of the recommended activities when the threat level rose to

Orange – half did not do any of the recommended activities. After knowing the details about the HSAS Orange and Red colors, 80.6 percent of the population in Columbus does not believe the HSAS color codes mean anything to them and do not feel it is worth their time or effort to do the activities. This seems to indicate a woeful lack of preparation on the part of households in Columbus, or that they just do not care or take the HSAS communication seriously as a threat to them.

As a result of informing the respondents about the recommended activities, there was a slight increase in the number of places that people would avoid if the threat level were to raise to Orange or Red. But most significantly was the avoidance of the highway system, which nearly doubled in the proportion of people who would avoid the highways if the threat level increased to Orange or Red. This is not surprising given the recommended activities to watch for travel advisories and expect traffic delays.

When asked again, with an established knowledge of the HSAS color-codes, respondents indicated that the HSAS was actually more effective to inform them of both a terrorist threat to the United States and to Columbus, Ohio. However, the overall proportions are still low, hovering around half of the people being convinced that it is effective to inform them.

At the time of this survey at the end of 2006, nearly 86 percent of households in

Franklin County had never visited the primary government website that is promoted by

232 the DHS to protect individuals and business from terrorism. Ready.gov is presently being vigorously promoted on television, print media, indoor and outdoor billboards, and on many Internet sites. Two campaigns exist, one in English (Ready.gov 2007b) and the other in Spanish (Listo), with the purpose of:

“Listo and its English language version Ready ask individuals to do three key things: get an emergency supply kit, make a family emergency plan, and be informed about the different types of emergencies that could occur and their appropriate responses. (Ready.gov 2007, pg 1).

So of those households in the city that have visited ready.gov (10.3 percent), how many of them have taken the three key steps above? First, only 11 percent of households in the county indicated they had made a kit and only 3 percent of them had visited ready.gov, indicating that it might not reflect the content “experts” say citizens should have. It is important to note that the terrorism disaster kit as it was called from 2003 to

2006 on ready.gov was changed to include broader circumstances of natural disasters and promoted as an emergency supply kit (Stebbins 2006). Asked if they still have the kit and whether it is updated, only 6.1 percent of households indicated they still have the kit and only half of them were current. Only 3 percent of households in Columbus, Ohio currently have a terrorism disaster kit. Second, only 34.6 percent of households have ever discussed a plan of action in the event of a terrorist attack, indicating that almost two- thirds of the households in Franklin County are without a basic plan of action if a terrorism event occurs. This obviously is contrary to the goals of preparedness at ready.gov. Of those people that said they had not discussed a plan of action, almost half of them (47.7 percent) said they now would talk to their immediate family about a plan after taking the survey.

233 All of this is to say that there appears to be a disjuncture between the people communicating by means of the HSAS to the households in Columbus, Ohio. It has been argued that a better, more geographically inform ed system that separates the HSAS into regions will be more effective to communicate at the level that most people operate – locally. It also should reduce fear, since theoretically the entire national threat level will not need to be raised, only specific regions. Households in Columbus are not prepared for a terrorist attack and the recommended actions outlined by the DHS color codes linked to threat level are not being taken seriously by people in Columbus. Possibly providing a more geographically informed HSAS can increase the awareness and participation of people to become more prepared. Clearly, this research has pointed out the paucity of people in Columbus who have discussed a plan of action with their family or are equipped with necessary supplies in the sad event of a terrorist attack. Obviously, more local campaigns are needed. A geographically informed HSAS might aid in helping people see the necessity of being prepared because they will recognize the threat level regionally and take a more vested interest in it because they will believe it actually pertains to them.

234

CHAPTER 9

CONCLUSIONS

“We must seek, above all, a world of peace; a world in which peoples dwell together in mutual respect and work together in mutual regard.” – President John F. Kennedy

(Anderson 1990, pg. 85)

9.1. Introduction

The geography of terrorism is a story of scale. It penetrates space and time, minds and hearts, even to the very core of our being. Because of its nature, the phenomenon of terrorism has a physical and a psychological component. The physical component operates in the geographic world through networks and exists as a clandestine and elusive agent, employing asymmetrical warfare against opponents. The psychological also acts in the geographic world by creating a spatial blanket of fear and terror to cause people to either panic, incite sympathetic uprising against governments, or to produce an unwanted result of courage to fight the fear and the terrorism.

235 This research has reintroduced the topic of terrorism and discussed it from the perspective of the discipline of geography. Five areas have been identified where geography stands to gain the greatest ground in terrorism research by adding unique views. Flint (2003) recognizes four of these as geographers’ expertise to: study regions, understand geopolitics of territory and borders, examine spatial scale, and understand spatial organization. I have introduced a fifth area where geographers or spatial experts can gain new ground in understanding terrorism through cartographic expertise.

Terrorism has always been difficult to define. It was redefined in the Chapter 2.1 with the use of geographic scale and provides a new consideration among terrorism experts as a way to define the topic. By incorporating geographic scale into the definition of terrorism, the phenomenon can be distinguished from other types of conflict. After defining terrorism, visualizing terrorism became the priority in goals of this research.

The geographic hazards paradigm represents an excellent conceptual and practical framework for building terrorism threat and vulnerability models (Cutter 2003, Montz et al. 2003, Mustafa 2005). Chapter 2.2 provides the background for the geographic hazards tradition and terrorism as a hazard.

Visualization science was introduced in Chapter 2.3 and both fields of scientific and information visualization were described. Geovisualization has emerged from visualization science and many of the techniques were discussed. Geovisualization can be applied to the areas that spatial experts can engage in terrorism research, which can be broken down into four distinct research foci – 1) terrorists and their networks, 2) vulnerability and security modeling, 3) emergency response, and 4) damage mitigation.

236 The work conducted throughout this research is positioned within vulnerability and security modeling has been done to create a way to understand terrorism by capturing the spatial physical dimension and spatial psychological dimension of terrorism. Within the area of mapping, geovisualization is at the threshold of a new ways to research terrorism and provide anti- and counter-terrorism implementation support.

The STOP model was built to address the spatial physical dimension of terrorism.

Chapters 3.1 and 4 describe the methodology and the analysis of the model. The real threat posed by terrorism was captured by the model based on past terrorism trends and varying levels of symbolic targets. The model was constructed to address terrorism at the local, city scale. In the case of STOP, Columbus Ohio served as a test case from implementation of the model.

The spatial psychological dimension of terrorism was captured from a survey of the Columbus population called the Columbus Terrorism Survey (CTS). The CTS results and analysis are given throughout Chapters 5, 6, 7, and 8, with particular attention to the psychological element of fear and how to go about mapping it in Chapter 6.

However, what types of limitations are exhibited from the STOP model? Is the mixed-method approach, which tries to address both aspects of terrorism, the real and psychological threat, the right approach to take? Finally, what about future work? What needs to be done and what avenues exist for expanding the STOP model, conducting more surveys, and using geovisualization in terrorism research?

237 9.2. STOP Limitations

A spatial physical dimension model has been built to capture real threat posed by terrorism. The model called the Strategic Threat Operation Program (STOP) was built in the geographic hazards tradition and tries to model threat in urban areas in the United

States. Using a geographic and mathematical approach, the model results in a

Vulnerability Threat Index (VTI) score that is applied throughout different parcels and objects in the city. The model itself is able to incorporate local past attacks of terrorism identifyed at the city geographic scale and be used for threat indexing within local cities or counties, as well as be used for comparisons between cities within the United States.

Is the STOP model useful and practical? A geographic and mathematical approach is a methodology that can be applied to all cities throughout the United States.

As a result, the VTI can be used throughout the country to obtain a useful measure of susceptible threat. It is practical because it is based on past terrorism incidents, providing an assessment of trend to show what is currently vulnerable to threat. The index score can be used as a measure to differentiate potential targets within the urban area.

What does the STOP model fail to incorporate? It does not incorporate already- existing levels of protection at places. This means that if places are less vulnerable because of some type of antiterrorism implementation, the overall vulnerability of the place is reduced. But to what degree does that have to do with threat? Is it possible that despite making a potential target less likely to terrorism by implementing protective measures, that it will in fact have less threat? Not necessarily, because although greater protection measures mean less damage to people and places, it does not necessarily

238 diminish the overall desire by terrorists to attack a place and in some cases could even promote it further. Places that have greater attention from anti-terrorism implementation may ac t as a more desirable target of terrorism because it indicates to terrorists that the place is valued more highly. As a result, although anti-terrorism implementation measures can be used, it does not translate directly to a reduction of threat from terrorism.

The STOP model takes into consideration the threat level of a place and how likely it is to be attacked based on a VTI index score, and therefore should be considered as a model to show what places are most likely threatened by an attack of terrorism. The results of the model should precipitate a discussion of how to protect places and people should an attack occur.

Should these results become public, would they promote terrorism in those places? It is possible that this effect could occur. However, more likely, any terrorists who have planned to attack Columbus, Ohio have already picked their targets for terror.

Therefore, the STOP model only tries to accurately mimic the real situation on the ground and show where the most likely places of attack will occur before the attack can occur. The model has been designed so that every city manager in the United States can perform his or her own analysis and can do so in the same method outlined in this research. The methodology has been designed to be practical to the point of easy replication (even if the cartographic design is more sophisticated then normally expected). Then anti-terrorism efforts can be focused on the top-tiered locations. The

STOP model attempts to model the real threat from terrorism that already exists and acts

239 as an anti-terrorism measure and a way for resource allocation to be more effectively used.

How much trust can be put into the STOP model results? STOP is like other geographic hazards models – it tries to approximate reality and does so with the creation of an index. It is not a predictive model in the sense that the highest VTI scores will necessarily translate into actual attacks. This is actually counter to the desires of the

STOP designer. The model is intended to provide a picture of threat level and to geovisualize the real situation that exists. It tries to incorporate variables that have been proven to be of consequence from previous terrorism attacks. Weights can be adjusted depending on the geographic context or the general STOP model weighting scheme can be used in cities throughout the United States with a high level of accuracy, as was demonstrated in the Chapter 4. However, the temporal aspect of terrorism is not built into the model.

It is true that many things operate on annual or seasonal variation. Terrorism trends show this same type of cycle (Rapoport 2004). The STOP model does not account for changes over time by weighting modern incidents more heavily than older attacks. In the same manner, temporal cycles that are weekly or daily are absent from the model.

Depending on interpretation, this may or may not be important. The STOP model has been designed to aid in resource allocation. So if stadiums, like Ohio Stadium, which have a large absence of crowds most of the time except game day, may be just as likely to be infiltrated regardless of crowds or not – the absence of people may or may not reduce the threat present from terrorism. In addition, the STOP model does not consider the

240 work patterns of population, or the day/night differences present in urban system patterns.

The temporal trend is an intriguing area of future research. Regardless of point of view, the temporal variable is not captured in the STOP model.

Overall, despite its limitations, the STOP model is an effective way to model and geovisualize the threat terrorism poses to United States cities. It captures the targets of terrorism most likely to be vulnerable from threat and gives a method for doing so in any city of the United States. GIS laypersons and experts can incorporate the framework within their specific area and model the physical threat from terrorism to their city.

9.3 Spatial Physical and Spatial Psychological Approaches

Overall, when comparing the STOP model with the CTS-created Columbus-

PTTP, the results help to formulate view of terrorism that captures the physical and psychological threat to the urban environment. The quantitative actualized model is required in some capacity to help show what really is likely to be a target based on past terrorism incidents, capturing possibly the target of terrorism. On the other hand, the perception-based model encapsulates not the target of terrorism so much as the goal of the terrorism: terror and fear through psychological attempts.

This research has tried to model the real physical and the psychological to gain a more holistic view of terrorism. In the case of the STOP model, the actual threat vulnerability from terrorism is modeled to reflect the current reality based on past incidents. Frequencies of events and the levels of magnitude are used to model reality.

The model can be applied to any urban environment in the United States without

241 difficulty. It is this high adaptability to any urban area with ease that makes this a good candidate model for adoption when trying to formalize terrorism threat to and within cities. The easier a model can be incorporated, as long as it captures an acceptable amount of reality, the more opportunity for its use and adoption. That is one reason the

STOP model has been built the way it has. By basing the weighting system on past terrorism incidents to establish a trend, the model can utilize both the actual events as well as a measure of severity of those events through magnitudes. Building in the population and symbology captures why one target would be chosen of another and the

STOP model incorporates this with ease to the practitioner. The data requirements are low. The most challenging part of the STOP model creation is the geovisualization techniques, which may be a limitation. Knowledge of GIS is necessary to build the spatially-based model. What is convenient is that most urban areas in the United States have expert GIS users already positioned within cities (many connected or hired by government agencies), so the STOP model can be built in conjunction with GIS-related entities.

In the same manner, the CTS has tried to capture the current perceptions of terrorism in the urban environment under study. Although the methodology was different than traditional Internet-based survey schemes, the same type of survey can be used in any U.S. city. Several of the questions can be re-asked in other places in a statistically sound probability-based manner. The answers can then be tallied using statistics software such as STATA or SPSS in the same way as was done in this research without too much

242 difficulty. Results can be mapped by GIS professionals and displayed for spatial exploratory data analysis (SEDA) as was explained in Chapter 7.

Since terrorism deals with the physical threat as well as the psychological threat, it only makes sense to conduct research that examines both. Geovisualization can accomplish this task coupled with the base foundation of geographic hazards research tradition and a probability-based survey. By geovisualizing terrorism, further analysis can be conducted to examine and explore new spatial questions pertaining to the topic.

9.4 Future Work

Combining the two approaches of physical real-threat modeling and perception- based modeling to help inform one another should be the goal of future research. In the case of reformulating the STOP model, the perception of terrorism might be incorporated as a variable in the model, possibly as a weight in the symbology. The temporal trend and analysis that looks at space in conjunction with time should be examined. Possibly new weighting schemes could include differences between non-modern and modern incidents, daily work populations of parcels or other areas to distinguish threat in a 24 hour cycle, and weighting targets according to past terrorism group tendencies. One avenue of work can be to incorporate tendencies of attack from specific terrorist groups. Terrorism proponents who attack only certain targets might be modeled with a weighting system to produce a more informed VTI.

The attitudes from the CTS are generalized to the population in such a way as to try and capture the cultural attitudes that exists about the topic. Those that would promote

243 and use terrorism consider cultural attitudes. If terrorism cells do exist in Columbus, as the Columbus Dispatch reported (Mayhood 2007) is a possibility, then it is likely that those in the cell have considered the perceptions of people to generate a shock and awe effect. It is never guaranteed that this will be the case. The STOP model only is able to approximate reality to a degree, as the CTS can only capture attitudes to a degree. For example, the Dispatch reporting that an al Qaeda operative was captured in Columbus in

2003 and may be part of a terrorism cell (even though this is denied by the FBI), is contradictory to what people in Columbus perceive. Only 13 percent of people responded very high, when asked what was the likelihood that a terrorist cell is operating in the

Columbus, Ohio area. Potentially in this case, the actual situation is very different from the perception. So too could the actualized model be from the perceived model and both be a degree from reality. Future work should try to consider both avenues when describing the threat of terrorism by trying to approximate and model terrorism and capture both the real physical and the real psychological dimensions of terrorism threat.

By incorporating this holistic view, specific details of future work can be discussed. First, more models that capture the real threat from terrorism should be built.

Additionally, a method might be created that does take into account new and implemented anti-terrorism measures, to model and incorporate existing security measures. Although this has yet to be proven to dissuade potential terrorists because they still continu e to plan attacks, it might be included in the modeling environment as an additional variable for the VTI. Research and training that works with city officials, first responders, and decision-makers might include summer workshops to train in geospatial

244 technology and how it can be used more effectively to meet the goals of anti- and counter-terrorism efforts.

The STOP model may be adaptable outside of the United States. The current model could provide a precedent for the way terrorism is modeled in other countries.

Using the worldwide TKB database as a reference for terrorism trends, similar models closely resembling the STOP model might be built in the same geographic hazards research tradition. Even in places that traditional have experienced high degrees of rural terrorism than urban terrorism, such as India, might benefit from the modeling STOP environment.

In the same manner, new terrorism surveys that are less comprehensive and more focused on specific and individualized questions should be done. People’s opinions and perceptions of terrorism should be a top priority if serious anti-terrorism efforts are being conducted – the psychological dimension of terrorism is real (Horgan 2005). If people

“feel” safer because they are educated about terrorism and the level of protection they have or do not have, then that potentially could go farther to reduce fear if an attack or threat of attack should occur. Mariane Pearl, who lost her husband Daniel, an accomplished journalist, to terrorism says it clearly,

“Terrorism is a psychological weapon. It stops you from claiming the world as your own. It stops you from relating to other people. It creates fear and hatred. The way to fight terrorists, as a citizen, is to deny them those emotions. Deny them fear, and they lose” (Smith 2007, p51).

Pearl, is denying terrorists that fear and as a result is conquering one facet of what terrorists try to employ – psychological fear. Surveys, and subsequently geovisualizations

245 of the results, can be used to understand terrorism more fully. The great potential for geovisualizing fear through geographic interpolation has yet to be unleashed.

The research goals and discussions throughout this research point out that there is much to be done by spatial experts who are interested in the topic of terrorism.

Geographers are equipped to understand the spatial dimension better than most, which is a gap in the terrorism literature. Through efforts to advance understanding of terrorism, it should be the hope of every researcher that their work will be used for the good of mankind for the advancement of knowledge. In the case of this subject matter, it is sublime to desire that advancement also mean greater protection of people and the things they value and a reduction of the evil of terrorism.

246

APPENDIX A

TERRORISM DEFINITIONS IN UNITED STATES CODE

247 (after Martin’s 2007 organization)

6 U.S.C. 101(15). Definitions [relating to Homeland Security] The term terrorism means any activity that– (A) involves an act that– (i) is dangerous to human life or potentially destructive of critical infrastructure or key resources; and (ii) is a violation of the criminal laws of the United States or of any State or other subdivision of the United States; and (B) appears to be intended-- (i) to intimidate or coerce a civilian population; (ii) to influence the policy of a government by intimidation or coercion; or (iii) to affect the conduct of a government by mass destruction, assassination, or kidnapping.

