Social Vulnerability in US Metropolitan Areas: Improvements in Hazard Vulnerability Assessment

Christopher T. Emrich

Bachelor of Arts University of South Florida, 1997

Master of Arts University of South Florida, 2000

------

Submitted in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy in the

Department of Geography

University of South Carolina

2005

______Major Professor Chair, Examining Committee

______Committee Member Committee Member

______Committee Member Dean of Graduate School

DEDICATION

This dissertation is dedicated to everyone who believed, trusted, supported,

amused, comforted, and generally had the patience to put up with me from the start of my

educational expedition until the completion of this document. I would not be where I am

today without the love and energy of these many people. Specifically, I dedicated this

manuscript to my wife and best friend, Laurie, without whose unconditional support I

would not have made it through this process, and to my family whose love and

understanding has helped me to endure this research and not loose my positive outlook.

To all those who find themselves in the same predicament that I was in not too

long ago – to far from the beginning to drop it all, and too far from the end to notice that

you have made progress along the journey – keep the faith, you will reach the goal line –

after all, if I can finish anyone can.

I would especially like to dedicate this piece of labor and love to my departed

Uncle LJ, Aunt Heidi, Grandma and Grandpa Emrich, and Noreen Flynn. Not a day went

by during the composition of this tome that their love and spiritual support could not be felt by me – especially on the days when the end seemed no where in sight. Their departure from this world has taught me to take hold of every moment and cherish every day that we have here on earth. Through their lives, I have learned about passion and

compassion, tolerance and empathy, pride and humility. I have grown into the person

that I am today because of the hand that they have played in my life.

iii

ACKNOWLEDGEMENTS

I would like to convey my most heartfelt gratefulness the many people without whom this dissertation would not have been completed. Particularly, I would like to thank Dr. Susan Cutter for her guidance, understanding, much needed structure, and endless support throughout the undertaking of this research. Sincere thanks also go to Dr.

Hodgson for his insight and contributions throughout my degree program, to Dr. Jakubs, for his patience, support and helpful assistance with many of my ‘small’ problems along the way, and to Dr. Weber for being there when no one else was.

Additionally, I would like to acknowledge all of the faculty and staff of the USC

Geography department, especially Capers – who has been there for moral support since the first day I met him – when he offered to take me fishing, and Elizabeth, whose jovial demeanor makes me smile every morning whether I am happy or not.

Special thanks is given to all of my fellow graduate students who have taken the good with the bad and are still there for support, especially all of the HRLers, past and present, who have put up with my moodiness, mindlessness, insanity and occasional normalcy through the years. Thanks for being there to bounce ideas off of and to set me straight when I had no idea that I was lost.

To all those unnamed persons who played a part in the completion of this dissertation – I thank you.

ABSTRACT

Social Vulnerability in United States Metropolitan Areas: Improvements in Hazard Vulnerability Assessment

Christopher T. Emrich

Metropolitan areas of the United States are becoming more and more hazardous places to live, subject to a myriad of threats from natural hazards, technological failures, and willful terrorist acts. Along with this increasing potential for catastrophe is a similar rise in the vulnerability of the residents who live there. This vulnerability manifests itself as the potential for harm, but it also describes the inability of people and places to adequately respond to and rebound from hazard events.

Theoretically based in the Vulnerability of Place Model, the spatial distribution of social vulnerability within two United States cities—Tampa-St. Petersburg, FL and Charleston, SC--was analyzed. The following research questions were posed: 1) What socioeconomic factors influence social vulnerability within urban areas?; 2) What characteristics of the built environment cause differential vulnerability within the urban realm?; and 3) What variations in lifelines and accessibility cause cities to exhibit higher or lower vulnerability to hazard events?

Variables representing three facets of social vulnerability -socio-economics, built environment, and accessibility--were standardized and placed in an un-weighted model. Experts in the natural hazards and disaster field were surveyed to determine the relative importance of variables in determining social vulnerability and these opinions were used

v as weights in a weighted model. The resulting models were compared to each other to formulate an understanding of how differential weighting of factors influenced each facet of social vulnerability.

Results indicate that although weighting does change overall vulnerability to some degree, the metropolitan pattern of vulnerability remains relatively constant across space, while local changes in all of the subcomponent facets as well as overall social vulnerability are more highly differentiated. These results provide an in depth look into those characteristics of a place or population that are most important in a spatial equation of social vulnerability. Such an understanding can aid policy makers, planners and individual families in planning for and reducing hazard impacts in the future through improvements in mitigation programs, zoning practices, and development policies.

Dissertation Director - Susan L. Cutter

TABLE OF CONTENTS

DEDICATION ...... iii

ACKNOWLEDGEMENTS ...... iv

ABSTRACT ...... v

LIST OF TABLES ...... x

LIST OF FIGURES ...... xv

CHAPTER ONE: Introduction ...... 1 1.1 Background ...... 1 1.2 Rationale ...... 2 1.3 Purpose and Research Questions ...... 4 1.4 Organization of the Dissertation ...... 5

CHAPTER TWO: Literature Review ...... 7 2.1 Risk in a Hazards Context ...... 7 2.2 The Concept of Vulnerability ...... 8 2.3 The Urban Dimension ...... 14 2.4 Health and Livability ...... 18 2.5 Quality of Life ...... 21 2.6 Accessibility and Lifelines ...... 24 2.7 The Concept of Vulnerability Assessment ...... 28 2.8 The Application of Vulnerability Assessment ...... 30 2.9 Summary ...... 36

CHAPTER THREE: Methodology ...... 38 3.1 Introduction ...... 38 3.2 Study Area ...... 39

vii 3.3 Data Sources and Availability ...... 42 3.3.1 Socioeconomic Vulnerability Data ...... 43 3.3.2 Built Environment Vulnerability Data ...... 43 3.3.3 Access, Lifeline, and Livability Data ...... 45 3.4 Research Design and Methods ...... 46 3.4.1 Theoretical Framework ...... 46 3.4.2 Unweighted Socioeconomic Vulnerability ...... 47 3.4.3 Unweighted Built Environment Vulnerability ...... 51 3.4.4 Unweighted Accessibility Vulnerability ...... 55 3.4.5 Unweighted Social Vulnerability ...... 60 3.5 The Survey Instrument ...... 60 3.5.1 Survey Sample ...... 63 3.5.2 Survey Construction...... 64 3.6 Weighted Vulnerability Indicators ...... 64 3.7 Summary ...... 68

CHAPTER FOUR: Results ...... 70 4.1 Unweighted Vulnerability Scores ...... 70 4.1.1 Unweighted Socioeconomic Vulnerability ...... 70 4.1.2 Unweighted Built-environment Vulnerability ...... 76 4.1.3 Unweighted Accessibility Vulnerability ...... 82 4.1.4 Unweighted Social Vulnerability ...... 87 4.2 Expert Weighted Vulnerability Scores ...... 94 4.2.1 Weighted Socioeconomic Vulnerability ...... 95 4.2.2 Weighted Built Environment Vulnerability ...... 102 4.2.3 Weighted Accessibility Vulnerability ...... 108 4.2.4 Weighted Social Vulnerability ...... 114 4.3 Comparing Unweighted and Weighted Vulnerability Scores ...... 119 4.3.1 Unweighted versus Weighted Socioeconomic Vulnerability ...... 119 4.3.2 Unweighted versus Weighted Built Environment Vulnerability ...... 127 4.3.3 Unweighted versus Weighted Accessibility Vulnerability ...... 133 4.3.4 Unweighted versus Weighted Social Vulnerability ...... 139

viii

CHAPTER FIVE: Conclusions...... 146 5.1 Introduction ...... 146 5.2 Addressing the Research Questions ...... 147 5.2.1 Socio Economic Indicators ...... 147 5.2.2 Built Environment Indicators ...... 149 5.2.3 Accessibility Indicators ...... 151 5.2.4 The Social Vulnerability Index ...... 152 5.3 Future Research ...... 154

REFERENCES ...... 156

APPENDIX A ...... 169

APPENDIX B ...... 174

APPENDIX C ...... 184

LIST OF TABLES

Table 2.1: Selected definitions of risk ...... 8

Table 2.2: Selected definitions of vulnerability ...... 11

Table 2.2 Continued: Selected definitions of vulnerability ...... 12

Table 2.2 Continued: Selected definitions of vulnerability ...... 13

Table 2.3: Selected definitions of quality of life ...... 23

Table 2.4: Selected definitions and themes related to accessibility ...... 26

Table 2.5: Selected definitions and themes related to lifelines ...... 27

Table 3.1: Selected Socio-demographic characteristics of Tampa – St. Petersburg, FL, Charleston, SC, Los Angeles, CA, New York, NY, and Washington D.C...... 41

Table 3.2: Data, sources and effect of socio-economic indicators on vulnerability ...... 44

Table 3.3: Data, sources and effect of built environment indicators on vulnerability ...... 45

Table 3.4: Data, sources and effect of accessibility and lifeline indicators on vulnerability...... 47

Table 3.5: Built Environment Vulnerability Scoring Procedure ...... 55

Table 4.1: Communities with the highest unweighted socio-economic vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 75

Table 4.2: Communities with the lowest unweighted socio-economic vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 76

Table 4.3: Block groups with the highest unweighted built environment vulnerability in Tampa – St. Petersburg, FL and Charleston, SC ...... 81

x Table 4.4: Block groups with the lowest unweighted built environment vulnerability in Tampa – St. Petersburg, FL and Charleston, SC ...... 82

Table 4.5: Communities with the highest unweighted accessibility vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 86

Table 4.6: Communities with the lowest unweighted accessibility vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 87

Table 4.7: Communities with the highest unweighted standardized social vulnerability in Tampa – St. Petersburg, FL ...... 91

Table 4.8: Communities with the highest unweighted standardized social vulnerability in Charleston, SC based on block groups within each community ...... 92

Table 4.9: Communities with the lowest unweighted standardized social vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ...... 93

Table 4.10: Communities with the lowest unweighted standardized social vulnerability in Charleston, SC based on block groups within each community ...... 94

Table 4.11: Socio-economic vulnerability factors and Delphi method weights ...... 96

Table 4.12: Communities with the highest weighted socio-economic vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 101

Table 4.13: Communities with the lowest weighted socio-economic vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 101

Table 4.14: Built environment vulnerability factors and Delphi method weights ...... 102

Table 4.15: Communities with the highest weighted built environment vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 107

Table 4.16: Communities with the lowest unweighted built environment vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 108

Table 4.17: Accessibility vulnerability factors and Delphi method weights ...... 109

xi Table 4.18: Communities with the highest weighted accessibility vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 113

Table 4.19: Communities with the lowest weighted accessibility vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 113

Table 4.20: Overall Social Vulnerability factors and Delphi method weights ...... 114

Table 4.21: communities with the highest weighted social vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 118

Table 4.22: Communities with the lowest weighted social vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community ...... 119

Table 4.23: Communities with the highest unweighted and weighted socioeconomic vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ...... 121

Table 4.24: Communities with the highest unweighted and weighted socioeconomic vulnerability in Charleston, SC based on block groups within each community ... 122

Table 4.25: Communities with the lowest unweighted and weighted socioeconomic vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ...... 123

Table 4.26: Communities with the lowest unweighted and weighted socioeconomic vulnerability in Charleston, SC based on block groups within each community ... 124

Table 4.27: Unweighted and weighted socio-economic vulnerability clusters for Tampa – St. Petersburg, FL ...... 125

Table 4.28: Unweighted and weighted socio-economic vulnerability clusters for Charleston, SC ...... 126

Table 4.29: Communities with the highest unweighted and weighted built environment vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ...... 128

Table 4.30: Communities with the highest unweighted and weighted built environment vulnerability in Charleston, SC based on block groups within each community ... 129

xii Table 4.31: Communities with the lowest unweighted and weighted built environment vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ...... 130

Table 4.32: Communities with the lowest unweighted and weighted built environment vulnerability in Charleston, SC based on block groups within each community ... 131

Table 4.33: Unweighted and weighted built environment vulnerability clusters for Tampa – St. Petersburg, FL ...... 132

Table 4.34: Unweighted and weighted built environment vulnerability clusters for Charleston, SC ...... 133

Table 4.35: Communities with the highest unweighted and weighted accessibility vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ...... 134

Table 4.36: Communities with the highest unweighted and weighted accessibility vulnerability in Charleston, SC based on block groups within each community ... 135

Table 4.37: Communities with the lowest unweighted and weighted accessibility vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ...... 136

Table 4.38: Communities with the lowest unweighted and weighted accessibility vulnerability in Charleston, SC based on block groups within each community ... 137

Table 4.39: Unweighted and weighted accessibility vulnerability clusters for Tampa – St. Petersburg, FL ...... 138

Table 4.40: Unweighted and weighted accessibility vulnerability clusters for Charleston, SC ...... 139

Table 4.41: Communities with the highest unweighted and weighted social vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ..... 140

Table 4.42: Communities with the highest unweighted and weighted social vulnerability in Charleston, SC based on block groups within each community ...... 141

Table 4.43: Communities with the lowest unweighted and weighted social vulnerability in Tampa – St. Petersburg, FL based on block groups within each community ..... 142

Table 4.44: Communities with the lowest unweighted and weighted social vulnerability in Charleston, SC based on block groups within each community ...... 143

xiii Table 4.45: Unweighted and weighted social vulnerability clusters for Tampa – St. Petersburg, FL ...... 144

Table 4.46: Unweighted and weighted social vulnerability clusters for Charleston, SC 145

xiv

LIST OF FIGURES

Figure 3.1: Charleston, SC and Tampa – St. Petersburg study areas ...... 40

Figure 3.2: Social Vulnerability Framework. Updated from Cutter 1996 ...... 48

Figure 3.3: Socioeconomic vulnerability data flow diagram ...... 52

Figure 3.4: Built Environment vulnerability data flow diagram ...... 56

Figure 3.5: Accessibility vulnerability data flow diagram ...... 59

Figure 3.6: Social vulnerability data flow diagram ...... 69

Figure 4.1: Unweighted socio-economic vulnerability for Tampa – St. Petersburg, FL . 71

Figure 4.2: Unweighted socio-economic vulnerability clusters for Tampa – St. Petersburg, FL ...... 71

Figure 4.3: Unweighted socio-economic vulnerability for Charleston, SC ...... 72

Figure 4.4: Spatial clusters of unweighted socio-economic vulnerability in Charleston, SC ...... 72

Figure 4.5: Unweighted built environment vulnerability for Tampa – St. Petersburg, FL ...... 77

Figure 4.6: Spatial clusters of unweighted built environment vulnerability in Tampa – St. Petersburg, FL ...... 77

Figure 4.7: Unweighted built environment vulnerability for Charleston, SC...... 78

Figure 4.8: Spatial clusters of unweighted built environment vulnerability in Charleston, SC ...... 78

Figure 4.9: Unweighted accessibility vulnerability for Tampa – St. Petersburg, FL ...... 83

Figure 4.10: Spatial clusters of unweighted accessibility vulnerability in Tampa – St. Petersburg, FL ...... 83

xv Figure 4.11: Unweighted accessibility vulnerability for Charleston, SC ...... 84

Figure 4.12: Spatial clusters of unweighted accessibility vulnerability in Charleston, SC ...... 84

Figure 4.13: Standardized unweighted social vulnerability for Tampa – St. Petersburg, FL ...... 88

Figure 4.14: Spatial clusters of Standardized unweighted social vulnerability for Tampa – St. Petersburg, FL ...... 88

Figure 4.15: Standardized unweighted social vulnerability for Charleston, SC ...... 89

Figure 4.16: Spatial clusters of standardized unweighted social vulnerability in Charleston, SC ...... 89

Figure 4.17: Weighted socio-economic vulnerability for Tampa – St. Petersburg, FL .. 97

Figure 4.18: Spatial clusters of weighted socio-economic vulnerability in Tampa – St. Petersburg, FL ...... 97

Figure 4.19: Weighted socio-economic vulnerability for Charleston, SC ...... 98

Figure 4.20: Spatial clusters of weighted socio-economic vulnerability in Charleston, SC ...... 98

Figure 4.21: Weighted built environment vulnerability for Tampa – St. Petersburg, FL104

Figure 4.22: Spatial clusters of weighted built environment vulnerability in Tampa – St. Petersburg, FL ...... 104

Figure 4.23: Weighted built environment vulnerability for Charleston, SC ...... 105

Figure 4.24: Spatial clusters of weighted built environment vulnerability in Charleston, SC ...... 105

Figure 4.25: Weighted accessibility vulnerability for Tampa – St. Petersburg, FL ...... 110

Figure 4.26: Spatial clusters of weighted accessibility vulnerability in Tampa – St. Petersburg, FL ...... 110

Figure 4.27: Weighted accessibility vulnerability for Charleston, SC ...... 111

Figure 4.28: Spatial clusters of weighted accessibility vulnerability in Charleston, SC 111

Figure 4.29: Weighted social vulnerability for Tampa – St. Petersburg, FL ...... 115

xvi

Figure 4.30: Spatial clusters of weighted social vulnerability in Tampa – St. Petersburg, FL ...... 115

Figure 4.31: Weighted social vulnerability for Charleston, SC ...... 116

Figure 4.32: Spatial clusters of weighted social vulnerability in Charleston, SC...... 116

xvii

CHAPTER ONE: Introduction

1.1 Background

There is little doubt that urban areas are increasingly becoming sites of environmental

risks for their residents, and despite a millennia-long struggle with natural hazards,

vulnerability, especially in urban areas, remains and in some areas is increasing (El-Sabh

and Murty 1988, IFRC/RCS 1999, Pelling 2003). Part of the problem is that society is

continually changing and generating new and different risks and hazards. Other key

factors include rapid population growth and subsequent environmental degradation, greater poverty, unsustainable development practices, the variability of climate, poor memories, and inadequate planning and assessment procedures (Bankoff 2001). As Flax et al (2002, 163) state, “Communities must identify exposure to hazard impacts to proactively address emergency response, disaster recovery, and hazard mitigation.”

Public policy makers have sought to anticipate the unexpected in order to reduce the risk to human life and safety posed by intermittently occurring natural and human-generated hazard events (Petak and Atkisson 1982). Unfortunately, much planning and mitigation falls apart when handling large or diverse spatial areas.

A major gap in the hazards literature exists in the area of urban vulnerability assessments that take into account factors such as accessibility, the makeup of the built environment, and socioeconomics as separate influences on social vulnerability. Better understanding of these aspects of hazard resilience will lead to more appropriate and

1 successful strategies for disaster mitigation in the long run. The information gathered

through this dissertation will help policy makers, private sector risk managers and even

homeowners decide what actions should be taken to prevent adverse impacts and prepare

for potential disaster in their area.

1.2 Rationale

Urban societies, whether well networked and solidly built or poorly constructed

and socially challenging, create new and complex hazards that will affect their

populations in one way or another. Typical large city problems such as segregation,

neighborhood degradation, increased road traffic, socioeconomic deprivations and

inequities in health, well-being and health-care accessibility, have become central issues

for many emergency managers and government agencies across the United States (Kamp

et al. 2003). If emergency managers can move away from the natural instinct to fight the

hazard itself, and instead accept the notion of resilience (the ability of people and places

to adequately respond to and rebound from disaster), then policy makers and planners

may begin to direct some resources away from repairing loss to enhancing skills and

other attributes known to minimize loss in the first place or to strengthen the capacity to

recover.

Particularly problematic in many metropolitan areas is rapid growth in population,

augmented by significant disparities in the distribution of environmental hazards relative

to the demographic characteristics of population subgroups. The number of people living

in urban centers has risen from 730 million in 1950 to 2.8 billion in 2000 and is expected to reach 4.6 billion in 2020 (El-Masri and Tipple 1997, US Census 2003). Such issues lead to an ever-expanding population that now resides in areas that are becoming less and

2 less safe. Accompanying this risk potential is a certain level of vulnerability to disaster.

This vulnerability, a reasonably new term with numerous meanings, has been an issue in which there has been relatively limited research, and thus very few empirical answers and results. The definition of vulnerability is discussed in detail below, but it should be noted at this juncture that risk and vulnerability are not synonymous. Nevertheless, regardless of how the term is identified, at the beginning of the 21st century, society is facing additional threats and risks, which may mean dealing with new types of hazards and disasters. The disasters of the future may or may not be bigger or worse, but they are likely to be more complex and require more sophistication in response and recovery

(Rubin 1998). In essence, the vulnerability of different sectors of society may also be changing. Unless we understand these issues we will be unable to develop effective prevention and preparedness programs that effectively mitigate impacts or sustain communities while recovering from impacts.

What is needed is better integration of social concepts and physical processes that will enhance the prediction of hazard impacts at the urban level (Cutter, 2003). This dissertation aims to achieve such a goal. Through the use of Geographic Information

Systems (GIS) we have capacity to map demographic and cultural phenomena more quickly and more intelligibly so as to display the results in more easily understood ways than were imaginable even a decade ago (Zerger 2002). Coupling this with the notion that having a better understanding of the phenomena with which we have to deal, whether they are of bio-physical, social, economic or psychological origin, will enable us to develop strategies and actions in the areas of prevention, response and recovery that better achieve their goals and maximize public safety (Buckle 2001).

3 1.3 Purpose and Research Questions

The purpose of this research is to gain a better understanding of the links between vulnerability and resilience within urban areas, and focuses on issues of access, socioeconomics, and the built environment. To this end, the objective of this research is to build and operationalize a framework for assessing and analyzing community vulnerability at the sub-metropolitan level. So far, science has not advanced a comprehensive framework to address these issues in an integrated manner and to enable an evaluation of physical, spatial, and social indicators (Kamp et al. 2003). To do so will require significant research into the topics of accessibility, trends in social vulnerability and new ideas on resilience in the face of disaster. The compilation of these issues will allow for the amalgamation of a theoretical framework of urban hazard vulnerability as well as a feasible, real world, protocol that can be implemented by emergency managers nationwide. If, “the ability of people to live a long and active life is clearly of the utmost importance to human development”, as stated by Bidani and Ravallion (1997, 125), then this research is on the right track. With this in mind, the following research questions will be addressed in order to advance a more developed and comprehensive understanding of urban social vulnerability and resilience to natural hazards:

How can current theories and frameworks of vulnerability assessment be upgraded to include issues of access to needed goods, resources, and lifelines in the urban context? How can the relative importance of individual indicators be uncovered and applied in such a framework so as to take into account differences in space, time and potential importance within and between urban areas? The following issues must be addressed in order to properly answer these research questions:

4 1. What are the socioeconomic factors that influence differences in the ability to

withstand and recover from hazard events within urban areas?

2. What are the characteristics of the built environment that cause differential

vulnerability within the urban realm?

3. What are the variations in lifelines and accessibility that cause cities to exhibit

higher or lower vulnerable to hazard events?

Unfortunately, no all-encompassing framework for metropolitan hazard vulnerability currently exists. Although studies of hazard vulnerability have been undertaken at the coarse county level, a research gap exists at the urban level that takes

into account issues of accessibility and lifelines, built environment composition and

vitality, and socioeconomic factors. Cutter (2001), states, “We have not adequately

developed the integration between natural sciences, engineering sciences, and social

sciences to produce credible vulnerability assessments at the local level (159).” There are

however many diverse concepts, ideas and theoretical constructs revolving around the

different aspects of social vulnerability which can be applied, at least theoretically, to an

all-inclusive understanding of urban vulnerability. These will be evaluated in detail in

the following literature review section in order to determine the usefulness and

interconnectivity of current theories and understandings of vulnerability to hazards. Of

particular interest for this dissertation are the topics of accessibility and built environment

composition and value and their implications on resilience for urban communities.

1.4 Organization of the Dissertation

Chapter 2 provides an in depth review of the literature pertaining to hazard risk,

vulnerability in the urban context, accessibility to needed goods and resources, and

5 livability as it relates to social vulnerability. Chapter 3 details the methods undertaken in the analysis of social vulnerability at the metropolitan level, starting with a description of

the study areas used in the research. This chapter then discusses data needs and

availability and continues with an outline of the research design and methods used in this

dissertation, and an explanation of the survey instrument employed to gather information

of the importance of each factor of social vulnerability. Chapter 4 presents the results of the unweighted and weighted vulnerability assessments for both study areas and provides a glimpse as to which areas are most vulnerable to hazard events. Chapter 5 discusses the implications of these findings in the broader hazard mitigation context. This chapter focuses also on the places within each study area that exhibit the greatest vulnerability as well as the factors of social vulnerability that were the most important according to the experts surveyed.

CHAPTER TWO: Literature Review

2.1 Risk in a Hazards Context

According to Glickman and Gough (1990, 30), "The meaning of risk has always

been fraught with confusion and controversy.” There seems no more appropriate

statement than this to convey the message surrounding the meaning of the term risk. The

word 'risk' has many differing uses in the study of natural hazards and involves a vast

number of disciplines.

Fischhoff et al. (1981) state that some misunderstandings between experts and lay

people seem to arise from these inconsistent definitions of risk. Many influential authors

define risk using terminology that could, and does, cause misinterpretation of its true

meaning. This sentiment is echoed by a Morgan (1993, 33), statement that, "Indeed,

uncertainty is at the heart of the definition of risk.” As such, Table 2.1 appraises the many different understandings of the word “risk” within the varied disciplines that study

its evolution and consequences. For this dissertation, the term risk is meant to be equal to

the probability that an event will occur to a given element (population, community,

structure). What is important to note is that although there are many different understandings and usages for the term “risk”, researchers must realize that “risk” is only a symptom of hazard, and that it is merely part of the problem of misunderstanding results from the many different disciplines active in the field of natural hazard research.

Table 2.1: Selected definitions of risk Hammer (1972): Risk as the sum of the possible alternative numbers of fatalities weighted by their probabilities.

Zenter (1979): Risk is simply the total number of deaths.

Ritter (1981): Risk is the probability of occurrence for an undesirable outcome.

UNDRO (1982): Risk is equal to loss divided by unit time.

Petak and Atkisson (1982): Risk is conceptually defined as being a function of two major factors: first, the probability that an event, or a series of events of various magnitudes, will occur, and second, the consequences of those events.

Crouch and Wilson (1982): Risk is the probability of an event multiplied by the severity of that event.

Crozier (1988): Risk is the expected number of lives lost, persons injured, damage to property and disruption of economic activity due to a particular natural phenomenon, and consequently the product of specific risk and elements at risk.

Ansell and Warton (1992): Risk can be represented in the form of a decision tree in which there is a choice between just two options, one of which will have only one possible outcome whilst the other option has two possible outcomes (gain or loss).

Covello and Merkhoffer (1993): Risk is, at minimum, a two-dimensional concept involving the possibility of an adverse outcome, and uncertainty over the occurrence, timing, or magnitude of that adverse outcome.

Cutter (1996b): Risk is the likelihood or probability that an event will occur.

Tobin and Montz (1997): Risk is the probability of occurrence multiplied by vulnerability.

What is most useful in terms of the definition of risk is that it encapsulates the physical, economic and structural aspects of the impacted society and does not focus on those factors that are found under a strict definition of vulnerability.

2.2 The Concept of Vulnerability

Although understanding risk is important, perhaps more imperative is a proper understanding of the elements of vulnerability. Vulnerability remains one of the most difficult aspects of hazards research to quantify and often relies heavily on indicators from available mass data such as the census. As such, current vulnerability assessments do not take into account all of the possible pressures related to diminished capacity and

8 decreased resilience. This statement is supported by the thoughts of King and

MacGregor (2000, 52),

“As the prediction of hazard impact and the establishment of safer building codes and warning systems have been improved, it has been the vulnerability of the human beings in the community that has emerged as the least known element.”

However, as Ferrier (1999, 2) states, “While many disasters cannot be predicted with any reliability, the social trends that impact on our ability to respond to disasters effectively can.” Employing advances in computer technology coupled with easily accessible demographic and socioeconomic databases have given emergency mangers a more accurate portrayal of baseline social information for use in planning activities (King

2001). Before establishing exactly how vulnerable any certain place is to hazards, we must first understand the basic concept of vulnerability. Many influential writers have seen vulnerability as one of the keys to understanding disaster, because it is correlated with the underprivileged, with past losses and with susceptibility to future losses (Blaikie et al. 1994, Cutter 1996a). Drawing from the numerous definitions of the term, it is clear

that social inequities along the lines of class, race, ethnicity, gender, age and national

origin are key elements in people's vulnerability to environmental calamities. The access

that people have to resources, including employment, health-care, social support,

financial credit, legal rights and education are part of what make them vulnerable to, or

secure from disaster.

