Abstract Utilization of Crowdsourcing And

ABSTRACT

UTILIZATION OF CROWDSOURCING AND VOLUNTEERED GEOGRAPHIC INFORMATION IN INTERNATIONAL DISASTER MANAGEMENT

by Julaiti Nilupaer

Large-scale disasters result in enormous impacts on vulnerable communities worldwide, and data acquisition has become a major concern in this time-critical situation: the limitations of geospatial technologies impede the real-time data collection, also the absent or poor data collection in some regions. With the current advances of Web 2.0, crowdsourcing and Volunteered Geographic Information (VGI) have become commonly used. As a potential solution to fill the gap of real-time geographic data, crowdsourcing and VGI enable timely information exchange through a voluntary approach and enhance amateur citizen participation. Importantly, such geographic information can substantially facilitate emergency coordination by fulfilling the needs of impacted communities and appropriately allocating relief supplies and funds. My research interest centers on the utilization of crowdsourcing and VGI for disaster management. Particularly, I work to explore their potential value and contributions by reviewing two notable and destructive disaster events as case studies: the 2011 Tohoku Earthquake and Tsunami, and the 2013 Typhoon Haiyan. In addition, I examine the challenges of this information and seek potential solutions. This research aims to contribute a comprehensive qualitative analysis of how Volunteer and Technical Communities (V&TCs) have used crowdsourced data and VGI to enhance the coordination of disaster management.

UTILIZATION OF CROWDSOURCING AND VOLUNTEERED GEOGRAPHIC INFORMATION IN INTERNATIONAL DISASTER MANAGEMENT

Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Arts

Julaiti Nilupaer

Miami University

Oxford, Ohio

2019

Advisor: Stanley Toops

Reader: John Maingi

Reader: Mary Henry

This Thesis titled

UTILIZATION OF CROWDSOURCING AND VOLUNTEERED GEOGRAPHIC INFORMATION IN INTERNATIONAL DISASTER MANAGEMENT

Julaiti Nilupaer

has been approved for publication by

The College of Arts and Science

and

Department of Geography

______Stanley Toops

______John Maingi

______Mary Henry

Table of Contents Chapter 1: Introduction 1 1.1Introduction 1 1.2 Research Questions 3 1.3Case Studies 4 1.3.1 2011 Tohoku Earthquake and Tsunami (Japan) 5 1.3.2 2013 Typhoon Haiyan (the Philippines) 5 1.4 Thesis Organization 6 Chapter 2: Literature Review 7 2.1 Introduction to Literature Review 7 2.2 Defining Disaster 7 2.2.1 The Contexts of Disaster 7 2.2.2 Hazard Vs. Disaster 8 2.2.3 Distinctions between Emergency, Disaster, and Crisis 9 2.2.4 Vulnerability, Resilience, and Risk 9 2.3 Disaster Management 11 2.3.1 Disaster Mitigation and Preparedness 12 2.3.2 Disaster Response and Recovery 13 2.4 GIS and Disaster Management 14 2.4.1 Evolution of GIS 15 2.4.2 The Role of GIS in a Disaster Management Cycle 15 2.4.2.1 GIS in Disaster Mitigation and Preparedness 16 2.4.2.2 GIS in Disaster Response and Recovery 17 2.4.2.3 GIS, Local Knowledge, and Disaster Management 18 2.4.3 Limitations of GIS in Disaster Management 19 2.5 Evolution of Web 2.0 20 2.5.1. Defining Web 2.0 20 2.5.2 Strengths and Potential Flaws 21 2.5.3 Core Concepts: Neogeography and Map Mash-ups 21 2.6 Crowdsourcing and VGI 22 2.6.1 Defining Crowdsourcing 22 2.6.2 Defining VGI 23 2.6.3 VGI Vs. Crowdsourcing 24 2.6.4 Contributions of VGI and Crowdsourcing for Disaster Management 25

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2.6.4.1 VGI in Disaster Mitigation and Preparedness 25 2.6.4.2 VGI in Disaster Response and Recovery 26 2.6.5 Challenges of VGI and Crowdsourcing for Disaster Management 27 2.6.5.1 Participant Motivation 27 2.6.5.2 Data Collection 28 2.6.5.3 Data Availability 29 2.6.5.4 Legal Issues 30 2.6.5.5 Data Quality 30 2.7 Conclusion to Literature Review 31 Chapter 3: Methods and Data Sources 32 3.1 Introduction to Methods and Data Sources 32 3.2 Bibliographic Review 32 3.2.1 Bibliographic Review Process 32 3.3 Document Analysis 33 3.3.1 ‘Document’ 33 3.3.2 Strengths and Potential Flaws 34 3.4 Secondary Data 34 3.4.1 The Contexts of Secondary Data 34 3.4.2 Advantages and Limitations 35 3.5 Conclusion to Methods and Data Sources 35 Chapter 4: Results 43 4.1 Introduction to Results 43 4.2 Review Results from Selected Research Papers 44 4.2.1 Classification by Contribution Year 48 4.2.2 Classification by Subject Areas 49 4.2.3 Classification by Institute Countries 50 4.2.4 Classification by Disaster Phases 51 4.2.5 Classification by Media 52 4.2.5.1 Volunteer and Technical Communities (V&TCs) 54 4.3 Review Results from Selected Documents 56 4.3.1 Classification by Document Types 57 4.3.1.1 Document Types on the 2011 Tohoku Earthquake and Tsunami 57 4.3.1.2 Document Types on the 2013 Typhoon Haiyan 58 4.3.2 Classification by Document Sources 59

4.3.2.1 Document Sources on the 2011 Tohoku Earthquake and Tsunami 59 4.3.2.2 Document Sources on the 2013 Typhoon Haiyan 60 4.3.2.3 Examples of Document Sources 60 Humanitarian Organizations 61 Crowdsourcing Communities 61 GIS Communities 62 4.3.3 Role of the Documents - Complementary or even Supplementary Data Source 62 4.4 Conclusion to Results 63 Chapter 5: Discussion 64 5.1 Introduction to Discussion 64 5.2 Stepping into a New Era – Milestones of Initiating VGI and Crowdsourcing Projects as well as Interfaces 66 5.2.1 Harvard Humanitarian Initiative (HHI) Project - Emergence of ‘Crisis Mapping’ 66 5.2.2 Post-Election Crisis in Kenya - Formation of Ushahidi 67 5.2.3 The First Interface between Worldwide Experts - ICCM Conference and Formation of Crisis Mappers Network 69 5.2.3.1 Physical Setting: ICCM Conference in 2009 69 5.2.3.1 Online Setting: Formation of Crisis Mappers Network 71 5.3 Continuing to Facilitate the Dialogues between Formal Humanitarian Organizations and Volunteer & Technical Communities (V&TCs) 72 5.3.1 2010 Haiti Earthquake 72 5.3.2 Introduction of Formal Humanitarian Organizations and Volunteer & Technical Communities (V&TCs) 73 5.3.3 Issues between Formal Humanitarian Organizations and Volunteer & Technical Communities (V&TCs) 73 5.3.4 Addressing Some Issues: Formation of Humanitarian OpenStreetMap Team (HOT) and Standby Task Force (SBTF) 74 Humanitarian OpenStreetMap Team (HOT) 74 Standby Task Force (SBTF) 75 5.4 Case Study 1: 2011 Tohoku Earthquake and Tsunami 75 5.4.1 OSM 76 5.4.1.1 QualityStreetMap (v.2) - New Coordinator Tool 77 5.4.2 Sinsai.info – A Collaboration between OSM, Ushahidi and SBTF 80 5.4.2.1 How Does Sinsai.info Work for Citizens? 83

5.4.3 Safecast 87 5.4.4. ALL311 91 5.4.5 ESRI Social Media Map 92 5.5 Further Formalization of the Dialogues between Two Groups - Establishment of Digital Humanitarian Network (DHN) 94 5.6 Case Study 2: 2013 Typhoon Haiyan 95 5.6.1 OSM 96 5.6.1.1 ‘Notes’ - New OSM Feature 97 5.6.1.2 Tasking Manager – New Tool for Mapping Disaster Response (online contribution) 98 5.6.1.3 OpenMapKit (OMK) – New Tool for Mapping Disaster Recovery (onsite contribution) 100 5.6.2 Ushahidi 106 5.6.2.1 ‘Haiyan.Crowdmap’ 106 5.6.2.2 ‘Philflood.Map’ 109 5.6.3 MicroMappers – A New Tool by SBTF, GIS Corps and ESRI 111 5.6.3.1 Image Clicker and Image Geo Clicker 112 5.6.3.2 Tweet Clicker and Tweet Geo Clicker 114 5.6.4 Google Crisis Response 118 5.7 Conclusion to Discussion 119 Chapter 6: Conclusion 121 6.1 Answering Research Questions 121 Research Question 1: How has the use VGI and Crowdsourcing enhanced the coordination across the four stages of a disaster management cycle? 121 Research Question 2: What have been the challenges of using VGI and crowdsourcing, and what are the potential solutions? 134 6.2 Challenges of This Research 142 6.2.1 Language Barrier 142 6.2.2 Unavailable Data Sources 142 6.2.3 Closed Data Sources 143 6.3 Contributions to Geography 143 6.4 Recommendations for Future Research 144 Appendix 1: Selected Research Papers on the 2011 Tohoku Earthquake and Tsunami 145 Appendix 2: Selected Research Papers on the 2013 Typhoon Haiyan 146 Appendix 3: Selected Documents on the 2011 Tohoku Earthquake and Tsunami 148 Appendix 4: Selected Documents on the 2013 Typhoon Haiyan 150 vi

References 152

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List of Tables

Table 1.1 Intensity, Demographic Impacts, and Economic Impacts of Two Case Studies Table 4.1 Bibliographic Review Process Table 4.2 Numbers of Selected Research Papers from Each Database Table 4.3 Results from Selected Research Papers Table 4.4 Distribution of Discussed Media Table 4.5 Distribution of Papers by Discussed V&TCs Table 4.6 Summary of Selected Documents on Two Case Studies Table 4.7 Numbers of Studies on Each V&TC Table 4.8 Summary of Targeted V&TCs that Mostly Discussed on Research Papers (R) and/or Documents (D) Table 5.1 Evolution of Using VGI/crowdsourcing from 2007-2013 Table 6.1 Eleven Advantages of Using VGI/Crowdsourcing in Disaster Management Table 6.2 Comparison of the Characteristics of OSM in Two Case Studies Table 6.3 Comparison of the Characteristics of Ushahidi in Two Case Studies Table 6.4 Technological Advancements of OSM and Ushahidi in Two Case Studies Table 6.5 Six Challenges of Using VGI/Crowdsourcing in Disaster Management

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List of Figures

Figure 2.1 Disaster as the Interface between Hazard and Vulnerability Conditions Figure 2.2 Phases and Activities of Disaster Management Figure 3.1 Bibliographic Review Process Figure 4.1 Distribution of Papers by Contribution Year Figure 4.2 Distribution of Papers by Subject Areas Figure 4.3 Distribution of Papers by Authors’ Institute Countries Figure 4.4 Distribution of Papers by Disaster Phases Figure 4.5 Distribution of Documents by Types (Japan) Figure 4.6 Distribution of Documents by Types (the Philippines) Figure 4.7 Distribution of Documents by Sources (Japan) Figure 4.8 Distribution of Documents by Sources (the Philippines) Figure 5.1 The First Ushahidi Platform Launched in Kenya, 2008 Figure 5.2 Participants of the 1st ICCM Conference Figure 5.3 Interface of Crisis Mappers Network Web Page Figure 5.4 Pre (2009) and Post-Earthquake (May 2011) of Sendai Region in Japan on OSM Map Figure 5.5 Interface of QualityStreetMap Figure 5.6a Interface of Sinsai.info (in Japanese) Figure 5.6b Interface of Sinsai.info (Translated by Google) Figure 5.7 Report Instructions on Sinsai.info Figure 5.8 Interface of Submitting a Report on Sinsai.info (in Japanese) Figure 5.9 Screenshot of Reports on Sinsai.info (Translated by Google) Figure 5.10 Japan Radiation Map Figure 5.11 Screenshot of bGeigie Nano Device Figure 5.12a Interface of ALL311 Web Page (in Japanese) Figure 5.12b Interface of ALL311 Web Page (Translated by Google) Figure 5.13 ESRI Social Media Map Figure 5.14 Digital Humanitarian Network (DHN) Community Interface Diagram Figure 5.15 Before and After the part of Tacloban City was mapped by HOT

Figure 5.16 Interface of Notes Feature Figure 5.17 Interface of Tasking Manager Figure 5.18 Interface of OMK Figure 5.19 Interface of Haiyan. Crowdmap by using the Ushahidi platform Figure 5.20 Comparison of Report Requirement on Reliability between Sinsai.info and Haiyan. Crowdmap Figure 5.21 Screenshot of Reports on Haiyan.Crowdmap Figure 5.22 Interface of Philflood.Map Figure 5.23 Crisis Map Produced by SBTF, GIS Corps and ESRI Figure 5.24 Interface of Image Clicker Figure 5.25 Interface of Image Geo Clicker Figure 5.26 Interface of Tweet Clicker Figure 5.27 Interface of Tweet Geo Clicker Figure 5.28 Typhoon Haiyan/Yolanda Crisis and Relief Map by Google Crisis Response Figure 5.29 Damage Assessment on Typhoon Haiyan/Yolanda Crisis and Relief Map

List of Acronyms and Abbreviations

CGI: Contributed Geographic Information DHN: Digital Humanitarian Network ESRI: Environmental Systems Research Institute GIS: Geographic Information Science GPS: Global Positioning Systems HHI: Harvard Humanitarian Initiative HOT: Humanitarian OpenStreetMap Team ICCM: International Conference of Crisis Mappers ICT: Information and Communications Technology NGO: Non-governmental Organization OCHA: United Nations Office for the Coordination of Humanitarian Affairs OMK: OpenMapKit OSM: OpenStreetMap SBTF: Standby Task Force SMS: Short Message Service UGC: User Generated Content UN: United Nations UN-SPIDER: United Nations Platform for Spaced-Based Information for Disaster Management and Emergency Response USGS: United States Geological Survey VGI: Volunteered Geographic Information V&TC: Volunteer and Technical Community/Volunteer and Technology Community

Dedication

To my beloved family from the land farthest away from the ocean, – Still embracing me with your tenderness deeper than the sea.