6 U.S.C. 444(2) . Definitions [relating to Anti-Terrorism Technology] (A) The term “act of terrorism” means any act that the Secretary determines . . . (i) is unlawful; (ii) causes harm to a person, property, or entity, in the United States, or in the case of a domestic United States air carrier or a United States-flag vessel (or a vessel based principally in the United States on which United States income tax is paid and whose insurance coverage is subject to regulation in the United States), in or outside the United States; and (iii) uses or attempts to use instrumentalities, weapons or other methods designed or intended to cause mass destruction, injury or other loss to citizens or institutions of the United States.

8 U.S.C. 1182(a)(3)(B). Excludable Aliens (iii) As used in this chapter, the term “terrorist activity” means any activity which is unlawful under the laws of the place where it is committed (or which, if it had been committed in the United States, would be unlawful under the laws of the United States or any State) and which involves any of the following: (I) The high jacking or sabotage of any conveyance (including an aircraft, vessel, or vehicle). (II) The seizing or detaining, and threatening to kill, injure, or continue to detain, another individual in order to compel a third person (including a governmental organization) to do or abstain from doing any act as an explicit or implicit condition for the release of the individual seized or detained. (III) A violent attack upon an internationally protected person (as defined in section 1116(b)(4) of Title 18) or upon the liberty of such a person. (IV) An assassination. (V) The use of any– (a) biological agent, chemical agent, or nuclear weapon or device, or (b) explosive, firearm, or other weapon or dangerous device (other than f or mere personal monetary gain), with intent to endanger, directly or indirectly, the safety of one or more individuals or to cause substantial damage to property. (VI) A threat, attempt, or conspiracy to do any of the foregoing. 248

(iv) As used in this chapter, the term “engage in terrorist activity” means, in an individual capacity or as a member of an organization– (I) to commit or to incite to commit, under circumstances indicating an intention to cause death or serious bodily injury, a terrorist activity; (II) to prepare or plan a terrorist activity; (III) to gather information on potential targets for terrorist activity; (IV) to solicit funds or other things of value for– (aa) a terrorist activity; (bb) a terrorist organization described in clause (vi)(I) or (vi)(II); or (cc) a terrorist organization described in clause (vi)(III), unless the solicitor can demonstrate that he did not know, and should not reasonably have known, that the solicitation would further the organization's terrorist activity; (V) to solicit any individual– (aa) to engage in conduct otherwise described in this clause; (bb) for membership in a terrorist organization described in clause (vi)(I) or (vi)(II); or (cc) for membership in a terrorist organization described in clause (vi)(III), unless the solicitor can demonstrate that he did not know, and should not reasonably have known, that the solicitation would further the organization's terrorist activity; or (VI) to commit an act that the actor knows, or reasonably should know, affords material support, including a safe house, transportation, communications, funds, transfer of funds or other material financial benefit, false documentation or identification, weapons (including chemical, biological, or radiological weapons), explosives, or training– (aa) for the commission of a terrorist activity; (bb) to any individual whom the actor knows, or reasonably should know, has committed or plans to commit a terrorist activity; (cc) to a terrorist organization described in clause (vi)(I) or (vi)(II); or (dd) to a terrorist organization described in clause (vi)(III), unless the actor can demonstrate that he did not know, and should not reasonably have known, that the act would further the organization's terrorist activity. . . .

18 U.S.C. 921(22). Definitions [relating to firearms] . . . .For purposes of this paragraph, the term “terrorism” means activity, directed against United States persons, which– (A) is committed by an individual who is not a national or permanent resident alien of the United States; (B) involves violent acts or acts dangerous to human life which would be a criminal violation if committed within the jurisdiction of the United States; and (C) is intended– (i) to intimidate or coerce a civilian population; (ii) to influence the policy of a government by intimidation or coercion; or (iii) to affect the conduct of a government by assassination or kidnapping. (a) As used in this chapter [addressing crimes involving firearms] –

18 U.S.C. 2331. Definitions [relating to the crime of terrorism] (1) the term “international terrorism” means activities that–

249 (A) involve violent acts or acts dangerous to human life that are a violation of the criminal laws of the United States or of any State, or that would be a criminal violation if committed within the jurisdiction of the United States or of any State; (B) appear to be intended– (i) to intimidate or coerce a civilian population; (ii) to influence the policy of a government by intimidation or coercion; or (iii) to affect the conduct of a government by mass destruction, assassination, or kidnapping; and (C) occur primarily outside the territorial jurisdiction of the United States, or transcend national boundaries in terms of the means by which they are accomplished, the persons they appear intended to intimidate or coerce, or the locale in which their perpetrators operate or seek asylum;

* * * (5) the term “domestic terrorism” means activities that– (A) involve acts dangerous to human life that are a violation of the criminal laws of the United States or of any State; (B) appear to be intended-- (i) to intimidate or coerce a civilian population; (ii) to influence the policy of a government by intimidation or coercion; or (iii) to affect the conduct of a government by mass destruction, assassination, or kidnapping; and (C) occur primarily within the territorial jurisdiction of the United States.

18 U.S.C. 2332b(g). Acts of terrorism transcending national boundaries – Definitions (5) the term “Federal crime of terrorism” means an offense that– (A) is calculated to influence or affect the conduct of government by intimidation or coercion, or to retaliate against government conduct; and (B) is a violation of – (i) section 32 (relating to destruction of aircraft or aircraft facilities), 37 (relating to violence at international airports), 81 (relating to arson within special maritime and territorial jurisdiction), 175 or 175b (relating to biological weapons), 175c (relating to variola virus), 229 (relating to chemical weapons), subsection (a), (b), (c), or (d) of section 351 (relating to congressional, cabinet, and Supreme Court assassination and kidnapping), 831 (relating to nuclear materials), 832 (relating to participation in nuclear and weapons of mass destruction threats to the United States) 842(m) or (n) (relating to plastic explosives), 844(f)(2) or (3) (relating to arson and bombing of Government property risking or causing death), 844(i) (relating to arson and bombing of property used in interstate commerce), 930(c) (relating to killing or attempted killing during an attack on a Federal facility with a dangerous weapon), 956(a)(1) (relating to conspiracy to murder, kidnap, or maim persons abroad), 1030(a)(1) (relating to protection of computers) , 1030(a)(5)(A)(i) resulting in damage as defined in 1030(a)(5)(B)(ii) through (v) (relating to protection of computers), 1114 (relating to killing or attempted killing of officers and employees of the United States), 1116 (relating to murder or manslaughter of foreign officials, official guests, or internationally protected persons), 1203 (relating to hostage taking), 1361 (relating to government property or contracts), 1362 (relating to destruction of communication lines, stations, or systems), 1363 (relating to injury to buildings or property within special maritime and territorial jurisdiction of the United States), 1366(a) (relating to destruction of an energy facility), 1751(a), (b), (c), or (d) (relating to Presidential and 250 Presidential staff assassination and kidnapping), 1992 (relating to terrorist attacks and other acts of violence against railroad carriers and against mass transportation systems on land, on water, or through the air), 2155 (relating to destruction of national defense materials, premises, or utilities), 2156 (relating to national defense material, premises, or utilities), 2280 (relating to violence against maritime navigation), 2281 (relating to violence against maritime fixed platforms), 2332 (relating to certain homicides and other violence against United States nationals occurring outside of the United States), 2332a (relating to use of weapons of mass destruction), 2332b (relating to acts of terrorism transcending national boundaries), 2332f (relating to bombing of public places and facilities), 2332g (relating to missile systems designed to destroy aircraft), 2332h (relating to radiological dispersal devices), 2339 (relating to harboring terrorists), 2339A (relating to providing material support to terrorists), 2339B (relating to providing material support to terrorist organizations), 2339C (relating to financing of terrorism), 2339D (relating to military-type training from a foreign terrorist organization), or 2340A (relating to torture) of this title; (ii) sections 92 (relating to prohibitions governing atomic weapons) or 236 (relating to sabotage of nuclear facilities or fuel) of the Atomic Energy Act of 1954 (42 U.S.C. 2122 or 2284); (iii) section 46502 (relating to aircraft piracy), the second sentence of section 46504 (relating to assault on a flight crew with a dangerous weapon), section 46505(b)(3) or (c) (relating to explosive or incendiary devices, or endangerment of human life by means of weapons, on aircraft), section 46506 if homicide or attempted homicide is involved (relating to application of certain criminal laws to acts on aircraft), or section 60123 ( b) (relating to destruction of interstate gas or hazardous liquid pipeline facility) of title 49; or (iv) section 1010A of the Controlled Substances Import and Export Act (relating to narco-terrorism).

22 U.S.C. 2656f. Annual Country Reports on Terrorism (d) As used in this section– (1) the term “international terrorism” means terrorism involving citizens or the territory of more than 1 country; (2) the term “terrorism” means premeditated, politically motivated violence perpetrated against noncombatant targets by subnational groups or clandestine agents; and (3) the term “terrorist group” means any group practicing, or which has significant subgroups which practice, international terrorism.

22 U.S.C. 2780(d). Transactions with Countries Supporting International Terrorism (d) Countries covered by prohibition. The prohibitions contained in this section apply with respect to a country if the Secretary of State determines that the government of that country has repeatedly provided support for acts of international terrorism. For purposes of this subsection, such acts shall include all activities that the Secretary determines willfully aid or abet the international proliferation of nuclear explosive devices to individuals or groups, willfully aid or abet an individual or groups in acquiring unsafeguarded special nuclear material, or willfully aid or abet the efforts of an individual or group to use, develop, produce, stockpile, or otherwise acquire chemical, biological, or radiological weapons.

251

APPENDIX B

CLEANING THE TKB SPANISH TERRORISM INCIDENTS DATA AND

DETERMINING INCIDENTS AS URBAN OR NON-URBAN

252 Data are always dirty; working with and cleaning imperfections within datasets is the normal modus operandi for database practitioners. In the case of the TKB Spanish data, the cleaning process involves the establishment of accuracy within the data.

Cleaning the data typically results in the correction of errors of commission and omission. Commission errors are characterized as database problems with existing data, while errors of omission deal with errors of missing data.

Commission errors in the database of Spanish incidents include errors of spelling, location assignment, and incident assignment. Spelling errors in the TKB database for

Spain include a variety of different mistakes. For some cities, there were obvious spelling errors, for example, the city of San Sebastian being labeled both as San Sebastian and

Saint Sebastian or the city of Vitoria being spelled incorrectly as Vittoria. Other spelling errors exist when comparing the TKB city spelling with the Spanish census spelling, such as with the city of Ordieia, which is Ordizia. It should also be noted, that since this dataset is updated often, spelling errors may continue to disappear with subsequent downloads. Such is the case with the original download in April of 2006, where the city of Azpetia has since been corrected to be spelled Azpeitia by the managers of the TKB database after the initial download. This change indicates that although downloads at the time of writing this paper may have no more spelling errors for Spain, other countries might.

Location assignment errors are numerous as well. The most obvious is the assigment of Spainish regions to that of the ‘City’ field. For example, incident 12853, which can be found online has Catalonia assigned to the ‘City’ field (TKB 12853

253 Incident 2007). Historical accounts confirm that this incident occurred in the city of

Barcelona on September 29, 2000, not the region of Catalonia (News Wire 2000b).

Additionally, there exists the assignment of incorrect cities in incident records. An historical account indicates that the incident on October 8, 2000 (TKB 13105 Incident

2007) occurred in the city of Salvatierra, not Alava (News Wire 2000). Errors of these types should be easily identified when attempting to assign geographic coordinates to an incident.

The final commission error is with incident assignment. When examining the description field and the historical account, it becomes apparent that in the TKB database some incidents should be separated into two or more incidents since they occur at two or more separate locations or the location assignment is completely wrong. For instance, incident 9513 states that two explosions occurred at two separate cities in the description field, yet the incident only appears as one record in the database and does not assign a city to the ‘City’ field (TKB 9513 Incident 2007). Furthermore, incident 10809, which occurred on February 15, 1998, indicates the city as Saint-Jean-de-Luz, which is not a city in Spain at all (TKB 10809 Incident 2007). This is true for incident 10808 attributed to Bayonne , as well, which is in France (TKB 10809 Incident 2007). Saint-Jean-de-Luz and Bayonne are cities in southern France that are near the border of the Basque region in

Spain.

There are also obvious errors of omission in the TKB database. Since the creators only used international terrorism incidents from 1968 to 1997 to construct the database, any domestic Spanish terrorism incidents during that time are not included, making a

254 complete analysis of all terrorism incidents during this time period impossible. To overcome the lack of domestic data, the combination of country-specific terrorism incidents is necessary. Such information can be found through historical archives and from previous research that focuses on specific countries or terrorist groups, such as the study conducted by Shabad and Ramo (1995). In addition to these missing data, there is also a reason to question the thoroughness of the international incidents. Incident 3728, which occurred on November 11, 1983 demonstrates this point (TKB 3728 Incident

2007). The description indicates that two businesses were bombed, the Bank of America and the Rank Xerox Corporation. However, not present in the TKB database is an incident that occurred on December 21, 1982, nearly identical to incident 3728, in which three bombs exploded at business of the Bank of America, Avis rental car, and Ford

Motor Company (News Wire 1982). Caution should be exercised by acknowledgment that this is an imperfect record of terrorism incidents, yet remains the best incident database currently available. After the TKB country-specific Spain data were cleaned and the location of the incidents were imputed, the combination of the census-defined urban locations and that of the TKB locations was done as describe below.

Since the TKB starts in 1968, census data were collected from the Instituto

Nacional de Estadistica (National Statistics Institute of Spain, INE) for 1968 to the present. Using these data for the census years of 1971, 1981, 1991, and 2001, which were downloaded from the INE website (INE 2007), and utilizing the definition for urban areas in Spain for each census, also found at INE for each census, four specific time periods of urban areas in the country were created. Since definitions of urban may change from one

255 census to the next, census-specific definitions are suggested to compensate for changes in urban growth and decline and the identification of such entities over time. A place might be categorized during one time period as non-urban and grow to become urban, which might result in incidents over time being non-urban but later urban. For example, in the case of Spain, the cities of Salou (no terrorism incident) and Sopelana (1 terrorism incident) were non-urban areas in 1991-2000, but became urban in 2001. From 2001-

2006, Salou had one terrorism incident, whereas, Sopelana saw an increase to 5 terrorism incidents.

Spanish urban areas are defined as cities having a population of 10,000 or more and remain consistent with each census. The goal is to be able to match the census data urban places with those listed in the TKB data, as shown in Figure B.1. In order for a match to occur, the location must be present for each incident. The database has 1293 incidents spanning from 1968 to 2006. Of these, 82 did not have a city assigned and 2 were incorrectly attributed to Spain but occurred in France. Utilizing the historical account archives of major newspapers and news wires the locations for 31 of these 82 incidents were identified. The remaining 51 incidents (3.9% of all incidents) were labeled as indeterminate and were excluded along with the 2 from France. As a result, there were

1240 total incidents used and determined to be either urban or non-urban, seen in Table

3.5.

256

Figure B.1 Methodological procedure for analysis of urban terrorism.

257

APPENDIX C

CRITICAL INFRASTRUCTURE AND PARCEL LIST FOR THE STOP MODEL IN

COLUMBUS, OHIO.

258

Critical Infrastructure Target Code No Infr astructure 0 Airports/Airlines 1 Abortio n Related 2 Business 3 Diplomatic 4 Educati onal Institutions 5 Food/W ater Supply 6 Government 7 Journal ists & Media 8 NGO 9 Police 10 Private Citizen & 11 Property Maritime 12 Military 13 Religio us Person/Place 14 Telecommunication 15 Terrorists 16 Tourists 17 Transpo rtation 18 Utilitie s 19 Other 20 Unknown 21

Table C .1 Critical Infrastructure target codes.