What remains is a general lack of consensus on the explanations, factors, and

measures of vulnerability, and spotty, largely uncoordinated, and physical based research

in academic and policy circles which significantly impedes real progress in various policy

arenas (Dow 1992, Kamanou and Morduch 2002, White and Haas 1975). The

9 relationships between the many terms used in vulnerability science are often unclear, and

the same term may have different meanings when used by different authors in different

contexts (Brooks 2003). Since there is little consistency in the definitions of

vulnerability, and no unique, identifiable, objectively optimal set of guidelines on the

subject, one would expect it to be quite difficult to operationalize the concept of using

specific variables or indicators (Cutter 1996a, Rashed and Weeks 2003b). The following

review depicts the wide range of definitions and concepts revolving around vulnerability

as it applies to natural hazards. Additionally, Table 2.2 reviews numerous authors’

understandings of what vulnerability is and how it can be defined or delineated (either

subjectively or objectively).

Kasperson et al., in 1995, proposed that vulnerability is a product of three

dimensions: exposure, resistance (the ability to withstand impacts), and resilience (the

ability to maintain basic structures and to recover from losses). Others, such as Cutter

(1996b), White and Haas (1975), Cannon (1994), and Par (1987) delve deeper to attempt

to explain these three dimensions. Rarely mentioned however, are the underlying causes

of increased social vulnerability to hazards or disaster events. Yet, vulnerability now

forms the cornerstone of international efforts aimed at reversing the downward spiral of

poverty, population increase, development and environmental degradation (Cutter

1996a). This major approach to hazard research, known as the social vulnerability paradigm, has looked at the way in which a variety of socioeconomic factors affects the

vulnerability of populations to hazards and disasters

over time. In the United States, the key characteristics that seem to influence disaster

vulnerability most are socioeconomic status, gender, and race or ethnicity. In the extent

10 Table 2.2: Selected definitions of vulnerability White and Hass (1975, 245): “The nations vulnerability to natural hazards in being increased by the following factors. 1. Shifts in population from country and city to suburban and exurban locations. More and more people live in unprotected flood plains, seismic risk areas and exposed coastal regions. 2. More people live in new and unfamiliar environments where they are totally unaware of potential risks and the possible ways of dealing with them. 3. The increasing size of corporations enlarges their capacity to absorb risks, which may result in plants being located in high-risk areas, or failure to adopt hazard resistant building methods. The location of these firms attracts job seekers and housing development to the same dangerous locations. 4. The rapid enlargement of the proportion of new housing starts accounted for by mobile homes means more families are living in dwellings which are easily damaged by natural hazards”

UNDRO (1982): Vulnerability is the susceptibility to damage or injury.

Susman et al. (1983): Vulnerability is the degree to which different classes in society are differentially at risk, both in terms of the probability of occurrence of an extreme physical event and the degree to which the community absorbs the effects of extreme physical events and helps different classes to recover.

Branch et al. (1984), Taylor (1990): Highlighted community resources, community social organization, and indicators of individual and community well-being as well as lifestyles, attitudes, beliefs and values and social organization as major factors in a communities ability to show resilience in the face of danger.

Commission for Racial Justice (1987): Hazard vulnerability is directly related to race and income.

Crozier (1988): Vulnerability is the degree of loss to a given element at risk or a set of such elements resulting from the occurrence of a natural phenomenon of a given magnitude and expressed on a scale from 0 (no damage) to 1 (total loss).

Aschauer (1988), Barro and Lee (1994), Easterly and Rubelo (1993), Dorrich et al. (2003): Highlighted community resources, community social organization, and indicators of individual and community well-being as well as lifestyles, attitudes, beliefs and values and social organization as major factors in a communities ability to show resilience in the face of danger.

Armour (1990): Views the social conditions that impact the ability of a community or social group to show resilience in the fact of danger in terms of three main categories. These are: people’s way of life – how they live, work, play, and interact with one another on a day-to-day basis, their culture – shared beliefs, customs, and values, and their community – its cohesion, stability, character, services, and facilities.

Panizza (1991): Vulnerability in terms of the degree to which a system, including population, buildings, infrastructures, economic activity, social organization and any expansion and development programs in an area may react adversely to the occurrence of a hazardous event.

Mohai and Bunyan (1992), U.S. EPA (1992), Williams (1996): Vulnerability includes socioeconomic conditions present that might effect exposure and response to hazards.

Grambling and Freudenburg (1992): Distinguished between six systems of the human environment that could aid in the explanation of how social systems would respond to and bounce back from hazard situations. These six systems are: biophysical and health systems, cultural systems, social systems, political/legal systems, economic systems, and psychological systems.

11 Table 2.2 Continued: Selected definitions of vulnerability Cutter (1993, 1994, 1996a, 1996b): Vulnerability is the likelihood that an individual or group will be exposed to and adversely affected by a hazard and that vulnerability is also the measure of a system’s capacity to absorb and recover from the occurrence of a hazardous event.

Cannon (1994): Vulnerability can be boiled down to three basic components: 1. The degree of resilience of the particular livelihood system of an individual or group, and their capacity for resisting the impact of a hazard; 2. The health component, which includes both the robustness of individuals and the operation of various social measures; 3. The degree of preparedness of an individual or group.

Blaikie et al. (1994): Vulnerability refers to social and material conditions derived from characteristics of individuals and groups that make them susceptible to harm and loss from environmental hazards and that constrain their ability to cope with the adversities of disasters.

Kasperson et al. (1995): Vulnerability is a product of three dimensions: exposure, resistance (the ability to withstand impacts), and resilience (the ability to maintain basic structures and to recover from losses)

Juslén (1995): A universal list of social impacts that would suit every case was not possible. Argued that a checklist would be useful, especially in the case of social impact assessment procedures. Defines several social impact categories to be considered when completing a survey of social impacts. These include: ‘standard’ social impacts concerning noise level, pollution, and so on, psychological impacts (such as community cohesion, disruption of social networks), anticipatory fear, impacts of carrying out the assessment, impacts on state and private services, and impacts on mobility (such as transportation, safety, obstacles).

Smith (1996): The concept of vulnerability implies a measure of risk combined with the level of social and economic ability to cope with the resulting event.

Tobin and Montz (1997):Vulnerability is a combination of the physical characteristics of natural hazards, political/economic factors, and social characteristics.

Hewitt (1997): Vulnerability is the attributes of persons, or activities and aspects of a community that can serve to increase damage from given dangers.

Alexander (1997): Vulnerability is defined as a measure of loss and as a measure of exposure to a loss.

Clark et al. (1998): People’s differential incapacity to deal with hazards, based on position of groups and individuals within both the physical and social worlds.

Gould (1998) Kreig (1998): The more economically depressed and desperate an area, the less capable it is of recognizing and rejecting such hazards and that communities with reduced access to economic opportunities are vulnerable to, and more accepting of, the health and environmental costs of hazard placement.

Mustafa (1998): Vulnerability is a state of defenselessness that renders a community powerless to withstand the debilitating effects of events commonly perceived as disaster or natural hazards.

Granger (1998): Elements at risk and their vulnerability are both relatively new areas of study and are focused on the development and understanding of the vulnerability of a wide range of the elements that are at risk within the community e.g. the buildings, infrastructures and people.

12 Table 2.2 Continued: Selected definitions of vulnerability Morrow (1999) and Buckle (2001) : Different groups within the community may be affected differentially; they may have different needs which become apparent at different times and these, in turn, require a flexible response, and different groups often possess vastly different capacities to manage the stress and difficulties of dealing with a disaster event.

Vanclay (1999): Adds the current political situation, the physical environment, and personal rights, freedoms and perceptions to Armour’s (1990) list of characteristics.

King and MacGregor (2000): Vulnerability includes a certain degree of resilience and the ability to recover from a disaster.

Kanbur and Squire (2001): Takes into account both exposure to serious risks and defenselessness against deprivation.

Heinz Center (2002): Vulnerability depends on the sensitivity of the natural and social system, adaptability, and the degree of exposure to disasters and is seen as the degree to which a system will respond to a given hazard event and the degree to which adjustments can moderate or offset potential damage.

Yohe and Tol (2002): Basic vulnerability is an external stress (or collection of stresses) that is a function of exposure, sensitivity, and adaptive capacity.

Lindsay (2003): Vulnerability extends into the realm of social and cultural factors that shape and form the circumstances of people’s lives - influencing their likelihood of exposure to health threats.

Rashed and Weeks (2003b): Urban vulnerability is a function of human behavior. The degree to which demographic and socioeconomic systems and physical assets are either susceptible or resilient to the impact of natural hazards.

Vale and Campanella (2005): Urban resilience, the opposite of vulnerability, is proposed simply as the physical capacity to bounce back from a significant obstacle. Propose that throughout history, nearly ever city has been resilient in the long run when looking at the disaster recovery model. Variations and impediments in recovery time are the important concepts in vulnerability. of disasters, special needs populations (elderly, disabled, lactating women, children under

5) are identified as among the most vulnerable (Parr, 1987). Differences in these factors result in a complicated system of stratification of wealth, power and status. This stratification results in differential decision making and perception of hazards, diverse types of mitigation techniques, an uneven distribution of exposure and vulnerability to hazards, disaster losses and other impacts and access to aid, recovery and reconstruction.

(Cannon 1994). White and Haas, 1975, attempt to explain vulnerability characteristics as follows:

13 “The nation’s vulnerability to natural hazards in being increased by the following factors. 1. Shifts in population from country and city to suburban and exurban locations. More and more people live in unprotected flood plains, seismic risk areas and exposed coastal regions. 2. More people live in new and unfamiliar environments where they are totally unaware of potential risks and the possible ways of dealing with them. 3. The increasing size of corporations enlarges their capacity to absorb risks, which may result in plants being located in high risk areas, or failure to adopt hazard resistant building methods. The location of these firms attracts job-seekers and housing development to the same dangerous locations. 4. The rapid enlargement of the proportion of new housing starts accounted for by mobile homes means more families are living in dwellings which are easily damaged by natural hazards” (White and Haas 1975, 245)

Note that in many instances, gender, race and ethnicity are not the key factors in increased exposure or vulnerability but rather are indicators of lower economic status and a relative lack of power. Hazard risk is primarily based on location, whereas, vulnerability to that same hazard is based, at least in part, on socioeconomic characteristics.

For this dissertation, the term vulnerability will be understood as a duel process involving first the factors of the built and natural environment that either increase or decrease the effects of hazards felt by an element at risk, and secondly as socioeconomic factors and processes that either hinder or enable said element’s ability to respond and recover from hazard events.

2.3 The Urban Dimension

A century ago, 25% of the global population resided in urban areas, now roughly

75% live in a metro area consisting of one or more central cities and a ring of suburbs

(Fitzpatrick and Lagory 2000, 7). “The speed at which urban building now takes place, contributes to hazard because it often leads to poor quality construction and haphazard development patters that take little account of risks (Uitto 1998, 9).” With so many

14 people occupying relatively small physical spaces containing limited resources and ever increasing demand on those resources, the metropolitan areas of the United States are not immune to the forces of nature as well as those posed by technological hazards. As

American cities become more geographically dispersed and increasingly complex with respect to infrastructure and the built environment, more and new kinds of urban vulnerabilities are brought about by the increasing dependence of communities on technology and more complex interactions within the urban systems (Rashed and Weeks

2003b, Pelling 2003). Adding to the inevitability of increased population in these urban realms are a host of other problems inherent to metropolitan areas. These include, but are not limited to, problems such as segregation, neighborhood degradation, increased road traffic, socioeconomic deprivations and inequities in health, well-being and health-care accessibility (Chardon 1999, Kamp et al. 2003, Pacione 2003).

Today, the geography of social vulnerability to hazards in the United States stretches from coast to coast, from city to city, and from neighborhood to neighborhood within cities. Although one cannot say that there exists a non-vulnerable urban community in the United States, there are clearly certain geographic areas and people within those areas that are more vulnerable than others (Brown et al. 1997, Rashed and

Weeks 2003a, Vale and Campanella 2005). The impact of urban destruction is not necessarily proportional to the scale of the disaster event. Rather, disaster impacts are largely a function of the meaning a disaster holds for those persons both in and near the impacted area – even those who live some distance away from the epicenter of the physical destruction (Vale and Campanella 2005). Additionally, Mitchell and Thomas

(2001, 77), state that,

15 “The juxtaposition of hazard events and vulnerable populations varies spatially and, consequently, so do losses. A direct relationship exists between the level of development and the type of losses that predominately occur.”

Furthermore, municipalities and civil society in general appear to have complementary

strengths and weaknesses when it comes to hazard vulnerability. Metropolitan areas have

the technical expertise and finances required to provide social protection from natural

hazards for socially vulnerable groups of people and to assist in increasing the capacity of such people for self-protection from disasters. However, what municipalities often lack is the detailed knowledge of these vulnerable groups and do not enjoy their trust (Wisner

2002). These statements embody the focus of this research. We know that resources are not equally distributed across the urban landscape and that critical resources are more accessible to some than to others, but the extent to which this is true for different metropolitan areas of the United States has yet to be explained. Ricketts and Mincy

(1990) state that inner cities in America have historically been characterized by high numbers of working age males without a job, households headed by women with children, households receiving welfare, and increased numbers of dropouts among the school aged population. Rashed and Weeks (2003b), postulate that urban vulnerability is conceptualized as a measure of the degree of coping abilities of human and physical systems of the urban place that are consistent with the principles of local sustainability.

They believe that a geographically-centered approach that focuses on the vulnerability of urban place and combines elements from the engineering and social science paradigms can help fill the gap between them and will pave the way for a better understanding of how vulnerability patterns evolve in urban areas.

16 Knowing how to formulate a better understanding of vulnerability to hazards in

the urban area is just one step in a long journey towards decreased social vulnerability

and increased resilience. What is needed is a broader understanding and application of such concepts into an applicable set of social vulnerability guidelines for urban areas.

We know that certain socio-demographic factors lead to increased vulnerability in the face of disaster. This fact, coupled with built environment and accessibility indicators such as significant lack of housing, infrastructure, job opportunities, and with scarce resources and unrealistic policies, is threatening health, economic productivity, environmental quality and social stability (El-Masri and Tipple 1997).

People living nearest the city center, in areas with large concentrations of poor minorities, are exposed to serious physical and mental health risks (Andrulis 1997,

Greenberg 1991). According to Fitzpatrick and Lagory (2000, 8), “This so called urban health penalty – the confluence of circumstances such as poor nutrition, poverty and unemployment with deteriorating housing, violence, and loss of services – has created a deepening health crisis on the inner city.” Known as Ghetto Poverty Areas, these poor communities where the household incomes of at least 40 percent of individuals are below the poverty line are far less resilient to disaster events than other more affluent neighborhoods.

The downward spiral of environmental and social degradation found within the confines of urban areas of the United States aggravates infectious disease, and diminishes the yield of nature, contributing to further poverty through its effects on people’s health and lack of resources (El-Masri and Tipple 1997). These structural effects on health and well being are not evenly distributed, but rather, are geographically concentrated such

17 that where one lives – especially where one grows up – exerts a profound effect on one’s life changes, general health, and well being (LeClere et al. 1997 1998, Logan and

Molotch 1987, Massey and Denton 1993, National Research Council 1993, Smith 1988).

Any local area will be composed of residents who vary from those most able to cope due to age, wealth, resources, both physical and intellectual, and with adequate access to information to those most vulnerable, with limited access to these resources, and most at risk if a disaster or crisis occurs (Marsh 2001). This concept of diminished capacity is further emphasized by Cicerchia (1999) who combines ‘city effects’ (positive effects of a concentration of people) with the so called ‘overload indicators’ (negative effects) to determined the extent to which urban areas are overdrawing on the goods, services and emergency capacities sustaining them.

2.4 Health and Livability

The decade of the 1960’s and the early 1970’s saw a spike in interest by the federal government in the identification of social well being and progress indicators (US

Department of Health, Education, and Welfare 1969, OMB 1973). Livability is a concept that has been researched and conceptualized by many academics in an attempt to understand all the factors that might indicate how and why certain places and environments are more appealing or satisfactory for human occupation than others. It encompasses broad human needs ranging from food and basic security to beauty, cultural expressions, and a sense of belonging to a community or a place.

Pacione (1990) states that livable is simply equal to “humane” and that livability is a quality that is not an attribute inherent in the environment but is behavior-related function of the interaction between environmental characteristics and personal

18 characteristics. Veenhoven (1996) states that livability is equivalent to habitability,

which in turn is equal to quality of life in the area or the degree to which its provisions

and requirements fit with the needs and capacities of its citizens. Newman (1999)

perceives livability as the human requirement for social amenity, including health and

well-being both individual and at the community level. Smith et al. (1997), identify

livability as the basic qualities that must exist for a community to be successful, consisting of survival, personal health and development, environmental health, comfort, and safety and security. Survival is the most basic indicator of livability involving adequacy of oxygen, food, water, shelter and clothing, and the ability to move around a territory to obtain the basic necessities of life. The National Research Council refers to livability as:

“The extent to which the attributes of a particular place can, as they interact with one another and with activities in other places, satisfy residents by meeting their economic, social, and cultural needs, promoting their health and well-being, and protecting natural resources and ecosystem functions (National Research Council 2002, 24).”

Additionally, the NRC lists those factors or characteristics of an urban area that might lead to a greater quality of life. These are: transportation (infrastructure, commuting, public transit, and vehicles, pedestrian friendly streets, ratio of bike paths to streets, % of street miles designated as bike route miles); ecosystem integrity (biodiversity, fish, land us, soil, surface water, wetlands); community involvement (volunteerism and connectedness, # of community gardens, distance between residences of extended families); and equity (diversity, employment types, income, children, finance). This list of variables and characteristics of urban areas does not attempt to quantify the importance of any particular variable and thus does little more than formulate a base level framework

19 for the conceptualization of livability. Others view livability as an ensemble concept

whose factors include or relate to a number of other complex characteristics or states,

including sustainability, quality of both life and place, and healthy communities (Norris

and Pittman 2000, Blassingame 1998).

Weinhold (1997) and Newman (1999), for example, simply list characteristics of

the environment, social landscape and economic structure that might make an area less

viable as a place to live or might lead to a more livable community. In the case of

Weinhold (1997), criteria for determining urban environmental health risk are: air quality, number of military facilities, drinking water quality, population density, number

of toxic releases, number of superfund sites, vehicle travel, toxic transfers, aircraft

operations, heating and cooling demand, number of manufacturers, number of CERCLIS

(Comprehensive Environmental Response, Compensation, and Liability Information

System) sites, and total agricultural acreage. Although this list contains many items that

one would think about when describing urban quality, Weinhold does not make an

attempt to place weights on these characteristics in order to construct a livability

framework. Along these same lines, Newman (1999) sees livability as a concept about

the human requirement for social amenity, health and well-being and includes both

individual and community well-being. In this instance, livability is about the human

environment though it can never be separated from the natural environment. He views

livability as a combination of the following characteristics: health, employment, income, education, housing, leisure activities, accessibility, urban design quality, and community.

Shafer et al. (2000) adds value to livability models through reference to the interaction between the human use and environmental domains, which give a picture of

20 how the concepts of livability, quality of life and sustainability relate to each other. In

this approach, livability is considered to be the result of the interaction between the

physical and social domain, sustainability as the resultant of the interaction between the

physical and economic domain. The interaction among these two domains is alternately

defined as sustainability and quality of life.

In conclusion, as stated by Landis and Sawicki (1998), neither standard livability

indicator sets, nor the more elaborate places-rated approaches that include many variables

are adequate to capture the many critical dimensions of urban livability and in essence,

urban vulnerability and resilience. Additionally, Szalai (1980) confirms the vastness of

the above literature review by referring to livability as a repository in which almost anything fits.

2.5 Quality of Life

Quality of life emerged as a concept within the social indicators movement of the

1960’s and questioned basic assumptions about the relationship between economic and

social well-being and the complex nature of individual and social material and immaterial

well-being (National Research Council 2002). A review of the literature found many

authors and academics with varying definitions of life quality and well being from those

who look solely at income as a measure of quality of life, to those who focus more on

qualitative characteristics of an environment and less on income as an indicator of well

being. According to Cummins (2000), there is a general consensus in the literature that

objective as well as subjective indicators are necessary in the study of the person- environment relationship. Several authors including Sen (1985, 1987), Khan (1991) and

Dasgupta (1990, 1993) have argued that the standard of living is a multidimensional

21 concept and that income alone cannot be an adequate measure of a person’s well being.

Conversely, the Hobijn and Frances (2001), view that a basic assumption of living

standard analysis is that the per capita income levels are a good proxy for living standards

and that convergence in income would indicate convergence in living standards. Table

2.3, illustrates the many concepts and definitions of the quality of life. Although some

main themes run through many of the definitions evaluated in this literature review, some major differences in quality of life indicators and concepts were found.

Mitchell et al. (2001) and Zolnik (2004), point out that there is no agreement yet on quality of life, neither in terminology nor in construction of methods or the criteria that comprise quality of life. In these approaches, quality of life consists of health, physical environment, natural resources, personal development and security. What is clear from this research is that although there are many different ideas on the topic of quality of life and well being, some key concepts flow through most of them. These include such things as personal health, mobility and access to needed resources, and connections within and around one’s living area.

The colorful diversity of concepts that have been encountered in the literature review demonstrates that there are many ways to conceptualize themes related to livability, environmental quality, quality of life, sustainability and ‘kin’ concepts (Kamp et al. 2003). It is clear that it is not possible to formulate one unequivocal conceptual framework of livability. This is mostly due to the fact that a broad range of disciplines has approached environmental quality and quality of life, each using their own languages, very often with subtle and sometimes massive differences. (Kamp et al. 2003). Whatever the agreed upon concept of livability, one big problem still remains. Measuring living

22 Table 2.3: Selected definitions of quality of life Maslow (1943) Lang (1994): Use a ‘hierarchy of needs’ as an organizing principle for human needs. The needs are physiological needs, safety and security needs, affiliation needs, esteem needs, and self-actualization needs.

Szalai (1980): Life quality is the degree of excellence or satisfactory character of life.

Appleyard (1981): Uses urban design principles in order to gain a better understanding of livability in urban areas. These include the important aspects of streets; safe from crime, clean unlettered appearance; convenient; free from traffic congestion; good for children; lined with affordable housing.

Lynch (1981): Distinguishes five categories of theories of ‘good city form’. These categories are: vitality (a healthy environment), sense (sense of place or identity), fit (a setting’s adaptability), access (to people, activities, resources, places, and information), and control (responsible control of the environment).

Cutter (1985): Centers around an individual’s wellbeing by focusing on general happiness and satisfaction with life and environment based on needs and desires, aspirations, lifestyle preferences, and other tangible and intangible factors.

Lennard (1987): Summarized principles involved in designing urban spaces that promote social life, and a sense of well-being, derived from traditional urban space design theory. A list of 10 basic design principles was comprised for the understanding of livability within the urban realm.

Smith (1994): Basic needs include physical security, economic security, and adequate protective housing,

Raphael et al. (1996), Veenhoven (1996), Diner and Suh (1997): Quality of life is the degree to which a person enjoys and is satisfied by the important possibilities of his/her life.

Musschenga (1997): Quality of life in terms of a concept called ‘the good life’ (A combination of: enjoyment, or positive mental states; satisfaction, or the evaluation of success in realizing a life plan or personal conception of the good life and excellence; and the virtuousness or value of a person’s activities).

Chueng (1997): Uses the term ‘the good life’ as a reference to the quality of life or well being of an individual. This concept of the idea of life quality, concerned with factors and characteristics that often cannot be objectively rationalized and thus are not comparable across time and space.

Smith et al. (1997): Life quality is a reference to distinguishing properties that promote a degree of excellence or high rank for an individual. This understanding of the concept uses six main categories of the quality of life principles. These are: livability, character, connection, mobility, personal freedom, and diversity.

Marans and Couper (2000): Life quality based on satisfaction research as well as concrete factors and characteristics of the environment is the residential satisfaction model, in which a distinction is made between different scale levels: house, neighborhood, city and community.

Evans (2001), Moser and Corroyer (2001), Kuo (2001): Quality of life in terms of ‘city effects’. Among these urban issues are crowding and behavior, housing quality and functioning of children, the amount of green in the neighborhood and coping behavior.

23 standards is difficult because it requires the combined interpretation of many possible

indicators. Hobijn and Frances (2001), discuss three issues that arise in particular when

dealing with livability assessment. These are: 1) the decision on which indicators to use

when measuring living standards, 2) the decision on whether the levels of the indicators

give a good representation of living standards or whether we should transform them in

order to construct a more representative data set, and 3) the choice of whether each

indicator should be analyzed separately or whether one should combine them and

construct one overall index of livability.

2.6 Accessibility and Lifelines

Access to goods, services, and emergency personal are key factors in the ability of

a person, household, or community to withstand the devastating effects of a disaster

situation. One of the basic objectives of healthcare planning in any part of the world is to

have an equal access to health care for all, irrespective of ability to pay (Murad 2001).

Health researchers, studying the varying vulnerability of illness within a population, have

found a set of social, physical and economic factors that are now referred to as the determinants of health. “These health factors are the same as those commonly associated with disaster vulnerability (Lindsay 2003, 291).” As such, access issues must be intertwined within any framework or concept of health, livability, quality of life or well- being.

If access is a major contributor to the impacts felt by persons during hazard events, one should consider those persons without proper access to be the most important people to target when delineating areas thought to need extra assistance both pre and post disaster. While efforts to educate the general public and local community disaster

24 responders in disaster management methods should not be slackened, a much larger

commitment to lifeline preparedness and accessibility in disaster situations is required

(Platt 1995). Studies of individual accessibility suggest that because of personal

constraints (i.e. lack of personal transportation) and the structure of the city, some good and service locations within urban areas are easier to reach for certain segments of the population (Kwan 1998). Additionally, while many problems in urban areas can be attributed to industrial decline and unemployment, others stem from the deteriorating physical environments and affect the standard of provision of housing, education,

transportation, health and other social services (Pacione 2003, 330).” Along these lines,

tables 2.4 and 2.5 review accessibility and lifeline research in order to identify those characteristics of areas, or factors that lead to inadequate accessibility to goods and

services not only during, but also pre and post disaster.

Major themes surrounding accessibility include issues of mobility, age constraints, minority concerns, and potential lifeline interruptions or disconnections.

Each of these concepts, when studied individually, may not imply a problem in access to goods and services for a particular person or community. However, when regarded together as indicators of accessibility, these fundamental ideas may point toward a more all inclusive understanding of the factors that lead to decreased accessibility and thus increased vulnerability to hazard events.

Access and lifelines play key roles in the determination of one’s vulnerability to hazard. The importance of social networks as a support mechanism, especially for low- income individuals and households, has long been noted in urban studies (Lewis 1966,

25 Table 2.4: Selected definitions and themes related to accessibility Lynch (1960): The idea of linkages within a community as an indication of access. Identifies five elements known to facilitate linkages of a community to a city as: paths (channels where people move), edges (linear elements such as rail lines), districts (sections of city with identifiable characteristics), nodes (entry points), and landmarks.

Tobler (1979): Everything is related to everything else, but closer things are more closely related. If no other factors were at work in the accessibility model, simple distance functions would be sufficient to address the issue of access to goods and services.

Cannon (1994): The ability of people to protect themselves is dependent upon their livelihood strength, and on their relationship to the state or other social and political structures. Lack of access to needed goods and services will impact those persons who most likely necessitate the most interaction with those goods and services before during and after a hazard event.

Handy (1994), Smith et al. (1997): All people, especially the elderly, children, and the physically challenged, need to have safe access to all places in the community, to other people, a variety of activities, resources, places and information. Use of a broader conceptualization of accessibility that treats physical mobility as only one component of a wider context for travel that includes the opportunities at travel destinations and the general costs of reaching those destinations.

Scott (2000): Mobility based measures simply quantify mobility or the physical ease of movement within a given environment. This ability to travel (both physically and economically) to and from locations within a community, city, state, or nation, is another factor that must be taken into account when conceptualizing accessibility.

Fitzpatrick and Lagory (2000): “The places that we live, work, and play in are fundamental resources, like time or money. The access we have to these resources dramatically affects our well-being (p.4).” View protection from risk in terms of availability of health professionals, community resources and supportive social networks. Consider more risky areas within the urban realm to have significantly poorer access to needed good and services.