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Acknowledgements

First of all, I would like to take this opportunity and express my enormous gratitude to Dr. Stanley Toops, for kindly, patiently, professionally advising and pushing me through the entire process of thesis writing. I would also like to express my genuine appreciation to my wonderful committee members, Dr. John Maingi and Dr. Mary Henry. Thank you for sharing valuable feedback for my research. In addition, I would also like to thank the staff and faculty from the Geography Department at Miami University. Debbi, thank you for providing timely solutions when I was confused with questions; Robbyn, thank you for being supportive of my thesis as well as career planning; Amelie and Dr. Green, thank you for the directions when I encountered challenges in my classes, and the moral support for my research. Also, thanks to the excellent Miami University Library and Graduate School staff members, for your assistance regarding the articles search, thesis defense presentation, APA citations as well as format check; and thanks to the incredible support from the Howe Writing Center. Further, to Mr. Markus Woltran, Director and Associate Program Officer of the United Nations for Outer Space Affairs, thank you for generously sharing the useful resources and providing the research directions for me. To Professor Suppasri Anawat from the Tohoku University (Japan), thank you for helping me with the recently published articles and the official documents from the Geospatial Authority of Japan. To Dr. Joseph Kerski from ESRI, thanks for sending me interesting maps for researching the case studies. To Dr. Alexander Zipf from the Heidelberg University, thanks for sharing me the original works regarding the Typhoon Haiyan. To Ushahidi Staff, thanks for offering the available resources to help me complete the analysis. Lastly, to my dearest graduate cohorts, especially Luci and Elina - ‘the days we grew together are the days that I will treasure.’ Sincerely, I wish you all the best in your life of adventures.

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Chapter 1: Introduction

1.1 Introduction

“To map the world, is to know it. To map the world alive is to change it before it’s too late”. - Meier, 2012a

A disaster can be defined as a large-scale event that occurs with or without warnings, resulting in tremendous disruption to the vulnerable communities (Teodorescu, 2014), severely impacting the aspects of socio-economics, culture, demography, and politics (Paul, 2011). Scientists have acknowledged the practice of Geographic Information Science (GIS) for its active role in and across each phase in disaster management (Tomaszewski, 2015). Specifically, they view GIS as one of the key Information and Communication Technology (ICT) tools for such management (Reinhardt, 2014). However, data acquisition has become a critical challenge when using GIS in such an urgent and immediate situation. For instance, the data might be difficult to collect, both in developed and less developed countries (Hartato, 2017). Also, the limitations of the geospatial technologies (e.g., satellite imagery is obstructed by severe weather or clouds) may obstruct the data collection (Poser & Dransch, 2010; McDougall, 2011; Triglav-Čekada & Radovan, 2012). Further, due to the insufficient resources and funds, the delay in acquiring information from official sources may cause more severe damages (Goodchild & Glennon, 2010). Yet, such time-critical disaster events demand abundant geographic information, as well as timely and effective communication (Haworth & Bruce, 2015). Particularly, the need for maps is urgent; therefore, the creation and distribution of maps are considered imperative to the coordination of disaster management (Erskine & Gregg, 2012). Due to the popularizing of the Internet, and the emergence of Web 2.0 – which was defined as a combination of peer production, cloud collaboration, and crowd-sourcing (Zook et al., 2010), the landscape of web mapping technologies has changed dramatically. Geo-data productions have been proliferating since 2005 Hurricane Katrina (Haklay et al., 2008; Kawasaki et al., 2013; Haworth & Bruce, 2015).

Subsequently, the terms Crowdsourcing and Volunteered Geographic Information (VGI) have come into public knowledge (Howe, 2006; Goodchild, 2007; Helft, 2009). The two terms share similarities as well as distinctions. Harvey (2013) contended that VGI should be viewed as geographic information collected with particular motivations, while crowdsourced information is collected without explicit purpose or even professional knowledge. In addition, VGI has limited control over the data reuse, whereas crowdsourcing does not (Harvey, 2013). As a potential solution to the challenge of conventional data acquisition (Goodchild, 2009), VGI enables sharable and accessible geographic information, through a voluntary approach from citizens (Haworth & Bruce, 2015). Importantly, VGI ideally fulfills the demand for real-time situations (Goodchild & Glennon, 2010). See et al. (2016) emphasized that crowdsourcing and VGI can be viewed as complementary to authoritative sources, instead of being seen as competitors or replacements to the traditional approaches. Haworth (2018) identified a variety of activities involving contributions to disaster response by sharing the open use of GIS, global crowdsourced mapping projects, and location-related posts through social media. Mooney et al. (2011) added that VGI and crowdsourcing could help to develop accessible and free maps, while the data collected from conventional GIS methods are time-consuming and expensive. Further, digital humanitarians can superimpose new measures in the conventional GIS approaches such as crowdsourcing, remote volunteer collaboration, and crisis mapping (Haworth & Bruce, 2015). Therefore, up-to-date, accessible, and relevant geographic information can be extensively utilized to ensure the fulfillment of needs from the affected communities, as well as the appropriate allocation of relief supplies and funding (Hartato, 2017). VGI brings opportunities for disaster management; meanwhile, it brings threats to authoritative mapping (Haworth & Bruce, 2015). There are a few issues with the use of VGI, for example, credibility concerns (Goodchild, 2007), and copyright conflicts (Haklay et al., 2014; See et al., 2016). All in all, the emerging field of VGI shows importance to GIS and geography (Haworth, 2018). Therefore, I propose to ascertain the practical use of VGI and crowdsourcing in disaster management, importantly, to explore the knowledge void from the existing literature, and fill such gap by presenting a comprehensive information collection. To demonstrate my research findings in details, I specifically focus on two notable and destructive disaster events as my case studies, including the 2011 Tohoku Earthquake and Tsunami (Japan), and the 2013 Typhoon

Haiyan (the Philippines). In particular, I examine the value of VGI and crowdsourcing that has been utilized to enhance disaster management. I also analyze the associated issues and challenges to explore potential solutions. Further, I employ Bibliographic Review and Document Analysis as my methodologies to conduct this research. In addition to the research papers selected from four electronic databases involving “Web of Science”, “Engineering Village”, “EBSCO” and “Google Scholar”, I also use primary and secondary data sources including electronic data/knowledge portals, newsletters, blogs, situation reports, conference presentations as well as printed books. Last but not least, this research aims to contribute to our geography society. With this comprehensive qualitative analysis, I work to achieve these following goals: first, the research helps our fellow geographers to learn more about the applications of this incredible geographic information, and thus broaden their knowledge in the field. Second, the research provides some interesting insights or innovative ideas to geospatial programmers who are developing more advanced geospatial technologies for improved emergency response. Third, importantly, the research helps to fill the information void and present an extensive collection from research papers as well as documents, therefore serving as a potential knowledge portal for researchers who need to use this information for research purposes.

1.2 Research Questions

The research questions were developed from the conceptual framework of literature involving the advancement of Web 2.0, crowdsourcing, and VGI, as well as the coordination of disaster management. With two case studies, this thesis strives to fill the information gap from the literature by examining the practical use of crowd-sourcing data and VGI across the four phases of a disaster management cycle. In particular, the thesis examines the contributions and potential challenges of these geographic data. Accordingly, this research works to address two main research questions:

Research Question 1: How has the use of VGI and crowdsourcing enhanced the coordination across the four stages of a disaster management cycle? Research Question 2: What have been the challenges of using VGI and crowdsourcing, and what are the potential solutions?

With the incorporation of two methodologies: Bibliographic Review and Document Analysis, as well as the extensive use of primary and secondary data, I answer Research Question 1 based on the two research datasets that I create. First Dataset: I design a search profile, with the indicators including keywords, search engine, time range, information that needs to be collected, inclusion and exclusion criteria, to filter and determine the most suitable research papers. By presenting the results of selected research papers, I examine the utilization of VGI and crowdsourcing within the four stages in disaster management. To illustrate, which Volunteer and Technical Community (V&TC) has used VGI and crowd-sourcing, how they have worked with the information to facilitate the emergency coordination, and which disaster phase has drawn more humanitarian attention and which phases have not. Second Dataset: However, the research papers might not be able to provide sufficient knowledge to perform the analysis. Therefore, I use a set of electronic and printed documentary materials as my complementary or supplementary datasets, including newsletter, blogs, journals, situation reports, data portals, conference slides, from additional primary and secondary data sources. Thus, I am able to fill the information void, synthesize the research findings; moreover, to answer the research questions thoroughly. With the Document Analysis approach, I answer Research Question 2 by reviewing the challenges of VGI and crowd-sourced data across the four stages of disaster management, based on the research findings presented in Question 1. In addition, I seek potential solutions and attempt to find out how the authors suggested to resolve the issues; lastly I share some of my perspectives.

1.3 Case Studies

The demographic as well as economic impacts caused by two catastrophes were shocking (Table 1.1), which have certainly drawn attention from all over the world. Both the domestic and international humanitarian aid had been put into best responding to the emergencies, after getting up-to-date situation information. With sufficient data, I used the quantified data to interpret the impacts of such disaster events and summarized some brief situation overview below.

Table 1.1 Intensity, Demographic Impacts, and Economic Impacts of Two Case Studies. Source: Author. Case Studies Intensity Demographic Impacts Economic Impacts

2011 Tohoku Earthquake 9.0 MW 11,600 killed, 16,450 missing $US 122-235 Billion (The And Tsunami (ReliefWeb-OCHA, 2011). World Bank, 2011)

2013 Typhoon Haiyan Category 5 6,000 killed, 14 million affected, 4 $US 12.9 Billion (NEDA, million displaced (ReliefWeb- 2013) OCHA, 2013b).

1.3.1 2011 Tohoku Earthquake and Tsunami (Japan) I chose the 2011 Tohoku Earthquake and Tsunami as my first case study because it’s considered the most massive earthquake that ever hit the nation (CNN Library, 2019), and shockingly, the 4th strongest earthquake in the world after 1900 (ReliefWeb-OCHA, 2011). On March 11, 2011, a massive tsunami was triggered by the magnitude of the 9.0 Mw earthquake in northeast Japan (仙台, Sendai Region). The 30-meter high tsunami waves inundated 433,000 square kilometers of the country, killed 11,600 people with 16,450 people reported missing, destroyed 11,700 homes and buildings, and impacted a variety of infrastructure (ReliefWeb- OCHA, 2011). In addition, the earthquake triggered a subsequent accident - severe nuclear emission of radioactive materials from the Fukushima Daiichi Nuclear Power Plant and caused worldwide concern (Government of Japan, 2012). Also, it damaged an estimated amount of $US 122-235 billion (The World Bank, 2011).

1.3.2 2013 Typhoon Haiyan (the Philippines) The second case study is the Category-5 Tropical Cyclone Haiyan (or called Super Typhoon Yolanda) in the Philippines that made landfall on 8 November 2013, which was marked as the deadliest event of 2013 in the Pacific and Asia for cruelly taking away 6,000 lives, affecting a large population of 14 million people, and having 4 million people displaced (ReliefWeb-OCHA, 2013b). The shallow beach allowed the storm surge to speed. Moreover, due to the vulnerable infrastructure and later triggered landslides, up to 70-80% of the total area was destroyed in the storm path, 1.2 million homes and buildings were damaged, over 6 million of it

5 were destroyed (Cogan, 2013). National Economic and Development Authority (NEDA, 2013) has contended that the total damage and loss were estimated at around $12.6 billion.

1.4 Thesis Organization

This thesis consists of six chapters. Chapter 1 presents an introduction of brief contexts regarding the use of VGI and crowdsourcing in disaster management. In addition to the research purpose, I formulated two research questions with a center on the two case studies, to examine the practical values and potential challenges. Chapter 2 examines the contexts of relevant literature, involving the individual phase and associated activities in a disaster management cycle, the interrelation between GIS and disaster management; the evolution of Web 2.0, and the background of crowdsourcing and VGI. Chapter 3 introduces two methodologies of Bibliographic Review and Document Analysis, as well as the primary and secondary data sources in this research, which contain research papers, electronic and printed documents. Chapter 4 explores both research datasets and targets seven Volunteer & Technical Communities (V&TCs) from the two case studies. Chapter 5 introduce the history of using VGI and crowdsourcing between 2007 to 2013, and focuses on discussing the advantages and issues of each V&TC on the case studies. Chapter 6 answers the research questions, summarizes three challenges in the process of research as well as the contributions to geography, and discuss the recommendations for future research.

Chapter 2: Literature Review

2.1 Introduction to Literature Review

The chapter comprises six segments of literature: Disaster, Disaster Management, GIS and Disaster Management, the Evolution of Web 2.0, Crowdsourcing as well as VGI. Specifically, I examined the background of the relevant literature, including (1) the contexts of disaster : distinctions with similar terms (emergency, crisis) and interrelations with important elements (hazard, vulnerability, resilience and risk) in the disaster literature, (2) the introduction of four main phases and associated activities in a disaster management cycle, (3) the evolution of GIS and its role in four phases of disaster management; (4) the development of Web 2.0 and its associated concepts (Neogeography and Map Mash-ups), (5) the contexts and distinctions of crowdsourcing and VGI, furthermore, the values as well as challenges of such information in disaster management. This literature helps to build the conceptual framework for answering the research questions.