259 LU TargetCode Description 100 0 AGRICULTURAL VACANT LAND 101 0 CASH FARM 110 0 VACANT LAND QUALIFIED FOR CAUV (Current Agricultural Use Value) 111 0 CASH FARM QUALIFIED FOR CAUV 112 0 LIVESTOCK OTHER THAN DAIRY AND POULTRY QUALIFIED FOR CAUV 113 0 DAIRY FARM QUALIFIED FOR CAUV 114 0 POULTRY FARM QUALIFIED FOR CAUV 115 0 FRUIT AND NUT FARM QUALIFIED FOR CAUV 116 0 VEGETABLE FARM QUALIFIED FOR CAUV 117 0 TOBACCO FARM QUALIFIED FOR CAUV 120 0 TIMBER OR FOREST LANDS 121 0 TIMBER OR FOREST LANDS QUALIFIED FOR CAUV 189 0 OTHER AGRICULTURAL 198 0 OTHER AGRICULTURAL 199 0 OTHER AGRICULTURAL USE QUALIFIED FOR CAUV 300 0 INDUSTRIAL VACANT LAND 301 0 LANDFILL OR OTHER INDUSTRIAL 302 0 LUMBER YARDS OR OTHER INDUSTRIAL 303 0 TANK FARMS OR OTHER INDUSTRIAL 380 0 MINES OR QUARRIES 390 0 GRAIN ELEVATORS 391 0 OPEN CODE OR OTHER INDUSTRIAL 392 0 OPEN CODE OR OTHER INDUSTRIAL 399 0 OTHER INDUSTRIAL STRUCTURES 400 0 COMMERCIAL VACANT LAND 406 0 STORAGE OVER RETAIL 408 0 OPEN CODE OR OTHER INDUSTRIAL 433 0 STORAGE OVER OFFICE (WALKUP) 456 0 PARKING GARAGE, STRUCTURES AND LOTS 457 0 PARKING LOT/STRUCTURE 468 0 OPEN CODE OR OTHER INDUSTRIAL 469 0 OPEN CODE OR OTHER INDUSTRIAL 472 0 OPEN CODE OTHER COMMERCIAL 473 0 OPEN CODE OTHER COMMERCIAL 474 0 OPEN CODE OTHER COMMERCIAL 476 0 OPEN CODE OTHER COMMERCIAL

Continued

Table C.2 STOP parcel coding designation. 260 Table C.2 continued

477 0 OPEN CODE OTHER COMMERCIAL 478 0 OPEN CODE OTHER COMMERCIAL 485 0 OPEN CODE OTHER COMMERCIAL 486 0 OPEN CODE OTHER COMMERCIAL 487 0 OPEN CODE OTHER COMMERCIAL 488 0 OPEN CODE OTHER COMMERCIAL 494 0 OPEN CODE OTHER COMMERCIAL 495 0 OPEN CODE OTHER COMMERCIAL 499 0 OTHER COMMERCIAL STRUCTURE 500 0 VACANT LAND QUALIFIED FOR CAUV 501 0 VACANT, UNPLATTED RESIDENTIAL LAND: 0 - 9.99 ACRES 502 0 VACANT, UNPLATTED RESIDENTIAL LAND: 10 - 19.99 ACRES 503 0 VACANT, UNPLATTED RESIDENTIAL LAND: 20 - 29.99 ACRES 504 0 VACANT, UNPLATTED RESIDENTIAL LAND: 30 - 39.99 ACRES 505 0 VACANT, UNPLATTED RESIDENTIAL LAND: 40 OR MORE ACRES 660 0 EXEMPT PROPERTY OWNED BY PARK DISTRICTS 661 0 OTHER EXEMPT 662 0 OTHER EXEMPT 663 0 OTHER EXEMPT 664 0 OTHER EXEMPT 665 0 OTHER EXEMPT 690 0 GRAVEYARDS, MONUMENTS AND CEMETERIES 691 0 OTHER EXEMPT 692 0 OTHER EXEMPT 693 0 OTHER EXEMPT 694 0 OTHER EXEMPT 695 0 OTHER EXEMPT COMMUNITY URBAN REDEVELOPMENT CORPORATION TAX ABATEMENTS (R.C. 700 0 1728.10) 701 0 OTHER ABATEMENT 702 0 OTHER ABATEMENT 703 0 OTHER ABATEMENT 704 0 OTHER ABATEMENT 705 0 OTHER ABATEMENT

Continued

261 Table C.2 continued

710 0 COMMUNITY REINVESTMENT AREA TAX ABATEMENTS (R.C. 3735.61) 711 0 OTHER ABATEMENT 712 0 OTHER ABATEMENT 713 0 OTHER ABATEMENT 714 0 OTHER ABATEMENT 715 0 OTHER ABATEMENT 720 0 MUNICIPAL IMPROVEMENT TAX ABATEMENTS (R.C. 5709.41) 721 0 OTHER ABATEMENT 722 0 OTHER ABATEMENT 723 0 OTHER ABATEMENT 724 0 OTHER ABATEMENT 725 0 OTHER ABATEMENT 730 0 MUNICIPAL URBAN REDEVELOPMENT TAX ABATEMENTS (R.C. 725.02) 731 0 ABATEMENT 732 0 ABATEMENT 733 0 ABATEMENT 734 0 ABATEMENT 735 0 ABATEMENT 740 0 OTHER TAX ABATEMENTS (R.C. 165.01 & 303.52) 741 0 ABATEMENT 742 0 ABATEMENT 743 0 ABATEMENT 744 0 ABATEMENT 745 0 ABATEMENT AGRICULTURAL LAND AND IMPROVEMENTS OWNED BY A PUBLIC UTILITY 800 0 OTHER THAN A RAILROAD MINERAL LAND AND IMPROVEMENTS OWNED BY A PUBLIC UTILITY OTHER 810 0 THAN A RAILROAD 899 0 ZERO-VALUED PARCELS 999 0 ZERO-VALUED PARCELS 304 1 AVIATION FACILITY OR OTHER INDUSTRIAL 305 3 RESEARCH/CHEMICAL LAB OR OTHER INDUSTRIAL 320 3 FOUNDRIES AND HEAVY MANUFACTURING PLANTS 330 3 MEDIUM MANUFACTURING AND ASSEMBLY 340 3 LIGHT MANUFACTURING AND ASSEMBLY - 0-10% OFFICE

Continued

262 Table C.2 continued

341 3 LIGHT MANUFACTURING: 11-20% OFFICE 342 3 LIGHT MANUFACTURING: 21-30% OFFICE 343 3 LIGHT MANUFACURING: OVER 30% OFFICE 350 3 WAREHOUSE: 0-5% OFFICE 351 3 WAREHOUSE: 6-15% OFFICE 352 3 WAREHOUSE: 16-25% OFFICE 353 3 WAREHOUSE: 26-35% OFFICE 354 3 WAREHOUSE: 36-50% OFFICE 355 3 DISTRIBUTION WAREHOUSE CENTERS OR OTHER INDUSTRIAL 356 3 AUTOMATED WAREHOUSE OR OTHER INDUSTRIAL 357 3 COLD STORAGE FACILITY OR OTHER INDUSTRIAL 358 3 MULTI-STORY WAREHOUSE OR OTHER INDUSTRIAL 360 3 INDUSTRIAL TRUCK TERMINALS 370 3 SMALL SHOPS (MACHINE, TOOL & DYE, ETC) 0-10% OFFICE 371 3 SMALL SHOPS (MACHINE, TOOL & DYE, ETC) 11-20% OFFICE 372 3 SMALL SHOPS (MACHINE, TOOL & DYE, ETC) OVER 20% OFFICE 375 3 OTHER INDUSTRIAL 405 3 OFFICE OVER RETAIL (WALKUP) 407 3 COMMERCIAL LAWN/GARDEN SALES OR OTHER INDUSTRIAL 416 3 COMMERCIAL CAMP GROUND 420 3 SMALL DETACHED RETAIL STRUCTURES (UNDER 10,000 SF) 421 3 SUPERMARKETS 422 3 DISCOUNT STORES AND JUNIOR DEPARTMENT STORES 423 3 CATALOG SHOWROOM SALES 424 3 FULL LINE DEPARTMENT STORE 425 3 NEIGHBORHOOD SHOPPING CENTER 426 3 COMMUNITY SHOPPING CENTER 427 3 REGIONAL SHOPPING CENTER 429 3 OTHER RETAIL STRUCTURES 430 3 RESTAURANT, CAFETERIA AND/OR BAR 432 3 RETAIL OVER OFFICE (WALKUP) 434 3 SUPPER CLUB/NIGHT CLUB 435 3 DRIVE-IN RESTAURANT OR FOOD SERVICE FACILITY 436 3 FAMILY RESTAURANT/DINING ROOMS, CAF╔

Continued

263 Table C.2 continued

437 3 OTHER FOOD SERVICE STRUCTURES 438 3 DRIVE THROUGH CARRYOUT 439 3 CONVENIENCE FOOD STORES OR OTHER FOOD SERVICE STRUCTURE 440 3 DRY CLEANING PLANTS AND LAUNDRIES 441 3 FUNERAL HOMES 444 3 FULL SERVICE BANKS 445 3 SAVINGS AND LOANS 447 3 OFFICE BUILDING (1 AND 2 STORIES) 448 3 WALK-UP OFFICE BUILDING (3 OR MORE STORIES) 449 3 ELEVATOR OFFICE BUILDING (3 OR MORE STORIES) 450 3 CONDOMINIUM OFFICE BUILDING 466 3 TRUCK/FARM EQUIPMENT SALES & SERVICE 467 3 USED CAR SALES LOT 479 3 DOG/CAT KENNELS 480 3 COMMERCIAL TRUCK TERMINALS 481 3 MINI WAREHOUSES 482 3 COMMERCIAL TRUCK TERMINALS 417 5 DAY CARE/PRESCHOOL 418 5 FRATERNITIES/SORORITIES 650 5 EXEMPT PROPERTY OWNED BY BOARDS OF EDUCATION 651 5 OTHER EXEMPT 652 5 OTHER EXEMPT 653 5 OTHER EXEMPT 654 5 OTHER EXEMPT 655 5 OTHER EXEMPT 670 5 EXEMPT PROPERTY OWNED BY COLLEGES ACADEMIES (PRIVATE) 671 5 OTHER EXEMPT 672 5 OTHER EXEMPT 673 5 OTHER EXEMPT 674 5 OTHER EXEMPT 675 5 OTHER EXEMPT 102 6 LIVESTOCK OTHER THAN DAIRY AND POULTRY 103 6 DAIRY FARM 104 6 POULTRY FARM

Continued

264 Table C.2 continued

105 6 FRUIT AND NUT FARM 106 6 VEGETABLE FARM 107 6 TOBACCO FARM 108 6 NURSERY 109 6 GREENHOUSE, VEGETABLE AND FLORACULTURE 190 6 INDUSTRIAL GREEN HOUSE 310 6 FOOD AND DRINK PROCESSING PLANTS AND STORAGE 600 7 EXEMPT PROPERTY OWNED BY UNITED STATES OF AMERICA 601 7 OTHER EXEMPT 602 7 OTHER EXEMPT 603 7 OTHER EXEMPT 604 7 OTHER EXEMPT 605 7 OTHER EXEMPT 610 7 EXEMPT PROPERTY OWNED BY STATE OF OHIO 611 7 OTHER EXEMPT 612 7 OTHER EXEMPT 613 7 OTHER EXEMPT 614 7 OTHER EXEMPT 615 7 OTHER EXEMPT 620 7 EXEMPT PROPERTY OWNED BY COUNTIES 621 7 OTHER EXEMPT 622 7 OTHER EXEMPT 623 7 OTHER EXEMPT 624 7 OTHER EXEMPT 625 7 OTHER EXEMPT 630 7 EXEMPT PROPERTY OWNED BY TOWNSHIPS 631 7 OTHER EXEMPT 632 7 OTHER EXEMPT 633 7 OTHER EXEMPT 634 7 OTHER EXEMPT 635 7 OTHER EXEMPT 640 7 EXEMPT PROPERTY OWNED BY MUNICIPALITIES 641 7 OTHER EXEMPT 642 7 OTHER EXEMPT

Continued

265 Table C.2 continued

643 7 OTHER EXEMPT 644 7 OTHER EXEMPT 645 7 EXEMPT PROPERTY OWNED BY METROPOLITAN HOUSING AUTHORITIES 446 8 RADIO/TV STATIONS 401 11 APARTMENTS: 4 - 19 RENTAL UNITS 402 11 APARTMENTS: 20 TO 39 RENTAL UNITS 403 11 APARTMENTS: 40+ RENTAL UNITS 404 11 APARTMENTS OVER RETAIL (WALKUP) 409 11 HOUSING -ELDERLY OR OTHER COMMERCIAL 412 11 NURSING HOME - FULL SERVICE AND PRIVATE HOSPITALS 413 11 NURSING HOME - CUSTODIAL 414 11 ROOMING HOUSES 415 11 MANUFACTURED HOME PARK 419 11 OTHER COMMUNITY HOUSING 431 11 APARTMENTS OVER OFFICE (WALKUP) 470 11 SINGLE FAMILY DWELLING, CONVERTED TO OFFICE USE 471 11 SINGLE FAMILY DWELLING, CONVERTED TO RETAIL USE 475 11 RETAIL CONDO 510 11 SINGLE FAMILY DWELLING ON PLATTED LOT 511 11 SINGLE FAMILY DWELLING ON UNPLATTED LAND: 0 - 9.99 ACRES 512 11 SINGLE FAMILY DWELLING ON UNPLATTED LAND: 10 - 19.99 ACRES 513 11 SINGLE FAMILY DWELLING ON UNPLATTED LAND: 20 - 29.99 514 11 SINGLE FAMILY DWELLING ON UNPLATTED LAND: 30 - 39.99 515 11 SINGLE FAMILY DWELLING ON UNPLATTED LAND: 40 OR MORE ACRES 520 11 TWO-FAMILY DWELLING ON PLATTED LOT 521 11 TWO-FAMILY DWELLING ON UNPLATTED LAND: 0 - 9.99 ACRES 522 11 TWO-FAMILY DWELLING ON UNPLATTED LAND: 10 - 19.99 ACRES 523 11 TWO-FAMILY DWELLING ON UNPLATTED LAND: 20 - 29.99 ACRES 524 11 TWO-FAMILY DWELLING ON UNPLATTED LAND: 30 - 39.99 ACRES 525 11 TWO-FAMILY DWELLING ON UNPLATTED LAND: 40 OR MORE ACRES 530 11 THREE-FAMILY DWELLING ON PLATTED LOT 531 11 THREE-FAMILY DWELLING ON UNPLATTED LAND: 0 - 9.99 ACRES 532 11 THREE-FAMILY DWELLING ON UNPLATTED LAND: 10 - 19.99 ACRES 533 11 THREE-FAMILY DWELLING ON UNPLATTED LAND: 20 - 29.99 ACRES

Continued

266 Table C.2 continued

534 11 THREE-FAMILY DWELLING ON UNPLATTED LAND: 30 - 39.99 ACRES 535 11 THREE-FAMILY DWELLING ON UNPLATTED LAND: 40 OR MORE ACRES 540 11 OTHER RESIDENTIAL 550 11 CONDOMINIUM RESIDENTIAL UNIT 551 11 OTHER RESIDENTIAL 552 11 OTHER RESIDENTIAL 553 11 OTHER RESIDENTIAL 554 11 OTHER RESIDENTIAL 555 11 OTHER RESIDENTIAL 559 11 CONDO GARAGE PARCEL 560 11 HOUSE TRAILERS OR MOBILE HOMES AFFIXED TO REAL ESTATE 599 11 OTHER RESIDENTIAL STRUCTURE 490 12 MARINE SERVICE FACILITIES 496 12 MARINAS (SMALL BOATS) 685 14 CHURCHES, PUBLIC WORSHIP 410 17 MOTEL/TOURIST CABINS 411 17 HOTEL 428 17 AMUSEMENT PARKS 460 17 THEATRES 461 17 DRIVE-IN THEATRES 462 17 GOLF DRIVING RANGES AND MINIATURE GOLF COURSES 463 17 GOLF COURSES 464 17 BOWLING ALLEYS 465 17 LODGE HALLS AND AMUSEMENT PARKS 491 17 RAQUETBALL COURTS 492 17 TENNIS BARNS 493 17 SWIMMING CLUB 497 17 AUTO RACETRACKS & HORSE TRACKS 498 17 SKATING RINKS 443 18 CARWASH-FULL SERVE/AUTO 451 18 GAS STATION - NO BAYS 452 18 AUTOMOTIVE SERVICE STATIONS 453 18 CAR WASH-SELF SERVE 454 18 AUTOMOBILE SALES AND SERVICE

Continued

267 Table C.2 continued

455 18 COMMERCIAL GARAGE 458 18 GAS STATION/CONVENIENCE FOOD STORE 459 18 GAS STATION/CAR WASH 483 18 BUS GARAGES & TERMINALS 840 18 RAILROAD REAL PROPERTY USED IN OPERATIONS 850 18 RAILROAD REAL PROPERTY NOT USED IN OPERATIONS 860 18 RAILROAD PERSONAL PROPERTY USED IN OPERATIONS 870 18 RAILROAD PERSONAL PROPERTY NOT USED IN OPERATIONS INDUSTRIAL LAND AND IMPROVMENTS OWNED BY A PUBLIC UTILITY OTHER 820 19 THAN A RAILROAD COMMERCIAL LAND AND IMPROVEMENTS OWNED BY A PUBLIC UTILITY 830 19 OTHER THAN A RAILROAD 871 19 UTILITY 880 19 PUBLIC UTILITY PERSONAL PROPERTY OTHER THAN RAILROADS 881 19 UTILITY 900 19 UTILITY 442 20 MEDICAL CLINICS AND OFFICES 484 20 HOSPITALS 489 20 HEALTH SPAS 680 20 CHARITABLE EXEMPTIONS (HOSPITALS, HOMES FOR THE AGED, ETC) 681 20 OTHER EXEMPT 682 20 OTHER EXEMPT 683 20 OTHER EXEMPT 684 20 OTHER EXEMPT

268

APPENDIX D

COLUMBUS TERRORISM SURVEY

269 Details: Probability-based survey with participation requests from postcards. Non- probability with email, television, and radio requests. Internet survey used surveymonkey.com and hosted at www.columbusterrorismsurvey.org. Screen capture below of entry page.

Figure D.1 Screen capture of Internet-based Columbus Terrorism Survey.

270

Consent for participation in research Protocol # 2006E0683

Welcome to the Columbus Terrorism Survey. Researchers at The Ohio State University are conducting a study on terrorism in Columbus, Ohio. This is an important educational survey and is based solely on your opinion. This survey is being used to evaluate the perception of terrorism and the effectiveness of the Homeland Security Advisory System in the area. Your participation is very important to the success of the project. The survey takes between 10 to 15 minutes to complete and has some interesting questions. Your participation is voluntary and you may exit the survey or skip a question at anytime. At the end of this survey, you will have the option to be entered into a random drawing to win a prize by providing your name and address. Your name and address are optional and are not required to take this survey. If you provide your name and address, it will be removed from your survey answers after the drawing for prizes occurs, to ensure your answers are anonymous. Thank you for your help and service to our community.

Contact information: Mei-Po Kwan or Jason VanHorn, [email protected], (614) 292- 6127 Question Responses Next Question Q0: DISPLAY: To participate and potentially win Yes GO TO Q1 a prize you must be 18 years or older. Were you born before November 30th, 1988? No GO TO S1 Q1: DISPLAY: With your permission, we would like to ask you some questions about your views Yes GO TO Q1a on terrorism. Your answers are completely voluntary and you may choose at anytime to skip any question by pressing the “Next>>” link or exit No GO TO S1 the survey by closing your Internet browser. Do we have your permission to begin the interview?

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Table D.1 The Columbus Terrorism Survey.