Frazier et al (2003): Claims that accessibility can be evaluated using a measure of distance, time, or cost. They see proximity as a surrogate for accessibility due to the difficulty of quantifying the concept.

Rashed and Weeks (2003a): The distribution of wealth in society is perhaps the most obvious variable, among several possible others (such as political rights, governmental compensations), that holds a direct relationship with the access to resources required to recover from the impact of a damaging event such as an earthquake hazard.

26 Table 2.5: Selected definitions and themes related to lifelines Lewis (1966), Lomnitz (1977), Fukuyaman (2001): The importance of social networks as a support mechanism, especially for low-income individuals and households. Understanding the factors that either positively or negatively effect the accessibility possessed by a community and its lifelines will enable researchers to uncover communities who exhibit a lower ability to cope due to lack of accessible goods, services, emergency responders.

Berkman and Beslow (1983), Umberson (1987): Persons with substantial social networks have better health and lower morality rates.

Brody (1985): Social ties can be significant in times of crisis.

Garmezy (1993), Hacker et al. (1994): Point towards three distinct forms of family protection that include family, school, and community. These lifelines form a protective barrier around individuals that have the ability to shield them from many of the negative impact not only from disasters, but also from the socially constructed dangers and hazards found within urban communities.

Enarson (2000): Community and household power struggles place residents at risk, for example through racial bias in insurance payments or racial segregation fostered by real estate and lending institutions (e.g. ‘redlining’ neighborhoods), economic barriers to safe housing, poverty rates among the elderly, race- and gender based job segregation and wage differentials, and exposure to personal violence.

Fitzpatrick and Lagory (2000): “While technological developments in transportation, communication, and information processing give humans new capacities to break down spatial barriers, a socially structured spatial environment produces new barriers (5).”

Buckle (2001): Community needs are assessed as health, education, youth services, housing, recreation, and cultural development. Although some of these factors are simply access related, at least half of them are dependent upon access and lifelines, or social and political connections that aid in the preparedness, response and recovery from a disaster effect.

Lomnitz 1977, Fukuyama 2001). What is important to remember is that understanding the factors that either positively or negatively affect the accessibility possessed by a community and its lifelines will enable researchers to uncover communities who exhibit a lower ability to cope due to lack of accessible goods, services, emergency responders.

There is a need for more integrated treatment of hazards and resource management issues in the research and policy arena (Mustafa 2002). This will provide planners and emergency managers with much needed spatial data on which parts of their communities or cities are in the most need of attention and exactly what kind of considerations they need in order to be more resilient during a hazard event.

27 2.7 The Concept of Vulnerability Assessment

It is clear to most involved in the assessment of hazard vulnerability that natural and related technological disasters are not problems that can be solved in isolation, but symptoms of more basic problems culturally created and based on the ways in which we view the natural world. Uitto (1998), states that vulnerability assessments, although widely employed in many cities across the globe, rely almost exclusively on data about the physical environment and neglect other vitally important aspects of social vulnerability. “It is thus concluded that it is time for a change in the prevailing thinking about how we cope with these hazards” (Mileti 1997, p1).

Cutter (1994) states that the goal of vulnerability assessment is to find practical ways of defining who is most vulnerable to global change and why. She asserts, for instance, that the most vulnerable people may not be in the most vulnerable places. For example, poor people can live in productive biophysical environments and be vulnerable, and wealthy people can live in fragile physical environments and live relatively well.

Alexander (1997) has proposed an approach to vulnerability assessment based on simple conceptual equations and asserts that vulnerability has a clearly identifiable locational dimension where people marginalized by class, politics or ethnicity are driven to the hazardous peripheries of place. This illustrates the fact that overall vulnerability can be broken down into several component parts based on different aspects of the problem. Bolin and Stanford (1998) follow this line of thinking by asserting that, vulnerability analysis shows that the problems people experience in disasters are frequently traceable to already-existing constraints on their access to resources and livelihoods (Bolin and Stanford 1998). Yet complications still remain in the explanation

28 of vulnerability assessment and changes in vulnerability. Tobin and Montz (1997)

believe that measuring the extent to which vulnerability has been altered is a difficult

undertaking, but certainly one that should be incorporated into any development plan as

well as in hazard mitigation planning and disaster relief programs. This statement speaks

volumes when it comes to vulnerability assessment. The assessment of vulnerability is

problematic in that much research is needed in order for it to become a usable tool for

mitigation strategies, public policy and community awareness.

In Cooperating with Nature, Burby (1998), states that vulnerability assessment

combines the information from hazard identification with an inventory of the existing

property and population exposed to a hazard. It provides information on whom and what

are vulnerable to a natural hazard within the geographic areas defined by hazard

identification. Vulnerability assessment can also estimate damage and casualties that will

result from various intensities of the hazard. Assessments attempt to predict how different types of property and population groups will be affected by a hazard.

Vulnerability functions are empirically derived relationships that describe the response of

populations, structures, or facilities to a range of hazard intensities. Vulnerability assessments characterize the exposed populations and property and the extent of injury and damage that may result from a natural hazard event of a given intensity in a given area. This concept of assessing vulnerability seems the most comprehensive and effective thus far. It uses information from hazard magnitude along with data of those affected by the event in order to attempt a picture of vulnerable populations.

Nevertheless, this framework has not been applied to vulnerability assessments to date.

29 Most of the vulnerability assessments to date have focused on larger geographic areas (national, state, or county). Some distance behind in development and practice are those instances where vulnerability assessments focus on sub-regional, sub-county, or the

metropolitan level (Bender 2002). There have, however, been some advances in the area

of vulnerability assessment at the community level. For example, Longhurst (1995, 269)

points out that, "As part of its International Decade for Natural Disaster Reduction

(IDNDR) activities, the Implementation and Applications Working Group of the UK

National Coordination Committee, organized a meeting on a topic that is exercising many

professionals at the moment: Community Vulnerability Assessment (CVA), The primary

objective of the workshop was to promote the development of appropriate response tools

and techniques to measure and analyze human vulnerability. Secondary objectives were

to promote inter- and multidisciplinary networking, and between diverse hazard

communities, highlight the importance of CVA as a component of risk assessment and

identify research gaps and indicate how they might be filled. Additionally, the National

Oceanic and Atmospheric Administration (NOAA) Coastal Services Center and the

Organization for American States (OAS) have sponsored four workshops specifically

aimed at understanding and sharing new and emerging techniques for assessing

vulnerability. These meetings, held annually since 2000 have been an outlet for

groundbreaking research in the areas of vulnerability analysis and have helped shape the

current direction of vulnerability assessments.

2.8 The Application of Vulnerability Assessment

Numerous studies were reviewed in order to understand the current state of social

vulnerability analysis as it applies to natural hazards . Four studies completed by: The

30 State of Wyoming (1977); The State of North Dakota (1977); The State of Washington

(1996) and Kitsap County, Washington (1998) have been identified, but they all lack a

proper methodology for assessment of multiple hazard vulnerability as noted below. In

addition, numerous studies were found to be of particular relevance to the issue of

vulnerability to natural hazards. The first, Social Vulnerability in Indianapolis (Maloney

1973) almost seems out of place in relation to the literature of the time. This study

coupled with the more recent, Assessing the Vulnerability of Coastal Communities to

Extreme Storms: The Case of Revere, MA., USA (Clark et al. 1998), A GIS-Based

Hazards Assessment for Georgetown County, South Carolina (Cutter et al. 2000), and

Social Vulnerability to Environmental Hazards (Cutter et al. 2003), provide a good base for defining and analyzing vulnerability on a community level.

The Wyoming Disaster Preparedness Program's Hazard Vulnerability Analysis, undertaken in 1977, represents some key factors that contribute to the analysis of the hazard potential for destruction, threat to life and economic loss from significant disaster agents (Wyoming Hazard Vulnerability 1977). It then goes on to declare that the ultimate outcome of the study is the development of a comprehensive disaster preparedness program for the State of Wyoming aimed at: (1) Reducing the incidence of and the vulnerability of the people to disaster events; (2) Establishing an effective disaster response capability; and (3) Expediting the rapid recovery from disasters through prompt and efficient use of assistance programs. The document conveys that its immediate objectives are to identify the natural and anthropocentric hazards to which the

State is subject, prioritizing those hazards which have the potential of expanding beyond the capability of local effort and recommending measures which would both decrease the

31 probability of a disaster occurrence and mitigate the effects, should a disaster strike.

The use of the term vulnerability in the title of this assessment seems to be very

erroneous in light of the fact that a mere three-quarters of a page was devoted to the

evaluation of the people who live in Wyoming. This document seems to focus more on

the potential and actual impact of disaster events than it does vulnerability. A table on

the final page of this document denotes this fact with its depiction of 'Disaster Effects by

County', in which it rates each county as high, moderate or low risk. This document

neither shows us vulnerabilities, nor does it explain the processes involved with

determining vulnerability. The time in which the document was completed might be one

of the major reasons behind the lack of understanding in the vulnerability arena, but more

recent studies have shown much the same approach to vulnerability analysis.

North Dakota's Hazard Vulnerability Analysis, completed in 1977, furthers the argument that vulnerability assessments have been dealt with on the same level as risk assessments. This document identifies the major hazard phenomena which pose a significant threat to life and property within North Dakota (Fuher et al. 1977). It looks at what has happened in North Dakota and suggests that from studies of actual and probable occurrences we can develop plans for the most recurrent and probable kinds of hazard phenomena (Fuher et al. 1977). This study shows another example of lack of knowledge about terms. It states, while discussing vulnerability to flood events, that the highest rate of vulnerability exists in areas which have continued to build in the identified flood plain areas (Fuher et al. 1977). This report does not mention socioeconomic factors, age, sex, or education as factors that influence vulnerability, but focuses solely on the locational

aspect of the people. This vulnerability analysis uses many of the same techniques as the

32 other 'early' vulnerability studies, such as tables showing disaster effects in terms of

severe to minimal damage and hazard potentials for all the counties in the state. It does

not touch on the true vulnerability of the people; rather it discusses the issue on a macro-

scale. (Fuher et al. 1977) concludes that the analysis took into consideration the past

history of disasters in the state and the potential for a particular type of emergency

situation. He goes on to state that until disaster prediction develops into a more exact

science, conclusions from such analyses as these must be general rather than specific.

Cover to cover, this report does not show us vulnerability; rather it represents an attempt

to report on disaster probabilities with the acknowledgment that prediction has yet to

become a precise science.

A more acceptable example of vulnerability assessment was undertaken in 1993

by Chang et al. (1993). This study stated that its focus was to obtain information and to assess seismic vulnerability of highly occupied or heavily used essential facilities, including schools, hospitals, fire stations, and bridges, in twenty counties in western

Tennessee that may be strongly affected by earthquakes in New Madrid seismic zones.

The study used a cost-effective preliminary seismic vulnerability evaluation system developed for Memphis and Shelby counties. The document depicted what criteria were

to be used in the study, how the study was conducted, and included study results that will

be important for future facility maintenance and improvement, earthquake loss estimates,

seismic hazard/risk reduction, and earthquake preparedness/rescue plans in the region.

This assessment appears at first glance to be along much the same lines as the other

vulnerability studies reviewed, but upon further examination it is evident that this report

has done what it set out to do. The main difference between this and the previous reports

33 centers on the removal of the human element from the assessment. Whereas the other studies gave a slight inclusion to the idea of vulnerability, this reports completely glosses over the idea. This document was not intended to identify social vulnerability to hazards, where the other studies attempt to characterize social vulnerability without taking all of the elements of vulnerability into account. This study provides a successful example of a screening procedure for prioritizing of essential facilities in an earthquake prone region in the central United States.

Petak, Atkisson and Gleye (1978) calculated the vulnerability of US populations to nine natural hazards: earthquakes, landslides, expansive soil, riverine flooding, storm surge, tsunami, tornado, hurricane, and severe wind. They derived expected annual losses by multiplying the probabilities of various intensities of hazards by the value of the physical structures, and adding results for all locals for which calculations were completed. They also computed estimated losses of life, housing units lost, residential dislocation, and unemployment resulting directly from natural hazards. The authors found that the vulnerability to natural hazards of the American population is seriously increasing. Indeed, vulnerability is increasing, but by what order of magnitude are people becoming more vulnerable? This book gives us the idea that Americans are more vulnerable now than ever, but it does not show us statistics that are relevant to individuals. These authors seem to have given us more of a risk analysis than a vulnerability assessment. Where a risk analysis aims at understanding the potential chance of impact of a hazard event, such as the potential for a direct hit from a hurricane, vulnerability assessment attempts to understand the underlying socio-demographic

34 makeup of a place that might lead to a more adverse effect before, during, and after a

disaster occurs.

The State of Washington Hazard Vulnerability Analysis (1996) shows much of

the same inequities as the above vulnerability analyses. Its purpose, as stated in the

introduction, is to provide information on potential large-scale hazards that exist or those that could impact Washington State. The report is intended to serve as a basis for state- level emergency management programs and to assist local jurisdictions in the development of similar documents focused on local hazards. This document is to be the foundation of effective mitigation, preparedness, response, and recovery activities. Once again, the term risk potential, the end result of a risk analysis, becomes a synonym for vulnerability when in fact it refers to quite different aspect of the disaster realm. This analysis is typical of most that have been researched in that it focuses on the physical hazard rather than on the actual vulnerability of persons within Washington State.

The more recent Kitsap County Hazard Vulnerability Study (Kitsap County

Department of Emergency Management 1998) states that vulnerability analysis is an element of hazard mitigation allowing emergency managers to set goals according to the public need for protection. It suggests that it enhances public and private agency

understanding and awareness, influencing the adoption of hazard mitigation programs.

Finally, this analysis supposedly reveals findings that serve as a basis for preparedness as

well as influencing effective response and recovery programs. The above statements

convey the message that this analysis of the vulnerability of Kitsap County is thorough and all encompassing, yet the opposite is actually true. This document is yet another attempt that falls into the realm of risk identification (physical analysis of potential

35 hazards), rather than vulnerability analysis. It points out the obvious elements of risk that

are encountered within the county, but does not touch on the elements of vulnerability as discussed earlier.

Numerous studies, three published relatively recently and the other dating back to

1973 seem the most relevant when dealing with social vulnerability. These studies,

(Maloney 1973), (Clark et al. 1998), (Cutter et al. 2000), and (Cutter et al. 2003) use a

definition of vulnerability assessment that includes statistical analysis of demographic characteristics to determine the areas that contain the most vulnerable populations.

Maloney appears to be a pioneer in the area of vulnerability as his research took place more than thirty years ago. Although Maloney does not include vulnerability to specific hazards, he does include valuable insight into the characteristics that make persons more or less vulnerable on a social level. Both of Cutter’s studies as well as the Clark et al. study do not specifically reference Maloney’s earlier attempt to quantify vulnerability,

yet these appear to be rooted in the same theoretical framework as their earlier

counterpart. All of these studies provide invaluable information on the areas of a community or society that currently contribute the most to a proper definition of vulnerability.

2.9 Summary

In summary, the literature was searched to determine first, the most effective meanings of the words "risk" and "vulnerability". The frameworks and concepts of social vulnerability to hazards put forth by the above research provide a much needed platform on which to advance in the field of hazard assessment. Finally, the literature review

attempted to determine the theory behind a sound methodology of vulnerability

36 assessment. Equations and theories on this topic assist research in the area of

vulnerability assessment by suggesting key elements involved in determining overall

community vulnerability to natural hazards.

Research has shown that a large gap in the literature exists in the areas social vulnerability at the sub county level, specifically for urban areas of the United States.

Moreover, the terms risk and vulnerability have often been viewed as two sides of the same coin. Attempts at risk and vulnerability assessments have proven that research into these fields is merely in its adolescence, not quite a “new science”, but with plenty of growing left to do before they are fully apprecieated. What is needed is a level of communication among researchers that strives to gain an understanding of key terms in the field of natural hazards assessment, including a more precise understanding and implementation of the terms risk, and vulnerability. With an easily followed natural hazards terminology there would most likely be a better flow of ideas and concepts that could lead to a more comprehensive understanding of risk and vulnerability. It is important then, that greater consideration be given to the advancement of ideas along the lines of vulnerability assessment methodology to ensure a sound base for future research in this area.

CHAPTER THREE: Methodology

3.1 Introduction

The preceding literature review suggests that one of the existing problems in the field of natural hazards is that there has been limited quantitative research in the area of urban vulnerability to disaster events. Emergency planners, managers, and countless numbers of people have suffered because of this lack of knowledge in the hazards research field. Indeed, there is no successful way to deal with this problem without a true index of urban vulnerability that takes into account socio economics, built environment factors, and accessibility to needed goods and services.

This dissertation proposes that by quantifying the qualitative research that has been undertaken in the field of vulnerability science it is possible to advance a framework for understanding urban vulnerability. As such, this chapter focuses on the methods used to operationalize an index of urban vulnerability for United States cities. Theoretically, one could assign values to the elements of vulnerability, manipulate those figures and establish a quantitative index of urban vulnerability. Such an index would have an enormous impact in disaster planning and regulatory fields, as well as on personal levels.

The implications of a true urban vulnerability index are so far reaching that it seems impossible to believe that the research in this arena has been so slow to come to fruition.

This chapter begins the process of quantitatively defining urban vulnerability and

38 resilience through the construction of a replicable set of protocols for the assessment of urban vulnerability.

3.2 Study Area

The study areas to be used in this dissertation are two major metropolitan areas within the contiguous United States, Charleston, South Carolina, and Tampa – St.

Petersburg, Florida (Figure 3.1). Located in the southeastern United States, these metropolitan areas are geographically situated on the central Coastal Plain of South

Carolina and the western coast of Central Florida, respectively. These urban areas were chosen in part because they conform to those areas designated by the United States

Geological Survey (USGS) and the National Geospatial Intelligence Agency (NGA) as among the top 133 most vulnerable urban areas in the nation. Additionally, these cities are of particular interest to those in the field of hazards research, as they are both located in hurricane prone coastal areas. The study areas were chosen primarily because of their importance to the USGS and NGA as prototypes for future research into metropolitan vulnerability. Additionally, because of their designation as “prototype” cities, demographic, economic, structural, and social data will be more readily available for assessment and analysis.

Both of these cities are ranked within the top 100 urban areas in the United States based on population. The Tampa – St. Petersburg metropolitan area, ranked 20th, had a

2000 population of 2,395,997, while Charleston, ranked 76th, had a 2000 population of

549,033. Table 3.1 displays some baseline demographic information about these two cities, in comparison with some of the top metropolitan areas within the United States.

39 Georgetown North Carolina

Berkeley Georgia

Dorchester

Charleston Atlantic Ocean

Charelston Urbainzed Area County Boundary

05102.5 Miles ´

Hernando Alabama Georgia Gulf Lake Orange of Sumter Mexico

Pasco Osceola

Polk

Hillsborough

Pinellas Tampa Tampa - St. Petersburg Bay Urbanized Area County Boundary 010205 Miles Manatee Hardee Highlands´

Figure 3.1: Charleston, SC and Tampa – St. Petersburg study areas

The standard deviations of these characteristics prove that although the two study areas

are not the largest or smallest cities within the United States, they do contain populations

with similar levels of general socioeconomic and demographic characteristics as some of

the most populated places in the country. As such, research, analysis, and results

40 Table 3.1: Selected Socio-demographic characteristics of Tampa – St. Petersburg, FL, Charleston, SC, Los Angeles, CA, New York, NY, and Washington D.C. City Name Tampa – St. Los New Washington Charleston Petersburg Angeles York D.C.

Population 2,395,997 549,033 16,373,645 12,689,665 7,608,070

51.82 50.90 50.52 52.25 53.01 % Female

population 0.90 0.96 1.02 0.93 1.05

% Population 11.15 14.03 15.62 15.97 20.22 below the poverty level 3.42 3.00 2.95 2.96 3.55 % New 23.53 30.28 17.51 20.79 32.89 Immigrants (Since 1995) 5.79 6.15 6.52 6.01 6.60 % Population 2.14 2.79 1.56 1.99 2.53 without access to telephone 0.43 0.49 0.50 0.44 0.45 6.25 7.26 8.50 7.43 6.27 % Population

under age 5 0.91 0.84 1.01 0.85 0.91 19.22 10.33 9.90 12.20 12.25 % Population

over age 65 4.26 3.50 3.56 3.37 3.36 % Houses with 0.46 0.85 0.96 1.12 1.37 inadequate

plumbing 0.36 0.30 0.30 0.31 0.35 facilities % Population 0.78 0.67 0.70 0.17 0.08 employed in

primary 0.32 0.31 0.31 0.32 0.34 industry % Population 11.69 12.33 9.73 14.17 8.54 employed in

health care 1.98 2.02 2.08 2.30 2.27 industry % Population 18.54 18.70 27.01 22.83 22.17 over age 25

without high 3.40 3.37 3.76 3.15 3.12 school diploma % Female 17.72 14.05 12.57 16.76 24.49 headed households 4.13 4.31 4.52 4.12 5.10 % Social 34.61 23.07 21.06 24.20 19.46 Security Recipients 6.74 5.35 5.50 5.32 5.70

41 garnered from the study areas chosen in this dissertation could apply directly to other major metropolitan areas within the United States.

3.3 Data Sources and Availability

Data must be available to measure the indicators associated with increased vulnerability and subsequent reduction in resilience in order for a successful framework of urban vulnerability to be implemented. Many of these needed data are spatial in nature, involving relationships between places, such as home and school, city and region, and include measures such as accessibility and emergency response times. Limitations on the availability of data for the urban environment have long been recognized by decision makers (OECD 1978). The fundamental point at this juncture is that there is a very real gap between some of the datasets provided by statewide mapping and surveying agencies and those required by emergency managers (McRae 2000). This point is highlighted by Cutter (2003) in this statement:

“The infrastructure for GI Science technology and data during emergencies is often non-existent or pieced together in an ad hoc fashion using a combination of local, county, state, federal and private providers and assets. The technical issues of data sharing, interoperability, power sources, and human resources often are insurmountable and pose major constraints on the use of GI Science for rapid response.” (Cutter 2003, 443)

Although this dissertation is not an exercise in locating and acquiring usable spatial data, it can be used as an example of truly how disjointed such data are even in today’s information age. It also illustrates the potential media from which these data can be found and applied to a better understanding of social vulnerability.

42 3.3.1 Socioeconomic Vulnerability Data

As discussed earlier, social statistics and socioeconomic data are relatively easy to

come by. The question then is, which data are useful for this research, and which data are

redundant and/or do not encapsulate the social themes of a particular place. Granger and

Johnson (1994) as well as Cutter et al. (2003) provide an excellent starting point from

which to decide on the types of data that should be gathered. Rather than compiling a

new set of variables from which to garner an understanding of urban level social

vulnerability to hazards, the socioeconomic variables chosen for this research conform to

those used in Cutter et al. 2003, social vulnerability to environmental hazards approach.

These variables were chosen because they have already been identified as important

indicators in vulnerability at the county level. Hence, applying them in a framework for

understanding social vulnerability at a sub-county scale is the next logical step in the

process. As such, Table 3.2 lists the variables collected for this research, sources of data,

and the theoretical effect of each variable on the overall social vulnerability in an urban

context.

3.3.2 Built Environment Vulnerability Data

Built environment indicators of vulnerable populations have previously been intertwined with other socioeconomic and demographic data in the understanding of social vulnerability (Blaikie et al. 1994, Tobin and Montz 1997, Cutter et al. 2003). To date, the built environment has not been empirically addressed as an independent factor in the delineation of vulnerability to hazards, although the Heinz Center (2002) has discussed the importance of the built environment as a factor in understanding vulnerability. In order to formulate a database that addresses these issues, data on the

43 concentration and vitality of the built environment will be garnered from various data sources from the federal government, as well as the GIS departments within the study areas. Table 3.3 lists the types of data to be used in the delineation of built environment indication of vulnerability, data sources, and their theoretical effect on vulnerability.

Table 3.2: Data, sources and effect of socio-economic indicators on vulnerability Effect on Socio-Economic Data Source Vulnerability (+) increase vulnerability (-) decrease vulnerability % Black Persons US Census Higher number (+) % Indian Persons US Census Higher number (+) % Asian Persons US Census Higher number (+) % Hispanic Persons US Census Higher number (+) % Children (under 5) US Census Higher number (+) % Elderly (over 65) US Census Higher number (+) % Female Persons US Census Higher number (+) Birth Rate US Census Higher rate (+) % Civilian Labor Force US Census Higher rate (+) unemployed Per Capita Income US Census Higher Income (-) Persons per household US Census Higher number (+) % Families earning > 100K US Census Higher incidence (-) % Families in poverty US Census Higher incidence (+) Median value of Owner US Census Higher value (+) Occupied Housing % Renter occupied units US Census Higher number (+) Median gross rent ($) US Census Higher (+) % Health Care Employment US Census Higher number (-) % Female headed household US Census Higher number (+) with no spouse % Over 25 without high US Census Higher number (+) school education % Civilian Labor Force US Census Higher number (+) % Female in Civilian Labor US Census Higher number (+) Force % Primary Industry US Census Higher number (+) % Trans., Comm., Utilities US Census Higher number (+) % Service Occupations US Census Higher number (+) Nursing Homes Residents Higher number (+) Social Security Recipients US Census Higher number (+) % International Migration US Census Higher number (+)

44 Table 3.3: Data, sources and effect of built environment indicators on vulnerability Effect on Built Environment Data Source Vulnerability (+) increase vulnerability (-) decrease vulnerability 1997 Economic Census Higher number (+) Retail Est. Study Area GIS Centers Higher earnings (-) Professional, Scientific, 1997 Economic Census Higher number (+) and Technical Service Study Area GIS Centers Higher earnings (-) Est. Administrative & Support, Waste 1997 Economic Census Higher number (+) Management, Study Area GIS Centers Higher earnings (-) Remediation Est. 1997 Economic Census Higher number (+) Educational Service Est. Study Area GIS Centers Higher earnings (-) Health Care & Social 1997 Economic Census Higher number (+) Assistance Service Est. Study Area GIS Centers Higher earnings (-) Arts, Entertainment, & 1997 Economic Census Higher number (+) Recreation Est. Study Area GIS Centers Higher earnings (-) Accommodation & Food 1997 Economic Census Higher number (+) Service Est. Study Area GIS Centers Higher earnings (-) 1997 Economic Census Higher number (+) Other Service Est. Study Area GIS Centers Higher earnings (-) Higher number (+) # Housing Units US Census

# Mobile Homes US Census Higher number (+) Higher number (+) # Manufacturing Est. 1997 Economic Census Higher earnings (-) Higher number (+) # Commercial Est. 1997 Economic Census Higher earnings (-)

3.3.3 Access, Lifeline, and Livability Data

The National Research Council (2002) recommends five broad issues surrounding improved data availability on livability indicators. These are: 1) The underlying dimensions of livability are neither completely separable nor mutually compensatory; 2)

There are essential crosscutting measures of livability that highlight the mutual interdependence of livability dimensions; 3) Multiple interrelated spatial scales and time

45 frames are operating within different dimensions of livability; 4) The assessment of livability fundamentally depends on data about both people and places; and 5) Although

every federal agency in the nation is charged with the critical responsibility to serve the

interests of the nation, each data program has been developed for carrying out agency

specific missions. In order to properly formulate a database that addresses these issues,

data on access and livability will be gleaned from numerous sources, both digital and

analog. Table 3.4 illustrates the types of data that were used in the access, lifeline and

livability vulnerability research, the data sources, and the theoretical importance of each

factor in an overall social vulnerability index. Research into public transportation

analysis and other sources such as the International Council of Shopping Centers (2000)

enabled the adequate representation, in a spatial framework, of the different access and

lifeline issues for the study areas.