2.2 Defining Disaster

2.2.1 The Contexts of Disaster

Figure 2.1 Disaster as the Interface between Hazard and Vulnerability Conditions Source: Paul, 2011

Fundamentally, the United Nations Center for Human Settlements (1994) defined natural disaster as the interface between hazards and vulnerability conditions (Figure 2.1), which

7 resulted from the most unexpected and sudden event, and vulnerability conditions refer to the dramatic damages to a human being as well as his built and natural environment. In addition, Wisner et al. (2004, p. 88) added that the factor of “deals with the amount of ‘access’ that people have to the capabilities, assets and livelihood opportunities that will enable them (or not) to reduce their vulnerability and avoid disaster” should be considered. Hence, a disaster should be resulting from a combination of hazards, vulnerability conditions, and “the insufficient capacity to reduce the potential negative consequences of risk” (Paul, 2011, p. 12). Consequently, the losses caused by such damages usually caused certain negative impacts on the aspects of socio-economics, culture, demography, and politics. Paul (2011) explained that the impacts of disasters are considered extremely widespread and destructive, in terms of eliminating the established years of development of the affected community, and slowing down the pace of the future reconstruction. In addition, scientists can measure the impacts of a disaster event by the number of casualties, injuries, or economic losses (Paul, 2011). These numbers indicate that the outside assistance and support on the scale from local to international will be substantially needed. National Research Council and Mapping Service Committee (2007) stated that the characteristics of disasters also lie in its scale and resources of the affected jurisdiction. Due to the large scale of such events that occurred so quickly, thus handling such an immediate situation leads to the depletion of a jurisdiction’s human resources, equipment, supplies, and funds (National Research Council and Mapping Service Committee, 2007). Additionally, disasters are considered quite an event of dynamics. Disasters can evolve according to natural factors and outside intervention, so the strategy plans should be flexible to cope with such an uncertain event (National Research Council and Mapping Service Committee, 2007). Moreover, disasters occur with a rare frequency, so the communities are often unprepared, and the local government finds it unnecessary to ready themselves with the associated plans and activities for the possibly incoming disaster (National Research Council and Mapping Service Committee, 2007).

2.2.2 Hazard Vs. Disaster To further illustrate Figure 2.1, the terms of hazard needs to be clarified. First of all, a hazard can be defined as “an extreme geophysical event that is capable of causing a disaster” (Alexander, 2000, p. 7). Similarly, Thywissen (2006) confirmed that every disaster starts with a

8 hazard. Even though the distinction between a hazard and disaster is quite clarified, the role of humans involved in hazards was not considered. Therefore, Chapman (1999, p. 3) concluded that, “a natural hazard should be defined as the interaction between a human community with a certain level of vulnerability and an extreme natural phenomenon, which may be geophysical, atmospheric, or biological in origin, resulting in major human hardship with significant material damage to infrastructure and/or loss of life or disease”. Therefore, the definition of vulnerability is the key to determine whether a natural hazard could be considered as an environmental disaster. Moreover, the other two relevant concepts are important to study hazard research.

2.2.3 Distinctions between Emergency, Disaster, and Crisis In addition to hazard, a few other associated terms often come into sight in the disaster literature, thus distinguishing the terms between them is necessary. Emergency (Johnson et al., 2000) is defined as “deviation from planned or expected behaviors or a course of events that endangers or adversely affects people, property, or the environment”, and it is small in geographic scale and can be managed by the local officials (e.g., local police and fire) (Tomaszewski, 2015). Disaster (ESRI, 2000; Tomaszewski, 2015) is larger in geographic scale and defined by the scope of an emergency. An emergency can turn into a disaster when its capability of managing the local resources is exceeded. Furthermore, the impacts caused by a disaster are greater than the local officials to handle; thus, the humanitarian aid on the local, state, or even national, the international level will be a demand. Crisis (Tomaszewski, 2015) is defined as a specific and temporary event in a dangerous situation (e.g., evacuation actions for people who are trapped in their homes).

2.2.4 Vulnerability, Resilience, and Risk Researchers use the concepts of vulnerability, resilience, and risk to analyze and understand the dynamics of disaster (Paul, 2011). First, considerably, there is a wide range of definitions in Vulnerability, including: (1) physical vulnerability: which refers to private property, infrastructure, economic vitality, habitat, productive ecosystem and natural environment; and (2) social vulnerability: which refers to the well-being of a human including death, injury and illness (Blaikie et al., 1994; National Research Council, 2006; Paul, 2011). The

9 vulnerability can be interpreted as the extent to “which a system acts adversely to the occurrence of a hazardous event,” in this context, the system refers to the “capacity to absorb and recover from a hazardous event” (Timmerman, 1981). Also, Paul (2011) emphasized that vulnerability and risk cannot be considered at the same time. Disasters are measured by the vulnerability of the affected communities instead of directly translating them into risk. Additionally, the term resilience is also an essential concept in disaster research. First of all, vulnerability and resilience are acknowledged as two complementary terms that together aim to a sustainable development, whose relationship could be seen as the improving resilience comes to reducing vulnerability (Foster, 1995). Tobin (1999) explained that if a community is considered resilient and sustainable, which will not only be able to diminish the impacts from a disaster, but also will recover from such event in a time-saving manner. Again, quite diverse literature is associated with resilience; thus, this term has different implications in different fields of research. Paul (2011) concluded that resilience can be defined as a measure or approach to increase the capacity of the affected communities to resist, cope and recover from a disaster event. Furthermore, another essential concept associated with a hazard is Risk. Johnson (2000) defined risk as “the potential or likelihood of an emergency to occur.” In addition, the risk is described as the combination of vulnerability and hazard (Alexander, 1993; Cova, 1999), the relationship is: Risk = elements at risk · (hazard · vulnerability) Therefore, the risk is defined as the function of the elements at risk, the hazard, and the associated vulnerability, and the elements at risk refer to the spatial information layers, such as the population and infrastructure (Alexander, 1993; Cova, 1999). Moreover, with spatial modeling, these spatial information layers can be combined to assess the values of hazard, vulnerability, and risk (Cova, 1999). Despite that, researchers focus on “consequence,” “loss,” and “vulnerability,” along with other elements to define risk. Mostly, the risk is often defined as the likelihood of a disaster event occurring multiplied by the consequences of such an event (Ansel & Wharton, 1992). Here, “consequence” refers to the measure of the impacts on the natural and human environments caused by disasters. Also, Thywissen (2006, p. 39) defined risk as a “function of hazard, vulnerability, exposure, and resilience.” However, the value of any component should

10 have been considered. If any value turns out to be 0, the value of risk will become 0, as well. In conclusion, the single one definition of risk has still not determined yet, due to its divergent meanings in diverse disciplines.

2.3 Disaster Management

Disasters are indeed large-scale and disruptive, yet cannot be prevented. The intervention to address such disasters has developed into a modern and complex framework, which involves the approaches that can reduce the vulnerability or diminish the impacts to the affected community, in terms of building the capacity to resist, adapt or change in the occurrence of disasters (Norris et al., 2008; Poser & Dransch, 2010). Baharin et al. (2009) described that the term of disaster management is the reactive approach that targets to improve the community resilience, mitigate the impacts of disasters, and thus prevent such disasters from expanding into a catastrophe. In addition, the International Federation of Red Cross (n.d.) defined disaster management as resource organization and management of human activities concerning the disaster cycle, which strives to prepare for, respond to, recover from and mitigate against the disasters.

Figure 2.2 Phases and Activities of Disaster Management Source: Poser and Dransch, 2010.

As Poser and Dransch (2010) concluded, disaster management can be interpreted as a continuous process including pre-disaster, during-disaster, and post-disaster activities, and the process is generally viewed as a cycle involving four phases: mitigation, preparedness, response,

11 and recovery. Each of these phases requires particular information on disaster responders (National Research Council and Mapping Science Committee, 2007).

2.3.1 Disaster Mitigation and Preparedness Disaster mitigation and disaster preparedness are the first two phases occurring before the disaster event, and both phases are inclusively related to one another, and the boundaries between two do not need to be specified exactly. Thus, in this section, I put together these phases to discuss the pre-disaster section of disaster management and especially focused on their associated activities and possible measures. Disaster mitigation is a continuous process consisting of risk identification, risk analysis, risk appraisal, and risk reduction (Figure 2.2). FEMA (n.d.; p.1) recognized mitigation as the “cornerstone of disaster management” and defined it as “any cost-effective action taken to eliminate or reduce the long-term risk of life and property from natural and technological hazards.” While, from the perspectives of Coppola (2007), there are five goals concerning mitigation, including risk likelihood reduction, risk consequences reduction, risk avoidance, risk acceptance, as well as risk transfer, sharing, or spreading. As a phase where it lies between pre-disaster and post-disaster period, it strives to achieve such goals by a set of measures such as land-use planning, technical approaches (e.g., disaster warning systems, disaster safe room), and capacity expanding in terms of social awareness and public education (National Research Council and Mapping Service Committee, 2007). In addition, Mileti (1999) added that building codes and standards should also be undertaken (e.g., a collection of laws, regulations, and policies adopted by a governmental legislative authority). Moreover, holding insurance enables economic damages to be shared through a wide population; therefore, this measure could aid the mitigation phase to some extent. Disaster preparedness is a phase that involves a series of pre-disaster activities in a short time which ensures the readiness of individuals, communities and local officials for an effective response. During this phase, a disaster event can be identified; thus, the strategic plans can be developed to address the concerns from the response and recovery. Furthermore, it consists of a set of actions such as emergency planning and training, operations of emergency monitoring, predicting, and early warning systems (National Research Council and Mapping Service Committee, 2007; Poser & Dransch, 2010).

Specifically, Paul (2011) explained that there are also other human actions involved in emergency planning and training, such as evacuation, exercise, and preparedness level ranging from household, community, to an organization, towards state and nation (e.g., planning, training, equipment, statutory authority). Meanwhile, Lindell and Praster (2003) also indicated that the warning process in the preparedness phase also involves a series of activities such as risk identification, risk assessment, risk reduction as well as a protective response.

2.3.2 Disaster Response and Recovery Likewise, disaster response and recovery are the last two phrases that take place in the aftermath of the disaster event. As a critical 72-hour response phase, the relevant activities within could be overlapped with the next phase of recovery, due to the difficulties in determining the boundary between two in large-scale disaster events (National Research Council and Mapping Service Committee, 2007; Paul 2011). Therefore, I placed them in the same section but discussed the two phases respectively, to avoid neglecting their distinct features. Additionally, to fully understand the disaster response and recovery, the knowledge of disaster relief is necessary. Disaster response is a crucial phase consisting of a set of prompt actions undertaken before, during, and after a disaster event (Mileti, 1999). Mitchell and Cutter (1997) agreed with this viewpoint by stating that it is considered more “narrowly” to define the response actions occurring only immediately after a disaster, such human actions will rescue the victims, diminish the impacts to property and facilitate the next phase of recovery. Concerning the value of the effective response, Eikenberry et al. indicated that an insufficient and poor disaster response leads to worsening the devastated situation, reflecting the failure of lacking relief support from government administration (e.g., the case of Hurricane Katrina). Plus, the delay of response will also lead to the tragedy of more losses and damages (Paul, 2011). Thus, Paul (2011) noted that to ensure a timely and adequate response, the need for local knowledge (i.e., socio-economic construction, political parties, religion, etc.) and, most importantly, the key role of coordination and communication (e.g., UN-OCHA). Lack of such appropriate coordination will result in the losses of valuable relief resources, thus exacerbating the plight of disaster-affected communities, and response efforts will not be delivered in time. A set of relevant activities are initiated to ensure an effective response (National Research Council and Mapping Service Committee, 2007; Paul, 2011), which includes the

13 operations of search and rescue; provision of basic commodities (e.g. water and food, temporary shelters) and emergency medical care; prevention of secondary hazards (e.g. fires, chemical spills, leaks from radioactive materials); identification and disposal of dead bodies; debris removal; after-disaster sheltering and housing; repairing utilities and key infrastructure and safety and security. Paul (2011) also mentioned that a primary hazard can often trigger a secondary hazard. Therefore, stopping the additional impacts during this stage is imperative. Disaster recovery is considered interchangeably with other terms such as rebuilding, reconstruction, restoration, rehabilitation, and post-disaster development (Quarantelli, 1998). The term recovery means taking the disaster-affected community back to its pre-disaster conditions (Mileti, 1999), and it aims to reverse the impacts of disasters, enhance safety and improve preparedness for future disasters (Paul, 2011). Disaster recovery is a post-disaster process that involves a broad range of activities such as damage assessment, rehabilitation, and reconstruction (Poser & Dransch, 2010). According to the National Research Council and Mapping Service Committee (2007), this phase is more specifically categorized into short-term and long-term associated activities. The short-term activities involve temporary housing/roofing, initial restoration, and repair of infrastructure and physical property, as well as financial aid. Meanwhile, Coppola (2007) and Mileti (1999) added a few other activities, such as the coordination of volunteers and donated goods. All these short- term activities engage in preparing the survivors for a rebuilding future. On the other hand, long-term activities include reconstruction of physical, social and economic infrastructure. Moreover, Paul (2011) added other activities, including organizing rehabilitation programs and creating job opportunities for survivors. The associated long-term activities strive to bring the affected population and surroundings back to the pre-disaster or even better conditions. As a result, these four phases in the disaster cycle interrelate with one another, and some actions do overlap. However, the provision of sufficient activities and suitable measures within the phases will contribute to comprehensive disaster management (Mileti, 1999).