271 Table D.1 continued

Q1a: Do you consider terrorism to be an important Very Important:5, issue today? Somewhat Important:4, Average Importance:3, GO TO Q1b Somewhat Unimportant:2, Not Important:1 Q1b: Was terrorism an important issue to you Very Important:5, before the events of September 11, 2001? Somewhat Important:4, Average Importance:3, GO TO Q2 Somewhat Unimportant:2, Not Important:1 Q2: In your opinion, what is the likelihood that Very High:5, High:4, terrorists will attack the United States in the next Average:3, Low:2, GO TO Q3 year? Very Low:1 Q3: Thinking about terrorism, what is the Very High:5, High:4, likelihood that a terrorist will attack the city of Average:3, Low:2, GO TO Q3b Columbus, Ohio in the future? Very Low:1 Q3b: The issue of terrorism in Columbus is Everyday:5, Often:4, something I talk about with my friends: Sometimes:3, Not GO TO Q4 Often:2, Never:1 What is the likelihood that a terrorist cell is Very High:5, High:4, operating in the Columbus, Ohio area? Average:3, Low:2, GO TO Q5 Very Low:1

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272 Table D.1 continued

Q5: If terrorists attacked, which of the following RANDOMIZED: do you feel are the most likely targets of terrorist [Shopping Malls, interest in Columbus? (Check All That Apply) Port Columbus Airport, The Ohio State University, The Ohio Capital building, Sports stadiums/Big Events, Electrical supply, GO TO Q6 water supply, schools, Downtown tall buildings, Highways, Your home, | (NOT RANDOMIZED): None, Other (TEXT BOX ENTRY)] Q6: Are you personally concerned that a terrorist Very concerned:5, will attack somewhere in Columbus, Ohio in the Somewhat next year? concerned:4, Average concern:3, GO TO Q7 Somewhat not concerned:2, Not concerned:1 Q7: How about within the next 5 years, will there Very concerned:5, be a terrorist attack in Columbus, Ohio? Somewhat concerned:4, Average concern:3, GO TO Q8 Somewhat not concerned:2, Not concerned:1

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273 Table D.1 continued

Q8: We have found some to be apprehensive Very apprehensive:5, about traveling, while others are not. Are you Somewhat apprehensive about going to any of the following apprehensive:4, places because of potential terrorism? Average apprehension:3, Little apprehension :2, Not apprehensive:1] For each of the following: RANDOMIZED: Shopping Mall: Port GO TO Q8b Columbus Airport: The Ohio State University: The Ohio Capital building: Sports stadiums/Big Events: Electrical Plant: Water Plant: Schools: Highways: Downtown tall buildings: Your home

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274 Table D.1 continued

Q8b: Do you avoid any of the following because RANDOMIZED: of the potential for terrorism? (Select All that Shopping Mall: Port Apply) Columbus Airport: The Ohio State University: The Ohio Capital building: Sports stadiums/Big Events: Electrical GO TO Q9 Plant: Water Plant: Schools: Highways: Downtown tall buildings: | NOT RANDOMIZED :Other (TEXT BOX)

Q9: Do you believe terrorism is more likely to Anywhere, Specific happen anywhere in the United States or in only in GO TO Q10 locations specific geographic locations? Q10: Do you think terrorism is more likely to In cities in general, occur in the United States: In rural areas in general, GO TO Q11 Equal chance in both cities and rural areas, Only in certain cities

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275 Table D.1 continued

Q11: Rank the cities below for their likelihood of [High Chance:5, terrorist attack (in the next couple of years): Likely:4, Average Chance:3, Not Likely:2, No Chance:1] [RANDOMIZED: New York City: Washington DC: GO TO Q12 Chicago: Los Angeles: Atlanta: Miami: Seattle: Dallas/Fort Worth: Boston: Las Vegas: Philadelphia: San Francisco: Denver: Columbus Q12: Rank the following Ohio cities in their [High Chance:5, likelihood of terrorist attacks: Likely:4, Average Chance:3, Not Likely:2, No Chance:1] GO TO Q13 [RANDOMIZED: Columbus: Cincinnati: Cleveland: Akron: Toledo: Dayton:

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276 Table D.1 continued

Q13: Rank the level of responsibility you expect [Completely from the government/law enforcement to protect responsible:5, you from terrorism in Columbus, Ohio. Somewhat responsible:4, Average responsibility:3, Little responsibility:2, No responsibility:1] GO TO Q14 [Local government/law enforcement: State government/law enforcement: National government/law enforcement] Q14: For whatever reason, is the government/law [Very adequate:5, enforcement protecting you adequately from Somewhat terrorism in Columbus, Ohio? adequate:4, Moderately adequate:3, Somewhat not adequate:2, Not adequate:1] GO TO Q15 [Local government/law enforcement: State government/law enforcement: National government/law enforcement]

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277 Table D.1 continued

Q15: Have you ever seen the Homeland Security Advisory System that looks like this? [HSAS Yes GO TO Q15a

No GO TO Q16b LOGO, a public domain image] Q15a: In your opinion, is the Homeland Security Advisory System helpful to you? [HSAS LOGO WILL BE INCLUDED AS A Yes, No GO TO Q15aa VISUAL] Q15aa: In your opinion, the purpose of the Inform me of a Homeland Security Advisory System is to: (Please terrorist threat to Check All that Apply) the United States: Inform me of a terrorist threat in my state: GO TO Q15b Inform me of a terrorist threat in my community: None of these: Other (TEXT BOX ENTRY) Q15b: How effective is the Homeland Security Very effective:5, Advisory System to notify you of a terrorist threat Somewhat to the United States? [HSAS LOGO WILL BE effective:4, INCLUDED AS A VISUAL] Average GO TO Q15c effectivness:3, Somewhat not effective:2, Not effective:1

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278 Table D.1 continued

Q15c: How effective is the Homeland Security Very effective:5, Advisory System to notify you of a terrorist threat Somewhat to Columbus, Ohio? [HSAS LOGO WILL BE effective:4, INCLUDED AS A VISUAL] Average GO TO Q15d effectivness:3, Somewhat not effective:2, Not effective:1] Q15d: Would you avoid any of the following [RANDOMIZED: locations because of the threat level rising to High Shopping Mall: Risk (Level Orange) on the Homeland Advisory Port Columbus Security System? (Check all that apply) [HSAS Airport: The Ohio LOGO WILL BE INCLUDED AS A VISUAL] State University: The Ohio Capital building: Sports stadiums/Big Events: Electrical Plant: Water Plant: Schools: GO TO Q15e Downtown tall buildings: Highways] NOT RANDOMIZED: [None of these: All of these, I would stay home Other (TEXT BOX ENTRY)]

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279 Table D.1 continued

Q15e: Would you avoid any of the following [RANDOMIZED: locations because of the threat level rising to Shopping Mall: Severe Risk (Level Red) on the Homeland Port Columbus Advisory Security System? (Check all that apply) Airport: The Ohio [HSA S LOGO WILL BE INCLUDED AS A State University: VISUAL] The Ohio Capital building: Sports stadiums/Big Events: Electrical Plant: Water Plant: Schools: GO TO Q16 Downtown tall buildings: Highways] NOT RANDOMIZED: [None of these: All of these, I would stay home Other (TEXT BOX ENTRY)] Q16: We have found some people who understand what each color means and others who do not, such as the difference between High Risk (Level Orange) and Severe Risk (Level Red). In your Yes, No GO TO Q16a opinion, are you reasonably aware of what each color means? [HSAS LOGO WILL BE INCLUDED AS A VISUAL] Q16a: Have you done any of the recommended activities that are associated with the Homeland Yes, No, Not sure GO TO Q16b Security Advisory System color scheme? [HSAS LOGO WILL BE INCLUDED AS A VISUAL]

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280 Table D.1 continued

Q16b: Here are the 2 highest risk levels and their meanings, please read them:

HIGH RISK (Level Orange) • Complete recommended actions at lower levels. Yes, I did some of Exercise caution when traveling, pay attention to GO TO Q18 travel advisories. the suggestions • Review your family emergency plan and make sure all family members know what to do. • Be Patient. Expect some delays, baggage searches and restrictions at public buildings. • Check on neighbors or others that might need assistance in an emergency. SEVERE RISK (Level Red) • Complete all recommended actions at lower levels. • Listen to local emergency management officials. Stay tuned to TV or radio for current information/instructions. Be prepared to shelter-in- place or evacuate, as instructed. • Expect traffic delays and restrictions. • Provide volunteer services only as requested. • Contact your school/business to determine status of No, I really haven’t workday. done the GO TO Q17 We have found that regarding the Homeland suggestions Security Advisory System, some people have learned more about it, while others ignore it. In your opinion, when the national threat level has changed to level High Risk (Level Orange) did it impact you enough so that you did some of the recommended activities suggested by the government? [HSAS LOGO WILL BE INCLUDED AS A VISUAL] Q17: Now knowing details about the 2 highest Yes, I will: No, it risk colors, will you now conduct some of the doesn’t make much GO TO Q18 recommended activities? [HSAS LOGO WILL difference to me BE INCLUDED AS A VISUAL]

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281 Table D.1 continued

Q18: Now knowing details about the 2 highest [RANDOMIZED: risk colors, would you avoid any of the following Shopping Mall: locations because of the national threat level rising Port Columbus to High Risk or Severe Risk on the Homeland Airport: The Ohio Advisory Security System? (Check all that apply) State University: [HSAS LOGO WILL BE INCLUDED AS A The Ohio Capital VISUAL] building: Sports stadiums/Big Events: Electrical Plant: Water Plant: Schools: GO TO Q19 Downtown tall buildings: Highways] NOT RANDOMIZED: [None of these: All of these, I would stay home Other (TEXT BOX ENTRY)] Q19: Knowing the details about the 2 highest risk Highly Effective:5, colors, how effective is the Homeland Security Somewhat Advisory System to notify you of a terrorist threat Effective:4, to the United States? [HSAS LOGO WILL BE Moderately GO TO Q19b INCLUDED AS A VISUAL] Effective:3, Somewhat Ineffective:2, Not Effective:1 Q19b: Knowing the details about the 2 highest Highly Effective:5, risk colors, how effective is the Homeland Somewhat Security Advisory System to notify you of a Effective:4, terrorist threat in Columbus, Ohio? [HSAS LOGO Moderately GO TO Q20 WILL BE INCLUDED AS A VISUAL] Effective:3, Somewhat Ineffective:2, Not Effective:1

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282 Table D.1 continued

Q20: The national government has made a website about being prepared in case of a terrorist attack, it Yes: No: Not Sure GO TO Q21 is http://www.ready.gov/. Have you ever visited the site http://www.ready.gov/? Q21: Have you ever made a terrorism disaster kit Yes GO TO 22 in case of a terrorist attack? No GO TO Q23 Q22: Do you still have the kit and is it current Yes, I have the kit: with supplies? (Check all that apply) No, I do not have the kit: Yes, it is GO TO Q23 current: No, it is not current Q23: Have you ever discussed a plan of action in Yes GO TO 24 the event of a terrorist attack with your immediate family? No GO TO 23b Q23b: After you have taken this survey, will you discuss a plan of action with your immediate Yes, No GO TO 24 family in the event of a terrorist attack? Q24: Before completing this survey, we would like to know a little about you. Female, Male GO TO Q25 Are you female or male? Q26: Do you consider yourself: RANDOMIZE [Caucasian, African-American, Asian/Asian- American, Hispanic/Latino, Native American, GO TO Q27 Middle Eastern/North African, Multi- Ethnic] NOT RANDOMIZED [Other] Q27: What year were you born?: Dropdown list GO TO Q28 1988 to 1886

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283 Table D.1 continued

Q28: I have lived in the Columbus area for: Less than 1 year, 1- 5 years, 5-10 years, 10-15 years, 15-20 GO TO Q29 years, More than 20 years Q29: What zip code do you live in? Text Box GO TO Q29b Q29b: Do you own your own home?

Yes, No GO TO Q30

Q30: My household income is: <$20,000 $20,000 – $30,000 $30,001 - $40,000 $40,001 - $50,000 $50,001 - $60,000 GO TO Q30b $60,001 - $70,000 $70,001 - $80,000 $80,001 - $90,000 $90,001 - $100,000 >$100,000 Q30b: Marital Status: Married, living with spouse Separated, Divorced Widowed GO TO Q31 Single, never married Domestic partnership

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284 Table D.1 continued

Q31: The highest education level I have achieved Did not graduate is: from high school

High School graduate/GED Some college, but no degree (yet) 2 year college GO TO Q32 degree 4 year college degree Postgraduate degree (MA, MBA, MD, PhD, JD, etc. Q32: To win a prize, we ask for your name and Yes, I would like mailing information. Your information will to be entered into GO QNAME remain confidential – we will not sell or give the random INFO out any personal information you have drawing to win a provided and your name will be removed from prize your survey answers once the drawing for prizes occurs. You will only be contacted by mail if you win a No, Thanks GO TO S1 prize. QNAME INFO TEXT BOXES FOR THE FOLLOWING: Name, Home GO TO S1 Address (Please provide home not business), City, State, Zip S1: Finally, how did you hear about this survey? Postcard, Television, Radio, Submit your TEXT BOX Other survey button. (please specify)

285

APPENDIX E

COLUMBUS TERRORISM SURVEY STATISTICAL RESULTS

286 Q1a: Do you consider terrorism to be an important issue today?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q1a_terror~t | Very I. | .6653992 .0291385 .6080238 .7227747 Somewhat I. | .2585551 .0270382 .2053152 .311795 Avg. Imp. | .0646388 .0151844 .0347397 .0945378 Somewhat UnI.| .0114068 .0065577 -.0015057 .0243194 Not I. | .0000000 ------

Table E.1 Do you consider terrorism to be an important issue today?

Q1b: Was terrorism an important issue to you before the events of September 11, 2001?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q1b_terr~911 | Very I. | .0608365 .014761 .0317713 .0899017 Somewhat I. | .3269962 .0289696 .2699533 .3840391 Avg. Imp. | .2965779 .0282059 .2410389 .352117 Somewhat UnI.| .2015209 .0247716 .1527441 .2502977 Not I. | .1140684 .0196311 .0754136 .1527233 ------

Table E.2 Was terrorism an important issue to you before the events of September 11,

2001?

287 Q2: In your opinion, what is the likelihood that terrorists will attack the United States in the next year?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q2_likelih~A | Very High | .2053232 .0249446 .1562058 .2544406 High | .3878327 .0300898 .328584 .4470814 Avg. | .2813688 .0277686 .2266909 .3360468 Low | .0988593 .0184318 .062566 .1351526 Very Low | .026616 .0099398 .007044 .0461879 ------

Table E.3 In your opinion, what is the likelihood that terrorists will attack the United

States in the next year?

Q3: Thinking about terrorism, what is the likelihood that a terrorist will attack the city of

Columbus, Ohio in the future?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 | Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q3_likelih~s | Very High | .0076046 .0053647 -.0029588 .0181679 High | .1406844 .0214715 .0984058 .182963 Avg. | .4144487 .0304215 .354547 .4743504 Low | .3003802 .0283093 .2446375 .3561229 Very Low | .1368821 .0212261 .0950866 .1786776 ------

Table E.4 Thinking about terrorism, what is the likelihood that a terrorist will attack the city of Columbus, Ohio in the future?

288 Q3b: The issue of terrorism in Columbus is something I talk about with my friends:

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q3b_friends | Everyday | .0114068 .0065577 -.0015057 .0243194 Often | .1026616 .0187432 .065755 .1395681 Sometimes | .3307985 .0290551 .2735872 .3880097 Not Often | .3840304 .0300348 .3248901 .4431707 Never | .1711027 .0232563 .1253096 .2168958 ------

Table E.5 The issue of terrorism in Columbus is something I talk about with my friends:

Q4: What is the likelihood that a terrorist cell is operating in the Columbus, Ohio area?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q4_cell_Co~s | Very High | .1292776 .0207188 .0884811 .1700741 High | .2243346 .0257601 .1736114 .2750578 Avg. | .4486692 .0307137 .3881922 .5091462 Low | .1596958 .0226218 .1151521 .2042395 Very Low | .0380228 .0118105 .0147673 .0612783 ------

Table E.6 What is the likelihood that a terrorist cell is operating in the Columbus, Ohio area?

289 Q5: If terrorists attacked, which of the following do you feel are the most likely targets

of terrorist interest in Columbus? (Check All That Apply)

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q5_Malls | .513308 .0308659 .4525312 .5740847 ------+------Q5_Airport | .6311787 .0297952 .5725101 .6898473 ------+------Q5_OSU | .3954373 .0301941 .3359833 .4548912 ------+------Q5_Capital | .3117871 .0286057 .2554607 .3681134 ------+------Q5_Stadiums | .7452471 .0269074 .6922648 .7982295 ------+------Q5_Electri~l | .2129278 .0252805 .1631489 .2627066 ------+------Q5_Water | .418251 .0304613 .3582708 .4782311 ------+------Q5_Schools | .1558935 .0224014 .1117839 .2000032 ------+------Q5_TallBuild | .4144487 .0304215 .354547 .4743504 ------+------Q5_Highways | .0874525 .0174452 .0531018 .1218031 ------+------Q5_Home | .0076046 .0053647 -.0029588 .0181679 ------+------Q5_None | .0076046 .0053647 -.0029588 .0181679 ------+------Q5_Other | .1102662 .0193425 .0721795 .1483528 ------

Table E.7 If terrorists attacked, which of the following do you feel are the most likely

targets of terrorist interest in Columbus?

290 Q6: Are you personally concerned that a terrorist will attack somewhere in Columbus,

Ohio in the next year?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q6_Concered | Very Con. | .0456274 .0128865 .0202531 .0710016 Somewhat Con.| .2015209 .0247716 .1527441 .2502977 Avg. Concern | .3460076 .0293759 .2881647 .4038506 Somewhat Not | .1863118 .0240443 .1389672 .2336564 Not Concerned| .2205323 .0256034 .1701177 .2709469 ------

Table E.8 Are you personally concerned that a terrorist will attack somewhere in

Columbus, Ohio in the next year?