3.4 Research Design and Methods

3.4.1 Theoretical Framework

As discussed earlier, this dissertation research is theoretically situated in the

findings of several key researchers in the fields of hazards and disasters. The theoretical

framework of this research is based in a general understanding of vulnerability as a two

part process involving first, factors that contribute to an initial lack of the ability of a

place or population to defend against the impact and effect of a hazard or disaster event;

and secondly, factors or characteristics of places or populations that hinder the ability to

adequately recover from an event in a timely manner. This research undertakes a multi

step process involving the spatial delineation of characteristics of the built environment,

accessibility to needed goods and services, and socioeconomics. The methodology for

46 Table 3.4: Data, sources and effect of accessibility and lifeline indicators on vulnerability. Effect on Accessibility Vulnerability Data Source (+) increase vulnerability (-) decrease vulnerability Grocery Store (Point) City GIS/Phone Book Greater Distance (+) Home Improvement Store City GIS/Phone Book Greater Distance (+) (Point) Discount Store (Point) City GIS/Phone Book Greater Distance (+) City GIS/Phone Book Fire Station (Point) Greater Distance (+) www.iso.com Police Station (Point) City GIS/Phone Book Greater Distance (+) City GIS/ City Planners Emergency Shelter (Point) Greater Distance (+) City Police Hospital (Point) City GIS/Phone Book Greater Distance (+) Health Care Facility (Point) City GIS/Phone Book Greater Distance (+) Pharmacy (Point) City GIS/Phone Book Greater Distance (+) Churches (Point) City GIS/Phone Book Greater Distance (+) Social Groups (Point) City GIS/Phone Book Greater Distance (+) School (Point) City GIS/Phone Book Greater Distance (+) Day Care Facility (Point) City GIS/Phone Book Greater Distance (+) Bus Route (Line) City GIS Departments Greater Distance (+)

this research is theoretically based within Cutter’s (1996a), hazards of place model of vulnerability. As such, Figure 3.2 shows the main focus of this dissertation research

within a broader understanding of social vulnerability. Note that the main focus of this

dissertation is centered on the advancement of techniques used in the delineation of social

vulnerability in urban areas. The remainder of this section will explain the processes and

procedures implemented in the spatial operationalization of Figure 3.2.

3.4.2 Unweighted Socioeconomic Vulnerability

The methodology for determining the socioeconomic vulnerability for each study area was based on previous research by Cutter et al. (2000, 2003). This research

47 provided a quality base from which to proceed with our social vulnerability assessment.

The factors in the Cutter method were decoupled in order that socioeconomic,

Geographic Biophysical Risk Context Vulnerability

Hazard Place Potential Vulnerability

Mitigation Social Social Vulnerability Fabric

Biophy sical Vulnerability Place Vulnerability

Built Environment Vitality

Geographic Context Built Environment Concentration

Social Vulnerability Access & Lifeline Indicators Social Fabric

Socio Economic Indicators

Figure 3.2: Social Vulnerability Framework. Updated from Cutter 1996

48 demographic, and built environment indicators could be analyzed as separate entities, each impacting socioeconomic vulnerability in different ways. Twenty-seven variables of socioeconomic and demographic characteristics for census block groups were made into standardized scores (z-scores) in order that all of them were equal in a summed vulnerability score. The factors used in the equation of unweighted socioeconomic vulnerability and their theoretical significance can be seen in table 3.2. The equation used to formulate this measure of social vulnerability is:

SEVI = PCIvul + MHvul + MRvul + INCvul + POVvul+ SSBvul + AMvul + NAvul + ASvul + Hvul + AGE5vul + AGE65vul + BRvul + IMvul + PPHvul + ROvul + NHSvul + NHRvul + FEMvul + FHHvul + HCWcul + CLFUvul + LFPvul + FLFPvul + EXTvul + TRANSvul + SERVvul

Where:

PCIvul = Per Capita Income vulnerability based on per capita income in each block group

MHVvul = Median house value vulnerability based on median house value in each block group

MRvul = Median rent vulnerability, based on median block group rent

INCvul = Income vulnerability based on percentage of households earning more than $100,000 per year in each block group

POVvul = Poverty vulnerability based on percentage of persons living below the poverty line in each block group

SSBvul = Social security vulnerability based on percentage of population collecting social security benefits in each block group

AMvul = African American vulnerability based on percentage of African Americans in each block group

NAvul = Native American vulnerability based on percentage of Native Americans in each block group

ASvul = Asian and Hawaiian Islander vulnerability based on percentage of Asians and Hawaiian Islanders in each block group

49 Hvul = Hispanic vulnerability based on percentage of Hispanic persons in each block group

AGE5vul = Young person vulnerability – based on percentage of persons under age five in each block group.

AGE65vul = Old person vulnerability – based on percentage of persons over age sixty- five in each block group

BRvul = Birthrate vulnerability – based on birthrate in each block group

Imvul = Immigration vulnerability – based on percentage of immigrants (within the last 10 years) in each block group

PPHvul = Persons per household vulnerability - based on average persons per household in each block group

ROvul = Renter occupied housing vulnerability – based on percentage of renter occupied housing units in each block group

NHSvul = No high school diploma vulnerability – based on percentage of persons over age 25 with no high school diploma in each block group

NHRvul = Nursing home resident vulnerability – based on percentage of nursing home residents in each block group

FEMvul = Female vulnerability – based on percentage of females in each block group

FHHvul = Female headed household vulnerability – based on percentage of female headed households with no spouse present in each block group.

HCWvul = Health care worker vulnerability – based on percentage of health care workers in each block group

CLFUvul = Civilian labor force unemployment vulnerability – based on percentage of unemployed persons in each block group

LFPvul = Labor force participation vulnerability – based on percentage labor force participation in each block group.

FLFP = Female labor force participation vulnerability – based on percentage female labor force participation in each block group

EXTvul = Extractive industry vulnerability – based on percentage employment in farming, fishing, and forestry occupations in each block group

50 TRANSvul = Transportation industry vulnerability – based on percentage employment in transportation, communications and other public utilities in each block group

SERVvul = Service industry vulnerability – based on percentage employment in service occupations in each block group.

These standardized variables were then summed to produce an unweighted socioeconomic vulnerability surface for each study area. These surfaces were then spatially appraised using a Moran’s I statistic to test for spatial clustering. The resulting surface of socio-economic clusters enabled the researcher to pinpoint places within each study area that exhibited either high levels of socioeconomic vulnerability or low levels of said vulnerability. A data flow diagram of this process can be seen in Figure 3.3.

3.4.3 Unweighted Built Environment Vulnerability

The main contributors to built environment vulnerability consist of measures of commercial and industrial development, residential density, and industry earnings. The methodology for delineating built environment vulnerability was based on metropolitan data surrounding industrial concentration, or a measure of the number of establishments, and vitality, the total sales amount for each industry. Nineteen variables pertaining to the characteristics of the built environment for census block groups and zip codes were made into standardized scores in order that all of them were equal in a summed vulnerability

Socio-economic Socio data at the Block Economic Group Level Factors TABULAR OPERATION Divide total value of every factor in each block group by total value for that factor in the study area

TABULAR OPERATION Divide the ratio of each factor in each block group by maximum ratio for that factor in the study area Socio Unweighted Socio- Economic Economic Factors Ratios

TABULAR OPERATION Multiply each factor by its Delphi Method weight

Weighted Socio- Economic Factors

Socio- economic weights from Delphi TABULAR OPERATION Sum all weighted factors for survey Socio-Economic each block group in the study Vulnerability Score area

Figure 3.3: Socioeconomic vulnerability data flow diagram

52 score. The factors used in the equation of unweighted built-environment vulnerability

and their theoretical significance can be seen in Table 3.3. The equation used to

formulate this measure of social vulnerability is:

BEVI = MANestvul + RETestvul + ACCestvul + ADMestvul + ARTestvul + PROFestvul + EDUestvul + HLTHestvul + OTRestvul + MHvul + HUvul + RETvalvul + ACCvalvul + ADMvalvul + ARTvalvul + PROFvalvul +EDUvalvul + HLTHvalvul + OTRvalvul

Where:

MANestvul = Manufacturing establishment density vulnerability – based on number of manufacturing establishments in each block group.

RETestvul = Retail establishment density vulnerability – based on number of retail establishments in each block group.

ACCestvul = Accommodation and food service establishment density vulnerability – based on number of accommodation and food service establishments in each block group.

ADMestvul = Administrative support establishment density vulnerability – based on number of administrative support establishments in each block group.

ARTestvul = Arts, entertainment and recreation establishment density vulnerability – based on number of arts, entertainment and recreation establishments in each block group.

PROFestvul = Professional establishment density vulnerability – based on number of professional establishments in each block group.

EDUestvul = Educational establishment density vulnerability – based on number of educational establishments in each block group.

HLTHestvul = Health care establishment density vulnerability – based on number of health care establishments in each block group.

OTRestvul = All “other” establishment density vulnerability – based on number of all “other” establishments in each block group.

MHvul = Mobile home density vulnerability – based on number of mobile homes in each block group.

53 HUvul = Housing unit density vulnerability – based on number of housing units in each block group.

RETvalvul = Retail establishment vitality vulnerability – based on value of all receipts for retail establishments in each block group.

ACCestvul = Accommodation and food service establishment vitality vulnerability – based on value of all receipts for accommodation and food service establishments in each block group.

ADMestvul = Administrative support establishment vitality vulnerability – based on value of all receipts for administrative support establishments in each block group.

ARTestvul = Arts, entertainment and recreation establishment vitality vulnerability – based on value of all receipts for arts, entertainment and recreation establishments in each block group.

PROFestvul = Professional establishment vitality vulnerability – based on value of all receipts for professional establishments in each block group.

EDUestvul = Educational establishment vitality vulnerability – based on value of all receipts for educational establishments in each block group.

HLTHestvul = Health care establishment vitality vulnerability – based on value of all receipts for health care establishments in each block group.

OTRestvul = All “other” establishment vitality vulnerability – based on value of all receipts for all “other” establishments in each block group.

Data complied from the United States Census, the United States Economic Census, the

USA Counties Data Book, and the State and Metropolitan Area Data Book were spatially segregated based on census block group. The data were then separated into two classes:

1) those factors that contribute to built environment concentrations in the study areas, and

2) those factors that contribute to built environment vitality in the study area. Once divided, each data set was spatially appraised in order to calculate the number of establishments and sales found in each census block group within the study areas. These

54 data were then normalized through the process seen in Table 3.5 so that no individual

variable skewed an overall vulnerability score. These standardized variables were then

Table 3.5: Built Environment Vulnerability Scoring Procedure Mean Built BE Built Environment (X) = (Y) = vulnerability Environment Value for all Value Census X + Score = (BE) Value Block Groups difference Block Maximum Y / (Retail (BGs) in between SA |X| Maximum Earnings) Study Area and BGs value of Y (SA) 1 $43,000 $2,206,500 $2,163,500 $17,780,040 0.99759 2 $168,000 $2,206,500 $2,038,500 $17,655,040 0.99057 3 $3,826,000 $2,206,500 -$1,619,500 $13,997,040 0.78533 4 $45,015 $2,206,500 $2,161,485 $17,778,025 0.99747 5 $17,823,040 $2,206,500 -15,616,540 $0 0.00000 6 $53,970 $2,206,500 $2,152,530 $17,769,070 0.99697 7 $2,190 $2,206,500 $2,204,310 $17,820,850 0.99988 8 $0 $2,206,500 $2,206,500 $17,823,040 1.00000 9 $1,875 $2,206,500 $2,204,625 $17,821,165 0.99989 10 $101,910 $2,206,500 $2,104,590 $17,721,130 0.99428

summed to produce an unweighted built environment vulnerability surface for each study area. The Moran’s I statistic was used to test for spatial clustering among the block groups in order to identify areas with high and low levels of built environment vulnerability. A data flow diagram of this process can be seen in Figure 3.4.

3.4.4 Unweighted Accessibility Vulnerability

The main factors affecting accessibility to needed goods and services and social lifelines surround the issues of mobility, physical distance and time, physical constraints in the built environment, and social network strength. To this end, the methodology for delineating access to needed goods, services, and emergency responders used a surrogate measure, distance. This surrogate was computed as the average distance from each census block group to the nearest service point or lifeline (e.g. hospital, bus route, police station, pharmacy, etc.). A review of the accessibility literature shows that specific

55 distances away from emergency services or basic needs establishments are used in the classification of access or under served communities. Generally, as distance from a

Landcover Data SELECT CONVERT Urban areas that are not Selected Areas to residential shapefile Urban Areas dissolved into one polygon Dissolve Based on Gridcode2 Urban Areas

Clip Urban Zip Codes Study area zip codes with dissolved urban areas Study Area Zip Codes1

Block Groups Union TABULAR OPERATION Study Area Block Groups with Urban Zip Codes3 Recalculate area of each urban zip code

TABULAR OPERATION TABULAR OPERATION Calculate the % of each Join zip code BE data block group belonging to with BGs based on %of Urban BG Zips each zip code each BG in each zip4

B.E. TABULAR OPERATION weights Weight each factor using TABULAR OPERATION from formulated Delphi Follow built environment Delphi weights from survey score procedure in table XXX survey

TABULAR OPERATION Sum all weighted Built Environment variables Vulnerability for each block group 1. The table associated with zip code polygon has built environment concentration and vitality data from the United States Economic Census 2. To make many polygons into one “urban area” polygon 3. To spatially join zip code information to each block group for later tabular functions. 4. Repeat for all values in built environment table. Figure 3.4: Built Environment vulnerability data flow diagram

56 critical needs facility increases, accessibility decreases, classifying certain areas as underserved or under serviced. A determination of accessibility for each census block group to each emergency good or service was computed once the distance from each block group to each service location or lifeline was ascertained. The factors used in the equation of unweighted accessibility vulnerability and their theoretical significance can be seen in Table 3.4. The equation used to formulate this measure of social vulnerability is:

AVI = PDvul + FDvul + PHvul + SHvul + GRvul + SGvul + DOvul + HIvul + SCvul + PTvul + HOvul + DCvul + DSvul + CHvul

Where:

PDvul = Police station accessibility vulnerability based on mean distance from each block group to the nearest police station location

FDvul = Fire station accessibility vulnerability based on mean distance from each block group to the nearest fire station location

PHvul = Pharmacy accessibility vulnerability based on mean distance from each block group to the nearest pharmacy location

SHvul = Emergency shelter accessibility vulnerability based on mean distance from each block group to the nearest emergency shelter location

GRvul = Grocery store accessibility vulnerability based on mean distance from each block group to the nearest grocery store location

SGvul = Social group accessibility vulnerability based on mean distance from each block group to the nearest social group location

DOvul = Doctor/healthcare accessibility vulnerability based on mean distance from each block group to the nearest general/family practitioner location

HIvul = Home improvement accessibility vulnerability based on mean distance from each block group to the nearest home improvement store location station

SCvul = School accessibility vulnerability based on mean distance from each block group to the nearest school location

57 PTvul = Public transportation accessibility vulnerability based on mean distance from each block group to the nearest public transportation route location

HOvul = Hospital accessibility vulnerability based on mean distance from each block group to the nearest hospital location

DCvul = Day care accessibility vulnerability based on mean distance from each block group to the nearest day care location

DSvul = Discount sore accessibility vulnerability based on mean distance from each block group to the nearest discount store location

CHvul = Church accessibility vulnerability based on mean distance from each block group to the nearest place of worship location

These standardized variables were then summed to produce an unweighted accessibility vulnerability surface for each study area. As before, these surfaces were then spatially appraised using a Moran’s I statistic. A data flow diagram of this process can be seen in

Figure 3.5.

Needed goods Goods and and services services point address data locations

Geocode SPATIAL ANALYST Distance from each point

SPATIAL ANALYST Zonal Statistic Distance from goods (MEAN) and services point

locations Average distance to good or service for each block group Block Groups within study area within study area

TABULAR OPERATION Create field Dist_Vul in Divide the distance for each Block Group by the maximum average distance for all block groups distance table

Access weights from TABULAR OPERATION Delphi Multiply Dist_Vul value by weights from Delphi Method survey Access survey for each block group Vulnerability for each block group

Figure 3.5: Accessibility vulnerability data flow diagram

59 3.4.5 Unweighted Social Vulnerability

The methodology for determining the overall social vulnerability for each study area was based on previous research by Cutter et al. (2000, 2003). The three main components of social vulnerability (socioeconomics, built environment, and accessibility) were standardized using z-scores and summed to create an overall unweighted social vulnerability surface for the study areas. The equation used to formulate this measure of social vulnerability is:

SOVI = AVI + BEVI + SEVI

Where:

AVI = Accessibility vulnerability

BEVI = Built-environment vulnerability

SEVI = Socioeconomic vulnerability

A Moran’s I statistic was applies to this surface as a test for spatial clustering.

The resulting surface of social vulnerability clusters enabled the researcher to identify places within each study area that exhibited either high or low levels of overall social vulnerability. A data flow diagram of this process can be seen in Figure 3.6.

3.5 The Survey Instrument

To determine the weights for each component in the social vulnerability index, a computer-based survey was designed to elicit expert opinions. The survey was based on a modified Delphi Method. This method of data collection, originally developed by the

RAND Corporation in 1969 for forecasting technological advances and impacts, is a group decision making process that makes use of a panel of geographically dispersed

experts. Delphi techniques are based on the concept that experts, in this case hazards and

60 disasters researchers and practitioners, calling on their insights and experience, are better

equipped to predict the future than theoretical approaches or extrapolation of current

trends.

One distinct advantage of the Delphi Method is that the experts do not need to be physically brought together in order to participate in the decision making process.

Additionally, the decision making process does not require complete agreement by all

panelists, since the majority opinion is represented by the median score of all opinions.

“Since the responses are anonymous, the pitfalls of ego, domineering personalities and

the ‘bandwagon or halo effect’ in responses are all avoided (Delphis, 1983).” Delphi is

able to overcome many of the downfalls of other group decision making efforts such as

the nominal group process technique, in which subjects must physically meet to discuss

ideas, or Cross Impact analysis, which evaluates components in relation to each other;

Delphi techniques treat each individual component of the analysis as an independent

variable.

During the last three decades, the Delphi Method has found a wide range of

applications in planning, evaluation, forecasting and issue-exploration in many areas.

However, in spite of this, there have been few applications in social policy. The

objective of most Delphi applications is the reliable and creative exploration of ideas or

the production of suitable information for decision making. One of the main

considerations important for Delphi applications to issues related to social policy and

public health is that the problem does not lend itself to precise analytical techniques but

can benefit from subjective judgments on a collective basis (Adler and Ziglio 1996).

Essentially, the Delphi method is primarily concerned with making the best you can of a

61 less than perfect fund of information (Dalkey 1968). Public Sector use of the Delphi

Method, as reviewed by McGraw et al. (1976) and Preble (1983), was initially aimed at exploration of new land use policy impacts, information systems for planning purposes, and the future sighting and organizational arrangements of hospitals and other health facilities. In the past decade, starting with the International Decade for Natural Disaster

Reduction (IDNDR), there has been an increasing commitment to social policy and public health issues on the part of international as well as national and local authorities and organizations. “This has brought about a wider range of functions, new institutions, the setting up of ad hoc committees and other decision making bodies involving civil servants, experts, politicians, administrators and the lay community (Ziglio 1996, 6).”

Unfortunately, the relationships and information exchanged between these groups has often been less than ideal, leading to the need for practical tools for improving the exchange of information for decision making. Historically the link between researchers and practitioners in the hazards field has been in line with this thought. The use of this

Delphi style survey in the dissertation is aimed at closing the knowledge gap between those researches the subject in the office and those applying techniques in the field.

In most cases, the criterion for deciding on a sample size for constructing a Delphi panel is not (and cannot be) a statistical one. The size of the expert panel will be variable. “The available literature suggests that with a homogeneous group of experts, good results can be obtained with even small panels of 10-15 individuals (Alder and

Ziglio 1996, 14).” However, in circumstances where various reference groups are involved, the sample size may be considerably larger as is a reduction in group error as group size increases. There are however, certain criteria used to define experts for use in

62 Delphi applications. These are outlined by Goldschmidt (1975) as: First the subject must

have knowledge and practical experience with the topic under exploration, and second,

the subjects must be willing to contribute to the investigation of the particular topic.

Essentially, subjects are only deemed experts to the degree that they produce responses

that are conclusively more significant than if a lay person were to fill out the survey.

Using the Delphi method does presume one theoretical assumption - that group

judgments from knowledgeable experts, achieved through the procedures associated with the Delphi Method, are more dependable than individual opinions.

3.5.1 Survey Sample

The sample population used in the survey was drawn from a list of researchers and practitioners in the fields of hazards and disasters. This list of persons was provided to the researcher by the Natural Hazards Center at Colorado University in Boulder, and represents all of the attendants to the Natural Hazards Research and Applications

Workshop, hosted annually by the Center. A total of 600 current researchers and practitioners were emailed a link to the online survey. Unfortunately, the release of the

survey coincided with the impact of hurricane Katrina to the Southeast United States, resulting in a total number of thirty-nine responses. However, since all of the participants at the workshop specialize in hazard and disaster research and application and the Delphi

method clearly states that a large sample size is not necessary, the number of responses to

the survey was adequate to proceed with the weighting portion of this research with

confidence. Respondents to the survey included twenty-three researchers/academics and

fourteen practitioners. Additionally, a broad range of experience was found among the

respondents, with eleven persons possessing greater than ten years of experience in the

63 hazard and disaster field. Furthermore, responses came from persons who currently work in all four areas of the emergency management cycle. Eight of the responses, however, were from persons affiliated with the University of South Carolina. This is important because it causes a potential bias in the conclusions drawn from this survey, although one could argue that students from the University of South Carolina are potentially more up to speed on the available literature pertaining to social vulnerability than many other academics.

3.5.2 Survey Construction

The online survey consisted of four main sections, three focusing on each of the three main facets of social vulnerability and one pertaining to how those three facets fit together to form overall social vulnerability. Each portion of the survey was designed so that all variables associated with a certain aspect of vulnerability were grouped together to allow the respondent to see all the available inputs to each component. The respondents were asked to quantitatively weight each set of vulnerability indicators according to how important they thought each indicator was in an overall equation of social vulnerability. The survey responses were automatically populated into an access database and were made into final weights ready for application in an equation of weighted social vulnerability. A final version of the online survey can be seen in

Appendix A.

3.6 Weighted Vulnerability Indicators

The standardized scores computed for each of the three broad indicators of social vulnerability (socioeconomic, built environment and accessibility) were weighted according to their general importance based on the responses to the survey. The

64 weighted vulnerability scores were calculated by multiplying each individual variable by

its weight, or output from the Delphi method survey. For example, the equation used to

formulate weighted socioeconomic vulnerability becomes:

wSEVI= wPCIvul + wMHvul + wMRvul + wINCvul + wPOVvul + wSSBvul + wAMvul + wNAvul + wASvul + wHvul + wAGE5vul + wAGE65vul + wBRvul + wIMvul + wPPHvul + wROvul + wNHSvul + wNHRvul + wFEMvul + wFHHvul + wHCWvul + wCLFvul + wLFPvul + wFLFPvul + wEXTvul + wTRANSvul + wSERVvul

Where:

wPCIvul = Per Capita Income vulnerability based on per capita income in each block group multiplied by its corresponding Delphi survey weight

wMHVvul = Median house value vulnerability based on median house value in each block group multiplied by its corresponding Delphi survey weight

wMRvul = Median rent vulnerability, based on median block group rent multiplied by its corresponding Delphi survey weight

wINCvul = Income vulnerability based on percentage of households earning more than $100,000 per year in each block group multiplied by its corresponding Delphi survey weight

wPOVvul = Poverty vulnerability based on percentage of persons living below the poverty line in each block group multiplied by its corresponding Delphi survey weight wSSBvul = Social security vulnerability based on percentage of population collecting social security benefits in each block group multiplied by its corresponding Delphi survey weight wAMvul = African American vulnerability based on percentage of African Americans in each block group multiplied by its corresponding Delphi survey weight wNAvul = Native American vulnerability based on percentage of Native Americans in each block group multiplied by its corresponding Delphi survey weight wASvul = Asian and Hawaiian Islander vulnerability based on percentage of Asians and Hawaiian Islanders in each block group multiplied by its corresponding Delphi survey weight

65 wHvul = Hispanic vulnerability based on percentage of Hispanic persons in each block group multiplied by its corresponding Delphi survey weight

wAGE5vul = Young person vulnerability – based on percentage of persons under age five in each block group multiplied by its corresponding Delphi survey weight wAGE65vul = Old person vulnerability – based on percentage of persons over age sixty- five in each block group multiplied by its corresponding Delphi survey weight wBRvul = Birthrate vulnerability – based on birthrate in each block group multiplied by its corresponding Delphi survey weight wIMvul = Immigration vulnerability – based on percentage of immigrants (within the last 10 years) in each block group multiplied by its corresponding Delphi survey weight

wPPHvul = Persons per household vulnerability - based on average persons per household in each block group multiplied by its corresponding Delphi survey weight wROvul = Renter occupied housing vulnerability – based on percentage of renter occupied housing units in each block group multiplied by its corresponding Delphi survey weight wNHSvul = No high school diploma vulnerability – based on percentage of persons over age 25 with no high school diploma in each block group multiplied by its corresponding Delphi survey weight wNHRvul = Nursing home resident vulnerability – based on percentage of nursing home residents in each block group multiplied by its corresponding Delphi survey weight wFEMvul = Female vulnerability – based on percentage of females in each block group multiplied by its corresponding Delphi survey weight wFHHvul = Female headed household vulnerability – based on percentage of female headed households with no spouse present in each block group multiplied by its corresponding Delphi survey weight wHCWvul = Health care worker vulnerability – based on percentage of health care workers in each block group multiplied by its corresponding Delphi survey weight

66 wCLFUvul = Civilian labor force unemployment vulnerability – based on percentage of unemployed persons in each block group multiplied by its corresponding Delphi survey weight

wLFPvul = Labor force participation vulnerability – based on percentage labor force participation in each block group multiplied by its corresponding Delphi survey weight wFLFP = Female labor force participation vulnerability – based on percentage female labor force participation in each block group multiplied by its corresponding Delphi survey weight wEXTvul = Extractive industry vulnerability – based on percentage employment in farming, fishing, and forestry occupations in each block group multiplied by its corresponding Delphi survey weight wTRANSvul = Transportation industry vulnerability – based on percentage employment in transportation, communications and other public utilities in each block group multiplied by its corresponding Delphi survey weight

wSERVvul = Service industry vulnerability – based on percentage employment in service occupations in each block group multiplied by its corresponding Delphi survey weight

The same procedure was followed for the built environment and accessibility

components. The weighted scores were then applied back to block group level geography

and a spatial layer of vulnerability (socioeconomic, built, and accessibility) was

completed for each study area. These surfaces were then assessed using a Moran’s I

statistic to test for spatial clustering.

The overall social vulnerability of the study areas was delineated through the

application of Delphi method weights to each of the three portions of the vulnerability

equation (socio-demographic, accessibility, and built environment). Overall social

vulnerability was calculated as the sum of the three weighted vulnerability subsets. The

factors used in the equation of unweighted social vulnerability include all three

67 components discussed above. The equation used to formulate this measure of social

vulnerability is:

wSOVI = wAVI + wBEVI + wSEVI

Where:

wAVI = Standardized weighted Accessibility vulnerability multiplied by its corresponding Delphi survey weight

wBEVI = Standardized wieighted Built-environment vulnerability multiplied by its corresponding Delphi survey weight

wSEVI = Standardized weighted Socioeconomic vulnerability multiplied by its corresponding Delphi survey weight The calculation of social vulnerability was accomplished through the use of GIS techniques and essentially displays the summation of the three “weighted” indicators of social vulnerability. The resulting GIS polygons lend themselves to rapid decomposition of the factors that might be found to increase or decrease vulnerability in certain areas.

These weighted social vulnerability surfaces were then spatially appraised using a

Moran’s I statistic. Figure 3.6 is a data flow diagram describing the formulation of

overall social vulnerability for the study areas.

3.7 Summary

This chapter described the procedures associated with delineating social

vulnerability in metropolitan areas. The use of expert weights in the operationalization of

an urban framework of vulnerability identification is a new twist on contemporary vulnerability assessments. Comparing the weighted to the unweighted vulnerability

surfaces will allow the research to identify those areas which are truly in need of

additional attention before, during and after hazard events and will allow future

researchers a more comprehensive understanding of the characteristics of a place or

68 population that give way to increased vulnerability and decreased resilience in the face of disaster.