2.4 GIS and Disaster Management

In this section, I will examine the development of GIS and explore its significance applied in the four phases of disaster management.

2.4.1 Evolution of GIS This originated from an influential mapping story of Dr. John Snow (Tomaszewski, 2015; GIS Lounge, 2019). Due to the huge disruption caused by the London Cholera outbreak in 1854, Dr. Snow had successfully identified the cause of such panic through mapping the affected locations (Tomaszewski, 2015). Thus, the first spatial analysis took place, and it led to the further revolutionary development of GIS (Tomaszewski, 2015). Before the 1960s, cartographers were only able to utilize paper maps to carry out their tasks in transportation planning and destination locating, along with the inaccurate data and difficulty in distance calibration (GIS Lounge, 2019). Thankfully, the term GIS (Geographic Information Systems) was initially coined by the “Father of GIS”- Dr. Roger Tomlinson from the development of the Canadian Geographic Information Systems in the early 1960s, which symbolized a pioneering period of GIS revolution that enables the antecedent computer mapping technology (Tomaszewski, 2015; GIS Lounge, 2019). From this prominent moment to the 1970s, the Harvard Laboratory for Computer Graphic and Analysis had been engaging in developments of computer mapping research and applications. Soon, the breakthrough of the first Vector GIS – ODYSSEY brought the revolution into a new stage– software commercialization (Tomaszewski, 2015). In the early 1980s, the most famous GIS software company was officially established with its new product, Arc/Info software. After that, a wide range of GIS software was launched, greatly enhancing such remarkable technology to the1990s (Tomaszewski, 2015). Then down the road to the 2010s, GIS was gradually introduced to the public and institutions, soon widely recognized as an essential tool for decision making in a variety of fields (GIS Lounge, 2019). Since then, GIS has stepped into a new era incorporating robust open-source, which enables GIS users to develop their GIS software through collaboration and sharing the software with the public, and, importantly, this mainstream has already marked an extraordinary segment of GIS revolution nowadays (GIS Lounge, 2019).

2.4.2 The Role of GIS in a Disaster Management Cycle Similarly, I grouped this section into two sub-sections and characterized the critically important role of GIS in each phrase of the disaster management cycle.

2.4.2.1 GIS in Disaster Mitigation and Preparedness In disaster mitigation, the role of GIS can be accurately defined as to “identify potential physical, virtual, and social vulnerabilities, that can ideally be mitigated or reduced through increased resilience efforts,” thus the most important mitigation activities are risk and vulnerability assessment (Tomaszewski, 2015, p. 233). In brief, Tomaszewski (2015) contended that the most common GIS techniques adopted in mitigation are the development of risk and vulnerability spatial indexes, which incorporates various social and physical variables. Cova (1999) agreed upon that GIS provides a mechanism to enhance risk management, including long- term assessment, planning, and forecasting, thus leading to the reduction of physical force from a disaster or minimization of vulnerability to that disaster. In this case, the term risk management is often considered as a mitigation activity (Alexander, 2000). In addition to vulnerability assessment and mapping (e.g., a focus on human-environment with implicit hazard component), as well as risk assessment and mapping (e.g., a focus on hazard and vulnerability), Cova (1999) added that another common GIS technique is hazard assessment and mapping (i.e., a focus on physical environment with implicit human vulnerability component), which can improve the mitigation process. In disaster preparedness, Tomaszewski (2015) classified three major GIS tasks associated with this phase: evacuation route planning, evacuation zone planning, public education, and citizen participation enhancement. First, as an illustration, the knowledge of how and where to quickly evacuate people is crucial; thus, the employment of GIS technology enables real-time decision support for such a time-sensitive situation. In particular, GIS can help to handle the ‘what-if scenarios’ (e.g., weather emergency; traffic congestion; damaged travel route), consequently substantially contribute to the urgent preparedness phase by saving a considerable amount of time on travel time, travel distance and expenses (Tomaszewski, 2015). Second, Cova (1999) agreed upon that GIS can indeed be used as an essential navigation tool in emergency vehicle routing; moreover, GIS provides spatial support in building hazard models, evacuation simulation models, furthermore rapid development in evacuation planning. Third, Tomaszewski (2015) emphasized that online mapping tools can facilitate information dissemination regarding disaster planning and preparedness for public education purposes. For example, during a technological training, citizens can practice multiple activities such as, how to immediately respond to a disaster strike, how the evacuation routes are planned and where the

16 shelters locate, simply by using their GPS built-in smartphone applications (Tomaszewski, 2015). Furthermore, GIS online mapping tools enable citizens to collect and share the relevant data of their neighborhood, therefore largely enhancing citizens’ participation in a disaster situation (Tomaszewski, 2015). This knowledge is defined as crowdsourcing (Howe, 2006), which will be discussed in the later section.

2.4.2.2 GIS in Disaster Response and Recovery Disaster response is considered as the most well-known phase for the most openly wide use of GIS and as well as disaster mapping (Tomaszewski, 2015). During such a time-sensitive and critical phase, GIS can integrate and disseminate the accurate, immediate, and relevant spatial data that can be used for mapping, modeling, and thus help decision-makers to develop and implement the response strategies (Cova, 1999). In regards to the role of GIS in initial disaster warning, GIS supports a variety of disaster response applications to deliver real-time situation information. Furthermore, the numbers of GIS products have been rapidly growing after a comprehensive framework of spatial data reference, situation inputs, data processing, and analytics (Tomaszewski, 2015). Moreover, GIS products are categories in paper maps, web- based maps, map and data services, and custom applications (Tomaszewski, 2015). In fact, paper maps are still considered vital since there might not be any power or internet connection in a disaster situation (Tomaszewski, 2015). Moreover, because of the complimentary disaster response data or products collected or created by a range of large information technology and data companies, online disaster response and geographic data streams have become a trendy type of GIS product, which aims to raise situation awareness and facilitate the public communication (Tomaszewski, 2015). Besides, GIS serves as a key tool in damage assessment, which is considered a crucial activity in disaster response to determine the extent of severity (Tomaszewski, 2015). The incorporation of a GPS built-in mobile device enables the field data collection and serves as a real-time database for assessment (Tomaszewski, 2015). Disaster recovery is often phased into short-term, intermediate, and long-term recovery (FEMA, 2011). Generally, GIS acts as a spatial inventory system as well as a coordination mechanism for all-phase recovery activities (Cova, 1999). Moreover, MacEachren (2005) added that the use of GIS also can promote dialogue through information visualization and sharing.

To begin with the short-term recovery (days), the use of GIS primarily coordinates the location-specific planning for redevelopment of shelters, housing, public health care, temporary local businesses, debris cleanup of main transportation routes and other recovery services, which is viewed as significant contribution to initial stage of recovery, especially in the case of multiple scatter regions impacted by a large-scale disaster (Johnson et al., 2000; Tomaszewski, 2015). Moreover, GIS also is employed in recovery analysis, disaster database design, assessment of risk, and vulnerabilities (Tomaszewski, 2015). Subsequently, during the intermediate recovery (weeks-months), the implementation of recovery activities still carries on; also, the use of GIS assists with the review of interim housing plans, debris removal, repair, and restoration of infrastructure (FEMA, 2011). Importantly, GIS provides a network analytical tool in restoring the critical infrastructure (e.g., power, electricity, water), in terms of conducting infrastructure vulnerability analysis (Tomaszewski, 2015). Besides, GIS is also used for risk education for the public and the prevention of disaster reoccurrence (Cova, 1999). At last, the long-term recovery (month-years) uses GIS to further ensures the ongoing recovery process, as well as facilitates recovery plans. However, long-term recovery is often seen as time and labor-consuming; thus, the implementation of recovery plans demands substantial financial investment (Johnson, 2000). Tomaszewski (2015) emphasized that the informative and visually expressive products built by GIS are proved to be useful and time-saving for stakeholders. For example, the use of GIS alleviates the overwhelming decision-making process in allocating recovery funding in terms of how and where (Johnson, 2000).

2.4.2.3 GIS, Local Knowledge, and Disaster Management Last but not least, besides the particular roles that GIS plays in each disaster phase, GIS also functions as an information management mechanism within the entire disaster management cycle (Tomaszewski, 2015). Griffiths (2006) explained that information management can be defined as the information collected from various sources and thus distributed to various users. Particularly, Tran et al. (2009) noted that the integration of local knowledge with GIS in disaster management is significant. Local knowledge was broadly defined as “the total of the knowledge and skills which people in a particular geographic area process, and which enable them to get the most out of their natural environment” (IK & DM, 1998). Tran et al. (2009) explained that local

18 knowledge is regarded as a key element in disaster risk identification, for the reason that this knowledge “allows the planners to survey the needs and opportunities rapidly in mitigation” (Twigg, 2004). However, local knowledge is rarely systematically recorded and only visually accessible to the local people (Hatfield, 2006), which can be a challenge for researchers who are unfamiliar with the study area. To break the limitation of such a powerful resource, the incorporation of GIS mapping techniques greatly advances such traditional participatory approaches for collecting and sharing the local knowledge (e.g., which area is vulnerable to the disaster strike) and enables the understanding and distribution of such knowledge without being physically present (Hatfield, 2006; Tran et al., 2009). Therefore, the process, available and accurate local knowledge will considerably help to develop the strategic plans and policies by decision-makers and practitioners, in terms of the various and indigenous data display of disaster vulnerabilities and risks (Tran et al., 2009). Overall, thanks to the backbone of disaster management - GIS, the terror, and insecurities resulting from huge disasters can be alleviated, to a large extent (Johnson, 2000).

2.4.3 Limitations of GIS in Disaster Management First, Poser and Dransch (2010) mentioned that, even though GIS contributes greatly with real-time geographic data for disaster management, the limitations of geospatial technologies still exist. Exemplified by the technologies of the in-situ and remote sensing systems. However, there are some parameters of the phenomenon that cannot be measured by such sensor systems (e.g., hailstorms). In addition, damaged sensors, disrupted communication, or the disconnected Internet may cause the sensor systems to be unavailable. Moreover, such sensor systems are unable to take measurements in weather-critical situations (e.g., heavy storms, etc.), since the clouds may obstruct the satellite images taken from remote sensing sensors. Further, there is usually a day delay for data collection and processing. McDougall (2011) and Triglav-Čekada et al. (2013) both agreed that there are certain limitations of satellite sensors. Additionally, data is collected from regions where the data is poor or absent (McDougall, 2011). Lastly, the traditional geographic information usually comes with high costs (Hartato, 2017).

2.5 Evolution of Web 2.0

As an essential enabling technology, Web 2.0 substantially popularizes the wide use of crowdsourcing and VGI, together with other technologies including georeferencing, geotags, Global Positioning Systems (GPS), and broad communication (Goodchild, 2007). In this section, I will explore the definition and significance of Web 2.0, along with its advantages and disadvantages (e.g., in the phase of disaster response), as well as the associated key concepts: Neogeograpy and Map Mash-ups.

2.5.1. Defining Web 2.0 Before the rise of Web 2.0, the early Web was considered “one-directional” (Goodchild, 2007), and the geographic information was considered costly and mostly inaccessible; furthermore, the information had to be created and distributed by professional experts, while ordinary people worldwide would act as passive recipients only. However, the evolution of Web 2.0 has altered many a practice, especially, the production of geographic information by worldwide volunteers, who play a new role in knowledge management including to create, develop, share and use such information (Haklay et al., 2008; Budhathoki et al., 2008; Haklay & Weber, 2008), therefore, the disaster situations will significantly be assisted. The responses from people to disasters, have facilitated the connection between people and the disaster area, and aided the disaster relief by contributing individual efforts (Zook et al., 2010). With the development of Web 2.0 and disaster mapping applications, it enables people to directly disseminate the information from ‘one-to-many’ fashion, evolving into a ‘many-to-many’ trend. Web 2.0 was initially defined as “the business revolution in the computer industry caused by the move to the Internet as a platform, and any attempt to understand the rules for success on the new platform” (O’Reilly, 2006). Web 2.0 is defined as peer production, cloud collaboration, or crowd-sourcing, the phenomenon refers to the ability of people from around the world to collaborate on projects that are often highly ambitious in both their scale and scope (Zook et al., 2010). Additionally, Goodchild et al. (2007) stated that Web 2.0 is “a bi-directional collaboration” that aims to gain, gather and publish the geographic information.

2.5.2 Strengths and Potential Flaws Zook et al. (2010) concluded that there are two important advantages of incorporating Web 2.0 in disaster response support. First, the multiple maps can be produced in a short time and help to relieve the technical stress from the aid agencies so that their limited resources, including the rescue labor, volunteers could be distributed elsewhere as needed. Second, individuals are able to report by using a variety of forms, such as the name-based databases that allow the report or search for the individuals. In addition, Goodchild (2007) stated that the environment of Web 2.0 allows the volunteers to contribute their technical efforts conveniently. On the other hand, when it comes to the potential flaws of using Web 2.0 in disaster relief, Zook et al. (2010) claimed that the validity and accuracy of data would be a concern. Even though the growing role of Web 2.0 has enabled ordinary people to participate in disaster relief on a broad scale, the knowledge gap between the experts and amateurs remains an issue; thus, the quality of data could be affected. However, this concern may be addressed in the future. Goodchild (2007) also added that, even though the contributors of data are volunteers without professional qualifications, the errors caused unintentionally could still be edited by experts through an extensive review process.