Q7: How about within the next 5 years, will there be a terrorist attack in Columbus,

Ohio?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q7_Concered5 | Very Con. | .1178707 .0199128 .0786613 .1570801 Somewhat Con.| .2205323 .0256034 .1701177 .2709469 Avg. Concern | .3079848 .0285092 .2518485 .3641211 Somewhat Not | .2205323 .0256034 .1701177 .2709469 Not Concerned| .1330798 .0209753 .0917782 .1743815 ------

Table E.9 How about within the next 5 years, will there be a terrorist attack in Columbus,

Ohio? 291 Q8: We have found some to be apprehensive about traveling, while others are not. Are you apprehensive about going to any of the following places because of potential terrorism? In Table 5.17, the scale was a five category Likert-type scale, which was coded in the analysis as 1 = Very Apprehensive, 2 = Somewhat Apprehensive, 3 =

Average Apprehension, 4 = Little Apprehension, and 5 = Not Apprehensive.

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized | Mean Std. Err. [95% Conf. Interval] ------+------Q8_Malls | 4.087452 .0692773 3.951041 4.223864 Q8_Airport | 3.771863 .0795395 3.615245 3.928481 Q8_OSU | 4.243346 .0662207 4.112953 4.373739 Q8_Capital | 4.224335 .065043 4.096261 4.352408 Q8_Stadiums | 3.775665 .0780166 3.622046 3.929285 Q8_Electri~l | 4.372624 .0618103 4.250915 4.494332 Q8_Water | 4.26616 .0684752 4.131328 4.400992 Q8_Schools | 4.43346 .057823 4.319603 4.547317 Q8_TallBuild | 4.068441 .0713172 3.928013 4.208869 Q8_Highways | 4.551331 .0489217 4.455001 4.647661 Q8_Home | 4.775665 .0356733 4.705423 4.845908 ------

Table E.10 Are you apprehensive about going to any of the following places because of

potential terrorism:

292 Q9: Do you believe terrorism is more likely to happen anywhere in the United States or only in specific geographic locations?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q9_Anywhere | Anywhere | .460076 .0307782 .3994719 .5206802 Specific locations| .539924 .0307782 .4793198 .6005281 ------

Table E.11 Do you believe terrorism is more likely to happen anywhere in the United

States or only in specific geographic locations?

Q10: Do you think terrorism is more likely to occur in the United States:

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q10_city_r~l | In cities | .5171103 .0308587 .4563476 .5778729 In rural areas | .0000000 Equal chance | .095057 .0181119 .0593935 .1307205 Only certain cit| .3878327 .0300898 .328584 .4470814 ------

Table E.12 Do you think terrorism is more likely to occur in the United States:

293 Q11: Rank the cities below for their likelihood of terrorist attack (in the next couple of years):

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q11_NYC | High | .581749 .0304613 .5217689 .6417292 Likely | .2737643 .0275353 .2195456 .3279829 Average | .1330798 .0209753 .0917782 .1743815 Not Likely | .0114068 .0065577 -.0015057 .0243194 No Chance | .0000000 ------+------Q11_DC | High | .6159696 .0300348 .5568293 .6751099 Likely | .243346 .0264986 .1911687 .2955234 Average | .1292776 .0207188 .0884811 .1700741 Not Likely | .0114068 .0065577 -.0015057 .0243194 No Chance | .0000000 ------+------Q11_Chicago | High | .269962 .0274149 .2159804 .3239435 Likely | .3612167 .0296635 .3028074 .419626 Average | .3079848 .0285092 .2518485 .3641211 Not Likely | .0608365 .014761 .0317713 .0899017 No Chance | .0000000 ------+------Q11_LA | High | .3193916 .0287921 .2626983 .3760849 Likely | .391635 .0301429 .3322818 .4509882 Average | .2395437 .0263568 .1876457 .2914417 Not Likely | .0494297 .0133859 .023072 .0757873 No Chance | .0000000 ------+------

Continued

Table E.13 Rank the cities below for their likelihood of terrorist attack (in the next couple of years):

294 Table E.13 continued

Q11_Atlanta | High | .1026616 .0187432 .065755 .1395681 Likely | .2395437 .0263568 .1876457 .2914417 Average | .4562738 .0307585 .3957084 .5168391 Not Likely | .1939163 .0244152 .1458414 .2419913 No Chance | .0076046 .0053647 -.0029588 .0181679 ------+------High | .121673 .0201877 .0819221 .1614239 Likely | .2357414 .026212 .1841284 .2873545 Average | .4562738 .0307585 .3957084 .5168391 Not Likely | .1863118 .0240443 .1389672 .2336564 No Chance | .0000000 ------+------Q11_Seattle | High | .0722433 .0159874 .0407632 .1037235 Likely | .1825095 .0238532 .1355411 .2294779 Average | .4904943 .0308712 .429707 .5512816 Not Likely | .2471483 .0266377 .1946972 .2995994 No Chance | .0076046 .0053647 -.0029588 .0181679 ------+------Q11_Dallas~h | High | .0646388 .0151844 .0347397 .0945378 Likely | .2281369 .0259138 .1771112 .2791626 Average | .4676806 .0308122 .4070095 .5283517 Not Likely | .2281369 .0259138 .1771112 .2791626 No Chance | .0114068 .0065577 -.0015057 .0243194 ------+------Q11_Boston | High | .1292776 .0207188 .0884811 .1700741 Likely | .2509506 .0267739 .1982312 .30367 Average | .4638783 .0307961 .4032389 .5245177 Not Likely | .1520913 .0221763 .1084248 .1957577 No Chance | .0038023 .0038006 -.0036814 .011286 ------+------Q11_Vegas | High | .1901141 .0242316 .1424007 .2378275 Likely | .3003802 .0283093 .2446375 .3561229 Average | .3231939 .0288819 .2663237 .3800641 Not Likely | .1596958 .0226218 .1151521 .2042395 No Chance | .026616 .0099398 .007044 .0461879 ------+------Q11_Philly | High | .0798479 .0167388 .0468882 .1128076 Likely | .2243346 .0257601 .1736114 .2750578 Average | .4980989 .0308766 .437301 .5588967 Not Likely | .1863118 .0240443 .1389672 .2336564 No Chance | .0114068 .0065577 -.0015057 .0243194 ------+------

Continued 295 Table E.13 continued

------+------Q11_SanFran | High | .148289 .0219464 .1050752 .1915027 Likely | .3003802 .0283093 .2446375 .3561229 Average | .3840304 .0300348 .3248901 .4431707 Not Likely | .1634981 .0228377 .1185293 .2084669 No Chance | .0038023 .0038006 -.0036814 .011286 ------+------Q11_Denver | High | .0342205 .0112265 .0121149 .0563262 Likely | .1406844 .0214715 .0984058 .182963 Average | .4828897 .0308587 .4221271 .5436524 Not Likely | .3117871 .0286057 .2554607 .3681134 No Chance | .0304183 .0106053 .0095358 .0513007 ------+------Q11_Columbus | High | .0342205 .0112265 .0121149 .0563262 Likely | .0722433 .0159874 .0407632 .1037235 Average | .4030418 .0302907 .3433976 .462686 Not Likely | .4258555 .0305354 .3657294 .4859816 No Chance | .0646388 .0151844 .0347397 .0945378 ------

296 Q12: Rank the following Ohio cities in their likelihood of terrorist attacks:

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q12_Columbus | High | .0988593 .0184318 .062566 .1351526 Likely | .2015209 .0247716 .1527441 .2502977 Average | .4410646 .0306616 .3806902 .5014391 Not Likely | .2243346 .0257601 .1736114 .2750578 No Chance | .0342205 .0112265 .0121149 .0563262 ------+------Q12_Cincy | High | .0570342 .0143212 .028835 .0852334 Likely | .1558935 .0224014 .1117839 .2000032 Average | .4562738 .0307585 .3957084 .5168391 Not Likely | .2737643 .0275353 .2195456 .3279829 No Chance | .0570342 .0143212 .028835 .0852334 ------+------Q12_Clevel~d | High | .1140684 .0196311 .0754136 .1527233 Likely | .2053232 .0249446 .1562058 .2544406 Average | .4638783 .0307961 .4032389 .5245177 Not Likely | .1749049 .0234593 .1287121 .2210977 No Chance | .0418251 .0123624 .0174828 .0661674 ------+------Q12_Akron | High | .0076046 .0053647 -.0029588 .0181679 Likely | .0380228 .0118105 .0147673 .0612783 Average | .2737643 .0275353 .2195456 .3279829 Not Likely | .4828897 .0308587 .4221271 .5436524 No Chance | .1977186 .0245952 .1492893 .246148 ------+------Q12_Toledo | High | .0190114 .0084334 .0024056 .0356172 Likely | .0608365 .014761 .0317713 .0899017 Average | .2851711 .0278815 .2302708 .3400715 Not Likely | .4448669 .0306885 .3844394 .5052945 No Chance | .1901141 .0242316 .1424007 .2378275 ------+------

Continued

Table E.14 Rank the following Ohio cities in their likelihood of terrorist attacks:

297 Table E.14 continued

------+------Q12_Dayton | High | .0418251 .0123624 .0174828 .0661674 Likely | .0646388 .0151844 .0347397 .0945378 Average | .2965779 .0282059 .2410389 .352117 Not Likely | .4562738 .0307585 .3957084 .5168391 No Chance | .1406844 .0214715 .0984058 .182963 ------

298

Q13: Rank the level of responsibility you expect from the government/law enforcement to protect you from terrorism in Columbus, Ohio

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q13_Local Gov/LawEnf | Completely Responsible| .2357414 .026212 .1841284 .2873545 Somewhat Responsible | .3840304 .0300348 .3248901 .4431707 Average Responsibility| .2965779 .0282059 .2410389 .352117 Little Responsibility | .0798479 .0167388 .0468882 .1128076 Not Responsible | .0038023 .0038006 -.0036814 .011286 ------+------Q13_State Gov/LawEnf | Completely Responsible| .2927757 .0281001 .2374448 .3481065 Somewhat Responsible | .460076 .0307782 .3994719 .5206802 Average Responsibility| .2129278 .0252805 .1631489 .2627066 Little Responsibility | .0342205 .0112265 .0121149 .0563262 Not Responsible | .0000000 ------+------Q13_National Gov/LawEn| Completely Responsible| .4258555 .0305354 .3657294 .4859816 Somewhat Responsible | .4334601 .0306022 .3732026 .4937176 Average Responsibility| .1140684 .0196311 .0754136 .1527233 Little Responsibility | .0152091 .0075576 .0003277 .0300906 Not Responsible | .0114068 .0065577 -.0015057 .0243194 ------

Table E.15 Rank the level of responsibility you expect from the government/law enforcement to protect you from terrorism in Columbus, Ohio.

299 Q14: For whatever reason, is the government/law enforcement protecting you adequately from terrorism in Columbus, Ohio?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q14_loc_pr~t | Very Adequate| .148289 .0219464 .1050752 .1915027 Somewhat Adqt| .2851711 .0278815 .2302708 .3400715 Moderate Adqt| .4106464 .0303798 .3508268 .470466 Smwt Not Adqt| .1064639 .0190467 .0689598 .143968 Not Adequate | .049 4297 .0133859 .023072 .0757873 ------+------Q14_state_~t | Very Adequate| .1444867 .0217115 .1017355 .1872379 Somewhat Adqt| .3422053 .0292989 .2845141 .3998966 Moderate Adqt| .3764259 .0299189 .3175137 .435338 Smwt Not Adqt| .1026616 .0187432 .065755 .1395681 Not Adequate | .0342205 .0112265 .0121149 .0563262 ------+------Q14_nation~t | Very Adequate| .21673 .0254435 .1666302 .2668299 Somewhat Adqt| .3536122 .0295238 .295478 .4117463 Moderate Adqt| .3079848 .0285092 .2518485 .3641211 Smwt Not Adqt| .0760456 .0163691 .0438139 .1082774 Not Adequate | .0456274 .0128865 .0202531 .0710016 ------

Table E.16 For whatever reason, is the government/law enforcement protecting you adequately from terrorism in Columbus, Ohio?

300 Q15: Have you ever seen the Homeland Security Advisory System that looks like this?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q15_seenHSAS | Yes | .9391635 .014761 .9100983 .9682287 No | .0608365 .014761 .0317713 .0899017 ------

Table E.17 Have you ever seen the Homeland Security Advisory System that looks like this?

301 Q15a: In your opinion, is the Homeland Security Advisory System helpful to you?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q15a_helpf~S | Yes | .5665399 .0306022 .5062824 .6267974 No | .4334601 .0306022 .3732026 .4937176 ------

Table E.18 In your opinion, is the Homeland Security Advisory System helpful to you?

302 Q15aa: In your opinion, the purpose of the Homeland Security Advisory System is to:

(Please Check All that Apply)

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Inform me of a | terrorist threat| in my community | .3231939 .0288819 .2663237 .3800641 ------+------Inform me of a | terrorist threat| in my state | .338403 .0292197 .2808676 .3959384 ------+------Inform me of a | terrorist threat| to the U.S.A. | .8479087 .0221763 .8042423 .8915752 ------+------None of these | | .0988593 .0184318 .062566 .1351526 ------+------Other Purpose | | .0874525 .0174452 .0531018 .1218031 ------

Table E.19 In your opinion, the purpose of the Homeland Security Advisory System is to:

303 Q15b: How effective is the Homeland Security Advisory System to notify you of a terrorist threat to the United States?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q15b_effec~S | Very Effectve| .1825095 .0238532 .1355411 .2294779 Somewhat Eftv| .3155894 .0287 .2590773 .3721014 Avg Effectnes| .2357414 .026212 .1841284 .2873545 Smwt Not Eftv| .1292776 .0207188 .0884811 .1700741 Not Effective| .1368821 .0212261 .0950866 .1786776 ------

Table E.20 How effective is the Homeland Security Advisory System to notify you of a terrorist threat to the United States?

Q15c: How effective is the Homeland Security Advisory System to notify you of a

terrorist threat to Columbus, Ohio?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 | Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q15c_effHS~s | Very Effectve| .095057 .0181119 .0593935 .1307205 Somewhat Eftv| .1863118 .0240443 .1389672 .2336564 Avg Effectnes| .2395437 .0263568 .1876457 .2914417 Smwt Not Eftv| .2205323 .0256034 .1701177 .2709469 Not Effective| .2585551 .0270382 .2053152 .311795 ------

Table E.21 How effective is the Homeland Security Advisory System to notify you of a terrorist threat to Columbus, Ohio? 304 Q15d: Would you avoid any of the following locations because of the threat level rising to High Risk (Level Orange) on the Homeland Advisory Security System? (Check all that

apply) &

Q15e: Would you avoid any of the following locations because of the threat level rising

to Severe Risk (Level Red) on the Homeland Advisory Security System? (Check all that

apply)

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 | Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q15de_Malls | Orange| .1787072 .0236582 .1321227 .2252917 Red| .2357414 .026212 .1841284 .2873545 ------+------Q15de_Airport| Orange| .269962 .0274149 .2159804 .3239435 Red| .391635 .0301429 .3322818 .4509882 ------+------Q15de_OSU | Orange| .0722433 .0159874 .0407632 .1037235 Red| .1178707 .0199128 .0786613 .1570801 ------+------Q15de_Capital| Orange| .1558935 .0224014 .1117839 .2000032 Red| .1673004 .0230492 .1219152 .2126855 ------+------

Continued

Table E.22 Would you avoid any of the following locations because of the threat level rising to High Risk (Level Orange) or Severe Risk (Level Red) on the Homeland

Advisory Security System?

305 Table E.22 continued

Q15de_Stadi~s| Orange| .269962 .0274149 .2159804 .3239435 Red| .3193916 .0287921 .2626983 .3760849 ------+------Q15de_Elect~l| Orange| .0798479 .0167388 .0468882 .1128076 Red| .1064639 .0190467 .0689598 .143968 ------+------Q15de_Water | Orange| .1026616 .0187432 .065755 .1395681 Red| .1140684 .0196311 .0754136 .1527233 ------+------Q15de_Schools| Orange| .0342205 .0112265 .0121149 .0563262 Red| .0380228 .0118105 .0147673 .0612783 ------+------Q15de_TallB~d| Orange| .1863118 .0240443 .1389672 .2336564 Red| .2509506 .0267739 .1982312 .30367 ------+------Q15de_Highw~s| Orange| .026616 .0099398 .007044 .0461879 Red| .0304183 .0106053 .0095358 .0513007 ------+------Q15de_None | Orange| .3992395 .0302433 .3396886 .4587905 Red| .2395437 .0263568 .1876457 .2914417 ------+------Q15de_StayH~e| Orange| .0722433 .0159874 .0407632 .1037235 Red| .2091255 .0251142 .1596741 .2585769 ------+------Q15de_Other | Orange| .0418251 .0123624 .0174828 .0661674 Red| .0342205 .0112265 .0121149 .0563262 ------

306 Q16: We have found some people who understand what each color means and others who do not, such as the difference between High Risk (Level Orange) and Severe Risk (Level

Red). In your opinion, are you reasonably aware of what each color means?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q16 | 1 | .851711 .0219464 .8084973 .8949248 2 | .148289 .0219464 .1050752 .1915027 ------

Table E.23 In your opinion, are you reasonably aware of what each color means?

Q16a: Have you done any of the recommended activities that are associated with the

Homeland Security Advisory System color scheme?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q16a | Yes| .2091255 .0251142 .1596741 .2585769 No| .4258555 .0305354 .3657294 .4859816 Not Sure| .365019 .0297304 .3064781 .4235599 ------

Table E.24 Have you done any of the recommended activities that are associated with the

Homeland Security Advisory System color scheme?