Unweighted Unweighted Built Unweighted Socio-Economic Environment Access Vulnerability for Vulnerability for Vulnerability for each block each block each block

TABULAR OPERATION TABULAR OPERATION TABULAR OPERATION Multiply socio-economic Multiply built environment Multiply accessibility vulnerability score by Delphi vulnerability score by Delphi vulnerability score by Delphi Method weight Method weight Method weight

Weighted Socio- Weighted Built Weighted Economic Environment Access Vulnerability for Vulnerability for Vulnerability for each block each block

TABULAR OPERATION Sum weighted Socio- economic, built environment, and access vulnerability scores

Social Vulnerability score for each block group

Figure 3.6: Social vulnerability data flow diagram

CHAPTER FOUR: Results

4.1 Unweighted Vulnerability Scores

An integral part of appreciating social vulnerability to hazard events in urban

areas is capturing a baseline understanding of the components that play into overall social

vulnerability. Along these lines, unweighted vulnerability scores were computed for all

three components of social vulnerability for baseline comparative purposes with the

Delphi weighted components. This section will focus on the results of these unweighted computations and will foster a more robust understanding of the actual variables as they present themselves in a pre weighted situation. Each component of the vulnerability

equation will be discussed individually and then in conjunction with each other in an explanation of unweighted overall social vulnerability.

4.1.1 Unweighted Socioeconomic Vulnerability

Each of the factors used to identify socioeconomic vulnerability were standardized across the study area and summed to produce an unweighted socioeconomic vulnerability score for each block group. Socioeconomic vulnerability scores and spatial clusters can be seen in Figures 4.1 and 4.2 for Tampa – St. Petersburg, Florida and in

Figures 4.3 and 4.4 for Charleston, South Carolina. The vulnerability scores in Figures

4.1 and 4.3 are classified as standard deviations from the mean in order that scores further

from the average score can easily be identified.

70 VULNERABILITY SCORE < -2.5 Std. Deviation -2.5 - -1.5 Std. Deviation -1.5 - -0.5 Std. Deviation -0.5 - 0.5 Std. Deviation µ 0.5 - 1.5 Std. Deviation 05102.5 1.5 - 2.5 Std. Deviation Miles > 2.5 Std. Deviation

Figure 4.1: Unweighted socio-economic vulnerability for Tampa – St. Petersburg, FL

Spatial Cluster µ High - High 05102.5 Miles Low - Low

Figure 4.2: Unweighted socio-economic vulnerability clusters for Tampa – St. Petersburg, FL

71 VULNERABILITY SCORE < -1.5 Std. Deviation -1.5 - -0.5 Std. Deviation µ -0.5 - 0.5 Std. Deviation 0.5 - 1.5 Std. Deviation 1.5 - 2.5 Std. Deviation 05102.5 Miles > 2.5 Std. Deviation

Figure 4.3: Unweighted socio-economic vulnerability for Charleston, SC

µ Spatial Cluster High - High 05102.5 Miles Low - Low

Figure 4.4: Spatial clusters of unweighted socio-economic vulnerability in Charleston, SC

72 The Moran’s I statistic was used to differentiate spatial clusters of both high and

low vulnerability. The statistic ranges from –1.0 to +1.0. There was a weak positive

spatial autocorrelation in both case study areas. In Tampa-St. Petersburg, for example,

the Moran’s I was 0.2615 at the 0.001 confidence level , while in Charleston, the Moran’s

I was 0.2497 at the 0.001 confidence level. When mapped, the spatial delineation

between clusters of high and low vulnerability are readily apparent (Figures 4.2 and 4.4).

Specifically, in Tampa – St. Petersburg, there are five main clusters of high vulnerability

areas across the three county area and only two substantive clusters of low

socioeconomic vulnerability, both located in Hillsborough County. Of the five clusters of high socioeconomic vulnerability in the Tampa-St. Petersburg study area, two are located in Hillsborough County, two in Pinellas County and one in Pasco County. These areas

are characterized by lower than average incomes (fewer families with yearly incomes

greater than $100,000). Additionally, the cluster in Pasco County shows higher levels of

persons over age sixty-five as well as persons without a high school diploma. The cluster

of high socio-economic vulnerability in eastern Hillsborough County is based on higher

number of persons per household than other areas and a higher than average number of persons employed in primary industry – two facts closely related to the large dependence on farming and the use of migrant workers in the area. Conversely, the communities in

Tampa-St. Petersburg with low socio-economic vulnerability have fewer people living in poverty and fewer people without a high school diploma. The distinctions are even

clearer across the Charleston study area, with two main clusters of high socioeconomic

vulnerability and only one main cluster of low vulnerability block groups surrounding the

downtown, Battery, and beach areas of Charleston. Although the clusters of low socio-

73 economic vulnerability in Charleston do display a large number of persons per

household, they also have generally low poverty and lower levels of female labor force

participation – all indications of middle and upper middle class households with stay at home mothers who are primary caregivers to larger families. The clusters of high socioeconomic vulnerability are characterized by low incomes, large number of people per household, and more African American residents.

In addition to the socioeconomic cluster maps, these vulnerability scores were spatially joined to city limit data for the study areas in order to gain a better understanding of how socioeconomic vulnerability differs statistically. Table 4.1 shows the communities and unweighted vulnerability scores of the top ten highest socioeconomically vulnerability places in Tampa and Charleston. Of particular interest in table 4.1 is the fact that five of the top ten most socio-economically vulnerable block groups are located within the City of Tampa. Within the Charleston study area, the City of Charleston-North Charleston is also home to five of the top ten most socioeconomically vulnerable populations, while Goose Creek-Hanahan contains three neighborhoods that display high levels of socioeconomic vulnerability. Most of the socio-economic vulnerability is a function of lower income levels. Table 4.2 shows communities and unweighted vulnerability scores of the top ten lowest socioeconomically vulnerable places in the two study areas. Again, the city of Tampa holds five of the top 10 least vulnerable places, while Citrus Park has two communities with modestly low socioeconomic vulnerability. The fact that the city of Tampa has five neighborhoods in both the most and least vulnerable categories suggests considerable

changes in socioeconomics across within the metro area.

74 Table 4.1: Communities with the highest unweighted socio-economic vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Brandon 13.72731 19.21963 Charleston Goose Creek - Tampa 12.08694 16.87964 Hanahan Goose Creek - Tampa 11.59130 14.83834 Hanahan Charleston - North Tampa 11.49204 14.61924 Charleston Charleston - North Clearwater 11.12155 13.81272 Charleston Goose Creek - St. Petersburg 10.93033 13.24338 Hanahan Tampa 10.78717 Summerville 12.94942 Charleston - North St. Petersburg 10.70457 12.65137 Charleston New Port Richey 10.64256 Summerville 12.51043 Charleston - North Tampa 10.56888 12.48003 Charleston

Additionally, the exclusion of Citrus Park from the most vulnerable and inclusion in the least vulnerable categories can be explained by the fact that this newly developed area is characterized by more highly educated, more wealthy, and less dense suburban populations. The Charleston study area shows much the same pattern as the Tampa-St.

Petersburg study area, with the Charleston – North Charleston area holding five places in the most and least vulnerable categories, suggesting substantial changes in socioeconomic and demographic conditions within the metro regions. Additionally,

Mount Pleasant has four communities with low socioeconomic vulnerability, characterized by low levels of poverty and social security beneficiaries as well as lower levels of renter occupied housing units and generally higher numbers of persons per

75 household. Much the same as Citrus Park, FL, Mount Pleasant, SC is a growing suburban area with a generally better educated middle to upper middle class population.

Table 4.2: Communities with the lowest unweighted socio-economic vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Tampa 1.75119 4.49750 Charleston Charleston - North Tampa 1.92920 4.80404 Charleston Citrus Park - Fern Charleston - North 2.57388 4.97029 Lake Charleston Charleston - North Tampa 2.78649 5.05122 Charleston Clearwater 2.81742 Mount Pleasant 5.19326 Brandon 2.88451 Mount Pleasant 5.24761 Citrus Park - Fern 2.91038 James Island 5.27877 Lake Charleston - North St. Petersburg 3.01137 5.36350 Charleston Tampa 3.03505 Mount Pleasant 5.42133 Clearwater 3.05598 Mount Pleasant 5.58527

4.1.2 Unweighted Built-environment Vulnerability

The factors used to identify built environment vulnerability for the study areas were converted into z-scores and summed to produce an unweighted built environment

vulnerability score for each block group. Figures 4.5 through 4.8 depict the scores for built environment vulnerability and associated spatial clusters for Tampa – St.

Petersburg, Florida and Charleston, South Carolina, respectively. As before, the vulnerability scores in these figures are classified as standard deviations from the mean in order that scores further from the average score can easily be identified.

76 VULNERABILITY SCORE < -1.5 Std. Deviation -1.5 - -0.5 Std. Deviation -0.5 - 0.5 Std. Deviation µ 0.5 - 1.5 Std. Deviation 05102.5 1.5 - 2.5 Std. Deviation Miles > 2.5 Std. Deviation

Figure 4.5: Unweighted built environment vulnerability for Tampa – St. Petersburg, FL

Spatial Cluster µ High - High 05102.5 Miles Low - Low

Figure 4.6: Spatial clusters of unweighted built environment vulnerability in Tampa – St. Petersburg, FL

77 VULNERABILITY SCORE < -0.5 Std. Deviations µ -0.5 - 0.5 Std. Deviations 0.5 - 1.5 Std. Deviations 1.5 - 2.5 Std. Deviations 05102.5 Miles > 2.5 Std. Deviations

Figure 4.7: Unweighted built environment vulnerability for Charleston, SC

µ Spatial Cluster High - High 05102.5 Miles Low - Low

Figure 4.8: Spatial clusters of unweighted built environment vulnerability in Charleston, SC

78 Spatial clusters of high and low built environment vulnerability were

differentiated through the application of a Moran’s I statistic to the vulnerability layers.

The results of this statistical test showed weak positive spatial autocorrelation in both

case study areas. Tampa-St. Petersburg had a Moran’s I of 0.2134 at the 0.001

confidence level , while Charleston had a Moran’s I of 0.2276 at the 0.001 confidence

level. When displayed in Figures 4.6 and 4.8, the spatial delineation between clusters of

high and low vulnerability are easily recognizable.

Five major clusters of high built environment vulnerability present themselves

across the Tampa – St. Petersburg study area. The central business district in Central

Pinellas County, one cluster along the boarder of Pinellas and Hillsborough Counties that

is characterized by a modest numbers of establishments and with relatively low levels of

income, one along North Dale Mabry Highway in central Hillsborough County which is

comprised of higher numbers of retail, accommodation and food service and

manufacturing establishments that do not bring in very high amounts of receipts and

another two along the western terminus of the Interstate 4 corridor into downtown

Tampa. One of these areas, Ybor City, is an old cigar factory area that is now host to

bars and clubs and the other is an old warehouse district that has lost to cheaper

competition along the I-4 corridor towards Orlando. Also, five clusters of low built

environment vulnerability can be seen across the study area. Two larger clusters are

present in east central and southeastern Hillsborough County. Both are characterized by

generally low numbers of manufacturing, retail, accommodation and food service establishments and generally modest levels of income. One smaller cluster in central

Hillsborough County is thriving as a new trade area that has grown with the gentrification

79 of the urban core. Additionally, one small cluster of block groups in south eastern St.

Petersburg and one moderate cluster in central Pinellas County are present. Both of these areas are dependent upon tourist dollars for their livelihood and contain large numbers of retail trade and accommodation and food service establishments.

Three main clusters of high built-environment vulnerability are present in

Charleston. One is to the northwest of downtown, one is to the east of the central city area, and one is a large cluster that encompasses many block groups in northern Berkeley

County. The two clusters nearest the city center suffer from lack of income sufficient enough to offset the number of establishments within each area, while the large area in

Berkeley County, which also has a large number of businesses and relatively small amounts of income associated with said establishments, is also characterized by a rather large number of mobile homes in comparison to the rest of the study area. Conversely, two smaller clusters representing low built-environment vulnerability are present in the study area. Both in Charleston County, one is located along the main shopping district in the downtown Charleston area and one is located north of the central city area. Both of these clusters are characterized by a relatively small number of businesses that bring in adequate income to the area.

These built-environment vulnerability scores were also spatially joined to city and town limit information for the study areas in order to gain a better statistical understanding of the variation in built environment vulnerability across the study areas.

Table 4.3 shows the communities and scores of the top ten most vulnerable places in

Tampa and Charleston according to built environment indicators.

80 Table 4.3: Block groups with the highest unweighted built environment vulnerability in Tampa – St. Petersburg, FL and Charleston, SC Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Tampa 12.95380 12.90870 Charleston Tarpon Springs 10.75316 Mount Pleasant 12.08443 Goose Creek - Tampa 10.31427 11.31694 Hanahan Charleston - North Boca Ciega 9.85830 11.15207 Charleston Goose Creek - Tampa 9.83842 10.84964 Hanahan Charleston - North Tampa 9.69724 10.74328 Charleston Tampa 9.64192 Mount Pleasant 10.28559 Charleston - North St. Petersburg 9.62582 10.22705 Charleston Charleston - North Tampa 9.57929 10.05409 Charleston Clearwater 9.54010 Mount Pleasant 10.03472

Again, the City of Tampa has a large number of highly vulnerable block groups in terms of built-environment indicators, with six communities showing up in the top ten most vulnerable communities. Fifty percent of the most vulnerable block groups in terms of built environment indicators in the Charleston study area are located within the confines of the city of Charleston-North Charleston, with an additional thirty percent located in Goose Creek. These communities are characterized predominantly by average numbers of housing units and larger numbers of accommodation and food service establishments with relatively low incomes. Table 4.4 shows the communities and scores of the top ten least vulnerable places in Tampa and Charleston according to built environment indicators. Of particular interest is the fact that the City of Tampa does not make the list of the ten least vulnerable communities and that eighty percent of the least

81 vulnerable places in the Charleston metropolitan area are found within the city of

Charleston-North Charleston.

Table 4.4: Block groups with the lowest unweighted built environment vulnerability in Tampa – St. Petersburg, FL and Charleston, SC Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North St. Petersburg 7.49776 7.33152 Charleston Charleston - North Central Pasco 7.74124 8.04562 Charleston Goose Creek - Clearwater 7.93751 8.05145 Hanahan Clearwater 7.96202 James Island 8.06046 Charleston - North Clearwater 7.96530 8.06186 Charleston Charleston - North Clearwater 7.97059 8.06977 Charleston Charleston - North St. Petersburg 7.97778 8.07085 Charleston Charleston - North Thonotosassa 7.98052 8.07098 Charleston Charleston - North Clearwater 7.98053 8.07497 Charleston Charleston - North Brandon 7.98885 8.07586 Charleston

4.1.3 Unweighted Accessibility Vulnerability

Each of the factors used in identifying accessibility vulnerability were converted

into z-scores and summed to produce an unweighted accessibility vulnerability score for

each block group. Accessibility vulnerability scores and spatial clusters can be seen in

Figures 4.9 and 4.10 for Tampa – St. Petersburg, Florida and in Figures 4.11 and 4.12 for

Charleston, South Carolina. The vulnerability scores in Figures 4.9 and 4.11 are

classified as standard deviations from the mean in order that scores further from the average score can easily be identified.

82 VULNERABILITY SCORE < -0.5 Std. Deviation -0.5 - 0.5 Std. Deviation µ 0.5 - 1.5 Std. Deviation 1.5 - 2.5 Std. Deviation 05102.5 Miles > 2.5 Std. Deviation

Figure 4.9: Unweighted accessibility vulnerability for Tampa – St. Petersburg, FL

Spatial Cluster µ High - High 05102.5 Miles Low - Low

Figure 4.10: Spatial clusters of unweighted accessibility vulnerability in Tampa – St. Petersburg, FL

83 VULNERABILITY SCORE < -0.5 Std. Deviations µ -0.5 - 0.5 Std. Deviations 0.5 - 1.5 Std. Deviations 1.5 - 2.5 Std. Deviations 05102.5 Miles > 2.5 Std. Deviations

Figure 4.11: Unweighted accessibility vulnerability for Charleston, SC

µ Spatial Cluster High - High 05102.5 Miles Low - Low

Figure 4.12: Spatial clusters of unweighted accessibility vulnerability in Charleston, SC

84 Application of a Moran’s I statistic allowed for the differentiation of spatial clusters depicting high and low accessibility vulnerability. A strong positive spatial autocorrelation was present in both study areas. Tampa – St. Petersburg had a Moran’s I of 0.8323 at the 0.001 confidence level, while in Charleston, the Moran’s I was 0.6762 at the 0.0001 confidence level. Once mapped, these areas of high and low vulnerability were clearly distinguishable in both study areas. As one would normally presume, the interiors of the study areas exhibit clusters of low accessibility vulnerability, while the outskirts of each study area generally display clusters of high accessibility vulnerability.

Two areas stand out in the Tampa – St. Petersburg study area as having clusters of high accessibility vulnerability, although they are within or near to the central cities of the study area. These are South Tampa and southern Pinellas County. These areas are vulnerable from an accessibility viewpoint because of their general lack of connectivity to the remainder of the city, and thus to essential goods and services. South Tampa, including MacDill Air Force base and the surrounding area, and South St. Petersburg have high accessibility vulnerability because they are both situated on the southernmost point of a peninsula. The tables found in Appendix B show the census block groups, county commission districts, and distances for the least serviced places to the nearest location for each good or service type in Tampa and Charleston study areas.

According to accessibility indicators, table 4.5 shows the communities and scores of the top ten most vulnerable places in Tampa and Charleston. As discussed earlier, all of these places fall on the outskirts of the central city area and are characterized by a general lack of accessibility to many if not all of the indicators of access to needed goods and services. Of particular interest is James Island, South Carolina. Although this

85 Table 4.5: Communities with the highest unweighted accessibility vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Central Pasco 11.56639 McClellenville 13.37764 Wimauma - Lithia 9.80741 James Island 9.16928 New Port Richey 9.62308 James Island 8.93762 Goose Creek - Thonotosassa 8.66999 8.40724 Hanahan St. Petersburg 8.54758 Mount Pleasant 7.49865 Wimauma - Lithia 8.02557 Summerville 7.28610 Plant City 7.78832 Mount Pleasant 7.28188 Citrus Park - Fern 7.49009 Bear Swamp 6.88744 Lake Goose Creek - Plant City 7.37688 6.75239 Hanahan Central Pasco 7.22529 James Island 6.71328

community is not found on either the top ten most vulnerable according to socioeconomics or the built environment, three places within this community are among the top ten most vulnerable according to accessibility.

Conversely, table 4.6 shows the communities and scores of the top ten least vulnerable places in Tampa and Charleston. As expected, all of these places fall within the confines of the central cities of each study area and have adequate access to all of the needed goods and services detailed in this research. Of particular interest is the fact that

Charleston-North Charleston account for all of the most accessible places in the

Charleston Metropolitan Area.

86 Table 4.6: Communities with the lowest unweighted accessibility vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North St. Petersburg 0.64692 0.80212 Charleston Charleston - North St. Petersburg 0.65390 0.81496 Charleston Charleston - North St. Petersburg 0.73834 0.82528 Charleston Charleston - North Tarpon Springs 0.74718 0.83465 Charleston Charleston - North St. Petersburg 0.75436 0.84386 Charleston Charleston - North St. Petersburg 0.77645 0.84742 Charleston Charleston - North Tampa 0.79851 0.85948 Charleston Charleston - North Clearwater 0.81270 0.88397 Charleston Charleston - North Clearwater 0.81869 0.88462 Charleston Charleston - North Clearwater 0.82522 0.89033 Charleston

4.1.4 Unweighted Social Vulnerability

Each of the factors used in the identification of social vulnerability were standardized across the study areas uszing z-scores and summed to produce an unweighted social vulnerability score for each block group. Figures 4.13 through 4.16 depict the scores for unweighted social vulnerability and associated spatial clusters for

Tampa – St. Petersburg, Florida and Charleston, South Carolina, respectively. As before, the vulnerability scores in these figures are classified as standard deviations from the mean in order that scores further from the average score can easily be identified.

87 VULNERABILITY SCORE < -1.5 Std. Dev. -1.5 - -0.5 Std. Dev. -0.5 - 0.5 Std. Dev. µ 0.5 - 1.5 Std. Dev. 05102.5 1.5 - 2.5 Std. Dev. Miles > 2.5 Std. Dev.

Figure 4.13: Standardized unweighted social vulnerability for Tampa – St. Petersburg, FL

Spatial Cluster µ High - High 05102.5 Miles Low - Low

Figure 4.14: Spatial clusters of Standardized unweighted social vulnerability for Tampa – St. Petersburg, FL

88 VULNERABILITY SCORE < -0.5 Std. Deviation µ -0.5 - 0.5 Std. Deviation 0.5 - 1.5 Std. Deviation

05102.5 > 1.5 Std. Deviation Miles

Figure 4.15: Standardized unweighted social vulnerability for Charleston, SC

µ Spatial Cluster High - High 05102.5 Miles Low - Low

Figure 4.16: Spatial clusters of standardized unweighted social vulnerability in Charleston, SC

89 Spatial clusters of high and low social vulnerability were differentiated through

the application of a Moran’s I statistic to the vulnerability layers. The results of this

statistical test showed moderately strong positive spatial autocorrelation in both case study areas. Tampa-St. Petersburg had a Moran’s I of 0.5566 at the 0.001 confidence level , while Charleston had a Moran’s I of 0.4438 at the 0.001 confidence level. When displayed in Figures 4.6 and 4.8, the spatial delineation between clusters of high and low vulnerability are easily identifiable. An agglomeration of the three major components of social vulnerability in an equally weighted fashion produces a cluster map resembling a spatial union of all three previous cluster maps for each study area, with high vulnerability clusters in the periphery and suburban spaces of the study areas and clusters of low social vulnerability in the numerous central city areas within each study areas.

Tables 4.7 and 4.8 show the communities, scores, and contribution of each of the

three components of social vulnerability of the top ten most socially vulnerable places in

Tampa and Charleston, respectively. The contribution that each of the three components

of vulnerability gives to the overall score of the top ten most vulnerable places changes

with each location, however, certain trends can be seen within and between study areas.

Generally, accessibility plays a much larger role in overall unweighted social

vulnerability in the Tampa – St. Petersburg metropolitan area than in the Charleston area.

Conversely, built environment indication of vulnerability is consistently high among the

most vulnerable communities in Charleston.

Tables 4.9 and 4.10 show the communities, scores, and contribution of each of the

three components of social vulnerability of the top ten least socially vulnerable places in

Tampa and Charleston, respectively. Built environment vulnerability is the most

90 influential component across the board for places in the Tampa – St. Petersburg metropolitan area as well as the Charleston urbanized area, while accessibility generally falls to the least influential component in the unweighted social vulnerability of the least vulnerable communities.

Table 4.7: Communities with the highest unweighted standardized social vulnerability in Tampa – St. Petersburg, FL Vulnerability Community Component Contribution Score Built Environment – 30.72% Central Pasco 2.18912 Accessibility – 45.68% Socioeconomic – 23.6% Built Environment – 32.88% New Port Richey 1.93761 Accessibility – 29.71% Socioeconomic – 37.96% Built Environment – 32.44% Wimauma - Lithia 1.90961 Accessibility – 44.31% Socioeconomic – 23.25% Built Environment – 34.45% New Port Richey 1.90102 Accessibility – 43.67% Socioeconomic – 21.88% Built Environment – 35.38% Brandon 1.87592 Accessibility – 11.31% Socioeconomic – 53.3% Built Environment – 32.3% Central Pasco 1.86297 Accessibility – 29.05% Socioeconomic – 38.65% Built Environment – 35.79% Port Richey 1.80522 Accessibility – 33.5% Socioeconomic – 30.71% Built Environment – 46.33% Tarpon Springs 1.79551 Accessibility – 14.48% Socioeconomic – 39.19% Built Environment – 34.54% Thonotosassa 1.79361 Accessibility – 41.7% Socioeconomic – 23.76% Built Environment – 39.05% New Port Richey 1.79048 Accessibility – 42.35% Socioeconomic – 26.29%

91 Table 4.8: Communities with the highest unweighted standardized social vulnerability in Charleston, SC based on block groups within each community Vulnerability Community Component Contribution Score Built Environment – 46.24% Charleston - North 2.16473 Accessibility – 7.61% Charleston Socioeconomic – 46.2% Built Environment – 31.16% McClellanville 2.06334 Accessibility – 48.47% Socioeconomic – 20.38% Built Environment – 43.2% Goose Creek – 2.02737 Accessibility – 24.9% Hanahan Socioeconomic – 31.9% Built Environment – 44.86% Goose Creek – 1.87281 Accessibility – 21.67% Hanahan Socioeconomic – 33.47% Built Environment – 40.8% Goose Creek – 1.80093 Accessibility – 10.44% Hanahan Socioeconomic – 48.77% Built Environment – 43.33% Goose Creek - 1.72292 Accessibility – 11.86% Hanahan Socioeconomic – 44.81% Built Environment – 40.53% James Island 1.69541 Accessibility – 28.63% Socioeconomic – 30.85% Built Environment – 39.53% Goose Creek – 1.67775 Accessibility – 37.47% Hanahan Socioeconomic – 23.0% Built Environment – 39.2% Mount Pleasant 1.67480 Accessibility – 32.51% Socioeconomic – 28.3% Built Environment – 39.62% James Island 1.66958 Accessibility – 40.02% Socioeconomic – 20.35%

92 Table 4.9: Communities with the lowest unweighted standardized social vulnerability in Tampa – St. Petersburg, FL based on block groups within each community Vulnerability Community Component Contribution Score Built Environment – 65.62% Clearwater 0.94407 Accessibility – 12.64% Socioeconomic – 21.74% Built Environment – 66.34% Clearwater 0.95759 Accessibility – 7.48% Socioeconomic – 25.98% Built Environment – 64.15% Tampa 0.96570 Accessibility – 14.83% Socioeconomic – 21.02% Built Environment – 63.7% Tampa 0.97250 Accessibility – 23.18% Socioeconomic – 13.12% Built Environment – 62.7% Tampa 0.98798 Accessibility – 12.08% Socioeconomic – 25.22% Built Environment – 62.19% Tampa 0.99608 Accessibility – 23.7% Socioeconomic – 14.11% Built Environment – 62.74% Tampa 1.00142 Accessibility – 11.12% Socioeconomic – 26.13% Built Environment – 61.47% Tampa 1.00768 Accessibility – 16.58% Socioeconomic – 21.94% Built Environment – 60.74% Citrus Park - Fern 1.01981 Accessibility – 18.47% Lake Socioeconomic – 20.79% Built Environment – 63.43% Tampa 1.03238 Accessibility – 9.76% Socioeconomic – 26.8%

93 Table 4.10: Communities with the lowest unweighted standardized social vulnerability in Charleston, SC based on block groups within each community Vulnerability Community Component Contribution Score Built Environment – 62.64% Charleston - North 1.00994 Accessibility – 11.33% Charleston Socioeconomic – 26.02% Built Environment – 61.61% Charleston - North 1.04064 Accessibility – 7.6% Charleston Socioeconomic – 30.79% Built Environment – 60.94% Charleston - North 1.04378 Accessibility – 7.89% Charleston Socioeconomic – 31.17% Built Environment – 61.19% Charleston - North 1.04787 Accessibility – 9.64% Charleston Socioeconomic – 29.18% Built Environment – 61.65% Charleston - North 1.05100 Accessibility – 14.57% Charleston Socioeconomic – 23.78% Built Environment – 59.37% James Island 1.05122 Accessibility – 11.38% Socioeconomic – 29.25% Built Environment – 59.12% Charleston - North 1.05862 Accessibility – 5.76% Charleston Socioeconomic – 35.12% Built Environment – 58.79% Charleston - North 1.05872 Accessibility – 6.24% Charleston Socioeconomic – 34.97% Built Environment – 60.54% Charleston - North 1.05905 Accessibility – 10.02% Charleston Socioeconomic – 29.44% Built Environment – 60.67% Charleston - North 1.06238 Accessibility – 17.3% Charleston Socioeconomic – 22.03%

4.2 Expert Weighted Vulnerability Scores

The results of the Delphi method survey give us a better understanding of the importance of each of the characteristics of a place or population as they apply to social vulnerability at the metropolitan level. This more in depth perspective of the significance of each characteristic allows for a more comprehensive and applicable index of urban

94 hazard vulnerability. To that end, this section will discuss the results of the expert survey

in each facet of overall social vulnerability in metropolitan areas.

4.2.1 Weighted Socioeconomic Vulnerability

Table 4.11 shows the weights applied to each variable in an expert weighted

understanding of socioeconomic vulnerability. These expert weight applied to each set of

variables were calculated as the arithmetic mean weight given by all survey respondents for each variable. It is clear from these weights that certain socio-demographic characteristics are considered to be much more important than others in an index for urban social vulnerability. Specifically, per capita income, the value of housing, poverty level, age, and unemployment were given the most weight by survey respondents.