2.5.3 Core Concepts: Neogeography and Map Mash-ups The core of the Web 2.0 is the concept of Neogeography. Di-Ann Eisnor defined the term as “a socially networked mapping platform which makes it easy to find, create, share and publish maps and places”, in addition to the definition, she added that as a “new geography” which contains the technologies that lie outside the field of traditional GIS (Haklay et al., 2008). Moreover, the Neogeography enables ordinary users to produce the maps on their own, share the geographic information with others, and disseminate the interpretation of such knowledge. Another concept that situates the center of Web 2.0 is the map mash-up, which refers to “websites that weave data from different sources into new integrated user services” (Batty et al., 2010). Moreover, mash-ups are categorized differently by the methods of data collection (Hakley et al., 2008). For instance, neogeography websites enable the capability of data exchange by innovative users. Moreover, some non-mapping websites allow innovative users to upload spatial information. Goodchild (2007) pointed out that, Google Earth and Google Maps enable the

21 widespread popularity of mash-ups, for its capability of uploading and distributing the geographic information created by the untrained amateurs via the Internet.

2.6 Crowdsourcing and VGI

As mentioned, Web 2.0 enables the widespread creation and distribution of geographic information for disaster management. As See et al. (2016) underlined, crowdsourcing and VGI can be viewed as complementary to authoritative sources instead of being seen as competitors or replacements to the traditional approaches. Thus, in this section, I will introduce the two important concepts: crowdsourcing and VGI. In detail, the characteristics and issues of VGI will be discussed, as well as the role in disaster management.

2.6.1 Defining Crowdsourcing Howe (2006) initially discovered crowdsourcing as a set of activities that are undertaken by a large group of practitioners for specific tasks or problems. Also, Hudson-Smith et al. (2009) described that when the data or information is used in the creation of content and becomes accessible and sharable through web-based services, then it will be defined as crowdsourcing. He also emphasized that the value of the crowd dynamics is unorganized and voluntary. Further, Cavelty et a. (2011) contended that crowdsourcing is an aggregation technique of disparate information. In addition, Estellés-Arolas and González-Ladrón-De-Guevara (2012, p. 10) claimed that “crowdsourcing is an online process that is distributed by the very nature of the Internet and it always involves the participation of the crowd.” According to an extensive bibliographic review of relevant literature, they have concluded with eight key criteria for verifying the crowdsourcing, including: (1) the form of crowd, (2) tasks of the crowd, (3) benefit for performing such tasks, (4) the form of the crowdsourcer/initiator, (5) benefit for crowdsourcer, (6) type of process, (7) type of call to use, and (8) medium to use. Therefore, Estellés-Arolas and González-Ladrón-De-Guevara formulated a comprehensive definition (2012, p.9-10):

“Crowdsourcing is a type of participative online activity in which an individual, an institution, a non-profit organization, or company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a

22 task. The undertaking of a task, variable complexity, and modularity, and in which the crowd should participate, bringing their work, money, knowledge, and/or experience, always entails mutual benefit. The user will receive the satisfaction of a given type of need, be it economic, social recognition, self-esteem, or the development of individual skills, while the crowdsourcer will obtain and utilize to their advantage what the user has brought to venture, whose form will depend on the type of activity undertaken.”

2.6.2 Defining VGI Goodchild originally coined the term of Volunteered Geographic Information (VGI) in 2007, when he defined VGI as organized large groups of amateur citizens with little/none professional qualification, voluntarily engaging in the creation of geographic information (e.g. Flickr, OSM), though the accuracy of such information needs to be assessed. Further, VGI is considered as a subset of User Generated Content (UGC), which comprises geographic reference, as UGC can be interpreted with two typical examples, including Facebook and Twitter (Craglia et al., 2012; Elwood et al., 2012). Some researchers have described this concept also as ‘collaborative mapping,’ ‘wikification of GIS,’ ‘participatory GIS,’ ‘web mapping 2.0’ (Neis & Zielstra, 2014). To begin with the nature of VGI, the term “volunteered” can differentiate VGI from any other form of UGC (Elwood et al., 2012). Also, Haworth and Bruce (2012, p. 5) concluded that “the motivations for volunteering or withholding will shape the dynamics of inclusion and exclusion in VGI development and influence data content.” Moreover, volunteering, as well as the geographic location, can be expressed as either explicit or implicit (Craglia et al., 2012). The explicit VGI can be exemplified as OpenStreetMap, whereas the implicit VGI can be seen as a social media post with tagging a location (Senaratne et al., 2017). Second, another keyword of VGI is “citizen science.” Goodchild (2007, p. 218) defined it as “communities or networks of citizens who act as observers in some domain of science.” On the one hand, some VGI projects require citizens with a fair level of knowledge and expertise (e.g., domains of land cover and land use mapping); thus it could be a factor that limits the expansion of VGI participants, but the professional experts are still able to contribute the geographic information. On the other hand, some VGI projects do not require any degree of

23 training or knowledge, which enables the enlarging participation of amateur citizens in a variety of domains. Also, Capineri (2016) mentioned other keywords in the contexts of VGI. In addition to the geographical references (e.g., geospatial references) that VGI contains, the content of such information as well as the authorship cannot be neglected. Although the producers of VGI are often ambiguous (Budhathoki et al., 2008), the contributions from diverse sources are still considered substantial (Haklay 2013; Coleman et al. 2009). Last but not least, Haworth (2018) concluded that VGI represents the changes in the ways that geospatial information is created and used, with significant implications in numerous fields, for instance, disaster management.

2.6.3 VGI Vs. Crowdsourcing Even though the boundaries of definition between crowdsourcing and VGI are considered quite vague, yet, Goodchild and Glennon (2010) noted that these two terms could be distinguished mainly in two hypotheses. First, crowdsourcing centralizes on the group work, whereas VGI focuses on individual efforts; thus, it is assumed that crowdsourcing can handle the tasks more efficiently than VGI. Although the crowd usually lacks professional knowledge, the work from the crowd is still largely acknowledged, since the crowd is able to exchange the knowledge and improve the results; Second, one of the characteristics of crowdsourcing is that every individual in this crowd is an observer. Therefore, the information proves to be more accurate than that gathered by a crowd consisting of more than one observer, rather than the data collected from just one individual observer. Despite the propositions discussed above, Harvey (2013) demonstrated a more comprehensive explanation. To begin with the illustration of broad crowdsourced geographic information, the most common example is that the mobile phone users collect the data without any professional knowledge or capability to control such collection. Therefore, according to the different purposes of user control, crowdsourced geographic information can be generally grouped into volunteered geographic information (VGI), as well as contributed geographic information (CGI). Then, in addition to the clarity of purposes for data collection, Harvey (2013) determined that another key distinction is the ability to control data collection and reuse. In sum, Harvey (2013) contended that VGI is defined as geographic information collected with clarity of

24 purpose and specific intention, whereas CGI is viewed as geographic information that has been collected without any relevant knowledge or explicit intention. Moreover, VGI has limited control over the data reuse, while CGI has none. Moreover, Hartato (2017) concluded that the VGI could be viewed as a form of crowdsourcing, despite the considerations regarding the motivations for such activity and the ability to control the data collection and usage.

2.6.4 Contributions of VGI and Crowdsourcing for Disaster Management To begin with, the advances in ICT, particularly exemplified by Web 2.0, have enabled data collection in an easy, quick, and systematic fashion from volunteers on a large scale (Gouveia et al., 2004). Further, neogeography promotes the proliferation of web-based geographic information technologies and enlarging the creation of individual geographic content and maps by non-experts (Leszczynski, 2014). Before this, the provision of most information used for emergencies was generally from public agencies, private companies, research institutes, or emergency professionals, as scientists mistakenly thought that an ordinary citizen with little professional knowledge could not be a knowledge producer. Now, agencies realize that the citizen volunteers for the emergency response can produce valuable information (Poser & Dransch, 2010; Goodchild, 2007).

2.6.4.1 VGI in Disaster Mitigation and Preparedness Disaster management requires the collection of accurate, up to date, yet different kinds of geographic information in different phases (Poser & Dransch, 2010). In particular, during the phases of mitigation and preparedness, static information is in need during the iteration of a disaster management cycle. For example, the information on hazard identification, quantification as well as vulnerability parameters (e.g., land use, action emergency planning). Poser and Dransch (2010) concluded that the key contribution of the public via VGI enables the effective communication of acceptable and unacceptable risk, coping strategies, mitigation measures as well as prioritization. Moreover, Pearce (2003) contended that disaster management policies have gradually recognized the importance of public participation in both disaster management planning and community planning; further, that increasing public participation leads to sustainable hazard mitigation. Additionally, Haworth (2018) indicated that disaster management

25 has progressed into a “collaborative activity and dynamic enterprise that facilitates the multiorganizational, intergovernmental, and intersectional cooperation.”

2.6.4.2 VGI in Disaster Response and Recovery In contrast to mitigation or preparedness, most researchers and volunteers have focused on the role of VGI in disaster response, due to the fact that the response information is more exposed and eye-catching in social media (Klonner et al., 2016). In particular, the response phase demands dynamic data, which contains the extent and intensity of a disaster event, along with the caused impacts and the current situation of activities. Moreover, this data needs to be monitored and updated on a continuous and regular basis by the technologies of the in-situ sensors and remote sensing systems. However, as discussed in Section 2.4.3.4, the sensors show certain limitations, and the issue concerning the data acquisition must be resolved in a time- critical situation. Therefore, practitioners can fill the gap by utilizing VGI in a disaster event (Poser & Dransch, 2010). In particular, Goodchild (2007) found that humans can act as sensors to provide contextual and filtered information because they are able to move around and observe the surroundings, synthesis with the local knowledge, thus interpret the situation. In particular, humans contain different senses (e.g., sight, hearing, smell, and taste), which enable them to measure different parameters of a disaster event (e.g., observation of increasing water levels, detection of cracking sound from an earthquake, the smell of the burning fire and taste of the contaminated water). Further, in contrast to physical sensors, human sensors are able to measure the parameters of more phenomena (e.g., hailstorms.). In addition to the limitation of geospatial technologies, sometimes, the complimentary geospatial data collected from some regions is unavailable or poor (McDougall, 2011), which also presents an opportunity for the use of VGI. Besides, Goodchild and Glennon (2010) pointed out that VGI acts as an alternative to the official sources, showing significance in time efficiency for information collection and open sharing. For illustration, due to insufficient funds and resources, authoritative data from official sources need to be verified through a long process before dissemination, therefore, the delay of such data usually causes more severe damages. In contrast, the volunteers today are fully prepared with digital equipment (e.g., cameras, digital maps, and GPS), which provides them with sufficient resources for data collection (Goodchild & Glennon, 2010).

Moreover, the evolution of VGI provides a new platform for data collection and dissemination in disaster management (Haworth & Bruce, 2015), therefore to increase the public awareness and facilitate the decision making for the government (Hartato, 2017). Additionally, authorities are able to communicate time-critical geographic information with the public directly and timely (Haworth & Bruce, 2015). Meanwhile, the public can contribute with important disaster-related information to the authorities directly and exchange the information with each other openly (Meier, 2012). Importantly, the collection of abundant information contributed by citizens, as well as the dissemination of this information from relief agencies, have played a critical role in effective disaster response (Gao et al., 2011; Abbasi et al., 2012; Haworth & Bruce, 2015). Further, VGI collection and dissemination, through the widespread propagation method of social media, can more thoroughly broadcast the messages (Gao et al., 2011; Haworth & Bruce, 2015).

2.6.5 Challenges of VGI and Crowdsourcing for Disaster Management Despite the contributions of VGI in enhancing disaster management, several issues can impede the VGI use, which is listed below: 2.6.5.1 Participant Motivation Creating crowdsourcing and VGI may enhance the authoritative datasets as potential competitors instead of replacing them (Neis et al., 2013; See et al., 2016). Haworth and Bruce (2015, p. 5) stated that “the motivations for volunteering or withholding will shape the dynamics of inclusion and exclusion in VGI development and influence data content.” Anonymous and Non-Anonymous Participants In particular, Goodchild (2007) noted two generic types of participants: the anonymous and identified citizens. On the one hand, personal satisfaction acts as a key incentive to participant in VGI collection and dissemination. By watching their contributions available through worldwide distribution, their need for recognition can thus be satisfied (e.g. anonymous projects or websites: OSM, Flickr).On the other hand, self-promotion is an important incentive for participants to actively engage in web-based disaster response projects, which leads to the large-scale exhibitionism of their personal websites such as blogs, thus publicizing their social popularity. Another reason for the active participation of the public is that, they simply aim to make the information available to their friends and relatives.

One-Way Information Flow and Two-Way Information Flow See et al. (2016) concluded with two categories of motivations as well. First, a one-way information flow, participants simply contribute to a good will and do not require any information for the return. Second, a two-way information flow, participants need tangible information in return after their engagements, such as access to data, or different kinds of advice.