307 Q16b: Here are the 2 highest risk levels and their meanings, please read them…

We have found that regarding the Homeland Security Advisory System, some people have learned more about it, while others ignore it. In your opinion, when the national threat level has changed to level High Risk (Level Orange) did it impact you enough so that you did some of the recommended activities suggested by the government?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q16b | Yes| .513308 .0308659 .4525312 .5740847 No| .486692 .0308659 .4259153 .5474688 ------

Table E.25 In your opinion, when the national threat level has changed to level High Risk

(Level Orange) did it impact you enough so that you did some of the recommended

activities suggested by the government?

308 Q17: Now knowing details about the 2 highest risk colors, will you now conduct some of the recommended activities?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q17 | Yes, I will | .1939163 .0244152 .1458414 .2419913 ------No, it does | not make much| difference to| me | .8060837 .0244152 .7580087 .8541586 ------

Table E.26 Now knowing details about the 2 highest risk colors, will you now conduct

some of the recommended activities?

309 Q18: Now knowing details about the 2 highest risk colors, would you avoid any of the following locations because of the national threat level rising to High Risk or Severe Risk on the Homeland Advisory Security System? (Check all that apply)

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q18_Malls | .2547529 .0269074 .2017705 .3077352 ------+------Q18_Airport | .4334601 .0306022 .3732026 .4937176 ------+------Q18_OSU | .1026616 .0187432 .065755 .1395681 ------+------Q18_Capital | .1863118 .0240443 .1389672 .2336564 ------+------Q18_Stadiums | .3498099 .0294509 .2918193 .4078005 ------+------Q18_Electr~l | .1254753 .0204563 .0851956 .165755 ------+------Q18_Water | .1330798 .0209753 .0917782 .1743815 ------+------Q18_Schools | .026616 .0099398 .007044 .0461879 ------+------Q18_TallBu~d | .2737643 .0275353 .2195456 .3279829 ------+------Q18_Highways | .0646388 .0151844 .0347397 .0945378 ------+------Q18_None | .2851711 .0278815 .2302708 .3400715 ------+------Q18_StayHome | .148289 .0219464 .1050752 .1915027 ------+------Q18_Other | .0570342 .0143212 .028835 .0852334 ------

Table E.27 Now knowing details about the 2 highest risk colors, would you avoid any of the following locations because of the national threat level rising to High Risk or Severe

Risk on the Homeland Advisory Security System?

310 Q19: Knowing the details about the 2 highest risk colors, how effective is the Homeland

Security Advisory System to notify you of a terrorist threat to the United States?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q19 | Very Effectve| .1520913 .0221763 .1084248 .1957577 Somewhat Eftv| .3802281 .0299778 .3212 .4392563 Avg Effectnes| .2357414 .026212 .1841284 .2873545 Smwt Not Eftv| .1292776 .0207188 .0884811 .1700741 Not Effective| .1026616 .0187432 .065755 .1395681 ------

Table E.28 Knowing the details about the 2 highest risk colors, how effective is the

Homeland Security Advisory System to notify you of a terrorist threat to the United

States?

311 Q19b: Knowing the details about the 2 highest risk colors, how effective is the Homeland

Security Advisory System to notify you of a terrorist threat in Columbus, Ohio?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q19b | Very Effectve| .1254753 .0204563 .0851956 .165755 Somewhat Eftv| .243346 .0264986 .1911687 .2955234 Avg Effectnes| .243346 .0264986 .1911687 .2955234 Smwt Not Eftv| .2129278 .0252805 .1631489 .2627066 Not Effective| .1749049 .0234593 .1287121 .2210977 ------

Table E.29 Knowing the details about the 2 highest risk colors, how effective is the

Homeland Security Advisory System to notify you of a terrorist threat in Columbus,

Ohio?

312 Q20: The national government has made a website about being prepared in case of a terrorist attack, it is http://www.ready.gov/. Have you ever visited the site

http://www.ready.gov/?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q20 | Yes | .1026616 .0187432 .065755 .1395681 No | .8669202 .0209753 .8256185 .9082218 Not Sure | .0304183 .0106053 .0095358 .0513007 ------

Table E.30 Have you ever visited the site http://www.ready.gov/?

313 Q21: Have you ever made a terrorism disaster kit in case of a terrorist attack?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262

------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q21 | Yes | .1102662 .0193425 .0721795 .1483528 No | .8897338 .0193425 .8516472 .9278205 ------

Table E.31 Have you ever made a terrorism disaster kit in case of a terrorist attack?

314 Q22: Do you still have the kit and is it current with supplies? (Check all that apply)

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Yes, still | have the kit | .0608365 .014761 .0317713 .0899017 ------+------Yes, it is | current | .0304183 .0106053 .0095358 .0513007 ------+------No it is not | current | .0304183 .0106053 .0095358 .0513007 ------+------No, don’t | have the kit | .0494297 .0133859 .023072 .0757873 ------

Table E.32 Do you still have the kit and is it current with supplies?

Q23: Have you ever discussed a plan of action in the event of a terrorist attack with your immediate family?

Number of strata = 1 Number of obs = 263 Number of PSUs = 263 Population size = 305608 Design df = 262 ------| Linearized Binomial Wald | Proportion Std. Err. [95% Conf. Interval] ------+------Q23 | Yes| .3460076 .0293759 .2881647 .4038506 No| .6539924 .0293759 .5961494 .7118353 ------

Table E.33 Have you ever discussed a plan of action in the event of a terrorist attack with

your immediate family?

315 Q23b: After you have taken this survey, will you discuss a plan of action with your immediate family in the event of a terrorist attack?

| Q23b Q23 | Yes No | Total ------+------+------Yes | 0 91 | 91 No | 82 90 | 172 ------+------+------Total | 82 181 | 263

Table E.34 After you have taken this survey, will you discuss a plan of action with your immediate family in the event of a terrorist attack?

316

BIBLIOGRAPHY

The 9/11 Commission (2004). The 9/11 commission report : Final report of the National Commission on Terrorist Attacks Upon the United States. New York: W.W. Norton.

Acevedo, W., & Masuoka, P. (1997). Time-series animation techniques for visualizing urban growth. Computers and Geosciences, 23 (4), 423-435.

Anderson, E. L. (2002). Assessing the risks of terrorism: A special collection of perspectives articles by former presidents of the society for risk analysis. Risk Analysis, 22 (3), 401-402.

Anderson, P. (1990). Great quotes from great leaders. Lombard, IL: Celebrating Excellence Publishing.

Anderson, S., & Gansneder, B. (1995). Using electronic mail surveys and computer monitored data for studying computer-mediated communication. Social Science Computer Review, 13 (1), 34-47.

Andrienko, G., & Andrienko, N. (1999). Interactive maps for visual data exploration. International Journal of Geographical Information Science, 13 (4), 355-374.

317

Andrienko, N., Andrienko, G., & Gatalsky, P. (2000). Supporting visual exploration of object movement. Computational Statistics, 16 417-433.

Anselin, L. (2004). GeoDa. Urbana-Champaign, IL: Produced by Luc Anselin and the Regents of the University of Illinois, Software Version 0.9.5-i.

Archer, T. (2003). Web-based surveys. Journal of Extension [On-Line], 41 (4) http://www.joe.org/joe/2003august/tt6.shtml.

Arey, J. A. (1972). The sky pirates. New York,: Scribner.

Association for Public Opinion Research (2004). Standard definitions: Final dispositions of case codes and outcome rates for surveys, 3rd edition. Lenexa, Kansas: AAPOR.

Armstrong, M., Xiao, N., & Bennett, D. (2003). Using genetic algorithms to create multicriteria class intervals for choropleth maps. Annals of the Association of American Geographers, 93 (3), 595-623.

Arquilla, J., Ronfeldt, D., & Zanini, M. (1999). Networks, netwar, and information-age terrorism. In I. Lesser (Ed.), Countering the new terrorism (pp. 39-84). Santa Monica: RAND Corporation.

Banks, R. (2000). Introduction: Survey and statistical computing in the new millennium. Social Science Computer Review, 18 (4), 381-383.

Barnes, T. J. (2004). A paper related to everything but more related to local things. Annals of the Association of American Geographers, 94 (2), 278-283.

Barrows, H. (1923). Geography as human ecology. Annals of the Association of American Geographers, 12 1-14.

318 Beck, R. (2003). Remote sensing and GIS as counterterrorism tools in the Afghanistan war: A case study of the Zhawar Kili region. The Professional Geographer, 55 (2), 170-179.

Beck, R. (2005). Reply to commentaries by O'Loughlin and Shroder. The Professional Geographer, 57 (4), 598-608.

Bellow, S. (2001). Facing history in Jerusalem. In B. Lewis (Ed.), A Middle East mosaic: Fragments of life, letters and history (pp. 469). New York: The Modern Library.

Bennis, P. (2003). Before & after : U.S. Foreign policy and the war on terrorism. New York: Olive Branch Press.

Berry, K. A., Markee, N. L., Fowler, N., & Giewat, G. R. (2000). Interpreting what is rural and urban for western U.S. Counties. Professional Geographer, 52 (1), 93- 105.

Bertin, J. (1967). Semiologie graphique. Madison: University of Wisconsin Press.

Best, S., Krueger, B., Hubbard, C., & Smith, A. (2001). An assessment of the generalizability of Internet surveys. Social Science Computer Review, 19 (2), 131- 145.

Bhagat, R. B. (2005). Rural-urban classification and municipal governance in India. Singapore Journal of Tropical Geography, 26 (1), 61-73.

bin Ladin, O. (1996). Declaration of war. In B. M. Rubin & J. C. Rubin (Eds.), Anti- American terrorism and the Middle East : A documentary reader, 2002 (pp. 137- 141). Oxford: Oxford University Press.

bin Ladin, O., Al-Zawahiri, A., Taha, A.-Y. R. I. A., Hamzah, S. M., & Rahman, F. (1998). Jihad against Jews and crusaders. In B. M. Rubin & J. C. Rubin (Eds.), Anti-American terrorism and the middle east : A documentary reader, 2002 (pp. 149-150). Oxford: Oxford University Press.

319 Bogen, K., & Jones, E. (2006). Risks of mortality and morbidity from worldwide terrorism: 1968-2004. Risk Analysis, 26 (1), 45-59.

Borchert, J. (1987). Maps, geography, and geographers. Professional Geographer, 39 (4), 387-389.

Bosnjak, M., & Tuten, T. (2003). Prepaid and promised incentives in web surveys – an experiment. Social Science Computer Review, 21 (2), 208-217.

Boulder County Wildfire Mitigation Group (2001). The whims manual. http://www.co.boulder.co.us/lu/wildfire/pdf/whims/whimsmanual.pdf (Last accessed: August 2, 2007).

Brewer, C. (1994). Color use guidelines for mapping and visualization. In A. MacEachren & D. R. F. Taylor (Eds.), Visualization in modern cartography (pp. 123-147). Oxford: Pergamon.

Brewer, C. (1996). Guidelines for selecting colors for diverging schemes on maps. The Cartographical Journal, 33 (2), 79-86.

Brewer, C. (1997). Spectral schemes: Controversial color use on maps. Cartography and Geographic Information Science, 24 (4), 203-220.

Brewer, C. (2006). Basic mapping principles for visualizing cancer data using Geographic Information Systems (GIS). American Journal of Preventive Medicine, 30 (2S), S25-S36.

Bruzewicz, A. (2003). Remote sensing imagery for emergency management. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 87-98). New York: Routledge.

Burton, I., & Kates, R. (1986). Geography, resources, and environment: Vol 1 selected writings of Gilbert F. White. Chicago: University of Chicago Press.

320 Buttenfield, B. (2004). Terrorism, information technology, and vulnerability. Annals of the Association of American Geographers, 94 (4), 994-996.

Byman, D. (2003). Should Hezbollah be next? Foreign Affairs, 82 (6), 54-66.

Card, S. (1996). Visualizing retrieved information: A survey. IEEE Graphics and Applications, (March), 63-67.

Chang, L. (2001). A comparison of samples and response quality obtained from RDD telephone survey methodology and Internet survey methodology. A Ph.D. dissertation at The Ohio State University.

Cho, H., & Larose, R. (1999). Privacy issues in Internet surveys. Social Science Computer Review, 17 (4), 421-434.

Chyba, C. (2002). Toward biological security. Foreign Affairs, 81 (3), 122-136.

Clancy, T. (2002). The sum of all fears. New York: Berkley Books.

Coaffee, J. (2003). Terrorism, risk and the city: The making of a contemporary urban landscape. Aldershot: Ashgate.

Cohen, B. (2004). Growth in developing countries: A review of current trends and a caution regarding existing forecasts. World Development, 32 (1), 23-51.

Combs, C. C. (1997). Terrorism in the Twenty-First century. Upper Saddle River: Prentice Hall.

Cordes, B., Jenkins, B. M., & Kellen, K. (1985). A conceptual framework for analyzing terrorist groups. Santa Monica: RAND.

321 Cordesman, A. H., & Center for Strategic and International Studies (2002). Terrorism, asymmetric warfare, and weapons of mass destruction : Defending the U.S. Homeland. Westport, Conn.: Praeger.

Coulson, M. (1987). In the matter of class intervals for choropleth maps: With particular reference to the work of George F. Jenks. Cartographica, 24 (2), 16-39.

Couper, M., Tourangeau, R., Conrad, F., & Crawford, S. (2004). What they see is what we get: Response options for web surveys. Social Science Computer Review, 22 (1), 111-127.

Couper, M., Traugott, M., & Lamias, M. (2001). Web survey design and administration. Public Opinion Quarterly, 65 230-253.

Covello, V., & Mumpower, J. (1985). Risk analysis and risk management: An historical perspective. Risk Analysis, 5 (2), 103-120.

Crawford, S., Couper, M., & Lamias, M. (2001). Web surveys - perceptions of burden. Social Science Computer Review, 19 (2), 146-162.

Crenshaw, M. (1998). The logic of terrorism: Terrorist behavior as a product of strategic choice. In W. Reich (Ed.), Origins of terrorism: Psychologies, ideologies, theologies, states of mind (pp. 7-24). Washington D.C.: Woodrow Wilson Center Press.

Cutter, S. (1993). Living with risk: The geography of technological hazards. New York: Edward Arnold.

Cutter, S. (2001). American hazardscapes. Washington DC: Joseph Henry Press.

Cutter, S. (2003). The vulnerability of science and the science of vulnerability. Annals of the Association of American Geographers, 93 (1), 1-12.

322 Cutter, S., Richardson, D., & Wilbanks, T. J. (2003). The geographical dimensions of terrorism. New York: Routledge.

Cutter, S., Richardson, D., & Wilbanks, T. J. (2004). The geographical dimensions of terrorism: Future directions. Annals of the Association of American Geographers, 94 (4), 1001-1002.

de Blij, H. (2004). Explicating geography's dimensions—an opportunity missed. Annals of the Association of American Geographers, 94 (4), 994-996.

de Blij, H. (2005). Why geography matters : Three challenges facing America : Climate change, the rise of china, and global terrorism. New York: Oxford University Press.

Defanti, T., Brown, M., & Mccormick, B. (1989). Visualization expanding scientific and engineering research opportunities. Computer, 22 (8), 12-25.

Deisler, J. P. F. (2002). A perspective: Risk analysis as a tool for reducing the risks of terrorism. Risk Analysis, 22 (3), 405-414.

Demers, M. N. (2005). Fundamentals of Geographic Information Systems. New York: John Wiley & Sons.

Dent, B. (1999). Cartography: Thematic map design. Boston: WCB/McGraw-Hill.

Dibiase, D. (1990). Visualization in the earth sciences. Earth and Mineral Sciences, Bulletin of the College of Earth and Mineral Sciences, The Pennsylvania State University, 59 (2), 13-18.

Dillman, D. A. (1978). Mail and telephone surveys : The total design method. New York: Wiley.

Dillman, D. A. (2000). Mail and Internet surveys : The tailored design method. New York: J. Wiley.

323 Dobson, M. (1973). Choropleth maps without class intervals: A comment? Geographical Analysis, 5 (4), 358-360.

Dorling, D. (1992). Stretching space and splicing time: From cartographic animation to interactive visualization. Cartography and Geographic Information Systems, 19 (4), 215-227.

Dorling, D. (1995). A new social atals of Britain. Chester: Wiley.

Dougenik, J., Chrisman, N., & Niemeyer, D. (1985). An algorithm to construct continuous area cartograms. The Professional Geographer, 37 (1), 75-81.

Dugan, L., Lafree, G., & Piquero, A. R. (2005). Testing a rational choice model of airline hijackings'. Criminology, 43 (4), 1031-1065.

Dunlap, C. J. (1998). Preliminary observations: Asymmetrical warfare and the western mindset. Carlisle Barracks, Pennsylvania: U.S. Army War College - Strategic Studies Institute.

Dykes, J. (1997). Exploring spatial data representation with dynamic graphics. Computers and Geosciences, 23 345-370.

Edsall, R. (2003). The parallel coordinate plot in action: Design and use for geographic visualization. Computational Statistics and Data Analysis, 43 605-619.

Ellis, J. (2004). MIPT: Sharing terrorism information resources., Proceedings of the Second Symposium Intelligence And Security Informatics, Berlin: Springer, 520- 525.

Ellis, J. (2004).Terrorism in the genomic age. In R. Howard & R. Sawyer (Eds.), Terrorism and counterterrorism: Understanding the new security environment (pp. 303-309). Guilford: McGraw-Hill.

324 Emporis (2007). Buildings of the city Columbus Ohio http://www.emporis.com/en/wm/ci/bu/?id=101043 (Last accessed: August 1, 2007).

Ettlinger, N., & Bosco, F. (2004). Thinking through networks and their spatiality: A critique of the US (public) war on terrorism and its geographic discourse. Antipode 36 (2), 249-271.

Eubank, W., & Weinberg, L. (2001). Terrorism and democracy: Perpetrators and victims. Terrorism & Political Violence, 13 (1), 155-164.

Evans, A. E., & Murphy, J. F. (1978). Legal aspects of international terrorism. Lexington, MA: Lexington Books.