Each of the factors used in the identification of socio-economic vulnerability were

converted to z-scores and summed for each study area so that no individual factor would

influence the final vulnerability score more than another. These standardized

socioeconomic vulnerability variables were then weighted according to their importance

in the Delphi method survey to produce a weighted socioeconomic vulnerability score for

each block group.

Weighted socio-economic vulnerability scores and associated spatial clusters can

be seen in Figures 4.17 and 4.18 for Tampa – St. Petersburg and in Figures 4.19 and 4.20

for Charleston. These vulnerability scores are also classified as standard deviations from

the mean in order that scores further from the average score can easily be identified.

95 Table 4.11: Socio-economic vulnerability factors and Delphi method weights DELPHI SOCIO-ECONOMIC VULNERABILITY FACTOR WEIGHT Economic Factors Per Capita Income 15.125 Median House Value 8.375 Median Rent 1.8125 Households earning more than $100,000 per year (%) 3.34375 Persons living below the poverty level (%) 14.25 Population collecting social security benefits (%) 3.375 Demographic Factors African Americans (%) 4.90625 Native Americans (%) 1.4375 Asian and Hawaiian Islanders (%) 0.9375 Hispanic persons (%) 2.9375 Age – less than 5 years (%) 5.71875 Age – over 65 years (%) 7.5 Birthrate 0.375 Immigration (within last 10 years) 3.1875 People per household 2.21875 Renter occupied housing units (%) 3.0625 Persons over 25 years with not high school diploma (%) 1.3125 Residents in nursing homes (%) 3.78125 Female population (%) 0.75 Female headed households with no spouse present 1.875 Employment Factors Health care workers (%) 1.78125 Civilian labor force unemployment (%) 5.09375 Population participation in labor force (%) 1.15625 Female participation in labor force (%) 1.93750 Employment in farming, fishing, and forestry occupations 1.34375 Employment in transportation, communication and other public 0.9375 utilities Employment is service occupations 1.46875

96 VULNERABILITY SCORE < -2.50 Std. Deviation -2.50 - -1.50 Std. Deviation -1.50 - -0.50 Std. Deviation µ -0.50 - 0.50 Std. Deviation 05102.5 0.50 - 1.50 Std. Deviation Miles > 1.50 Std. Deviation

Figure 4.17: Weighted socio-economic vulnerability for Tampa – St. Petersburg, FL

Spatial Cluster µ High - High 05102.5 Miles Low - Low

Figure 4.18: Spatial clusters of weighted socio-economic vulnerability in Tampa – St. Petersburg, FL

97 VULNERABILITY SCORE < -2.50 Std. Deviation -2.50 - -1.50 Std. Deviation µ -1.50 - -0.50 Std. Deviation -0.50 - 0.50 Std. Deviation 0.50 - 1.50 Std. Deviation 05102.5 Miles > 1.50 Std. Deviation

Figure 4.19: Weighted socio-economic vulnerability for Charleston, SC

µ Spatial Cluster High - High 05102.5 Miles Low - Low

Figure 4.20: Spatial clusters of weighted socio-economic vulnerability in Charleston, SC

98 Spatial clusters of high and low weighted socio economic vulnerability were

differentiated through the application of a Moran’s I statistic to the vulnerability layers.

The results of this statistical test showed a moderately strong positive spatial

autocorrelation in both case study areas. Tampa-St. Petersburg had a Moran’s I of

0.3087 at the 0.001 confidence level , while Charleston had a Moran’s I of 0.3108 at the

0.001 confidence level. When displayed in Figures 4.18 and 4.20, the spatial delineation

between clusters of high and low vulnerability are easily recognizable.

The pattern of clusters seen in Figure 4.18 is much the same as the unweighted

socioeconomic vulnerability clusters in Figure 4.2, with a few exceptions. First, the

cluster of highly vulnerability block groups in central Hillsborough County has grown

considerably, from twenty-five to forty-seven block groups, as has the cluster of highly

vulnerable block groups in eastern Hillsborough County. This area, known as Suitcase

City, is situated near the University of South Florida and is characterized primarily by

lower income, transitional, renter populations who are often employed in service industry

occupations. Additionally, two clusters of low socioeconomic vulnerability have grown

through the use of expert weighting. Both of these clusters, technically located in north

Tampa, are in areas where income is ever increasing, new homes are being built and young families are constantly moving in.

Figure 4.20 displays much the same picture of socio-economic vulnerability as its unweighted counterpart (Figure 4.4). One major difference however, is the fact that the portions of the Charleston study area that exhibited high socioeconomic vulnerability clustering have decreased in number when expert weights were applied to the data.

Specifically, the singular cluster of high socioeconomic vulnerability previously covering

99 much of the northern portion of the metropolitan area has been broken into two smaller

clusters.

These vulnerability scores were also spatially joined to city limit data for the

study areas in order to gain a better understanding of how weighted socioeconomic

vulnerability differs statistically in the study areas. Table 4.12 shows the communities

and weighted vulnerability scores of the top ten highest socioeconomically vulnerability

places in Tampa and Charleston.

Not surprisingly, the City of Tampa contains five of the top ten most

socioeconomically vulnerable communities in the greater metropolitan area. Charleston

– North Charleston contains six of the top ten most vulnerable communities in the study

area. Table 4.13 shows the communities and weighted vulnerability scores of the top ten

least socioeconomically vulnerability places in Tampa and Charleston. The city of

Tampa contains seventy percent of the least vulnerable communities in the Tampa – St.

Petersburg metropolitan area. When compared to the high socioeconomic vulnerability

table, this table exemplifies the fact that real differences in socioeconomics and

demographics exist across the study area. Additionally, Clearwater has two communities

in both the most and least vulnerable tables, solidifying the fact that space is important when looking at hazard vulnerability. Much of the same can be seen in the Charleston study area, with an even split in least socioeconomically vulnerable communities between

Charleston – North Charleston and Mount Pleasant. The communities within the

Charleston – North Charleston area are located in the affluent Battery District, while the

Mount Pleasant communities are located across the river from the downtown Charleston area.

100 Table 4.12: Communities with the highest weighted socio-economic vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Tampa 51.79754 83.11253 Charleston Goose Creek - Tampa 47.70146 64.00686 Hanahan Charleston - North Brandon 43.69100 62.85185 Charleston Charleston - North Tampa 42.26352 59.80687 Charleston Charleston - North Tampa 40.29959 59.24302 Charleston Goose Creek - St. Petersburg 39.89572 56.49531 Hanahan Clearwater 39.11989 Summerville 56.01854 Clearwater 38.92868 Summerville 54.72057 Charleston - North Tampa 38.76142 54.18887 Charleston Charleston - North New Port Richey 38.36001 53.18129 Charleston

Table 4.13: Communities with the lowest weighted socio-economic vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Tampa 8.21176 11.53984 Charleston Charleston - North Tampa 8.50459 15.12137 Charleston Charleston - North Tampa 10.51007 15.30527 Charleston Tampa 10.81507 Mount Pleasant 17.74104 Charleston - North Clearwater 10.86328 17.81434 Charleston Plant City 11.06978 Mount Pleasant 18.95123 Charleston - North Tampa 11.07130 21.54480 Charleston Tampa 11.16341 Mount Pleasant 21.69911 Clearwater 11.28080 Mount Pleasant 21.95177 Tampa 11.40030 Mount Pleasant 22.46021

101 4.2.2 Weighted Built Environment Vulnerability

Table 4.14 shows the weights applied to each variable in an expert weighted understanding of built environment vulnerability. It is clear from these weights that certain characteristics of the humanly constructed environment are considered to be much more important than others in an index for urban social vulnerability. Specifically, the number of mobile homes, housing units, manufacturing establishments, health care establishments, and earnings in administrative support and waste management were given the most weight by survey respondents.

Table 4.14: Built environment vulnerability factors and Delphi method weights DELPHI BUILT ENVIRONMENT VULNERABILITY FACTOR WEIGHT Composition Factor Mobile home density 19.03333 Housing unit density 18.2 Manufacturing establishment density 9.16667 Retail trade establishment density 3.43333 Professional establishment density 2.66667 Educational establishment density 3.76667 Administrative support and waste management establishment 4.6 density Health care and social assistance establishment density 9.06667 Arts, entertainment and recreational establishment density 1.03333 Accommodation and food service establishment density 3.9 All “other” services density 1.26667 Value Factors Retail earnings 4.53333 Professional earnings 2.16667 Administrative support and waste management earnings 6.1 Educational service earnings 1.26667 Health care and social assistance earnings 3.06667 Arts, entertainment and recreational earnings 1.43333 Accommodation and food service earnings 3.0 All “other” services earnings 2.3

The factors used to identify built environment vulnerability for the study areas were converted into z-scores and summed to produce a weighted built environment

102 vulnerability score for each block group. Figures 4.21 through 4.24 depict the weighted scores for built environment vulnerability and associated spatial clusters for Tampa – St.

Spatial clusters of high and low weighted built environment vulnerability were differentiated through the application of a Moran’s I statistic to the vulnerability layers.

The results of this statistical test showed weak positive spatial autocorrelation in both case study areas. Tampa-St. Petersburg had a Moran’s I of 0.3319 at the 0.001 confidence level , while Charleston had a Moran’s I of 0.2559 at the 0.001 confidence level. When displayed in Figures 4.22 and 4.24, the spatial differences in clusters of high and low vulnerability are easily decipherable.

The spatial clusters surface of weighted built environment vulnerability for the

Tampa – St. Petersburg metropolitan area, seen in Figure 4.22, shows three major clusters of high built environment vulnerability. Specifically, two clusters can be seen in Pinellas

County, one in the St. Petersburg downtown area and one that stretches west from the boarder between Hillsborough and Pinellas Counties all the way to Clearwater Beach, and one main cluster located in Pasco County. While the first two clusters are characterized by increased numbers of establishments with relatively low incomes and also high numbers of residential housing units, the cluster in Pasco county is primarily characterized by a large number of mobile homes and a relatively inactive economy compared to the whole study area.

103 VULNERABILITY SCORE < -0.50 Std. Deviation -0.50 - 0.50 Std. Deviation µ 0.50 - 1.50 Std. Deviation 05102.5 > 1.50 Std. Deviation Miles

Figure 4.21: Weighted built environment vulnerability for Tampa – St. Petersburg, FL

Spatial Cluster µ High - High 05102.5 Miles Low - Low

Figure 4.22: Spatial clusters of weighted built environment vulnerability in Tampa – St. Petersburg, FL

104 VULNERABILITY SCORE < -0.50 Std. Deviation µ -0.50 - 0.50 Std. Deviation 0.50 - 1.50 Std. Deviation 1.50 - 2.50 Std. Deviation 05102.5 Miles > 2.50 Std. Deviation

Figure 4.23: Weighted built environment vulnerability for Charleston, SC

µ Spatial Cluster High - High 05102.5 Miles Low - Low

Figure 4.24: Spatial clusters of weighted built environment vulnerability in Charleston, SC

105 The weighted built environment cluster surface for the Charleston metropolitan

area (Figure 4.24) shows three main clusters of high vulnerability and two smaller clusters with low vulnerability. Of the three clusters of high built environment, two are located in the northern part of the urban area where economic activity is far less than in the central city area. The other main cluster of high built environment vulnerability is

located in the Goose Creek area, where the number of houses, and commercial

establishments slightly outweighs the value brought in by those establishments.

These vulnerability scores were also joined to city limit data for the study areas in

order to gain a better understanding of how weighted built environment vulnerability

differs statistically across space. Table 4.15 shows the communities and weighted

vulnerability scores of the top ten most vulnerability places in Tampa and Charleston

according to built environment indicators.

Clearwater, Florida stands out in this table with five of the top ten most vulnerable communities, according to built environment indicators. This is mainly because of an increased number of accommodation and food service establishments that are dependant upon tourist dollars for their survival and an ever increasing number of

homes and mobile homes in this beautiful beach town.

Table 4.16 shows the communities and weighted vulnerability scores of the top

ten least vulnerability places in Tampa and Charleston according to built environment

indicators. Clearwater stands out again as an anomaly when it comes to weighted built

environment vulnerability. Although the top three most vulnerable places according

weighted built environment vulnerability can be founding Clearwater, this city also

106 Table 4.15: Communities with the highest weighted built environment vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Clearwater 58.41930 69.17895 Charleston Goose Creek - Clearwater 57.02279 59.71701 Hanahan Clearwater 56.70990 Mount Pleasant 55.43047 Charleston - North St. Petersburg 51.89479 52.40690 Charleston Goose Creek - Tampa 51.11970 48.36388 Hanahan Charleston - North Tarpon Springs 50.46150 47.73378 Charleston Goose Creek - Clearwater 49.85577 47.37006 Hanahan Charleston - North Clearwater 47.01685 47.22621 Charleston Charleston - North Port Richey 46.21600 47.16889 Charleston Goose Creek - Tarpon Springs 45.03722 45.64251 Hanahan

contains five of the least vulnerable communities when looking at weighted built environment vulnerability. This fact is primarily due to the large dependence on a thriving upscale tourist industry along the gulf coast of Pinellas County. The same holds true for Charleston, which contains eighty percent of the least vulnerable places according to weighted built environment vulnerability. The city of Charleston’s tourist economy, along with its trendy downtown shopping area and significant lack of mobile homes allow many communities a low weighted built environment vulnerability score.

107 Table 4.16: Communities with the lowest unweighted built environment vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North St. Petersburg 7.49776 7.33152 Charleston Charleston - North Central Pasco 7.74124 8.04562 Charleston Goose Creek - Clearwater 7.93751 8.05145 Hanahan Clearwater 7.96202 James Island 8.06046 Charleston - North Clearwater 7.96530 8.06186 Charleston Charleston - North Clearwater 7.97059 8.06977 Charleston Charleston - North St. Petersburg 7.97778 8.07085 Charleston Charleston - North Thonotosassa 7.98052 8.07098 Charleston Charleston - North Clearwater 7.98053 8.07497 Charleston Charleston - North Brandon 7.98885 8.07586 Charleston

4.2.3 Weighted Accessibility Vulnerability

Table 4.17 shows the weights applied to each variable in an expert weighted

understanding of accessibility vulnerability. It is clear from these weights that certain

characteristics of the access and lifelines are considered to be much more important than

others in an index for urban social vulnerability. Specifically, proximity to hospitals,

emergency shelters, and fire stations were given the most weight by survey respondents,

with others such as proximity to doctors, grocery stores, public transportation, police

stations, churches, and social groups given moderate amounts of weight in an overall understanding of accessibility vulnerability.

108 Table 4.17: Accessibility vulnerability factors and Delphi method weights ACCESSIBILITY DELPHI WEIGHT VULNERABILITY FACTOR Proximity to hospital 15.46667 Proximity to doctor/health care facility 7.6 Proximity to grocery store 6.83333 Proximity to fire station 10.16667 Proximity to home improvement store 3.1 Proximity to public transportation 5.4 Proximity to discount store 1.33333 Proximity to police station 6.5 Proximity to pharmacy 4.43333 Proximity to church 6.76667 Proximity to social group 6.23333 Proximity to school 4.83333 Proximity to day care 2.23333 Proximity to emergency shelter 19.1

Each of the factors used in the identification of accessibility vulnerability were converted into z-scores and summed for each block group in both study areas. These standardized accessibility vulnerability variables were then weighted according to their importance in the Delphi method survey to produce a weighted accessibility vulnerability score for each block group. Weighted accessibility vulnerability scores and spatial clusters are depicted in Figures 4.25 and 4.26 for Tampa – St. Petersburg, Florida and in

Figures 4.27 and 4.28 for Charleston, South Carolina.

Spatial clusters depicting high and low weighted accessibility vulnerability for the study areas were identified by applying a Moran’s I statistic. A strong positive spatial autocorrelation was present in both study areas. Tampa – St. Petersburg had a Moran’s I of 0.8496 at the 0.001 confidence level, while in Charleston, the Moran’s I was 0.7255 at the 0.0001 confidence level. Once mapped, these areas of high and low vulnerability were clearly distinguishable in both study areas. As one would normally presume, the

109 VULNERABILITY SCORE < -0.50 Std. Deviation -0.50 - 0.50 Std. Deviation µ 0.50 - 1.50 Std. Deviation 1.50 - 2.50 Std. Deviation 05102.5 Miles > 2.50 Std. Deviation

Figure 4.25: Weighted accessibility vulnerability for Tampa – St. Petersburg, FL

Spatial Cluster µ High - High 05102.5 Miles Low - Low

Figure 4.26: Spatial clusters of weighted accessibility vulnerability in Tampa – St. Petersburg, FL

110 VULNERABILITY SCORE < -0.5 Std. Deviation µ -0.5 - 0.5 Std. Deviation 0.5 - 1.5 Std. Deviation

05102.5 > 1.5 Std. Deviation Miles

Figure 4.27: Weighted accessibility vulnerability for Charleston, SC

µ Spatial Cluster High - High 05102.5 Miles Low - Low

Figure 4.28: Spatial clusters of weighted accessibility vulnerability in Charleston, SC

111

interiors of the study areas exhibit clusters of low accessibility vulnerability, while the

outskirts of each study area generally display clusters of high accessibility vulnerability.

The weighted spatial cluster surfaces for each of the study areas (Figures 4.26 and

4.28) do not show significant differences from the unweighted accessibility cluster

surfaces (Figures 4.10 and 4.12) mainly because of the strong spatial auto correlation of

the original unweighted surface and the simple fact that accessibility is a function of

distance. Since more of the needed goods and services are located near the central

business districts within each study area, the obvious truth is that access will decrease the

further one travels from the central city area.

Gaining a more robust understanding of statistical differences in weighted

accessibility vulnerability across space was accomplished by joining this vulnerability

data to city limit data for the study areas. Tables 4.18 and 4.19 show the communities

and weighted vulnerability scores of the top ten most and least vulnerability places in

Tampa and Charleston according to accessibility indicators. The places in these tables

follow closely to the information conveyed in the cluster maps. One can see that places

further from the city centers which normally contain the most goods and services are

more vulnerable from an accessibility standpoint, and those that are closest to the central

city area exhibit less accessibility vulnerability because more needed goods and services

are located within major urban hubs.

112 Table 4.18: Communities with the highest weighted accessibility vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Central Pasco 80.08478 McClellenville 95.86131 New Port Richey 67.28349 James Island 71.50145 Wimauma - Lithia 66.59421 James Island 69.22057 Goose Creek - St. Petersburg 60.58262 58.79872 Hanahan Thonotosassa 59.71027 Mount Pleasant 58.62425 Wimauma - Lithia 57.77549 Mount Pleasant 58.07817 Plant City 55.87270 James Island 54.48322 New Port Richey 53.63986 James Island 54.09276 Central 53.24434 Mount Pleasant 51.97721 Pasco Wimauma - Lithia 51.86392 Summerville 50.79746

Table 4.19: Communities with the lowest weighted accessibility vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Tarpon Springs 4.60358 6.22778 Charleston Charleston - North St. Petersburg 4.75221 6.39264 Charleston Charleston - North St. Petersburg 5.00106 6.55647 Charleston Charleston - North Clearwater 5.03591 7.94397 Charleston Charleston - North St. Petersburg 5.38671 7.96674 Charleston Charleston - North St. Petersburg 5.46065 8.09780 Charleston Charleston - North St. Petersburg 5.48923 8.12285 Charleston Charleston - North St. Petersburg 5.49420 8.16668 Charleston Goose Creek - St. Petersburg 5.53461 8.26738 Hanahan Charleston - North Clearwater 6.00988 8.35555 Charleston

113 4.2.4 Weighted Social Vulnerability

Table 4.20 shows the weights applied to each variable in an expert weighted

understanding of social vulnerability. It is clear from these weights that the three facets

of overall social vulnerability are not considered equally important in an index for urban social vulnerability. Specifically, socioeconomic and built environment vulnerability were given almost equal weights, while accessibility was given nearly 10 points less weight overall.

Table 4.20: Overall Social Vulnerability factors and Delphi method weights SOCIAL VULNERABILITY DELPHI WEIGHT FACTOR Socioeconomic Vulnerability 36.55172 Built Environment Vulnerability 36.86207 Accessibility Vulnerability 26.58621

Each of the factors used in the identification of social vulnerability were standardized for the study areas by computing z-scores so that no individual factor would

influence the final vulnerability score more than another. These standardized social

vulnerability variables were then weighted according to their importance in the Delphi

method survey to produce a weighted social vulnerability score for each block group.

Figures 4.29 and 4.30 depict weighted social vulnerability scores and clusters of high and

low weighted social vulnerability for Tampa-St. Petersburg, Florida, while Figures 4.31

and 4.32 show the same information for Charleston, South Carolina.

114 VULNERABILITY SCORE < -1.50 Std. Deviation -1.50 - -0.50 Std. Deviation -0.50 - 0.50 Std. Deviation µ 0.50 - 1.50 Std. Deviation 05102.5 1.50 - 2.50 Std. Deviation Miles > 2.50 Std. Deviation

Figure 4.29: Weighted social vulnerability for Tampa – St. Petersburg, FL

Spatial Cluster µ High - High 05102.5 Miles Low - Low

Figure 4.30: Spatial clusters of weighted social vulnerability in Tampa – St. Petersburg, FL

115 VULNERABILITY SCORE < -1.50 Std. Deviation µ -1.50 - -0.50 Std. Deviation -0.50 - 0.50 Std. Deviation 0.50 - 1.50 Std. Deviation 05102.5 Miles > 1.50 Std. Deviation

Figure 4.31: Weighted social vulnerability for Charleston, SC

µ Spatial Cluster High - High 05102.5 Miles Low - Low

Figure 4.32: Spatial clusters of weighted social vulnerability in Charleston, SC

116 Application of a Moran’s I statistic to the weighted social vulnerability layers enabled the delineation of spatial clusters of high and low weighted social vulnerability.

The results of this statistical test showed moderately strong positive spatial utocorrelation in both case study areas. Tampa-St. Petersburg had a Moran’s I of 0.4222 at the 0.001 confidence level , while Charleston had a Moran’s I of 0.3288 at the 0.001 confidence level. When displayed in Figures 4.30 and 4.32, the spatial delineation between clusters of high and low vulnerability are easily identifiable.

Although not given as much weight as the other factors, accessibility plays a big part in the fact that clusters of high social vulnerability essentially ring the two study areas. Since these areas had such high levels of accessibility vulnerability before the expert weighting, the fact that they were not given as much importance in overall social vulnerability did not fully retard their impact on overall vulnerability.

In addition to the weighted social vulnerability cluster maps, these vulnerability scores were spatially joined to city limit data for the study areas in order to gain a better understanding of how social vulnerability differs statistically across space. Table 4.21 shows the communities and weighted vulnerability scores of the top ten most socially vulnerability places in Tampa and Charleston. Once again, Clearwater stands out with four communities in the top ten most vulnerable places in the Tampa – St. Petersburg metropolitan area. These places generally have a greater number of houses, a larger number of commercial establishments, and often limited access to important services such as emergency shelters. Charleston – North Charleston and Goose Creek – Hanahan account for eighty percent of the most vulnerable communities in the Charleston metropolitan area.

117 Table 4.21: communities with the highest weighted social vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Clearwater 67.68543 77.51578 Charleston Goose Creek – New Port Richey 67.17756 65.06926 Hanahan Goose Creek - Central Pasco 66.73843 61.03358 Hanahan Charleston - North Clearwater 65.56438 60.02870 Charleston Goose Creek – St. Petersburg 64.58512 57.48492 Hanahan Clearwater 63.16232 McClellenville 57.46448 Goose Creek – Brandon 63.07071 56.23185 Hanahan Tarpon Springs 62.80025 Summerville 53.96252 Charleston - North Central Pasco 61.79708 53.60195 Charleston Charleston - North Clearwater 61.63854 53.29852 Charleston

These places, located in and close to central city area, are characterized by a generally

high number of housing units and commercial establishments. Table 4.22 shows the

communities and weighted vulnerability scores of the top ten least socially vulnerable

places in Tampa and Charleston. The fact that Clearwater shows up again with three of

the top ten least vulnerable communities exemplifies that fact that changes over space in

access, socio-demographics and the composition and vitality of the built environment can

impact how one area might have the capacity to handle a disaster event more easily than

another area within the same municipality. The same holds true for the Charleston

metropolitan area, where a full ninety percent of the least vulnerable communities are located within the city of Charleston, which also contains four of the most vulnerable.

118 Table 4.22: Communities with the lowest weighted social vulnerability in Tampa – St. Petersburg, FL and Charleston, SC based on block groups within each community Tampa – St. Petersburg Charleston Vulnerability Vulnerability Community Community Score Score Charleston - North Tampa 27.62892 25.70573 Charleston Charleston - North Tampa 28.54381 25.99263 Charleston Charleston - North Clearwater 28.66049 26.49575 Charleston Charleston - North Plant City 28.69658 27.11335 Charleston Clearwater 29.24675 James Island 27.86809 Charleston - North Tampa 29.43086 28.48254 Charleston Citrus Park - Fern Charleston - North 29.54963 29.62810 Lake Charleston Charleston - North Tampa 29.62026 29.89440 Charleston Charleston - North Clearwater 29.67870 29.98403 Charleston Charleston - North Clearwater 29.84500 29.98627 Charleston

4.3 Comparing Unweighted and Weighted Vulnerability Scores

Understanding how differences in expert weighting play into a framework of urban social vulnerability is an important part of this research. Specifically, where the weighted vulnerability scores are significantly different than the unweighted scores

within each study area is key in the discovery of which people and places are most

vulnerable to hazard events in metropolitan areas. To that end, this section discusses the

differences between the unweighted vulnerability scores and the weighted vulnerability scores for all three facets of social vulnerability as well as overall urban social vulnerability.

4.3.1 Unweighted versus Weighted Socioeconomic Vulnerability

119 As discussed earlier, the most important characteristics of a person or place that lead to increased social vulnerability according to the experts surveyed through this research were per capita income, the value of housing, poverty level, age, and unemployment. Since these variables were given the most weight in an understanding of socioeconomic vulnerability, one would assume that places within each study area with higher levels of said characteristics would become more vulnerable and places with lower levels of each would exhibit lower socioeconomic vulnerability. A correlation of the ranked weighted and unweighted socioeconomic vulnerability scores for the two study areas showed strong and significant correlations of 0.952 for Tampa – St. Petersburg and

0.947 for Charleston between the two scores, indicating that weighting socioeconomic vulnerability does not have a significant impact on changes in rank in a global setting

(taking into account the whole study area), although local differences (variations across a smaller spatial area such as a neighborhood to community) can be seen throughout the study areas. Along these lines, tables 4.23 through and 4.26 show the communities, vulnerability scores and rankings of the most and least vulnerable places in the Tampa –

St. Petersburg and Charleston metropolitan areas according to their unweighted and weighted socioeconomic vulnerability scores. Minor differences can be seen in the rankings of the most vulnerable places in Tampa – St. Petersburg with all but two of the top ten unweighted communities showing up as the most vulnerable places once the expert weights were applied to the data. These two areas are characterized by high levels of unemployment as well as high levels of low per capita income. The same is true for the Charleston metropolitan area, where all but one of the top ten unweighted communities

120 Table 4.23: Communities with the highest unweighted and weighted socioeconomic vulnerability in Tampa – St. Petersburg, FL based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Brandon 13.72731 Tampa 51.79754 3RD

Tampa 12.08694 Tampa 47.70146 2ND

Tampa 11.59130 Brandon 43.69100 1ST

Tampa 11.49204 Tampa 42.26352 10TH

Clearwater 11.12155 Tampa 40.29959 4TH

St. Petersburg 10.93033 St. Petersburg 39.89572 8TH

Tampa 10.78717 Clearwater 39.11989 5TH

St. Petersburg 10.70457 Clearwater 38.92868 42ND

New Port Richey 10.64256 Tampa 38.76142 21ST

Tampa 10.56888 New Port Richey 38.36001 9TH

121 Table 4.24: Communities with the highest unweighted and weighted socioeconomic vulnerability in Charleston, SC based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Charleston - Charleston - 19.21963 83.11253 1ST North Charleston North Charleston Goose Creek - Goose Creek - 16.87964 64.00686 2ND Hanahan Hanahan Goose Creek - Charleston - 14.83834 62.85185 4TH Hanahan North Charleston Charleston - Charleston - 14.61924 59.80687 12TH North Charleston North Charleston Charleston - Charleston - 13.81272 59.24302 5TH North Charleston North Charleston Goose Creek - Goose Creek - 13.24338 56.49531 3RD Hanahan Hanahan Summerville 12.94942 Summerville 56.01854 7TH Charleston - 12.65137 Summerville 54.72057 9TH North Charleston Charleston - Summerville 12.51043 54.18887 8TH North Charleston Charleston - Charleston - 12.48003 53.18129 10TH North Charleston North Charleston were also found to be in the top ten most socioeconomically vulnerable places. This community, however, was ranked twelfth in unweighted socioeconomic vulnerability and exhibits low numbers of families earning greater than $100,000 per year as well as low level of general labor force participlation.