2.6.5.2 Data Collection Essential Use of Localization Unlike the georeferenced data gathered from stationary and location-specific sensor systems, sometimes the information collected from humans is not localized; thus, the information is not usable. However, the widespread utility of GPS built-in digital devices including mobile phones or tablets enables humans to incorporate the use of localization in reporting an urgent event. Thus, the contributors need to understand the importance of employing GPS-enabled products before recording any information (Poser & Dransch, 2010). Bias – Data Quality Reduction Data quality could be impacted by limited knowledge and training of volunteers, additionally, the quality could be impacted by bias caused by the volunteers. When amateur volunteers are affected by a huge disaster, which may cause intentional or unintentional data acquisition, including biased or inaccurate information (Poser & Dransch, 2010). In particular, the impacted individuals or households may get emotional, which may unintentionally affect their subjectivity of judgments during a disaster event. Another example is that the more severely impacted individuals or households tend to be more willing to report the situation, which leads to a bias that only the disaster events above a certain threshold can be informed and broadcast (Poser & Dransch, 2010). Worst, the public may intentionally exaggerate the impacts so that they can make a profit when pleading compensation (Poser & Dransch, 2010). Such data will undoubtfully affect the quality of VGI and cause other issues. Therefore, the measures to address such issues is essential. Example of a Data Collection Approach Poser and Dransch (2010) pointed out that one of the approaches that efficiently obtain information is the Internet-based survey, which aims to collect the exact information in a

28 required format for a specific task. However, this approach requires volunteers with a certain extent of knowledge, the ability to filter the important knowledge from large amounts of data, moreover, the willingness to carry on such as demanding task.

2.6.5.3 Data Availability Digital Divide – Limited Access to Data Digital Divide in the context of VGI (Goodchild, 2007) refers to a phenomenon that, though a growing number of citizens in developed countries have access to the information through the connected Internet and mobile devices; unfortunately, there is a large population in developing countries do not have such access. Further, the issues of language and alphabet are other obstacles to data access. Therefore, Goodchild (2007) argued that users should take into consideration the limitation concerning internet access and mobile devices so that all the citizens encountering the disaster would have access to crowdsourcing and VGI. Different Levels of Data Availability See et al. (2016) contended that data availability varies from unavailable (used only internally), only available to contributors, only available after registration and log-in, or more open to everyone. Thus, the data within the levels of availability also varies, such as only available from viewing on a map interface, available in downloadable formats (e.g., KML, KMZ, XML, etc.) and some available via API (See et al. 2016). However, such levels of access may impede the use of crowdsourcing and VGI due to the insufficient professional knowledge of volunteers. Characteristics of Uncertainty – Act as Supplementary Source Due to the fact that the VGI contains the characteristics of uncertainties, such as the amount, category, producer, and other components of such information. In contrast to conventional sensor systems, VGI contributed from the public cannot be predicted or planned forehead. Therefore, only if the accuracy and reliability were assured, VGI would be able to act supplementary to the authoritative geographic information database (Poser & Dransch, 2010; Goodchild & Glenn, 2010).

2.6.5.4 Legal Issues Goodchild (2007, p. 220) mentioned that VGI is sometimes referred to “asserted” geographic information, for the reason that “its creator asserts its content without citation, reference, or other authority.” Thus, it may cause associated issues. Data License – Efforts from All Data licensing is considered a major issue when integrating VGI with authoritative datasets, which causes copyright conflicts (Scassa, 2013; Neis & Zielstra, 2014; See et al., 2016). Due to the fact that the authoritative sources usually contain copyright of users’ permission to access and use the information, whereas VGI and crowdsourcing do not. Therefore, both VGI operators, contributors, and users should be cautious of the copyright issues, such as providing documents of evidence regarding their rights to access and use the information (Scassa, 2013). Authorities from diverse places of the world have allowed the adoption of open data licenses for VGI and crowdsourcing and enabled the successful integration with authoritative datasets (Singleton et al., 2016). Causes of Legal Issues Chen et al. (2017) summarized two reasons regarding legal issues. First, VGI projects are lack of diverse perspectives from the government and citizens, which leads to a limited research angle. Second, researchers and policymakers are still in the progress of establishing the relevant laws and regulations; thus, the legal void takes a certain time to fill in.

2.6.5.5 Data Quality Lastly, the major issue concerning the use of VGI is its unknown quality; thus, researchers found it essential to develop approaches to assess the data provided by non-experts. To scientifically assess the quality, Poser and Dransch (2010) contended with two concepts: accuracy and credibility. Accuracy Researchers centralize on the level of similarity between the data created and the real- world phenomena described (Devillers & Jeansoulin, 2006), and such an approach is termed as “internal data quality.” While the “external data quality” as a complementary concept, as Poser and Dransch (2010, p .7) stated, “which assesses the suitability of a data set for a specific task in

30 a specific area… also relies on the measures of internal data quality and explicitly stated objectives and requirements of the intended use.” Credibility Poser and Dransch (2010) argued that credibility is based on the trust and reputation as proxies for data quality, which depends on the users to rate the credibility of other users and the information they contributed. This ‘quality-as-credibility’ (Flanagin & Metzger, 2008) is particularly useful when individual perceptions or vague concepts are aimed at rather than objective properties.

2.7 Conclusion to Literature Review

With this solid basis for comprehensive knowledge for interpreting VGI, crowdsourcing, and disaster management, I am positive that the research questions can be answered in depth.

Chapter 3: Methods and Data Sources

3.1 Introduction to Methods and Data Sources

This chapter introduces two methods and two data sources that I employed in my research. The methods include Bibliographic Review and Document Analysis. Bibliographic Review is considered a fundamental method in the research. I designed a search profile with a few indicators: database, search range, keywords, information to be collected, inclusion criteria as well as exclusion criteria. By following the profile, a comprehensive review of selected research papers can be carried out, and high-quality research can thus be developed. Document Analysis particularly applies to case-study based research (Yin, 1994). Since I used a considerable number of documents as an additional data source, this approach enabled the extensive use of these complementary data outside the academic databases. That being said, I incorporated the research papers as well as documentary materials to create two research datasets so that the knowledge gap can be filled, and research questions can be answered thoroughly.

3.2 Bibliographic Review

A bibliographic review is a scientific process of analyzing and elaborating the research literature. It vastly provides researchers with a solid ground of literature. With this fundamental approach, researchers are able to obtain the most topic-relevant information, ascertain the research objectives, and answer the research questions (Esquirol-Caussa et al., 2017).

3.2.1 Bibliographic Review Process A review process of bibliographies was developed (Esquirol-Caussa et al., 2017), which involves five stages: (1) The answerable research questions should be prepared while defining the research topic. (2) A search profile included with data search and collection should be established. For instance, the appropriate databases, keywords, and most relevant information should be determined. (3) A critical analysis of literature should be conducted, which involves a comprehensive evaluation, interpretation, and classification. Meanwhile, irrelevant information should be eliminated by following certain criteria. (4) The review should be completed through the previous steps. They facilitate constructing an extensive theoretical framework for research. (5) and conclusions.

Figure 3.1 Bibliographic Review Process Source: Esquirol-Caussa et al., 2017.

3.3 Document Analysis

3.3.1 ‘Document’ Qualitative research requires abundant literature, documentary materials, and data collection, so documents play a key role as complementary data in the analysis. Merriam (1988, p. 118) indicated that “Documents of all types can help the researcher uncover meaning, develop understanding, and discover insights relevant to the research problem.” Bowen (2009) added that documents also allow researchers to acquire rich background knowledge, contexts, supplement research data from other resources, identify changes and development and verify the research findings or corroborate evidence. In general, O’Leary (2014) defined three types of documents, which involves: 1) Public Records: the official records produced by organizations. For example, annual reports, policy manuals, strategic plans, and handbooks. 2) Personal Documents: the first-person documentation includes experience, thoughts, ideas — for example, blogs, Facebook posts, Twitter posts, reflections, and journals. 3) Physical Evidence: ‘physical objects found within the research setting (often called artifacts).’ For example, handbooks, agendas, posters/flyers and training materials. Thus, I employed the classification to conduct a review and evaluation of the documentary materials (Bowen, 2009), which includes a variety of forms: both electronic (internet-transmitted and computer-based) and printed government reports; NGO’s and Global Geospatial Institutional research and situation reports; books and brochures; United Nations conference presentations; journals and newsletter; Geospatial datasets (e.g. maps, satellite imageries, GIS data) from data/knowledge portals, documentary films and videos. Most of them were found on the Internet, some of them were browsed in the database at Miami University Library. Document analysis is considered as an effective analytical method in qualitative research. It aims to obtain understanding and knowledge of research through data examination and data 33 interpretation (Corbin & Strauss, 2008; Rapley, 2007). It is crucial to determine the usefulness and authenticity of the documents. For this reason, researchers need to understand the strengths and weaknesses of this approach.

3.3.2 Strengths and Potential Flaws As Bowen (2009) stated that, document analysis generally turns out to be a less time- consuming and less costly method. Also, most documents are available on the Internet and more accessible to researchers nowadays. Moreover, instead of being obstructive and reactive, documents are stable in the research process since the data in the documents were already collected. Additionally, Yin (1994) added that the documents cover a large range of time and events. They provide the exactness of names, references, and details of events. However, Bowen (2009) indicated that the documents often do not provide researchers with sufficient details to answer the research questions; thus, we need to understand that the documents were created as a former independent study. Yin (1994) also pointed out that access to the documents sometimes could be denied, and they will be unable to be retrieved. In addition, there are limitations in reviewing a complete collection of documents due to the ‘biased selectivity,’ which might reflect the focus/specialization of the organizations’ efforts or the preferences of the author.

3.4 Secondary Data

3.4.1 The Contexts of Secondary Data Secondary data can be referred to as ‘relatively large databases that individual researchers would not be able to gather themselves’ (Martin & Pavlovskaya, 2009, p. 173). Even though the researchers do not originate the secondary data, the data is often recognized as an essential ‘primary source’ for research. There are plentiful types of secondary data, which can be qualitative or/and quantitative data, such as published scientific studies, archives, or data collection, and they are mostly created and provided by government agencies, non-government organizations, private corporations. In making use of secondary data, White (2010) claimed that the data can be used for three purposes: context, comparison, and analysis for research. He explained that, when secondary data plays a role as a context, it can provide descriptive information of the research focus; as a source of comparative evidence, it can help with verifying

34 the conclusions that come from the authors of academic journals; and as raw materials for analysis, the secondary data enables a much less time-consuming data search. Therefore, it is important to evaluate the advantages and limitations of the secondary data before we incorporate the data into the research.

3.4.2 Advantages and Limitations Martin and Pavlovskaya (2009) concluded with three strengths of using secondary data. First, the extensive spatial coverage, as well as the growing accessibility with the lowest costs, greatly enables the wide use of their research projects. Second, as a creation by well-trained professionals, secondary data are considered consistently well-organized and suitable. With the standard and comparable information, secondary data are highly acknowledged for its legitimacy. Moreover, White (2010, p. 62) argued the government produces secondary data in ways that individual researchers cannot compete with, which tends to be much more powerful. Importantly, secondary data can identify the gaps in interpretation and fill the knowledge void, and therefore, to provide a comprehensive research basis. Meanwhile, Martin and Pavlovskaya (2009) also suggested that researchers should be aware of the potential flaws of secondary data, for example: (1) the risk of trapped in the data- driven research questions. Researchers are unable to answer the research questions because of the secondary data they find, are not initially designed for them. Many datasets are created from the original purpose. That being said, researchers need to reinterpret or redesign the data to meet the research needs or modify their research questions. (2) Partial representation. It is admitted that secondary datasets contain incomplete information - only about selected phenomena or aspects. (3) The ambiguity of categories. The considerations of the vague and obscure data and variables need to be taken to avoid the complications for research. (4) Concerns about privacy and random errors.

3.5 Conclusion to Methods and Data Sources

In this chapter, I introduced two research approaches including Bibliographic Review and Document Analysis. A scientific literature review provided the researcher with an excellent grounding in the literature. Document analysis of various sources such as government reports, newsletters, journals, blogs, books, magazine articles, as well as conference presentations/videos

35 allowed the researcher to draw evidence from many perspectives. I used two types of datasets, research papers and documentary materials from government organizations, non-government organizations and community sources. Suitable approaches and useful data sources would facilitate the successful completion of this research.

Chapter 4: Results

4.1 Introduction to Results

Research Question 1: How has the use of VGI and crowdsourcing enhanced the coordination across the four stages of a disaster management cycle?

Research Question 2: What have been the challenges of using VGI and crowdsourcing, and what are the potential solutions?

In this chapter, I employed the Bibliographic Review and Document Analysis approaches to examine 20 English language research papers and 50 documents. I proposed to find out which media has used VGI and crowdsourcing in the two case studies. With the targeted media, I will be able to discuss their advantages and disadvantages in Chapter 5, and answer the questions in Chapter 6. By adopting Bibliographic Review, I have created two datasets to implement comprehensive research (Table 4.1). First Dataset – Research Papers: I designed a search profile and determined a set of suitable and useful research papers. To comprehensively examine the 20 papers, I classified the content into five dimensions (Table 4.1). According to the statistics, I determined the five examples of VGI/crowdsourcing use on the Tohoku Earthquake, including OpenStreetMap (OSM), Ushahidi, Safecat, ALL311, and ESRI; also four examples on the Typhoon Haiyan, including OSM, Ushahidi, MicroMappers and Google Crisis Response. Second Dataset – Documents: However, due to the language barrier that I was unable to interpret the Japanese language papers, and there was insufficient information on the existing English language papers, I decided to create a second dataset. I incorporated 50 documents, including reports, knowledge portals, blogs, newsletters, books, conference slides, theses and dissertations as the additional data sources (Table 4.1). With this diverse knowledge outside academia, I was able to interpret the knowledge more concisely and analyze the research questions more comprehensively; importantly, the documents allowed me to acquire a rich knowledge background and extend my research findings.