Evans, J., & Mathur, A. (2005). The value of online surveys. Internet Research: Electronic Networking Applications and Policy, 15 (2), 195-219.

Fabrikant, S. (2000). Spatialized browsing in large data archives. Transactions in GIS, 4 (1), 65-78.

Fabrikant, S., & Buttenfield, B. (2001). Formalizing semantic spaces for information access. Annals of the Association of American Geographers, 91 (2), 263-280.

Farrell, W. R. (1982). The US government response to terrorism: In search of an effective strategy. Boulder: Westview Press.

FEMA (2003). Risk management series, insurance, finance, and regulation primer for terrorism risk management in buildings. Washington D.C.: United States Federal Emergency Management Administration.

Flint, C. (2003). Terrorism and counterterrorism: Geographic research questions and agendas. Professional Geographer, 55 (2), 161-169.

325 Flint, C. (2003b). Geographies of inclusion/exclusion. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 53-58). New York: Routledge.

Flint, C. (2005). Dynamic metageographies of terrorism: The spatial challenges of religious terrorism and the "war on terrorism". In C. Flint (Ed.), The geography of war and peace (pp. 198-216). Oxford: Oxford University Press.

Franklin County Tax Auditor Office, F (2006). Franklin county tax auditor data CD

Foucault, M. (2003). Society must be defended. Lectures at the college de France. New York: Picador.

Fowler, W. W. (1981). Terrorism data bases: A comparison of missions, methods, and systems. Santa Monica: RAND.

Frenkel, K. A. (1988). The art and science of visualizing data. Communications of the ACM, 31 (2), 111-121.

Gaile, G., & Willmott, C. (2003). Geography in America at the dawn of the 21st Century. Oxford: Oxford University Press.

Galloway, G. (2003). Emergency preparedness and response - lessons learned from 9/11. In S. Cutter, D. Richardson &T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 27-34). New York: Routledge.

GeoVista Center (2000). The Apoala project. http://www.geovista.psu.edu/grants/apoala/index.htm (Last accessed: August 2, 2007).

Golding, D. (1992). A social and programmatic history of risk research. In S. Krimsky & D. Golding (Eds.), Social theories of risk (pp. 23-52). Westport: Praeger.

326 Goodchild, M. (2003). Data modeling for emergencies. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 105-110). New York: Routledge.

Goodchild, M. (2004). GIScience, geography, form, and process. Annals of the Association of American Geographers, 94 (4), 709-714.

Goodchild, M. (2004b). The validity and usefulness of laws in Geographic Information Science and geography. Annals of the Association of American Geographers, 94 (2), 300-303.

Graham, S. (2006). Cities and the 'war on terror'. International Journal of Urban and Regional Research, 30 (2), 255-276.

Gregory, K. (2000). The changing nature of physical geography. London: Arnold.

Griffith, D. (2004). Using maps to plug security gaps: Fact or fantasy? Annals of the Association of American Geographers, 94 (4), 998-1001.

Groves, R. M. (2004). Survey methodology. Hoboken, N.J.: Wiley-Interscience.

Gunaratna, R. (2002). Inside al Qaeda : Global network of terror. New York: Columbia University Press.

Guo, D., Liao, K., & Morgan, M. (2007). Visualizing patterns in a global terrorism incident database. Environment and Planning B: Planning and Design (in press).

Gurganus, A. (2003). Why we fed the bomber. New York Times, New York: June 8, Weekend Edition, pg 13.

Gusein-Zade, S., & Tikunov, V. (1993). A new technique for constructing continuous cartograms. Cartography and Geographic Information Systems, 20 (3), 167-173.

327 Haimes, Y. Y., & Longstaff, T. (2002). The role of risk analysis in the protection of critical infrastructures against terrorism. Risk Analysis, 22 (3), 439-444.

Hannah, M. (2006). Torture and the ticking bomb: The war on terrorism as a geographical imagination of power/knowledge. Annals of the Association of American Geographers, 96 (3), 622-640.

Harrower, M. (2001). Visualizing change: Using cartographic animation to explore remotely-sensed data. Cartographic Perspectives, 39 30-42.

Harrower, M., & Brewer, C. (2003). Colorbrewer.Org: An online tool for selecting colour schemes for maps. The Cartographical Journal, 40 (1), 27-37.

Heerwegh, D., & Loosveldt, G. (2002a). An evaluation of the effect of response formats on data quality in web surveys. Social Science Computer Review, 20 (4), 471-484.

Heerwegh, D., & Loosveldt, G. (2002b). Web surveys: The effect of controlling survey access using pin numbers. Social Science Computer Review, 20 (1), 10-21.

Hewitt, K. (1997). Regions of risk: A geographical introduction to disasters. Harlow: Longman.

Heyman, E., & M ickolus, E. F. (1980). Observations on "why violence spreads". International Studies Quarterly, 24 (2), 299-305.

Hill, A., & Cutter, S. (2001).Methods for determining disaster proneness. In S. Cutter (Ed.), American hazardscapes (pp. 13-36). Washington D.C.: Joseph Henry Press.

Hoffman, B. (1998). Inside terrorism. New York City: Columbia University Press.

Hoffman, B. (1999).Terrorism trends and prospects. In I. Lesser, B. Hoffman, J. Arquilla, D. Ronfeldt, M. Zanini & B. M. Jenkins (Eds.), Countering the new terrorism (pp. 7-38). Santa Monica: RAND.

328

Hoffman, B., & Claridge, D. (1998). The RAND-St. Andrews chronology of international terrorism and noteworthy domestic incidents, 1996. Terrorism & Political Violence, 10 (2), 135-180.

Hoffman, B., & Hoffman, D. K. (1995). The RAND-St. Andrews chronology of international terrorism, 1994. Terrorism & Political Violence, 7 178-229.

Hoffman, B., & Hoffman, D. K. (1996). The RAND-St. Andrews chronology of international terrorist incidents, 1995. Terrorism & Political Violence, 8 (3), 87- 127.

Horgan, J. (2005). The psychology of terrorism. London: Taylor & Francis Routledge.

House, D., & Kocmoud, C. (1998). Continuous cartogram construction. Proceedings, of Visualization ’98. IEEE: Research Triangle Park, 197-204, 533.

Howard, R. (2004).Understanding al Qaeda's application of the new terrorism - the key to victory in the current campaign. In R. Howard & R. Sawyer (Eds.), Terrorism and counterterrorism: Understanding the new security environment (pp. 75-85). Guilford: McGraw-Hill.

INE (2007). Instituto Nacional de Estadistica. http://www.ine.es (Last accessed: August 2, 2007).

Inselberg, A. (1985). The plane with parallel co-ordinates. The Visual Computer, 1 (2), 69-91.

Inselberg, A. (2007). Home of parallel coordinates. http://www.math.tau.ac.il/~aiisreal/ (Last accessed: August 2, 2007).

Issac, J. (2004). Social science and liberal values in a time of war. Perspectives on Politics, 2 475-483.

329 Jaccard, J., & Wan, C. (1996). LISREL approaches to interaction effects in multiple regression. Newbury Park: Sage Publications.

James, M., Wotring, C., & Forrest, E. (1995). An exploratory study of the perceived benefits of electronic bulletin board use and their impact on other communication activities. Journal of Broadcasting and Electronic Media, 39 (1), 30-50.

Jenks, G. F. (1977). Optimal data classification for choropleth maps. Lawrence: Department of Geography, University of Kansas.

Jenks, G. F., & Caspal, F. (1971). Error on choroplethic maps: Definition, measurement, reduction. Annals of the Association of American Geographers, 61 (2), 217-244.

Johnston, R. (1997). Geography and geographers: Anglo-American human geography since 1945. London: Arnold.

Johnston, R. (2004). Geography, GIS, and terrorism. Annals of the Association of American Geographers, 94 (4), 996-998.

Juergensmeyer, M. (2000). Terror in the mind of God : The global rise of religious violence. Berkeley: University of California Press.

Kates, R., & Kasperson, J. (1983). Comparative risk analysis of technological hazards: A review. Proceedings, National Academy of Sciences, 80 7027-7038.

Kaye, B., & Johnson, T. (1999). Research methodology: Taming the cyber frontier techniques for improving online surveys. Social Science Computer Review, 17 (3), 323-337.

Keeter, S., Miller, C., Kohut, A., Groves, R., & Presser, S. (2000). Consequences of reducing nonresponse in a national telephone survey. Public Opinion Quarterly, 64 (2), 125-148.

330 Keim, D. (2001). Visual exploration of large datasets. Communications of the ACM, 44 (8), 38-44.

Kelly, C. (2002). AFIP's Department of Environmental and Toxicologic Pathology provides critical DOD, homeland defense programs. AFIP Letter, 160 (4), 1,6.

Kelmelis, J., & Loomer, S. (2003). Critical infrastructure. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 127-138). New York: Routledge.

Kidron, M., & Segal, R. (1991). The state of the world atlas. London: Simon & Schuster.

Kleinfield, N. (1993). Casual choices of workday prove fatal to 5 at disaster. New York Times, New York: February 28, Section A, pg 1.

Krygier, J. (1994). Sound and geographic visualization. In A. MacEachren & D. R. F. Taylor (Eds.), Visualization in modern cartography (pp. 345). Oxford: Pergammon.

Kunreuther, H. (2002). Risk analysis and risk management in an uncertain world. Risk Analysis, 22 (4), 655-664.

Kwan, M.-P. (2003). Intelligent emergency response systems. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 111-116). New York: Routledge.

Kwan, M.-P. (2004). Beyond difference: From canonical geography to hybrid geographies. Annals of the Association of American Geographers, 94 (4), 756-763.

Kwan, M.-P. (2007). Affecting geospatial technologies: Toward a feminist politics of emotion. The Professional Geographer, 59 (1), 22-34.

Laqueur, W. (1987). The age of terrorism. Boston: Little, Brown.

331 Laqueur, W. (1996). Postmodern terrorism: New rules for an old game. Foreign Affairs, 75 (5), 24-36.

Laqueur, W. (1999). The new terrorism: Fanaticism and the arms of mass destruction. New York: Oxford University Press.

Levy, P. S., & Lemeshow, S. (1999). Sampling of populations : Methods and applications. New York: Wiley.

Lipton, E. (2006). Come one, come all, join the terror target list. The New York Times, New York: July 12, Section A, pg 1.

Lucieer, A., & Kraak, M.-J. (2004). Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty. International Journal of Geographical Information Science, 18 (5), 491-512.

MacEachren, A. (1982). Map complexity: Comparison and measurement. The American Cartographer, 9 (1), 31-46.

MacEachren, A. (1994). Visualization in modern cartography: Setting the agenda. In A. MacEachren & D. Taylor (Eds.), Visualization in modern cartography (pp. 1-12). Oxford: Pergammon.

MacEachren, A. (1995). How maps work: Representation, visualization, and design. New York: Guilford Press.

MacEachren, A., & Kaack, M.-J. (1997). Exploratory cartographic visualization: Advancing the agenda. Computers and Geosciences, 23 (4), 335-342.

MacEachren, A., Wachowicz, M., Edsall, R., & Haug, D. (1999). Constructing knowledge from multivariate spatiotemporal data: Integrating geographic visualization with knowledge discovery in database methods. International Journal of Geographical Information Science, 13 (4), 311-334.

332 Mackinlay, J. (1986). Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5 (2), 110-141.

Macpherson, A., Mcconnell, J., Vance, A., & Vanchan, V. (2006). The impact of U.S. Government antiterrorism policies on Canada-U.S. Cross-border commerce: An exploratory study from western New York and southern Ontario. Professional Geographer, 58 (3), 266-277.

Mamadouh, V. (2005).Geography and war, geographers and peace. In C. Flint (Ed.), The geography of war and peace (pp. 26-60). Oxford: Oxford University Press.

Martin, E. (2007). “Terrorism” and related terms in statute and regulation: Selected language. Washington D.C.: Congressional Research Service: The Library of Congress.

Matthews, L. J., & Army War College (U.S.). Strategic Studies Institute. (1998). Challenging the united states symmetrically and asymmetrically : Can America be defeated? Carlisle Barracks, PA: Strategic Studies Institute, U.S. Army War College.

Mayhood, K. (2007). Terror suspect says he wasn't a player. Columbus Dispatch, Columbus: June 15, Section A, pg 1.

Mccormick, B., Defanti, T., & Brown, M. (1987). Visualization in scientific computing. Computer Graphics, 21 (6), Entire Issue.

McGarrel, E. (2007). DHS and critical infrastructure assessment. Personal communication on February 1 by Verbal communication.

Mckinnon, D., 1934- (1986). Everything you need to know before you're hijacked. San Diego: House of Hits.

Merari, A. (1999). Attacks on civil aviation: Trends and lessons. In P. Wilkinson & B. M. Jenkins (Eds.), Aviation terrorism and security (pp. 9-26). London: Frank Cass.

333 Merriam-Webster (2005). Definition of terrorism. http://www.m-w.com/dictionary/terrorism (Last accessed: August 2).

Mickolus, E. F. (1980). Transitional terrorism: A chronology of events 1968-1979. London: Aldwych Press.

Mickolus, E. F. (1981). Combating international terrorism: A quantitative analysis. A Ph.D. dissertation at Yale University.

Mickolus, E. F. (1987). Comment - terrorists, government, and numbers: Counting things versus things that count. Journal of Conflict Resolution, 31 (1), 54-62.

Miller, H. (2003). Transportation and communication lifelines disruption. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 145-152). New York: Routledge.

Miller, H. (2004). Tobler's first law and spatial analysis. Annals of the Association of American Geographers, 94 (2), 284-289.

Miller, T., Kobayashi, M., Caldwell, E., Thurston, S., & Collett, B. (2002). Citizen surveys on the web: General population surveys of community opinion. Social Science Computer Review, 20 (2), 124-136.

MIPT (2006). Memorial Institute for the Prevention of Terrorism terrorism knowledge Base. http://www.tkb.org (Last accessed: August 2, 2007).

Mitchell, J. (2003). Urban vulnerability to terrorism as hazard. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 17-26). New York: Routledge.

Moellering, H. (1976). The potential uses of a computer animated film in the analysis of geographical patterns of traffic crashes. Accident Analysis & Prevention, 8 215- 227.

334 Moellering, H. (1980). The real-time animation of three-dimensional maps. The American Cartographer, 7 (1), 67-75.

Monmonier, M. (1992). Summary graphics for integrated visualization in dynamic cartography. Cartography and Geographic Information Systems, 19 (4), 247-260.

Montz, B., Cross, J., & Cutter, S. (2003). Hazards. In G. Gaile & C. Willmott (Eds.), Geography in America at the dawn of the 21st century (pp. 481-493). New York: Oxford University Press.

Mueller, J. (2004). The remnants of war. Ithica: Cornell University Press.

Murray, A., & Grubesic, T. (2007). Critical infrastructure. London: Springer.

Mustafa, D. (2005). The terrible geographicalness of terrorism: Reflections of a hazards geographer. Antipode, 37 (1), 72-92.

Nacos, B., Bloch-Elkon, Y., & Shapiro, R. Y. (2007). The threat of international terrorism after 9/11: News coverage and public perceptions http://www.publicopinionpros.com/features/2007/feb/nacos.asp (Last accessed: July 15, 2007).

National Parks Service, N. P. (2004). Lists of national historic landmarks. http://www.nps.gov/nhl/designations/listsofNHLs.htm (Last accessed: August 1, 2006).

Neilson Net Ratings (2003). U.S. Market penetration. http://www.clickz.com/showPage.html?page=2222631 (Last accessed: August 1, 2007).

New York Times (2004). 2004 election interactive graphics. http://www.nytimes.com/packages/html/politics/2004_ELECTIONRESULTS_G RAPHIC/ (Last accessed: August 2, 2007).

335 News Wire (1982). Terrorists bomb three U.S. Companies. United Press International, December 22, International news.

News Wire (2000). Fire attacks in Basque country damage buildings and radio tower. Agence France Presse English, October 8, International news.

News Wire (2000b). Parcel bomb blast at Spanish newspaper injures two. Associated Press Worldstream, September 29, International news.

Office of the Inspector General (2006). Progress in developing the national asset database. Washington D.C.: United States Department of Homeland security.

Office of the President (2002). Homeland security Presidential Directive 3 http://www.whitehouse.gov/news/releases/2002/03/20020312-5.html (Last accessed: August 2, 2007).

Office of the President (2003). Homeland security Presidential Directive/hspd-5 http://www.whitehouse.gov/news/releases/2003/02/20030228-9.html (Last accessed: August 2, 2007).

Office of the President (2003b). Physical protection of critical infrastructures and key assets. http://www.whitehouse.gov/pcipb/physical.html (Last accessed: August 2, 2007).

Ohio Historical Center (2004). Central Ohio sites. http://www.ohiohistory.org/places/central.html (Last accessed: August 1, 2007).

O'Kelly, M., Kim, H., & Kim, C. (2006). Internet reliability with realistic peering. Environment and Planning B: Planning and Design, 33 325-343.

O'Loughlin, J. (2005). The war on terrorism, academic publication norms, and replication. The Professional Geographer, 57 (4), 588-591.

336 Openshaw, S. (1984). The modifiable areal unit problem. Norwick: GeoBooks.

Orr, S. (2005). New technology and research: An analysis of Internet survey methodology in political science. PS: Political Science and Politics, 38 (2), 263- 267.

Oswald, D. (1997). Fire insurance maps : Their history and applications. College Station: Lacewing Press.

Oxford English Dictionary (2005). Definition of terrorism. http://dictionary.oed.com/cgi/entry/50249598?single=1&query_type=word&quer yword=terrorism&first=1&max_to_show=10 (Last accessed: August 2).

Pape, R. A. (2005). Dying to win: The strategic logic of suicide terrorism. New York: Random House.

Paté-Cornell, E. (2002). Fusion of intelligence information: A Bayesian approach. Risk Analysis, 22 (3), 445-454.