One community in the Tampa Bay area sticks out in Table 4.25 as having low weighted socioeconomic vulnerability even though it ranked nearly 25% more vulnerable when using unweighted indication of vulnerability. This community, located in Plant

City, although characterized by relatively low labor force participation, has a very high per capita income and a very high number of families earning more than $100,000

122 Table 4.25: Communities with the lowest unweighted and weighted socioeconomic vulnerability in Tampa – St. Petersburg, FL based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Tampa 1.75119 Tampa 8.21176 2ND

Tampa 1.92920 Tampa 8.50459 1ST Citrus Park - 2.57388 Tampa 10.51007 4TH Fern Lake Tampa 2.78649 Tampa 10.81507 9TH

Clearwater 2.81742 Clearwater 10.86328 10TH

Brandon 2.88451 Plant City 11.06978 279TH Citrus Park - 2.91038 Tampa 11.07130 16TH Fern Lake St. Petersburg 3.01137 Tampa 11.16341 55TH

Tampa 3.03505 Clearwater 11.28080 18TH

Clearwater 3.05598 Tampa 11.40030 35TH

123 Table 4.26: Communities with the lowest unweighted and weighted socioeconomic vulnerability in Charleston, SC based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Charleston - Charleston - 4.49750 11.53984 1ST North Charleston North Charleston Charleston - Charleston - 4.80404 15.12137 3RD North Charleston North Charleston Charleston - Charleston - 4.97029 15.30527 2ND North Charleston North Charleston Charleston - 5.05122 Mount Pleasant 17.74104 5TH North Charleston Charleston - Mount Pleasant 5.19326 17.81434 4TH North Charleston Mount Pleasant 5.24761 Mount Pleasant 18.95123 6TH Charleston - James Island 5.27877 21.54480 8TH North Charleston Charleston - 5.36350 Mount Pleasant 21.69911 10TH North Charleston Mount Pleasant 5.42133 Mount Pleasant 21.95177 9TH

Mount Pleasant 5.58527 Mount Pleasant 22.46021 21ST

per year, suggesting larger middle and upper middle class families where only one parent works and the other stays at home as a caregiver.

In addition to the actual weighted and unweighted vulnerability scores, these areas were appraised for spatial clusters of high and low socioeconomic vulnerability in order that changes in vulnerable places could be more easily recognized. Tables 4.27 and 4.28 show the communities and number of high and low spatial clusters based on their weighted and unweighted socioeconomic vulnerability scores. The number of high

124 Table 4.27: Unweighted and weighted socio-economic vulnerability clusters for Tampa – St. Petersburg, FL Unweighted Clusters Weighted Clusters Community High – High Low - Low High – High Low - Low Boca Ciega 0 6 0 3

Brandon 3 30 2 29

Central Pasco 0 0 0 0 Citrus Park - 0 6 0 13 Fern Lake Clearwater 19 14 18 8

Gibsonton 0 0 0 0

New Port Richey 17 0 16 0 Palm River - 0 0 0 0 East Tampa Plant City 6 0 2 0

Port Richey 19 0 21 0

Ruskin 0 0 0 0

St. Pete Beach 0 3 0 3

St. Petersburg 16 3 20 7

Tampa 44 70 57 76

Tarpon Springs 9 0 9 2

Thonotosassa 0 0 0 0 Wimauma - 0 1 0 1 Lithia

125 Table 4.28: Unweighted and weighted socio-economic vulnerability clusters for Charleston, SC Unweighted Clusters Weighted Clusters Community High – High Low - Low High – High Low - Low Bear Swamp 0 0 0 0 Charleston – 8 15 10 12 North Charleston Goose Creek – 9 0 6 0 Hanahan James Island 0 8 0 7

McClellanville 0 0 0 0

Mount Pleasant 0 8 0 11

Summerville 0 0 0 0

vulnerability clusters in the Tampa – St. Petersburg metropolitan area decreased for all communities when expert weights were applied to the equation of socioeconomic vulnerability, except for Port Richey, St. Petersburg, and the City of Tampa.

However, although many places within the city of Tampa essential became more

vulnerable according to socioeconomic variables, the number of low vulnerability areas

also increased, widening the gap between those most and least able to cope with disaster.

The city of Charleston – North Charleston also had an increase in places exhibiting high

weighted socioeconomic vulnerability and a decrease in low vulnerability places, while

Goose Creek saw a decrease in areas considered to be the most socioeconomically

vulnerable and Mount Pleasant increased the number of its low vulnerability

communities after expert weights were entered into the equation.

126 4.3.2 Unweighted versus Weighted Built Environment Vulnerability

As previously mentioned, the most important characteristics of a place that lead to increased built environment vulnerability according to the experts surveyed through this research were the number of housing units, the number of mobile homes, the number of manufacturing and health care establishments and the value of earnings in administrative support and waste management enterprises. A correlation of the ranked weighted and unweighted built environment vulnerability scores for the two study areas showed a moderate and significant correlation of 0.602 for Tampa – St. Petersburg and a strong and significant correlation of 0.952 for Charleston. These correlations indicate that weighting built environment vulnerability has more of an impact in one study area than it does in the other based on changes in rank in a global setting, although local variations in unweighted and weighted built environment vulnerability are easilty spotted when looking at the different case study areas.

The weighted built environment vulnerability scores and rankings of the most and least vulnerable places within each study area can be seen in Tables 4.29 through 2.32

The rankings of the most vulnerable places according to weighted built environment indicators changed drastically for the Tampa – St. Petersburg metropolitan area due to numerous factors including large numbers of mobile homes and housing units as well as differences in the types of economic endeavors across the area. The most drastic change in rankings, from nearly average built environment vulnerability to the 7th most

vulnerable place can be seen in Clearwater. This particular community is characterized

by a very large number of housing units and mobile homes and a relatively small number

of manufacturing establishments. Additionally, the most vulnerable community

127 Table 4.29: Communities with the highest unweighted and weighted built environment vulnerability in Tampa – St. Petersburg, FL based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Tampa 12.95380 Clearwater 58.41930 149TH

Tarpon Springs 10.75316 Clearwater 57.02279 47TH

Tampa 10.31427 Clearwater 56.70990 14TH

Boca Ciega 9.85830 St. Petersburg 51.89479 67TH

Tampa 9.83842 Tampa 51.11970 1ST

Tampa 9.69724 Tarpon Springs 50.46150 2ND

Tampa 9.64192 Clearwater 49.85577 654TH

St. Petersburg 9.62582 Clearwater 47.01685 10TH

Tampa 9.57929 Port Richey 46.21600 12TH

Clearwater 9.54010 Tarpon Springs 45.03722 24TH

according to weighted built environment indicators, was 149th overall in unweighted built

environment vulnerability. This community has the highest amount of mobile homes in

the whole study area and also has a large number of single family housing units. The

number of mobile homes and housing units are treated equally in an unweighted built

environment equation, however, in an expert weighted equation of built environment vulnerability each of these variables is made substantially more influential. The

Charleston metropolitan area is characterize by much smaller shifts in built environment vulnerability ranks based on the unweighted and weighted methods. The largest shift in

128

Table 4.30: Communities with the highest unweighted and weighted built environment vulnerability in Charleston, SC based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Charleston - Charleston - 12.90870 69.17895 1ST North Charleston North Charleston Goose Creek - Mount Pleasant 12.08443 59.71701 5TH Hanahan Goose Creek - 11.31694 Mount Pleasant 55.43047 2ND Hanahan Charleston - Charleston - 11.15207 52.40690 15TH North Charleston North Charleston Goose Creek - Goose Creek - 10.84964 48.36388 16TH Hanahan Hanahan Charleston - Charleston - 10.74328 47.73378 8TH North Charleston North Charleston Goose Creek - Mount Pleasant 10.28559 47.37006 3RD Hanahan Charleston - Charleston - 10.22705 47.22621 6TH North Charleston North Charleston Charleston - Charleston - 10.05409 47.16889 4TH North Charleston North Charleston Goose Creek - Mount Pleasant 10.03472 45.64251 22ND Hanahan

ranks is from 22nd most vulnerable to 10th most vulnerable. This community in Mount

Pleasant has relatively few mobile homes, however, it has the second largest number of single family housing units in the whole study area.

Once again, the Tampa – St. Petersburg metropolitan area shows a marked shift in rankings among the least vulnerable communities. None of the least vulnerable communities in an unweighted equation of built environment vulnerability are found in the least vulnerable weighted places. The main factors behind this major shift center

129 Table 4.31: Communities with the lowest unweighted and weighted built environment vulnerability in Tampa – St. Petersburg, FL based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank St. Petersburg 7.49776 Brandon 15.11861 165TH

Central Pasco 7.74124 Plant City 15.19455 75TH Citrus Park – Clearwater 7.93751 15.23952 94TH Fern Lake Clearwater 7.96202 Brandon 15.24210 123RD

Clearwater 7.96530 Plant City 15.25537 291ST Citrus Park – Clearwater 7.97059 15.26200 93RD Fern Lake Citrus Park – St. Petersburg 7.97778 15.28449 91ST Fern Lake Thonotosassa 7.98052 Brandon 15.28596 377TH Citrus Park – Clearwater 7.98053 15.38180 403RD Fern Lake Brandon 7.98885 Tampa 15.39802 116TH

130 Table 4.32: Communities with the lowest unweighted and weighted built environment vulnerability in Charleston, SC based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Charleston - 7.33152 James Island 24.52003 5TH North Charleston Charleston - Charleston - 8.04562 24.65364 3RD North Charleston North Charleston Goose Creek - Charleston - 8.05145 24.71842 2ND Hanahan North Charleston Goose Creek - James Island 8.06046 24.92794 4TH Hanahan Charleston - Charleston - 8.06186 24.97766 8TH North Charleston North Charleston Charleston - Goose Creek - 8.06977 25.04194 13TH North Charleston Hanahan Charleston - Charleston - 8.07085 25.05829 9TH North Charleston North Charleston Charleston - Charleston - 8.07098 25.10335 11TH North Charleston North Charleston Charleston - Charleston - 8.07497 25.10663 10TH North Charleston North Charleston Charleston - 8.07586 Summerville 25.10783 19TH North Charleston

around the fact that all of these areas have very low numbers of housing units and mobile homes and very low levels of manufacturing establishments and better than average income from the commercial establishments located there. The same characteristics hold true for the least vulnerable places in Charleston, however, the shifts in rank are not nearly as drastic as those seen in the Tampa Bay area.

Variations in spatial clustering of the least and most vulnerable places could also be seen in both study areas. It is these variations at the local level that aid in the understanding of exactly where planning and mitigation strategies should be implemented to combat increases in vulnerability. Tables 4.33 and 4.34 show the communities and

131 number of high and low spatial clusters based on their weighted and unweighted built environment vulnerability scores.

Table 4.33: Unweighted and weighted built environment vulnerability clusters for Tampa – St. Petersburg, FL Unweighted Clusters Weighted Clusters Community High – High Low - Low High – High Low - Low Boca Ciega 6 0 5 0

Brandon 13 29 1 14

Central Pasco 0 0 0 0 Citrus Park - 4 5 1 4 Fern Lake Clearwater 14 35 32 23

Gibsonton 0 0 0 0

New Port Richey 5 0 20 0 Palm River - 1 1 0 7 East Tampa Plant City 0 8 0 0

Port Richey 4 0 26 0

Ruskin 1 0 0 0

St. Pete Beach 23 17 2 0

St. Petersburg 5 0 27 27

Tampa 37 42 8 86

Tarpon Springs 18 0 16 0

Thonotosassa 0 5 0 0 Wimauma - 0 5 0 0 Lithia

132 Table 4.34: Unweighted and weighted built environment vulnerability clusters for Charleston, SC Unweighted Clusters Weighted Clusters Community High – High Low - Low High – High Low - Low Bear Swamp 0 0 0 0 Charleston – 8 22 6 22 North Charleston Goose Creek – 8 1 10 1 Hanahan James Island 0 0 0 0

McClellenville 0 0 0 0

Mount Pleasant 6 0 4 0

Summerville 1 0 3 0

Brandon, Florida had a substantial decrease in the numbers of high and low

vulnerability places between unweighted and weighted equations of built environmenet

vulnerability, while Clearwater, New Port Richey and Port Richey all had substantial

increases in the number of high vulnerability areas. This indicates intense development

in these areas, while at the same time showing no real growth in economic development.

There is not very much difference in the spatial clusters present in the Charleston area,

with Goose Creek and Summerville gaining two high vulnerable communities each and

Charleston and Mount Pleasant each loosing two high vulnerability places.

4.3.3 Unweighted versus Weighted Accessibility Vulnerability

As discussed above, the most important characteristics of a place that lead to increased accessibility vulnerability according to the experts surveyed through this research were proximity to emergency shelters, hospitals, health care facilities, and police stations. A correlation of the ranked weighted and unweighted accessibility vulnerability

133 scores for the two study areas showed strong and significant correlations of 0.954 for

Tampa – St. Petersburg and 0.957 for Charleston. These correlations indicate that weighting accessibility vulnerability has little impact in the rank of overall accessibility vulnerability when compared to unweighted accessibility vulnerability in a global setting, although local differences, especially in places with low accessibility vulnerability, are clear throughout the study areas. Tables 4.35 through 4.38 show the communities, vulnerability scores and rankings of the least and most vulnerable places in the Tampa –

St. Petersburg and Charleston metropolitan areas according to their unweighted and weighted accessibility vulnerability.

Table 4.35: Communities with the highest unweighted and weighted accessibility vulnerability in Tampa – St. Petersburg, FL based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Central Pasco 11.56639 Central Pasco 80.08478 1ST Wimauma - 9.80741 New Port Richey 67.28349 3RD Lithia Wimauma - New Port Richey 9.62308 66.59421 2ND Lithia Thonotosassa 8.66999 St. Petersburg 60.58262 5TH

St. Petersburg 8.54758 Thonotosassa 59.71027 4TH Wimauma - Wimauma - 8.02557 57.77549 6TH Lithia Lithia Plant City 7.78832 Plant City 55.87270 7TH Citrus Park - 7.49009 New Port Richey 53.63986 13TH Fern Lake Plant City 7.37688 Central Pasco 53.24434 10TH Wimauma - Central Pasco 7.22529 51.86392 12TH Lithia

134 Table 4.36: Communities with the highest unweighted and weighted accessibility vulnerability in Charleston, SC based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank McClellenville 13.37764 McClellenville 95.86131 1ST

James Island 9.16928 James Island 71.50145 2ND

James Island 8.93762 James Island 69.22057 3RD Goose Creek - Goose Creek - 8.40724 58.79872 4TH Hanahan Hanahan Mount Pleasant 7.49865 Mount Pleasant 58.62425 5TH

Summerville 7.28610 Mount Pleasant 58.07817 7TH

Mount Pleasant 7.28188 James Island 54.48322 10TH

Bear Swamp 6.88744 James Island 54.09276 12TH Goose Creek - 6.75239 Mount Pleasant 51.97721 13TH Hanahan James Island 6.71328 McClellenville 95.86131 6TH

The lack of drastic changes in the rankings on these tables are clear evidence that

no amount of weight placed on individual aspects of accessibility to goods and services can overcome the fact that the outskirts of the study areas are characterized by such lack

of accessibility.

The same general trend holds true when looking at the least vulnerable places

according to accessibility indicators. Although there was a minor amount of shift in the

ranks between the weighted and unweighted scores, in general, those places with

adequate accessibility in an unweighted equation of vulnerability remained also had

135 Table 4.37: Communities with the lowest unweighted and weighted accessibility vulnerability in Tampa – St. Petersburg, FL based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank St. Petersburg 0.64692 Tarpon Springs 4.60358 4TH

St. Petersburg 0.65390 St. Petersburg 4.75221 2ND St. St. Petersburg 0.73834 5.00106 3RD Petersburg Tarpon Springs 0.74718 Clearwater 5.03591 8TH

St. Petersburg 0.75436 St. Petersburg 5.38671 12TH

St. Petersburg 0.77645 St. Petersburg 5.46065 11TH

Tampa 0.79851 St. Petersburg 5.48923 2ND

Clearwater 0.81270 St. Petersburg 5.49420 13TH

Clearwater 0.81869 St. Petersburg 5.53461 6TH

Clearwater 0.82522 Clearwater 6.00988 22ND

high access in a weighted equation of accessibility vulnerability. The shifts in rank that

are present are due mainly to emergency shelter proximity and proximity to hospitals.

In addition to the actual weighted and unweighted vulnerability scores, these areas were appraised for spatial clusters of high and low accessibility vulnerability in order than

changes in vulnerable places could be more easily recognized. Along these lines, tables

4.39 and 4.40 show the communities and number of high and low spatial clusters based

on their weighted and unweighted accessibility vulnerability scores. Generally, across

the Tampa Bay area, there is a decrease in places with high accessibility vulnerability and

136 Table 4.38: Communities with the lowest unweighted and weighted accessibility vulnerability in Charleston, SC based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Charleston - Charleston - 0.80212 6.22778 2ND North Charleston North Charleston Charleston - Charleston - 0.81496 6.39264 1ST North Charleston North Charleston Charleston - Charleston - 0.82528 6.55647 6TH North Charleston North Charleston Charleston - Charleston - 0.83465 7.94397 7TH North Charleston North Charleston Charleston - Charleston - 0.84386 7.96674 16TH North Charleston North Charleston Charleston - Charleston - 0.84742 8.09780 18TH North Charleston North Charleston Charleston - Charleston - 0.85948 8.12285 56TH North Charleston North Charleston Charleston - Charleston - 0.88397 8.16668 22ND North Charleston North Charleston Charleston - Goose Creek - 0.88462 8.26738 21ST North Charleston Hanahan Charleston - Charleston - 0.89033 8.35555 32ND North Charleston North Charleston

an increase of places with low accessibility vulnerability when expert weights are applied to the data. However, the City of Tampa, under an expert weighted understanding of accessibility vulnerability, saw an almost doubling of high vulnerability places and a

decrease in the number of low vulnerability places. These areas of increased accessibility

vulnerability are mainly found in the South Tampa area, where access is limited due to the fact that the area is a Peninsula and is a considerable distance from the nearest emergency shelter. The same trend hold true for the Charleston study area, with the

exception of James Island and Mount Pleasant, which both saw an increase in places with high accessibility. The main issues in these areas is lack of adequate access to emergency

137 Table 4.39: Unweighted and weighted accessibility vulnerability clusters for Tampa – St. Petersburg, FL Unweighted Clusters Weighted Clusters Community High – High Low - Low High – High Low - Low Boca Ciega 0 6 0 14

Brandon 24 3 20 8

Central Pasco 11 0 11 0 Citrus Park - 26 0 28 0 Fern Lake Clearwater 1 70 1 81

Gibsonton 10 0 11 0

New Port Richey 9 9 9 6 Palm River - 4 0 10 0 East Tampa Plant City 38 0 32 1

Port Richey 7 0 8 1

Ruskin 5 0 5 0

St. Pete Beach 2 0 2 0

St. Petersburg 4 149 5 157

Tampa 37 182 63 172

Tarpon Springs 7 2 6 6

Thonotosassa 16 0 17 0 Wimauma - 11 0 11 0 Lithia

138 Table 4.40: Unweighted and weighted accessibility vulnerability clusters for Charleston, SC Unweighted Clusters Weighted Clusters Community High – High Low - Low High – High Low - Low Bear Swamp 2 0 2 0 Charleston – 0 71 0 66 North Charleston Goose Creek – 5 1 3 5 Hanahan James Island 9 0 13 0

McClellenville 1 0 1 0

Mount Pleasant 8 0 13 0

Summerville 10 0 5 0

shelters as both of these areas are near the coast where planners have, appropriately, not

designated shelters.

4.3.4 Unweighted versus Weighted Social Vulnerability

The importance of each of the three facets of social vulnerability is not equal when expert opinions are taken into account. Although socioeconomics and built environment indicators are nearly weighted equally, accessibility indication of vulnerable population is given almost ten points less weight in an overall weighted understanding of social vulnerability. This may lead one to believe that access to needed goods and services is not as crucial in disaster situations as sociodemographic characteristics of a place and population and built environment factors. Correlating the ranked weighted and unweighted overall social vulnerability scores for the two study areas showed strong and significant correlations of 0.930 for Tampa – St. Petersburg and 0.950 for Charleston.

These correlations indicate that weighting the facets of social vulnerability does not

139 influence overall changes in rank at the global scale, although local differences in unweighted and weighted social vulnerability can be clearly identified throughout the study areas.

Along these lines, tables 4.41 through 4.44 show the communities, vulnerability scores and rankings of the least and most socially vulnerable places in the Tampa – St.

Petersburg and Charleston metropolitan areas according to their unweighted and weighted social vulnerability scores.

Table 4.41: Communities with the highest unweighted and weighted social vulnerability in Tampa – St. Petersburg, FL based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Central Pasco 2.18912 Clearwater 67.68543 71ST New Port 1.93761 New Port Richey 67.17756 2ND Richey Wimauma - 1.90961 Central Pasco 66.73843 1ST Lithia New Port 1.90102 Clearwater 65.56438 65TH Richey Brandon 1.87592 St. Petersburg 64.58512 24TH

Central Pasco 1.86297 Clearwater 63.16232 70TH

Port Richey 1.80522 Brandon 63.07071 5TH

Tarpon Springs 1.79551 Tarpon Springs 62.80025 8TH

Thonotosassa 1.79361 Central Pasco 61.79708 6TH New Port 1.79048 Clearwater 61.63854 116TH Richey

140 Table 4.42: Communities with the highest unweighted and weighted social vulnerability in Charleston, SC based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Charleston - Charleston - 2.16473 77.51578 1ST North Charleston North Charleston Goose Creek – McClellenville 2.06334 65.06926 4TH Hanahan Goose Creek – Goose Creek - 2.02737 61.03358 3RD Hanahan Hanahan Goose Creek – Charleston - 1.87281 60.02870 11TH Hanahan North Charleston Goose Creek – Goose Creek – 1.80093 57.48492 5TH Hanahan Hanahan Goose Creek - 1.72292 McClellenville 57.46448 2ND Hanahan Goose Creek – James Island 1.69541 56.23185 6TH Hanahan Goose Creek – 1.67775 Summerville 53.96252 19TH Hanahan Charleston - Mount Pleasant 1.67480 53.60195 18TH North Charleston Charleston - James Island 1.66958 53.29852 20TH North Charleston

Generally, large shifts in rank between unweighted and weighted equations of social vulnerability are seen across the Tampa – St. Petersburg study area since accessibility is not treated with the same importance as socioeconomics and the built environment. However, one community in Central Pasco, ranked most vulnerable in an unweighted understanding of social vulnerability was ranked third in a weighted understanding, and the place ranked second based on an unweighted definition was also ranked second in the weighted understanding of social vulnerability. Overall, five of the top ten unweighted places were also found in the top ten weighted vulnerability ranks.

141 Table 4.43: Communities with the lowest unweighted and weighted social vulnerability in Tampa – St. Petersburg, FL based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Clearwater 0.94407 Tampa 27.62892 4TH

Clearwater 0.95759 Tampa 28.54381 5TH

Tampa 0.96570 Clearwater 28.66049 46TH

Tampa 0.97250 Plant City 28.69658 662ND

Tampa 0.98798 Clearwater 29.24675 11TH

Tampa 0.99608 Tampa 29.43086 8TH Citrus Park - Fern Tampa 1.00142 29.54963 84TH Lake Tampa 1.00768 Tampa 29.62026 58TH Citrus Park - 1.01981 Clearwater 29.67870 2ND Fern Lake Tampa 1.03238 Clearwater 29.84500 15TH

The Charleston study area exhibits much the same trend in unweighted versus weighted social vulnerability with six of the top ten unweighted places also emerging in the top ten weighted places. The same tendencies in rank shifts can be seen in both of the tables showing the top ten least vulnerable places in Tampa – St. Petersburg and

Charleston, SC, tables 4.43 and 4.44 respectively. This fact indicates that, for the most part, the weights applied to social vulnerability influence the most and least vulnerable places far less than those places that fall somewhere in the middle of the vulnerability scale. Essentially, the places that exhibit either high or low unweighted vulnerability

142 Table 4.44: Communities with the lowest unweighted and weighted social vulnerability in Charleston, SC based on block groups within each community UNWEIGHTED WEIGHTED

VULNERABILITY VULNERABILITY Vulnerability Vulnerability Unweighted Community Community Score Score Rank Charleston - Charleston - 1.00994 25.70573 10TH North Charleston North Charleston Charleston - Charleston - 1.04064 25.99263 1ST North Charleston North Charleston Charleston - Charleston - 1.04378 26.49575 5TH North Charleston North Charleston Charleston - Charleston - 1.04787 27.11335 4TH North Charleston North Charleston Charleston - 1.05100 James Island 27.86809 6TH North Charleston Charleston - James Island 1.05122 28.48254 37TH North Charleston Charleston - Charleston - 1.05862 29.62810 22ND North Charleston North Charleston Charleston - Charleston - 1.05872 29.89440 9TH North Charleston North Charleston Charleston - Charleston - 1.05905 29.98403 19TH North Charleston North Charleston Charleston - Charleston - 1.06238 29.98627 2ND North Charleston North Charleston

scores will also exhibit the same general placement in a weighted understanding of vulnerability.

In addition to the actual weighted and unweighted vulnerability scores, these areas were appraised for spatial clusters of high and low social vulnerability in order than changes in vulnerable places could be more easily recognized. Along these lines, tables

4.45 and 4.46 show the communities and number of high and low spatial clusters based on their weighted and unweighted social vulnerability scores. The Tampa – St.

Petersburg study area shows a marked decrease in high weighted social vulnerability across the study area, with the exception of some of the municipalities in Pinellas and

143 Table 4.45: Unweighted and weighted social vulnerability clusters for Tampa – St. Petersburg, FL Unweighted Clusters Weighted Clusters Community High – High Low - Low High – High Low - Low Boca Ciega 0 8 2 6

Brandon 11 7 7 20

Central Pasco 11 0 11 0 Citrus Park - 23 1 12 2 Fern Lake Clearwater 10 54 22 38

Gibsonton 6 0 4 0

New Port Richey 17 0 23 0 Palm River - 2 0 0 0 East Tampa Plant City 39 0 26 0

Port Richey 14 0 25 0

Ruskin 5 0 1 0

St. Pete Beach 1 1 0 0

St. Petersburg 4 77 10 62

Tampa 31 115 29 98

Tarpon Springs 15 0 15 0

Thonotosassa 17 0 11 0 Wimauma - 10 0 9 0 Lithia

144 Table 4.46: Unweighted and weighted social vulnerability clusters for Charleston, SC Unweighted Clusters Weighted Clusters Community High – High Low - Low High – High Low - Low Bear Swamp 2 0 0 0 Charleston – 4 40 4 23 North Charleston Goose Creek – 10 1 9 2 Hanahan James Island 3 0 2 0

McClellenville 1 0 1 0

Mount Pleasant 7 0 7 1

Summerville 8 0 8 0

Pasco Counties. Specifically, St. Petersburg, Clearwater, Port Richey and New Port

Richey all show increases in highly vulnerable cluster. This is mainly due to the amount of weight applied to the accessibility vulnerability indicators. Essentially, those areas closer to the city centers dropped out of the high social vulnerability clusters and those places further away, such as Brandon, show increases in low vulnerability clusters.

Clusters closest to the city center that were high under an unweighted demarcation of social vulnerability no longer displayed high social vulnerability, while clusters that were previously low vulnerability and near the city center also dropped into the no cluster category.