Table 4.1 Bibliographic Review Process Source: Esquirol-Caussa et al. (2017) and Author Bibliographic Review Process In This Thesis (Esquirol-Caussa et al., 2017) (Author) Step 1: Defining a Topic Two Research Questions were formed in Chapter 1: -Use of Crowdsourcing/VGI to enhance disaster management. -Challenges and potential solutions to such information use. Step 2: Literature Search and Compilation First Dataset: 20 research papers (See Appendix 1 and 2). Second Dataset: 50 Documents (See Appendix 3 and 4). Step 3: Critical Analysis of the Literature Five dimensions to analyze the research papers: Contribution Year, Subject Areas, Institute Countries, Disaster Phases, and Media. Three dimensions to analyze the documents: Document Types, Document Sources, and the Role of the Documents. Step 4: Writing the Review Seven targeted V&TCs will be analyzed in Chapter 5, including the advantages, challenges as well as solutions. Step 5: Developing the Conclusions Research Questions are answered in Chapter 6.

4.2 Review Results from Selected Research Papers

Study Design - Search Profile With the search profile that I explained in Chapter 3, I am able to select the most relevant papers. Specifically, (1) Database: I used four electronic databases, including Web of Science, Engineering Village, EBSCO host, and Google Scholar. (2) Search Range: I mainly examined the papers ranging from 2011 (when the Tohoku Earthquake occurred) to the present 2019. (3) Keywords: I searched the literature with these keywords: ‘Disaster Management,’ ‘Disaster Response,’ ‘Crowdsourcing,’ ‘Volunteered Geographic Information,’ ‘Crisis Mapping.’ Also, the titles of case studies are named differently, such as ‘Japan Earthquake, Sendai Earthquake, Great East Japan Earthquake and Tsunami’, ‘Yolanda Typhoon,’ and I needed to search for all

44 types of names so that important papers would not be omitted. (4) Information Needs to Be Collected: Authors with Contribution Year, Institute Countries, Disaster Phases and Discussed Media. The information will be further classified with analysis. (5) Inclusion Criteria: Due to the fact that Geography is an interdisciplinary subject, the papers that I review came from a variety of research fields, including Disaster Science, Environmental Science, Information Systems, Computer Science, and Engineering. (6) Exclusion Criteria: The papers are out of scope will not be considered, such as the field of Statistics, Political Science. In addition, without professional translation, Japanese, Spanish or any non-English language papers would not be considered. Also, the citations and abstracts only were not taken into consideration.

Table 4.2 Numbers of Selected Research Papers from Each Database. Source: Author. Database Web of Science Engineering EBSCO Google Scholar Total Village Research Case Study Papers (=20) 2011 Tohoku 2 3 1 5 10 Earthquake 2013 Typhoon 3 6 0 1 10 Haiyan

Therefore, with the search profile, I have determined 10 research papers for Tohoku Earthquake and 10 research papers for Typhoon Haiyan (Table 4.2). To illustrate, research papers refer to the combination of journal and conference papers in this thesis - 11 journal papers and nine conference papers (See Appendix 1 and 2). Notably, (1) Tohoku Earthquake: two papers are from Web of Science, one paper from EBSCO, five papers from Google Scholar; (2) Typhoon Haiyan: three papers are from Web of Science, six papers from Engineering Village and one paper from Google Scholar. The details of the selected papers will be demonstrated in the following section.

Results of Selected Research Papers Next, I conducted a comprehensive bibliographic analysis of the selected 20 research papers, and a summary of results is listed below:

Table 4.3 Results from Selected Research Papers (Listed by Contribution Year). Source: Author. Author and Institute Case Study Disaster Media Contribution Year Country Phase (Gao et al., 2011) USA Tohoku Earthquake Response Twitter Ushahidi

(Meier, 2012) USA Tohoku Earthquake Response Twitter Ushahidi Standby Task Force

(Mizushima et al., Japan Tohoku Earthquake Response Ushahidi 2012) Twitter, Facebook

(Sekimoto, 2013) Japan Tohoku Earthquake Response Ushahidi Twitter

(Yamamoto, 2013) Japan Tohoku Earthquake Response Ushahidi ALL311 ESRI Twitter, Facebook YouTube

(Chan & Comes, 2014) USA and Typhoon Haiyan Response OSM Norway SBTF

(Hayakawa et al., 2014) Japan Tohoku Earthquake Response OSM

(Liu, 2014) USA Typhoon Haiyan Response OSM Wikis (Vieweg et al., 2014) Qatar Typhoon Haiyan Response MicroMappers Twitter

(Westrope et al., 2014) USA and France Typhoon Haiyan Recovery OSM YouTube

(Palen et al., 2015) USA Typhoon Haiyan Response OSM Wikis

(Brown et al., 2016) USA and Japan Tohoku Earthquake Response Safecast

(Coletti et al., 2017) USA Tohoku Earthquake Response Safecast

(Dittus et al., 2017) UK Typhoon Haiyan Response OSM Wikis

(Kuo et al., 2017) Germany Typhoon Haiyan Recovery OSM Standby Task Force Twitter, Facebook Flickr

(Mejri et al., 2017) Italy and Japan Typhoon Haiyan Recovery OSM Ushahidi Twitter, Facebook

(Muto & Kohtake, Japan Typhoon Haiyan Preparedness OSM 2017) Response Google Crisis Response

(Roberts & Doyle, USA Typhoon Haiyan Response OSM 2017) Twitter, Facebook Wikis

(Shibuya, 2017) Japan Tohoku Earthquake Response Ushahidi Twitter, Facebook YouTube

(Park & Johnson, 2019) USA Tohoku Earthquake Response Ushahidi Safecast Twitter

The 20 selected English language research papers are listed in chronological order by contribution year, containing a variety of information regarding authors’ institute countries, discussed disaster phases, and media (Table 4.3). In addition, these papers helped to ascertain the widespread popularity of VGI and crowdsourcing use from a global perspective, to reveal the research gap in some disaster phase, and importantly, to target the prevalent examples of VGI/crowdsourcing use in each disaster event. The results will be further reviewed in the following sub-sections, through a classification framework with five dimensions: contribution year, subject areas, institute countries, disaster phases, and media.

4.2.1 Classification by Contribution Year

0 2011 2012 2013 2014 2015 2016 2017 2018 2019

Japan Philippines

Figure 4.1 Distribution of Papers by Contribution Year. Source: Author.

The 20 papers were produced ranging from 2011 to 2019, and they do not entirely correspond to the timeline of four different phases in the cycle of disaster management. Some researchers have their projects immediately contributed within the same year of the crisis attacked, while some researchers expressed their long-term interests in the use of VGI/crowdsourcing. Even years after the disaster struck, these international scholars chose to continue exploring the potential use of VGI and crowd-sourced information on overall disaster management. For example, regarding the 2011 Tohoku Earthquake studies, one journal paper that discussed the disaster response was released immediately in the same year, while the rest of the papers that also addressed the disaster response phase were created from 2012 to 2019 (Figure 4.1). In particular: Meier (2012), Mizushima et al. (2012), Sekimoto (2013), Yamamoto (2014), Hayakawa et al. (2014), Brown et al. (2016), Coletti et al. (2017), Shibuya (2017) as well as Park and Johnson (2019). Also, four papers that showed their research interests on the Haiyan disaster response as well as recovery were produced one year after the crisis occurred, involving Chan & Comes (2014), Liu (2014), Vieweg et al. (2014), and Westrope et al. (2014), while the rest of the six papers ranged from 2015 to 2017. Specifically, Palen and his colleagues who together worked on a conference paper in 2015. Interestingly, there is a dramatic rise on year 2017 (Figure 4.1), with

48 five papers that discussed disaster preparedness, response as well as recovery, including Dittus et al. (2017), Kuo et al. (2017), Mejri et al. (2017), Muto & Kohtake (2017) as well as Roberts et al. (2017).

4.2.2 Classification by Subject Areas

0 Disaster Science Information Systems Computer Science Engineering

Japan Philippines

Figure 4.2 Distribution of Papers by Subject Areas. Source: Author.

The researchers who contributed their knowledge on the VGI/crowdsourcing use are from a variety of fields. According to the diversity of the published journals as well as attended conferences (See Appendix 1 and 2), 20 research papers are generally grouped into four categories of subject areas, including Disaster Science, Information Systems, Computer Science as well as Engineering (Figure 4.2). In particular: (1) Disaster Science: 40% (5 out of 20) of the total research papers focused on the VGI/crowdsourcing use. Park & Johnson (2019) and Mejri et al. (2017) published their works on the broad field of disaster. Additionally, Brown et al. (2016) and Coletti et al. (2017) centered on the Radioactivity, the tertiary hazard caused by the Tohoku tsunami. Roberts et al. (2017) researched on the flood damage assessment, the secondary hazard caused by the typhoon. (2) Information Systems: There are 30% (6 out of 20) of the total papers that discussed the VGI use (four on Japan, two on the Philippines). During the Tohoku Earthquake, Meier (2012), Mizushima et al. (2012), Hayakawa et al. (2014) as well as Shibuya (2017) have worked on their projects on the VGI use. While during the Typhoon Haiyan, Vieweg et al. (2014), and Kuo et al.

(2017) have engaged in their research on the crowd-sourced mapping. (3) Computer Science: 20% (4 out of 20) of all the papers were produced in this area, including one on Japan and three on the Philippines. For instance, Gao et al. (2011) discovered the use of VGI in the Tohoku Earthquake. Liu (2014), Palen et al. (2015), and Dittus et al. (2017) contributed their knowledge for the typhoon relief. (4) Engineering: 25% (5 out of 20) of all papers came from the area of Engineering. For illustration, Sekimoto (2013) and Yamamoto (2014) published their conference papers on the Japanese study. Chan & Comes (2014), Westrope et al. (2014), as well as Muto and Kohtake (2017) conducted their research on the Typhoon Haiyan.

4.2.3 Classification by Institute Countries

0 USA Qatar Japan Italy Germany Norway France

Tohoku Earthquake Typhoon Haiyan

Figure 4.3 Distribution of Papers by Authors’ Institute Countries. (Note: listed from North America, Asia, to Europe) Source: Author.

The two case studies drew international attention from both independent and collaborative scholars (Figure 4.3). Interestingly, there are just American and Japanese experts who worked on the Tohoku Earthquake, while a wide range of global researchers from North America, Asia, and Europe, especially Japan, made efforts to the Typhoon Haiyan study. In particular: (1) Tohoku Earthquake: Japan showed the status of being ‘self-sufficient’ as a developed country (The World Bank, 2011), with five papers and a collaborative project. Also, the USA shows a significant number with 45% contributions. (2) Typhoon Haiyan: researchers from diverse backgrounds contributed their knowledge particularly in this case study. To

50 illustrate: researchers from the USA produced four papers, Qatar, Japan, Germany respectively contributed 10%, and Italy, Norway, and France completed three collaborative projects.

4.2.4 Classification by Disaster Phases

Recovery

Response

Preparedness

Mitigation

0 2 4 6 8 10

Philippines Japan

Figure 4.4 Distribution of Papers by Disaster Phase. Source: Author.

Different disaster phases provide different approaches to reduce vulnerability, mitigate the impacts and improve the resilience for the disaster-affected community (Poser & Dransch, 2010; Norris et al., 2008). Therefore, it is crucial to understand the research state in the field of such management. As can be seen in Figure 4.4, approximately 83% of the papers (16.5 studies out of 20) showed strong research interests in the area of disaster response. Disaster response is a unique phase in the cycle of disaster management that often gets large-scale attention from the public (Tomaszewski, 2015). As a time-sensitive and critical stage, information becomes more exposed and eye-catching in social media (Klonner et al., 2016). This is particularly exemplified by the two case studies where the massive scope, media exposure, and overall international attention provided an excellent opportunity for people who wanted to help with the situation (Tomaszewski, 2015). Victims from the crisis can also act as disaster responders themselves. Fewer papers examined preparedness or recovery, 2.5% (0.5 out of 20) of total projects addressed preparedness issues, and 15% (3 out of 20) papers were produced on the recovery

51 field. Further, Figure 4.4 reveals a substantial research need for ‘mitigation’-related research for completing emergency management. In particular, (1) Tohoku Earthquake: All of the twenty papers discussed the disaster response efforts. (2) Typhoon Haiyan: In addition to 65% of the research papers regarding disaster response, Muto and Kohtake (2017) initiated projects for disaster preparedness. Also, Westrope et al. (2014), Kuo et al. (2017), as well as Mejri et al. (2017) researched the recovery process. Thus, in this thesis, I will focus on studying disaster response with the existing literature.

4.2.5 Classification by Media Lack of geospatial information has been a critical challenge for emergency management, but volunteers can create such information and disseminated through social networks (Li & Goodchild, 2010). The literature verified that social media tools are significant components for emergency response (Table 4.3), and thus the crowdsourced-information and VGI can be gathered and distributed by citizens, researchers, and emergency responders (Simon et al., 2015).