Peterson, M. (1979). An evaluation of unclassed crossed line choropleth mapping. The American Cartographer, 6 (1), 21-37.

Peterson, M. (1992). Creating unclassed choropleth maps with postscript. Cartographic Perspective, 12 4-6.

Peterson, M. (1995). Interactive and animated cartography. Englewood: Prentice Hall.

Phillips, D. (1973). Skyjack: The story of air piracy. London: Harrap.

Phillips, J. D. (2004). Doing justice to the law. Annals of the Association of American Geographers, 94 (2), 290-293.

337

Pilch, R. (2004). The bioterrorist threat in the United States. In R. Howard & R. Sawyer (Eds.), Terrorism and counterterrorism: Understanding the new security environment (pp. 208-242). Guilford: McGraw-Hill.

Pillar, P. (2004).The dimensions of terrorism and counterterrorism. In R. Howard & R. Sawyer (Eds.), Terrorism and counterterrorism: Understanding the new security environment (pp. 24-45). Guilford: McGraw-Hill.

Polling Report (2001). ABC news/Washington post poll Sept. 11, 2001. http://www.pollingreport.com/terror10.htm (Last accessed: August 1, 2007).

Preston, R. (1994). The hot zone. New York: Random House.

Raisz, E. (1938). General cartography. New York: McGraw-Hill.

RAND (2007). About the RAND corporation http://www.rand.org/about/ (Last accessed: May 30, 2007).

Rapoport, D. (2004). The four waves of modern terrorism. In A. Cronin & J. Ludes (Eds.), Attacking terrorism: Elements of a grand strategy (pp. 46-74 ). Washington DC: Georgetown University Press.

Ready.Gov (2004). Citizen guidance on the Homeland Security Advisory System. http://www.ready.gov/america/_downloads/emergency_preparedness/are_you_rea dy.pdf (Last accessed: Sept 2, 2004).

Ready.Gov (2007). Listo campaign fact sheets. http://www.ready.gov/america/_downloads/factsheets (Last accessed: July 5, 2007).

Ready.Gov (2007b). Ready America. http://www.ready.gov/america/index.html (Last accessed: August 2, 2007).

338

Reiter, L. (1990). Earthquake hazard analysis: Issues and insights. New York: Columbia University Press.

Riskind, J., & Torry, J. (2003). Al- Qaida among us. Columbus Dispatch, Columbus: June 20, Section A, pg 1.

Rittschof, K., Stock, W., Kulhavy, R., Verdi, M., & Johnson, J. (1996). Learning from cartograms: The effects of region familiarity. Journal of Geography, 95 (2), 50- 58.

Robinson, A. H. (1947). Foundation of cartographic methodology. A Ph.D. dissertation at The Ohio State University.

Robinson, A. H. (1952). The look of maps; an examination of cartographic design. Madison: University of Wisconsin Press.

Robinson, A. H., Morrison, J., Muehrcke, P., Kimerling, A., & Guptill, S. (1995). Elements of cartography. New York: Wiley.

Robinson, K. K., Crenshaw, E. M., & Jenkins, J. C. (2006). Ideologies of violence: The social origins of Islamist and leftist transnational terrorism. Social Forces, 84 (4), 2009-2026.

Ross, J. I. (1994). Research note: Hypotheses about political terrorism during the gulf conflict, 1990-1991. Terrorism & Political Violence, 6 (2), 224-234.

Roster, C., Rogers, R., Albaum, G., & Klein, D. (2004). A comparison of response characteristics from web and telephone surveys. International Journal of Market Research, 46 (3), 359-373.

Salama, S., & Hansell, L. (2005). Does intent equal capability? Al-Qaeda and weapons of mass destruction. Nonproliferation Review, 12 (3), 615-653.

339

Sandler, T., & Scott, J. (1987). Terrorist success in hostage-taking incidents'. Journal of Conflict Resolution, 31 (1), 35-53.

Savitch, H. V. & Ardashev, G. (2001). Does terror have an urban future? Urban Studies 38 (13), 2515-2533.

Savitch, H. V. (2005). An anatomy of urban terror: Lessons from Jerusalem and elsewhere. Urban Studies, 42 (3), 361-395.

Scheuer, M. C. (2004). Imperial hubris : Why the west is losing the war on terror. Washington D. C: Brassey's Publication.

Schmid, A. P., & Jongman, A. J. (1984). Political terrorism : A research guide to concepts, theories, data bases, and literature. Amsterdam: North-Holland.

Scholl, N., Mulders, S., & Dent, R. (2002). Online qualitative market research: Interviewing the world at a fingertip. Qualitative Market Research, 5 (3), 210- 223.

Shabad, G., & Ramo, F. J. L. (1995). Political violence in a democratic state: Basque terrorism in Spain. In M. Crenshaw (Ed.), Terrorism in context (pp. 410-469). University Park: The Pennsylvania State University Press.

Sheppard, E. (2001). Quantitative geography: Representations, practices, and possibilities. Environment and Planning D: Society and Space, 19 535-554.

Shroder, J. (2005). Remote sensing and GIS as counterterrorism tools in the Afghanistan war: Reality, plus the results of media hyperbole. The Professional Geographer, 57 (4), 592-597.

Sidaway, J. (1994). Geopolitics, geography, and "terrorism" in the Middle East. Environment and Planning D: Society and Space, 12 357-372.

340

Silke, A. (2001). When sums go bad: Mathematical models and hostage situations. Terrorism & Political Violence, 13 (2), 49-66.

Sills, S., & Song, C. (2002). Innovations in survey research: An application of web-based surveys. Social Science Computer Review, 20 (1), 22-30.

Slack, J. D. (1990). Information, training, and assistance needs of municipal governments. Public Administration Review, 50 (4), 450-455.

Sloan, S. (1998). Terrorism and asymmetry. In L. J. Matthews (Ed.), Challenging the United States symmetrically and asymmetrically : Can America be defeated? (pp. 173-194). Carlisle Barracks, PA: Strategic Studies Institute, U.S. Army War College.

Slocum, T. A., Mcmaster, R. B., Kessler, F. C., & Howard, H. H. (2005). Thematic cartography and geographic visualization. Upper Saddle River: Pearson/Prentice Hall.

Slovic, P. (1992).Perception of risk: Reflections on the psychometric. In S. Krimsky & D. Golding (Eds.), Social theories of risk (pp. 117-152). Westport: Praeger.

Smart Money (2007). Map of the market. http://www.smartmoney.com/marketmap/ (Last accessed: August 2, 2007).

Smith, N. (2003). American empire: Roosevelt's geographer and the prelude to globalization. Berkeley: University of California Press.

Smith, S. (2007). Angelina wants to save the world. Newsweek, CXLIX (26), 46-53.

STATA SE 9.2 (2006). Statistics Data Analysis Special Edition. College Station, TX: Produced by StataCorpk, Software Version 9.2.

341 St. John, P. (1991). Air piracy, airport security, and international terrorism : Winning the war against hijackers. New York: Quorum Books.

Staff and News Reports (2003). Hawaii stays at general risk for terrorism http://starbulletin.com/2003/02/08/news/story2.html (Last accessed: August 2, 2007).

Stammen, K. (2004). Columubs just not average enough. Columbus Dispatch, Columbus: June 5, Section Business, pg 1C.

Stannard, M. (2006). 9/11: Five years later. San Francisco Chronicle, San Francisco: September 6, Section A, pg 1.

Starr, C. (1969). Social benefit versus technological risk: What is our society willing to pay for safety? Science, 165 1232-1238.

Stebbins, M. (2006). DHS updates Ready.Gov…sort of. http://www.fas.org/blog/ssp/michael_stebbins/ (Last accessed: July 5, 2007).

Stern, J. (1999). Getting and using the weapons. In J. Stern (Ed.), The ultimate terrorists (pp. 48-68). Boston: Harvard University Press.

Stern, J. (2003). Terror in the name of God: Why religious militants kill. New York: Ecco.

Sui, D. Z. (2004). GIS, cartography, and the ‘‘third culture’’: Geographic imaginations in the computer age. The Professional Geographer, 56 (1), 62-72.

Sui, D. Z. (2004b). Tobler's first law of geography: A big idea for a small world? Annals of the Association of American Geographers, 94 (2), 269-277.

Survey Monkey (2006). Design features. http://www.surveymonkey.com/Home_FeaturesDesign.aspx (Last accessed: August 2, 2007).

342

Swoboda, W., Muhlberger, J., Weitkunat, N., & Schneeweib, R. (1997). Internet surveys by direct mailing. Social Science Computer Review, 15 (3), 242-255. Taillon, J. P. D. B. (2002). Hijacking and hostages: Government responses to terrorism.Westport: Praeger.

Ten Thirteen Productions (1998). The X-files: Fight the future. Ten Thirteen Productions, released by 20th Century Fox: VHS tape.

Thomas, D., & Mitchell, J. (2001). Which are the most hazardous states? In S. Cutter (Ed.), American hazardscapes (pp. 115-156). Washington D.C.: Joseph Henry Press.

Thomas, J. (1997). Agony relived as U.S. Pursues McVeigh death. New York Times, New York: June 5, Section A, pg 1.

Thomas, J. (1998). Reduced term for witness to bombing plot. New York Times, New York: May 28, Section A, pg 18.

Thrower, N. (1959). Animated cartography. The Professional Geographer, 11 (6), 9-12.

Tiat, M. (2003). The need for a national spatial data infrastructure. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 77-86). New York: Routledge.

TKB Definition (2006). TKB data methodologies. http://www.tkb.org/RandSummary.jsp?page=method (Last accessed: August 2, 2007).

TKB 3728 Incident (2007). Unknown group attacked business target (Nov. 15, 1983, Spain). http://www.tkb.org/Incident.jsp?incID=3728 (Last accessed: May 23, 2007).

343 TKB 7759 Incident (2007). Al-Qaeda attacked government target (Sept. 11, 2001, United States). http://www.tkb.org/Incident.jsp?incID=7759 (Last accessed: May 23, 2007).

TKB 9513 Incident (2007). Basque fatherland and freedom (ETA) attacked business target (Dec. 1, 2001, Spain). http://www.tkb.org/Incident.jsp?incID=9513 (Last accessed: May 23, 2007).

TKB 10808 Incident (2007). Unknown group attacked business target (Feb. 15, 1998, Spain). http://www.tkb.org/Incident.jsp?incID=10808 (Last accessed: May 23, 2007).

TKB 10809 Incident (2007). Unknown group attacked business target (Feb. 15, 1998, Spain). http://www.tkb.org/Incident.jsp?incID=10809 (Last accessed: May 23, 2007).

TKB 12853 Incident (2007). First of October antifascist resistance group (GRAPO) attacked journalists & media target (Sept. 29, 2000, Spain). http://www.tkb.org/Incident.jsp?incID=12853 (Last accessed: May 23, 2007).

TKB 13105 Incident (2007). Unknown group attacked telecommunication target Oct. 8, 2000, Spain). http://www.tkb.org/Incident.jsp?incID=13105 (Last accessed: May 23, 2007).

Tobler, W. (1963). Geographic area and map projections. Geography Review, 53 (1), 59 78.

Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46 (2), 234-240.

Tobler, W. (1973). Choropleth maps without class intervals? Geographical Analysis, 5 (3), 262-265.

Tobler, W. (1986). Pseudo-cartograms. The American Cartographer, 13 (1), 43-50.

344

Tobler, W. (2004). On the first law of geography: A reply. Annals of the Association of American Geographers, 94 (2), 304-310.

Tobler, W. (2004b). Thirty five years of computer cartograms. Annals of the Association of American Geographers, 94 (1), 58-73.

Treihish, L. A. (1992). Climatology of global stratospheric ozone. Yorktown Heights, NY: IBM Corporations.Videotape.

Tuathail, G. (1998). Deterritorialized threats and global dangers: Geopolitics, risk society and reflexive modernization. Geopolitics, 3 (1), 17-31.

Tuathail, G. (2000). The postmodern geopolitical condition: States, statecraft, and security at the millennium. Annals of the Association of American Geographers, 90 (1), 166-178.

Tuathail, G. (2003). Just out looking for a fight”: American affect and the invasion of Iraq. Antipode, 35 (5), 856-870.

Tufte, E. (2000). Visual explanations : Images and quantities, evidence and narrative. Cheshire: Graphics Press.

Tuten, T., Galesic, M., & Bosnjak, M. (2004). Effects of immediate versus delayed notification of prize draw results on response behavior in web surveys: An experiment. Social Science Computer Review, 22 (3), 377-376.

United States Census Bureau (2005). Fact Sheet – Franklin County, Ohio. http://factfinder.census.gov/servlet/ACSSAFFFacts?_event=&geo_id=05000US3 9049&_geoContext=01000US%7C04000US39%7C05000US39049&_street=&_ county=Franklin+county&_cityTown=Franklin+county&_state=04000US39&_zi p=&_lang=en&_sse=on&ActiveGeoDiv=&_useEV=&pctxt=fph&pgsl=050&_su bmenuId=factsheet_1&ds_name=null&_ci_nbr=null&qr_name=null®=null%3 Anull&_keyword=&_industry= (Last accessed: August 3, 2007).

345 United States Census Bureau (2006). Definition: Urban & rural. https://ask.census.gov/cgi- bin/askcensus.cfg/php/enduser/std_adp.php?p_faqid=623&p_created=109215023 8&p_sid=I*24MeIi&p_accessibility=0&p_redirect=&p_lva=&p_sp=cF9zcmNoP TEmcF9zb3J0X2J5PSZwX2dyaWRzb3J0PSZwX3Jvd19jbnQ9NzAmcF9wcm9k cz0mcF9jYXRzPSZwX3B2PSZwX2N2PSZwX3BhZ2U9MSZwX3NlYXJjaF90 ZXh0PXVyYmFuIGRlZmluaXRpb24*&p_li=&p_topview=1 (Last accessed: August 2, 2007).

United States Congress (2004). Public transportation terrorism prevention and response act of 2004 report (to accompany H.R. 5082) (including cost estimate of the congressional budget office). U.S. Government Printing Office.

United States Department of Homeland Security (2007). Chronology of changes to the homeland security advisory system. http://www.dhs.gov/xabout/history/editorial_0844.shtm (Last accessed: August 2, 2007).

United States Department of State (1985-2004). Patterns of global terrorism. Washington D.C.: United States Department of State.

United States Department of Transportation Federal Aviation Administration (1981). U.S. and foreign registered aircraft hijackings. Washington, D.C.: The Office of Civil Aviation Security.

Vasconcellos, R. (1993). Knowing the Amazon through tactile graphics. Bournemouth, UK: International Cartographic Association (pp. 206-210).

Wallis, H., & Robinson, A. H. (1987). Cartographical innovations: An international handbook of mapping terms to 1900. Tring: Map Collector Publications in association with the International Cartographic Association.

Ware, C. (2004). Information visualization: Perception for design. San Francisco: Elsevier.

346 Weber, C., & Buttenfield, B. (1993). A cartographic animation of average yearly surface temperatures for the 48 contiguous United States:1897-1986. Cartography and Geographic Information Systems, 20 (3), 141-150.

Weimann, G., & Brosius, H. B. (1988). The predictability of international terrorism: A time-series analysis. Terrorism & Political Violence, 11 (6), 491-502.

Weinberg, L., & Eubank, W. (1999). Terrorism and the shape of things to come. Terrorism & Political Violence, 11 (4), 94-105.

Weisberg, H. F., Krosnick, J. A., & Bowen, B. D. (1996). An introduction to survey research, polling, and data analysis. Thousand Oaks, Calif.: Sage Publications.

Whitcomb, M., & Porter, S. (2004). E-mail contacts - a test of complex graphical designs in survey research. Social Science Computer Review, 22 (3), 370-376.

White, G. F. (1988). Paths to risk analysis. Risk Analysis, 8 171-175.

Whittaker, D. (2004). Terrorists and terrorism in the contemporary world. New York: Routledge.

Wilbanks, T. (2003). Energy systems and infrastructures. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 139-144). New York: Routledge.

Wilhelmson, R., Jewett, B., Shaw, C., Wicker, L., Arrott, M., Bushell, C., Bajuk, M., Thingvols, J., & Yost, J. (1990). A study of numerically modeled storm. The International Journal of Supercomputer Applications, 4 (2), 20-36.

Witte, J., Amoroso, L., & Howard, P. (2000). Research methodology: Method and representation in Internet-based survey tools–mobility, community, and cultural identity in survey2000. Social Science Computer Review, 18 (2), 179-195.

Wood, D. (1992). The power of maps. New York: The Guilford Press.

347 Wood, D. (2006). Map art. Cartographic Perspectives, 53 5-14.

Worboys, M., & Duckham, M. (2004). GIS: A computing perspective. Boca Raton: CRC Press.

Worldmapper (2007). Worldmapper. http://www.worldmapper.org (Last accessed: August 2, 2007).

Wright, R., Ganster, P., & Dow, D. (2003). Transborder diaster management. In S. Cutter, D. Richardson & T. Wilbanks (Eds.), The geographical dimensions of terrorism (pp. 35-44). New York: Routledge.

Wu, S.-Y. (2002). Assessing the vulnerability to environmental hazards - a HERO protocol. http://hero.geog.psu.edu/products/protocol_vuln.pdf (Last accessed: August 2, 2007).

Xiao, N., & Armstrong, M. (2006). Choroware: A software toolkit for choropleth map classification. Geographical Analysis, 38 (1), 102-121.

Yoo, S., & Kwan, K. (2004). Visfiles: Taking stock of visualization in scientific computing. Computer Graphics, 38 (3), 4-6.

Young, C. (2005). The Cindy Sheehan you don't know. The Boston Globe, Boston: August 22, Section Op-Ed, pg A11.

Zlotnik, H. (2004). World urbanization: Trends and prospects. In T. Champion & G. Hugo (Eds.), New forms of urbanization: Beyond the urban-rural dichotomy (pp. 43-64). Aldershot: Ashgate.

348