145

CHAPTER FIVE: Conclusions

5.1 Introduction

Conceptualizing a framework for identifying social vulnerability is a process that

is still in its infancy. Vulnerability science has come a long way since its inception, however, at present the processes used in the identification of vulnerable places and

populations is still as much an art as a science. Quantitative models attempted to define

vulnerability in numerical terms using principal component analysis to weight model

outputs. Yet the question of which variables are most important and represent a comprehensive understanding of social vulnerability remains. Although the general

characteristics of vulnerable populations are known (e.g., those in poverty, those who

reside in mobile homes, those with limited access to needed goods and services), the importance of each variable in a numeric equation of vulnerability remains unknown.

This research attempted to further our understanding of vulnerability science by applying weights to the known characteristics of vulnerability. Such weights were determined through the use of a modified Delphi method survey of experts in the fields of disaster research and practice. This survey allowed participants to decide the importance of individual variables among a larger set of indicators representing socio-economic, built environment, and accessibility—the constituent parts of overall social vulnerability.

These weights were then applied to their standardized variables and then summed to

create the component indices. Once computed, the three components---socio-economic,

146 built environment, and accessibility—were then also weighted by the expert opinion.

The weighted scores were then summed to produce the total social vulnerability score.

5.2 Addressing the Research Questions

Three questions guided this dissertation research: 1) What socioeconomic factors influence differences in the ability to withstand and recover from hazard events within urban areas?; 2) What characteristics of the built environment cause differential vulnerability with the urban realm?; and 3) What variations in lifelines and accessibility cause these cities to be more or less vulnerable to a hazard event?

5.2.1 Socio Economic Indicators

Using an unweighted equation of socioeconomic vulnerability allowed each variable in the equation to influence socioeconomic vulnerability equally. As such, the significance of individual variables in the final socioeconomic vulnerability scores was not constant across the study areas. Spatial differences in the numbers and amounts of specified variables meant that vulnerability scores for each block group in the study area were based on the set of variables in that spatial area. In an unweighted explanation of socioeconomic vulnerability, places that had increased levels of poverty also had higher levels of persons per household and lower levels of education, higher unemployment, and higher levels of service sector employment. Conversely, places with greater numbers of families earning over $100,000 per year also had higher median house values, fewer renter occupied housing units and fewer persons over age sixty-five. However, it is only through weighting of each set of variables that the importance of individual characteristics becomes evident.

147 The application of expert weights to the standardized socioeconomic vulnerability data used in this research changed the overall pattern of socio-economic vulnerability in a few distinct ways. Since a majority of expert weight was given to relatively few socioeconomic variables, namely per capita income, the value of housing, poverty level, age, and unemployment, variations in these characteristics largely changed the distribution of vulnerability across the study areas. For example, although eight places in

Tampa – St. Petersburg and nine places in Charleston were found in both the top ten unweighted and weighted rankings, two places had significant movement in the rankings

(moving from less vulnerable to more vulnerable) by the application of expert weights.

These shifts in rank indicate that those areas exhibited higher than normal numbers of poor persons, lower valued housing, and unemployment, while displaying normal levels of all other socioeconomic variables. This lack of significant change in ranks indicates that these areas had substantially high levels of poverty, unemployment and older persons to begin with, such that when expert weights were applied, they merely multiplied already existing inequities in these areas.

In conclusion, although using expert weights better defines socioeconomic vulnerability at the urban level, the overall results are not significantly different than those found using standardized un-weighted variables in an equation of socioeconomic vulnerability for the study areas. However, the use of expert weights also indicates that socioeconomic vulnerability could be roughly calculated using a smaller set of variables if time or data availability were issues, which had to be overcome. Additionally, since a smaller set of characteristics was deemed most important in terms of understanding socioeconomic vulnerability, it seems only reasonable that a smaller set of policies and

148 practices must be implemented in order to reverse the increases in vulnerability caused by

high incidences of these characteristics. Policy makers, planners, communities, and

individuals don’t necessarily need to be concerned about the whole suite of

socioeconomic characteristics if they are interested in making a positive change in resilience; they need to shift focus onto those characteristics deemed most important by

the expert survey respondents—per capita income, housing value, poverty level, age, and

unemployment.

5.2.2 Built Environment Indicators

The second research question examined those characteristics of the built environment that produce differential vulnerabilities in the urban area. The main built environment characteristics that cause differential vulnerability across the study areas relate to two broad fields: First, those variables that capture the current state of concentration in terms of number of buildings in an area, and second, the amount of money that is brought into an area by said buildings. Using an unweighted equation of built environment vulnerability gives each variable the same general influence on overall vulnerability, however, some distinctions can be made over space in the two study areas.

Generally, those places with higher numbers of housing units and mobile homes have a much smaller number of industrial and commercial establishments. This can be easily pictured if one thinks about suburban communities, which generally have limited numbers of commercial establishments in their immediate vicinities, save the occasional gas station or convenience store. Additionally, in these study areas, places that had higher numbers of manufacturing establishments also had smaller numbers of residential units as well as smaller numbers of commercial establishments. This is not a stretch to

149 imagine as many consider heavy industry to be dirty and smelly, two characteristics

neither commercial establishment, nor homeowners have on the top of their location

wishlist.

Applying expert judgments in a spatial understanding of built environment

vulnerability had a moderate impact in the rankings of the most vulnerable places (on this dimension) in Tampa – St. Petersburg and a lesser effect in the rankings of the most vulnerable places in the Charleston study area. These changes in overall built

environment rankings were mainly due to the fact that a small set of variables was given

the most importance, by experts, in the overall equation of vulnerability. According to

the experts, the number of mobile homes, housing units, manufacturing, and health care

establishments, as well as value of receipts in administrative and waste remediation

enterprises are most important in determining overall built environment vulnerability.

Seven of the top ten most vulnerable places in Tampa – St. Petersburg, using a

weighted equation, are not found in the top ten most vulnerable using an un-weighted

equation. However, in the Charleston study area, eight of the top ten most vulnerable

places in a weighted understanding are also in the top ten in an un-weighted equation of

built environment vulnerability. The same trend holds true for the least vulnerable places

on the built environment dimension within each study area. While there are moderate

shifts in the rankings of the least vulnerable places in Tampa – St. Petersburg according

to weighted built environment indicators, these are not apparent in the Charleston study

areas.

This difference between study areas suggests that the use of weights is important

for understanding differences in built environment vulnerability from place to place. The

150 expert weights, when applied to Tampa – St. Petersburg, caused major shifts in vulnerability because of generally disparate baseline conditions across the study area.

Some clusters of places contained large numbers of housing units, mobile homes, and virtually no manufacturing and commercial establishments. It is only through the application of expert weights to the factors of built environment vulnerability that certain clusters become evident, since these differences across space did not generally present themselves in an unweighted built environment vulnerability. Conversely, the Charleston metropolitan area does not have the same types of differences in built environment variables as Tampa – St. Petersburg, with more places exhibiting moderate levels of mobile homes and housing units rather than fewer places containing a majority of units.

5.2.3 Accessibility Indicators

The final research question examined the role of lifelines and accessibility to them as a part of the vulnerability equation. Specifically, the question addressed the geographic variability in this access and how this varied between the study areas and within them. In general terms, as distance from the central city area increases, accessibility to a majority of the goods and services located within the urban core decreases, for the study areas. There are however, a few exceptions to this rule in both study areas. Predominantly, fire station locations provide adequate coverage to most of the study areas and distance to the nearest church in both study areas leads to the belief that God is always close by (no census block group greater than 3.5 miles away from a place of worship).

The use of expert opinion in an understanding of accessibility vulnerability for metropolitan areas did not significantly change the overall rankings of those places found

151 to be most vulnerable in an un-weighted equation of accessibility vulnerability for the

two study areas. However, the use of expert weights did change the rankings of those places with the lowest accessibility vulnerability for the study areas. These changes in

overall accessibility vulnerability were mainly caused by access to those places given the

most influence in an overall weighted understanding of accessibility vulnerability,

namely, access to emergency shelters, health care and police stations. Essentially, these

places became less vulnerable because they were closer to the facilities thought to be

more necessary during times of disaster even though they might have been further away,

on average, from most of the other accessibility indicators.

In conclusion, the use of an expert weighted understanding of accessibility

vulnerability did not significantly change the rankings of those places most vulnerable.

The fact that moderate changes in the rankings of the least vulnerable places were found

after applying weights indicates that the use of weights is important for an overall spatial

understanding of vulnerability. Additionally, since such a limited number of accessibility elements were given such great influence in an overall understanding of weighted accessibility vulnerability, those areas with limited access to data or those with a need for a rapid assessment of accessibility could use the set of variables found to be most influential by experts in an overall equation of accessibility vulnerability rather than the full set of accessibility variables used in this dissertation.

5.2.4 The Social Vulnerability Index

Unweighted indication of social vulnerability for specific places in each block group were influenced differentially by the three main components of vulnerability.

Generally, higher unweighted social vulnerability for the Tampa – St. Petersburg

152 metropolitan area is heavily influenced by accessibility factors, while in Charleston, socioeconomics play a heavy role in high vulnerability. On the other hand, places

exhibiting lower vulnerability for both study areas were mainly influenced by low built environment indication of vulnerability as well as low accessibility vulnerability.

The application of expert weights to each of the three facets of overall social vulnerability for urban areas (socio-economics, built environment, and accessibility) had a moderate effect on the rankings of those places found to be most and least vulnerable in an un-weighted equation of overall social vulnerability. The un-weighted and weighted overall social vulnerability ranks were highly correlated, yet local changes within communities clearly could be identified when expert weights were applied to the equation of social vulnerability. These changes in overall social vulnerability from the unweighted to the weighted case were a result of the fact that the experts did not weight each of the three facets equally. Specifically, experts gave less influence to accessibility compared to built environment and socio-economics, which were roughly proportional to each other.

In conclusion, although the use of a weighted equation of social vulnerability did

not significantly change the ranks of places compares to their un-weighted social

vulnerability ranks for the entire metro region, changes at the sub-metro or local

community level were easily seen across the study areas. For policy purposes, decreases in overall social vulnerability can be achieved locally by focusing mitigation and planning on the most important component for each community, rather than implementing broad brush approaches that might miss the more intricate place-based differences in social vulnerability that are present at different localities.

153 5.3 Future Research

The analysis and findings garnered from this dissertation are ground breaking in the field of urban vulnerability assessment. As described in the literature review, an all- encompassing metropolitan area vulnerability protocol has yet to be established and operationalized for the United States. Such a protocol will enable planners and developers, city governments and individuals to make more informed decisions surrounding many aspects of hazard mitigation, preparation, and recovery. Additionally, this research acts as a new dimension of vulnerability assessment, one that encapsulates factors and characteristics not only from the built environment and socioeconomic arenas, but also from the perspective of access to lifelines.

Expanding on the Hazards of Place Model of Vulnerability (Cutter 1996a), this dissertation provides a broader understanding of the dynamics that factor into the calculation of social vulnerability. This dissertation spatially and quantitatively defines issues surrounding access to goods and services and lifeline indication of vulnerability, as well as built environment and socioeconomic indicators in order to expand the social portion of the hazards of place model. Conceptually framing and implementing these augmentations to the model enables future research aimed at minimizing vulnerability to have a greater and possibly more direct impact than previously possible.

Future research needs to focus on two main areas. Initially issues of applicability and comparability in other urban areas is paramount to determining how well this method holds up to larger cities within the United States. Additionally, the methods used in this research do not allow for direct comparison between urban areas. For this reason, future research should be aimed at modifying the current methodology to include characteristics

154 such as spatial area and population or income as measures from which to standardize the

available data. This type of standardization will allow for direct statistical analysis not

only within, but also between cities of any size. The second main area of future research

includes the further decomposition of the expert survey to look at specific differences in opinion between academics and researchers. Results from this endeavor would help to bridge the gap between what is known in the world of academia and what is practiced in the real world by emergency managers and planners. This research begins to bridge the gap between theory and practice, but only its successful implementation by practitioners will allow it to reach its full potential.

155

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APPENDIX A

The Delphi Survey used to gather expert opinion on the factors of vulnerability

Figure A.1: Page 1 of the Delphi Method Vulnerability Survey

169

Figure A.2: Page 2 of the Delphi Method Vulnerability Survey

170

Figure A.3: Page 3 of the Delphi Method Vulnerability Survey

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Figure A.4: Page 4 of the Delphi Method Vulnerability Survey

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Figure A.5: Page 5 of the Delphi Method Vulnerability Survey

Figure A.6: Page 6 of the Delphi Method Vulnerability Survey

173

APPENDIX B

Tables showing block groups, county commission districts and distances in Charleston, SC and Tampa – St. Petersburg, FL for a set of accessibility indicators

Table B.1: Block groups with least access to hospitals in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 121010320012 Pasco 2 12.85 121010320023 Pasco 2 11.87 121010320011 Pasco 2 11.65 121010316001 Pasco 2 11.07 120570139111 Hillsborough 4 11.06 121010320031 Pasco 2 10.60 120570115091 Hillsborough 2 10.51 121010320022 Pasco 1 & 2 10.38 120570115042 Hillsborough 2 10.13 121010317011 Pasco 2, 3 & 4 10.04

Table B.2: Block groups with the least access to doctors in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 121010319001 Pasco 1 & 2 8.27 121010317011 Pasco 2, 3 & 4 7.14 120570139111 Hillsborough 4 6.84 120570102082 Hillsborough 2 6.22 121010316003 Pasco 3 6.02 120570103032 Hillsborough 2 6.00 120570115042 Hillsborough 2 5.69 120570139092 Hillsborough 4 5.62 121010317031 Pasco 4 5.62 120570103031 Hillsborough 2 5.61

174

Table B.3: Block groups with the least access to grocery store in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 121010319001 Pasco 1 & 2 4.99 120570101082 Hillsborough 4 4.14 120570102082 Hillsborough 2 3.88 121010317011 Pasco 2, 3 & 4 3.63 121030201032 Pinellas 6 3.63 120570102061 Hillsborough 2 3.55 120570115101 Hillsborough 2 3.48 120570115113 Hillsborough 2 3.43 120570115053 Hillsborough 2 3.32 120570132071 Hillsborough 4 3.15

Table B.4: Block groups with the least access to grocery store in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 120570139122 Hillsborough 4 5.71 121010319001 Pasco 1 & 2 5.02 120570139092 Hillsborough 4 5.01 121010318011 Pasco 5 4.49 120570139121 Hillsborough 4 4.45 121010317011 Pasco 2, 3 & 4 4.12 120570139102 Hillsborough 4 3.98 120570115051 Hillsborough 2 3.96 120570102082 Hillsborough 2 3.92 120570139091 Hillsborough 4 3.86

Table B.5: Block groups with the least access to home improvement stores in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 121030201032 Pinellas 6 6.40 120570139122 Hillsborough 4 6.08 120570139111 Hillsborough 4 5.60 120570115054 Hillsborough 2 5.55 120570115052 Hillsborough 2 5.43 121010321012 Pasco 2 5.28 120570139092 Hillsborough 4 5.27 121010319001 Pasco 1 & 2 5.12 120570115043 Hillsborough 2 5.06 120570102061 Hillsborough 2 4.98

175

Table B.6: Block groups with the least access to public transportation in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 121010319001 Pasco 1 & 2 12.14 121010320012 Pasco 2 8.85 121010320023 Pasco 2 8.71 121010320022 Pasco 1 & 2 8.04 121010321012 Pasco 2 7.76 120570139111 Hillsborough 4 7.34 120570102061 Hillsborough 2 7.23 120570102051 Hillsborough 2 7.21 121010320011 Pasco 2 6.67 120570102053 Hillsborough 2 6.50

Table B.7: Block groups with the least access to discount store in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 120570139111 Hillsborough 4 7.75 121010319001 Pasco 1 & 2 7.35 120570102061 Hillsborough 2 6.27 121010317011 Pasco 2, 3 & 4 6.11 120570139122 Hillsborough 4 6.03 120570102052 Hillsborough 2 5.92 120570102071 Hillsborough 2 5.90 120570102053 Hillsborough 2 5.82 120570102082 Hillsborough 2 5.71 120570102072 Hillsborough 2 5.68

Table B.8: Block groups with the least access to police station in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 120570139111 Hillsborough 4 12.46 120570139112 Hillsborough 4 11.37 120570139122 Hillsborough 4 10.73 120570134094 Hillsborough 4 10.42 120570139102 Hillsborough 4 10.27 120570139121 Hillsborough 4 10.08 120570132083 Hillsborough 4 10.08 120570139103 Hillsborough 4 9.78 121010321012 Pasco 2 9.72 120570132082 Hillsborough 4 9.70

176

Table B.9: Block groups with the least access to pharmacy in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 120570101062 Hillsborough 4 9.85 120570101064 Hillsborough 4 9.49 120570127011 Hillsborough 4 9.35 120570127012 Hillsborough 4 9.13 120570127023 Hillsborough 4 8.86 120570126003 Hillsborough 4 8.83 120570127021 Hillsborough 4 8.76 120570125023 Hillsborough 4 8.75 120570101071 Hillsborough 4 8.71 120570101063 Hillsborough 4 8.49

Table B.10: Block groups with the least access to church in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 121010317011 Pasco 2, 3 & 4 3.45 121030271031 Pinellas 4 2.72 121010317031 Pasco 4 2.62 121010319001 Pasco 1 & 2 2.54 121030201032 Pinellas 6 2.37 121030244053 Pinellas 6 2.24 120570117083 Hillsborough 1 2.20 120570139111 Hillsborough 4 2.16 120570102082 Hillsborough 2 2.15 120570073009 Hillsborough 1 2.05

Table B.11: Block groups with the least access to social groups in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 120570101032 Hillsborough 2 5.51 120570101051 Hillsborough 2 5.08 120570101052 Hillsborough 4 4.87 120570102082 Hillsborough 2 4.69 120570103032 Hillsborough 2 4.57 120570115091 Hillsborough 2 4.53 120570103043 Hillsborough 2 4.37 120570130045 Hillsborough 4 4.18 120570101061 Hillsborough 4 4.17 120570130032 Hillsborough 4 4.16

177 Table B.12: Block groups with the least access to schools in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 121030201032 Pinellas 6 6.28 121010319001 Pasco 1 & 2 5.10 121030201033 Pinellas 6 4.32 121030280023 Pinellas 6 4.18 120570102082 Hillsborough 2 3.43 121010317011 Pasco 2, 3 & 4 3.41 120570101082 Hillsborough 4 3.39 120570139111 Hillsborough 4 3.36 121030280022 Pinellas 6 3.28 121030271031 Pinellas 4 2.93

Table B.13: Block groups with the least access to day care facilities in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 121010319001 Pasco 1 & 2 7.07 121030201032 Pinellas 6 5.53 120570139111 Hillsborough 4 4.60 121010317011 Pasco 2, 3 & 4 4.58 120570115091 Hillsborough 2 4.58 120570115042 Hillsborough 2 4.43 120570131001 Hillsborough 4 4.18 121030280023 Pinellas 6 4.16 120570141052 Hillsborough 1 4.14 121010312022 Pasco 2 & 5 3.99

Table B.14: Block groups with the least access to emergency shelters in Tampa – St. Petersburg, FL County Commission Block Group Distance (miles) District 120570072003 Hillsborough 1 9.46 120570072002 Hillsborough 1 9.45 120570072001 Hillsborough 1 9.31 120570073009 Hillsborough 1 9.31 120570070004 Hillsborough 1 9.04 120570071005 Hillsborough 1 9.02 120570071001 Hillsborough 1 8.81 120570071004 Hillsborough 1 8.71 120570069002 Hillsborough 1 8.64 120570070003 Hillsborough 1 8.61

178 Table B.15: Block groups with least access to hospitals in Charleston, SC County Commission Block Group Distance (miles) District 450190050004 Charleston 2 16.07 450150207011 Berkeley 6 & 7 11.47 450350105011 Dorchester 1 & 6 9.84 450350105012 Dorchester 6 9.84 450190020042 Charleston 9 9.61 450190046012 Charleston 1 8.77 450350105021 Charleston 6 8.59 450190046013 Charleston 2 8.44 450190049002 Charleston 2 8.31 450190020041 Charleston 9 8.06

Table B.16: Block groups with the least access to doctors in Charleston, SC County Commission Block Group Distance (miles) District 450190050004 Charleston 2 9.06 450190020042 Charleston 9 7.15 450350105012 Dorchester 6 6.99 450190020041 Charleston 9 6.59 450350105021 Charleston 6 6.51 450350105011 Dorchester 1 & 6 5.99 450150207011 Berkeley 6 & 7 5.34 450190020033 Charleston 8 & 9 5.03 450190020034 Charleston 9 4.99 450190026095 Charleston 6 4.92

Table B.17: Block groups with the least access to grocery store in Charleston, SC County Commission Block Group Distance (miles) District 450190020041 Charleston 9 4.73 450190020042 Charleston 9 4.72 450350108011 Dorchester 3 & 7 4.04 450190050004 Charleston 2 4.03 450150207031 Berkeley 2 & 3 3.63 450190020034 Charleston 9 3.52 450190049002 Charleston 2 3.34 450190020051 Charleston 9 3.33 450190046013 Charleston 2 3.32 450190046011 Charleston 1 3.17

179 Table B.18: Block groups with the least access to fire stations in Charleston, SC County Commission Block Group Distance (miles) District 450350108011 Dorchester 3 & 7 3.81 450150207031 Berkeley 2 & 3 3.74 450190050004 Charleston 2 3.51 450190026101 Charleston 6 3.42 450350108062 Dorchester 3 & 4 3.03 450350108061 Dorchester 4 2.97 450190046013 Charleston 2 2.89 450350105012 Dorchester 6 2.82 450350105023 Dorchester 3, 5 & 7 2.66 450150210003 Berkeley 2 2.64

Table B.19: Block groups with the least access to home improvement stores in Charleston, SC County Commission Block Group Distance (miles) District 450190050004 Charleston 2 8.73 450150207011 Berkeley 6 & 7 6.75 450190020042 Charleston 9 6.51 450190020041 Charleston 9 6.03 450190026095 Charleston 6 5.32 450190020034 Charleston 9 4.67 450190020033 Charleston 8 & 9 4.47 450150207071 Berkeley 3 & 6 3.88 450350108011 Dorchester 3 & 7 3.80 450190020051 Charleston 9 3.68

Table B.20: Block groups with the least access to public transportation Charleston, SC County Commission Block Group Distance (miles) District 450190050004 Charleston 2 20.78 450350105011 Dorchester 1 & 6 14.63 450350105012 Dorchester 6 14.56 450190046013 Charleston 2 13.19 450350105021 Charleston 6 13.16 450190046012 Charleston 1 13.00 450190049002 Charleston 2 12.85 450150207011 Berkeley 6 & 7 12.74 450190049001 Charleston 2 12.01 450350106011 Dorchester 1 & 6 11.76

180 Table B.21: Block groups with the least access to discount store in Charleston, SC County Commission Block Group Distance (miles) District 450190050004 Charleston 2 13.64 450190046012 Charleston 1 6.53 450190049002 Charleston 2 6.30 450190046013 Charleston 2 6.05 450150207011 Berkeley 6 & 7 5.97 450190049001 Charleston 2 5.86 450190026095 Charleston 6 5.11 450190020042 Charleston 9 4.72 450350105011 Dorchester 1 & 6 4.57 450190020041 Charleston 9 4.54

Table B.22: Block groups with the least access to police station in Charleston, SC County Commission Block Group Distance (miles) District 450190050004 Charleston 2 12.20 450190046012 Charleston 1 8.12 450150207011 Berkeley 6 & 7 7.40 450190046011 Charleston 1 6.77 450350108011 Dorchester 3 & 7 6.55 450350105023 Dorchester 3, 5 & 7 6.14 450350105021 Charleston 6 5.81 450190026095 Charleston 6 5.73 450190046013 Charleston 2 5.45 450190046014 Charleston 1 5.16

Table B.23: Block groups with the least access to pharmacy in Charleston, SC County Commission Block Group Distance (miles) District 450190050004 Charleston 2 13.07 450150207011 Berkeley 6 & 7 7.06 450350105011 Dorchester 1 & 6 6.45 450350105012 Dorchester 6 6.42 450190046012 Charleston 1 6.15 450190046013 Charleston 2 5.38 450190049002 Charleston 2 5.38 450190026095 Charleston 6 5.29 450190020042 Charleston 9 5.21 450190049001 Charleston 2 4.92

181 Table B.24: Block groups with the least access to church in Charleston, SC County Commission Block Group Distance (miles) District 450190020041 Charleston 9 3.74 450190020042 Charleston 9 3.64 450150207031 Berkeley 2 & 3 3.06 450190050004 Charleston 2 3.00 450190049002 Charleston 2 2.82 450350108011 Dorchester 3 & 7 2.43 450190020034 Charleston 9 2.39 450190026101 Charleston 6 2.07 450190020033 Charleston 8 & 9 2.01 450190049001 Charleston 2 1.99

Table B.25: Block groups with the least access to social groups in Charleston, SC Least accessible block County Commission Distance (miles) groups District 450190050004 Charleston 2 4.66 450190020042 Charleston 9 4.59 450350108011 Dorchester 3 & 7 4.19 450190026095 Charleston 6 4.14 450190020041 Charleston 9 4.08 450150207011 Berkeley 6 & 7 3.37 450150207031 Berkeley 2 & 3 3.31 450190046012 Charleston 1 3.15 450190020033 Charleston 8 & 9 3.14 450190046011 Charleston 1 3.01

Table B.26: Block groups with the least access to schools in Charleston, SC County Commission Block Group Distance (miles) District 450190050004 Charleston 2 7.53 450190049001 Charleston 2 4.67 450190049002 Charleston 2 4.60 450190020041 Charleston 9 4.21 450150207011 Berkeley 6 & 7 4.07 450190020042 Charleston 9 3.99 450190026095 Charleston 6 3.95 450190049003 Charleston 2 3.61 450190026101 Charleston 6 3.20 450350108011 Dorchester 3 & 7 3.02

182 Table B.27: Block groups with the least access to day care facilities in Charleston, SC County Commission Block Group Distance (miles) District 450150207011 Berkeley 6 & 7 6.87 450190050004 Charleston 2 5.74 450190049002 Charleston 2 5.10 450190049001 Charleston 2 4.44 450350105011 Dorchester 1 & 6 4.37 450190020041 Charleston 9 4.19 450190026095 Charleston 6 3.94 450350108011 Dorchester 3 & 7 3.85 450190020042 Charleston 9 3.75 450150207071 Berkeley 3 & 6 3.62

Table B.28: Block groups with the least access to emergency shelters in Charleston, SC County Commission Block Group Distance (miles) District 450190049001 Charleston 2 14.69 450190020042 Charleston 9 14.58 450190049002 Charleston 2 14.29 450190050004 Charleston 2 14.23 450190049003 Charleston 2 14.21 450190020041 Charleston 9 13.68 450190048001 Charleston 2 12.82 450190046042 Charleston 2 12.32 450190020033 Charleston 8 & 9 12.28 450190046043 Charleston 2 12.23

183

APPENDIX C

Moran’s I Statistical Outputs for Local Indicator of Spatial Autocorrelation (LISA) for Tampa – St. Petersburg, FL and Charleston, SC

Figure C.1: Tampa Standardized unweighted social vulnerability Moran’s I Permutations

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Figure C.2: Tampa unweighted socioeconomic vulnerability Moran’s I Permutations

Figure C.3: Tampa unweighted accessibility vulnerability Moran’s I Permutations

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Figure C.4: Tampa unweighted built environment vulnerability Moran’s I Permutations

Figure C.5: Charleston Standardized unweighted social vulnerability Moran’s I Permutations

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Figure C.6: Charleston unweighted accessibility vulnerability Moran’s I Permutations

Figure C.7: Charleston unweighted socioeconomic vulnerability Moran’s I Permutations

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Figure C.8: Charleston unweighted built environment vulnerability Moran’s I Permutations

Figure C.9: Charleston weighted socioeconomic vulnerability Moran’s I Permutations

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Figure C.10: Charleston weighted accessibility vulnerability Moran’s I Permutations

Figure C.11: Charleston weighted built environment vulnerability Moran’s I Permutations

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Figure C.12: Charleston weighted social vulnerability Moran’s I Permutations

Figure C.13: Tampa weighted accessibility vulnerability Moran’s I Permutations

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Figure C.14: Tampa weighted socioeconomic vulnerability Moran’s I Permutations

Figure C.15: Tampa weighted built environment vulnerability Moran’s I Permutations

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Figure C.16: Tampa weighted social vulnerability Moran’s I Permutations

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