Table 4.4 Distribution of Discussed Media. Source: Wending et al. (2013) for formatting categories. The author indicated each case study for each category. No. Categories of Media Examples Japan Philippines 1. Social Networking Facebook 3 3

Content Sharing YouTube 2 1 Flickr 0 1

Collaborating Knowledge Sharing Social Wikis 0 4 Media

Blogging and Microblogging Twitter 7 4

2. Volunteer and Technology Communities OSM 2 9 / Volunteered & Technical Communities Ushahidi 7 1 (V&TCs) Safecast 3 0 Standby Task Force 1 2 ALL311 1 0 ESRI 1 0 MicroMappers 0 1 Google Crisis 0 1 Response

According to Wendling et al. (2013, p. 11), as a critical component of crowdsourcing, social media has been recognized for being “collaborative, participatory, decentralized, popular, and accessible” to enhance risk and crisis communication. Thus, I examined the discussed media and indicated the numbers of discussed papers for each case study (Table 4.4). As can be seen in Table 4.4, the discussed media can be grouped into two categories: the general social networks and the Volunteer and Technology Communities. Wendling et al. (2013) sub-categorized the general social media into four types, including Social Networking, Content Sharing, Collaborating Knowledge Sharing Social Media, as well as Blogging and Microblogging. In particular, (1) Social Networking: has generally brought users together for their shared common interests. For instance, Facebook (https://www.facebook.com/) has been mentioned in the Tohoku Earthquake (Mizushima et al., 2012; Yamamoto, 2013; Shibuya, 2017), as well as the Typhoon Haiyan (Kuo et al., 2017; Mejri et al., 2017). As a well-known social platform with over 901 million users, it allows the users to timely connect with friends and relatives during a crisis (Wendling et al., 2013). (2) Content Sharing: YouTube (https://www.youtube.com/) was mentioned both in the Tohoku Earthquake (Yamamoto, 2013; Shibuya, 2017) as well as Typhoon Haiyan (Westrope et al., 2014), which allowed users to view or upload videos and exchange disaster information. Especially, Shibuya (2017) explained that YouTube played an important role during the Japanese Radiation Leak. The mayor of Fukushima posted a video contending that there were insufficient relief supplies and calling for more volunteers (Shibuya, 2017). Then, many citizens actively contributed to the relief goods allocation activity. Moreover, Westrope et al. (2014) said that YouTube served as a video tutorial tool for new volunteers during the OSM Damage Assessment Mapping. Further, the photos from Flickr (https://www.flickr.com/) were proven as useful disaster information in the Haiyan crisis through open sharing (Kuo et al., 2017). (3) Collaborating Knowledge Sharing Social Media: users from the Sendai Earthquake utilized wikis for emergency response (Liu, 2014; Palen et al., 2015; Dittus et al., 2017; Roberts & Doyle, 2017). Wiki websites enabled communications between different stakeholders in a crisis management situation (Wendling et al., 2013). (4) Blogging and Microblogging: Twitter (https://twitter.com/) has been considerably discussed for its substantial benefits for enhancing emergency management on seven papers for each case study. With more than 140 million users (Wendling et al., 2013), Twitter served as a critical platform for people to share timely facts and receive real-time disaster warnings. For instance,

Meier (2012b) indicated that, because of the emerging post-tsunami ‘Twittersphere,’ the volunteers were able to map an average of 3,000 tweets per week during the first month of deployment. Also, Vieweg et al. (2014) explained that highly accessible networks, extensive Twitter use (ranked 10th in the world), and English proficiency allowed many residents in the Philippines to tweet about the disaster situation in English. It greatly saved time for content translation. The second category on the Table 4.4 is Volunteer and Technology Communities/Volunteered & Technical Communities (V&TCs): which refers to ‘ICT-enabled and volunteer communities that apply and leverage their technical skills in collecting, processing, and managing data in support of response efforts for disasters and humanitarian crises’ (Liu, 2014, p. 413). It is considered the key media for emergency navigation. OpenStreetMap (OSM), Ushahidi, and Standby Task Force have been studied in both case studies. Safecast, ALL311, and ESRI have been only discussed in the Tohoku Earthquake. Also, Micro Mappers and Google Crisis Response are two V&TC examples, particularly for Typhoon Haiyan.

4.2.5.1 Volunteer and Technical Communities (V&TCs) There are generally eight types of V&TCs were discussed in the papers. OSM, Ushahidi, SBTF, Safecast, ALL311, and ESRI were discussed in the Tohoku studies; OSM, Ushahidi, SBTF, MicroMappers, Google Crisis Response were discussed in the Haiyan studies (Table 4.5). OpenStreetMap (https://www.openstreetmap.org/) is a global community that strives to provide open access to geographical data and mapping, and it is often referred to as a map of ‘Wikipedia’ (Hayakawa et al., 2014) The literature identified the flexible application of OSM on both case studies and strong research interests in the Philippines crisis enhancement. Specifically, only two conference papers on the Tohoku Earthquake and eight papers as well as conference papers on the Typhoon Haiyan. The researchers extensively explored the new features, coordination as well as editing tools of OSM. Ushahidi (https://www.ushahidi.com/) serves as an open crowdsourcing crisis information platform to meet local needs. Overall, seven papers discussed the use of Ushahidi in the Japanese crisis. These researchers showed their interest in exploring the benefits as well as challenges in the launch of Ushahidi for this triple Japanese disaster. However, only one paper broadly discussed Ushahidi on the Typhoon Haiyan.

Standby Task Force (SBTF) (https://www.standbytaskforce.org/) works as an online network that gathers international tech-savvy volunteers (SBTF, n.d.). The volunteers work to contribute with their professional expertise and technical skills (SBTF, n.d.). Notably, SBTF played an essential role in both the Tohoku Earthquake and Typhoon Haiyan. The details will be discussed in chapter 5. Safecast (https://blog.safecast.org/) is an international and volunteer-based organization that was developed immediately after the Japanese radiation disaster (Safecast, n.d.). In total, three papers discussed the deployment of Safecast, including Brown et al. (2016), Coletti et al. (2017), as well as Park and Johnson (2019). Since this V&TC was mainly designed to map the radiation extent (Safecast, n.d.), it was not used in the Typhoon Haiyan case. ALL311 (https://web.archive.org/web/20110519005458/all311.ecom-plat.jp/) was viewed as an open-source geospatial knowledge portal and extensively accessed by the local volunteer researchers (Yamamoto, 2013). However, there was limited documentation on this V&TC. ESRI ( https://www.esri.com/en-us/home) posts blogs and newsletters in sharing the latest information on geospatial mapping technology and provides access to a variety of map products, satellite imageries, and GIS data (ESRI, n.d.). However, ESRI was merely discussed in one paper regarding the Japanese crisis. MicroMappers (https://micromappers.wordpress.com/about-info/) is an innovative tool that was developed by SBTF during Typhoon Haiyan (Meier, 2015). This tool was used for collecting information from tweets and images contributed by the citizens (Meier, 2015). Since it was not launched until 2013, MicroMappers was not discussed in any papers on Tohoku Earthquake and Tsunami. Google Crisis Response (https://crisisresponse.google/) strives to provide critical disaster-related information concerning natural disasters as well as humanitarian crises; the popular products include Google Maps, Google Earth, and Google Person Finder (Google Crisis Response, n.d.). However, the team has made a creative contribution regarding the Typhoon Haiyan, which will be introduced in chapter 5. To sum up, due to the insufficient information on OSM (Japan), Safecast (Japan), Ushahidi (Philippines), Google Crisis Response (Philippines), MicroMappers (Philippines), I incorporated a variety of documents as the complementary data sources in this research (Appendix 3 and 4), and the results are discussed in the following section.

Table 4.5 Distribution of Papers by Discussed V&TCs (Note: Blank area refers to “Not Applicable” or “Not Discussed”). Source: Author. Case Studies 2011 Tohoku Earthquake and Tsunami 2013 Typhoon Haiyan V&TCs OSM Hayakawa et al., 2014; Chan & Comes, 2014; Liu, 2014; Mizushima et al., 2012 Westrope et al., 2014; Palen et al., 2015; Dittus et al., 2017; Kuo et al., 2017; Mejri et al., 2017; Muto & Doyle, 2017; Roberts & Doyle, 2017 Ushahidi Gao et al., 2011; Meier, 2012; Mizushima et Mejri et al., 2017 al., 2012; Sekimoto, 2013; Yamamoto, 2013; Shibuya, 2017; Park & Johnson, 2019 SBTF Meier, 2012 Chan & Comes, 2014; Kuo et al., 2017 Safecast Brown et al., 2016; Coletti et al., 2017; / Park & Johnson, 2019 ALL311 Yamamoto, 2013 / ESRI Yamamoto, 2013 / MicroMappers / Vieweg et al., 2014 Google Crisis / Muto & Kohtake, 2017 Response

4.3 Review Results from Selected Documents Qualitative research demands an abundant literature review within and outside academia; thus, the documentary materials play a complementary or supplementary role in this research. In addition, documents are able to identify changes and developments, verify the research findings, or corroborate the evidence (Bowen, 2009). Importantly, these documents enable the synthesis of this information from research papers and extend the research findings. Merriam contended that: “documents of all types can help the researcher uncover meaning, develop understanding, and discover insights relevant to the research problem” (1988, p. 118). Thus, in order to fill the information void from the academic literature and probe my research questions extensively, I utilized a set of electronic and printed documentary materials, including book chapters, reports, newsletters, magazine articles, blogs, conference slides,

56 conference videos, tutorial videos, data and knowledge portals, theses as well as a dissertation to thoroughly examine the two case studies (Table 4.6).

Table 4.6 Summary of Selected Documents on Two Case Studies (Note: one document was used in both case studies). Source: Author. Documents Book Report Newsletter Blog Slides Video Web Data Thesis and Total

Case Studies Chapter Page Portal Dissertation Japan 3 3 3 6 3 1 2 2 2 25 Philippines 3 2 7 9 0 2 1 1 1 26 Total 6 5 10 15 3 3 3 4 2 50

As Table 4.6 presents, there are a total of 50 documents used in this thesis. Specifically, 25 documents on the Tohoku Earthquake and 26 on the Typhoon Haiyan. All these documents significantly helped to explore the research questions and complete this project. In the following section, I examined these documents from three dimensions: ‘Document Type’, ‘Document Source’, and the ‘Role of Documents’.

4.3.1 Classification by Document Types I classified the document type based on different case studies. From the results of the classified categories, I was able to examine the diversity of document type and learn the original, innovative, conclusive perspectives from different authors.

4.3.1.1 Document Types on the 2011 Tohoku Earthquake and Tsunami Among the 25 documents on the 2011 Tohoku Earthquake, I have found a wide variety of document types. Approximately 23% (6 out of 25) of the total are blogs (Figure 4.5), following by three newsletters, three reports, three book chapters, three conference slides, two web pages, as well as a knowledge portal, a data portal, a tutorial video, a master’s thesis, and lastly, a dissertation. Due to the fact that I was unable to comprehend several Japanese journal articles, the acquisition of translated Japanese blogs and conference slides proved to be useful for this thesis.

Book Chapter Report Newsletter Blog Slides Video Web Page Knowledge Portal Thesis and Dissertation

Figure 4.5 Distribution of Documents by Types (Japan). Source: Author.

4.3.1.2 Document Types on the 2013 Typhoon Haiyan Among the 26 documents on the 2013 Typhoon Haiyan study, approximately 35% (9 out of 26) were blogs, and 27% (7 out of 26) were newsletters. Following three book chapters, two reports, two conference videos, and a knowledge portal as well as a dissertation. In this case, I did not need to use translated blogs and conference slides since these documents were already written in English. Therefore, these documents saved me time from digging around the web and using Google Translate.

Book Chapter Report Newsletter Blog Slides Video Web Page Knowledge Portal Thesis and Dissertation

Figure 4.6. Distribution of Documents by Types (the Philippines). Source: Author.

4.3.2 Classification by Document Sources In this section, I classified the data sources according to the different case studies and introduced the main data sources that I used. For example, the Humanitarian Organizations, General Media, GIS Communities, and Crowdsourcing Communities.

4.3.2.1 Document Sources on the 2011 Tohoku Earthquake and Tsunami

Humanitarian Organizations Media GIS Communities Crowdsourcing Communities Other

Figure 4.7 Distribution of Documents by Source (Japan). Source: Author.

I categorized the 25 document sources into five categories: Humanitarian Organizations, GIS Communities, Crowdsourcing Communities, Media, and Other (Figure 4.7). Regarding the Japanese case study, the documents from Crowdsourcing Communities taking up 40% of all the documents (10 out of 25), the sources include “OpenStreetMap Wiki”, “iRevolutions”, “Sinsai.info”, “Ushahidi”, and “STS Forum on the East Japan Disaster.” This is followed by GIS Communities which comprises 5 out of 25 total sources, including “GIS Lounge”, “Center for Security Studies”, “ESRI”, “ISPRS”, and “OpenGeospatial.org.” Next, the Humanitarian Organization category contains “The World Bank” and the “UN.” The General Media used in this research includes the “LA Times” and “Europe Interview.” Other sources utilized in this thesis are book chapters, slides, theses, and a dissertation.

4.3.2.2 Document Sources on the 2013 Typhoon Haiyan

Humanitarian Organizations Media GIS Communities Crowdsourcing Communities Other

Figure 4.8 Distribution of Documents by Source (the Philippines). Source: Author.

Concerning the Typhoon Haiyan case, I continued to categorize the data sources into the five previous types. However, these data sources appear to be more diverse than the previous ones. Crowdsourcing Communities still act as an essential data source, approximately taking up 38% of 26 documents (Figure 4.8). The sources include “Humanitarian OpenStreetMap Team (HOT)”, “CrisisMappers”, “Standby Task Force (SBTF)”, “Ushahidi”, “iRevolutions”, and “OpenStreetMap Wiki.” This is followed by 19% of the data sources coming from Humanitarian Organizations, such as the “American Red Cross,” “the UN,” and “the World Bank.” In addition, 19% of the sources are from the general media, for example, “Wired”, “CISCO Newsroom”, “Aljazeera America”, “the Guardian”, and “the Atlantic.” Only two documents come from GIS Communities such as “Geoawesomeness” and “GIS Lounge.” Other data sources include one dissertation and three book chapters.

4.3.2.3 Examples of Document Sources In the following sub-sections, I will introduce the major data sources that I used in this thesis, including Humanitarian Organizations, Crowdsourcing/VGI Communities, and GIS communities.

Humanitarian Organizations UN-SPIDER (http://www.un-spider.org/; United Nations Platform for Spaced-Based Information for Disaster Management and Emergency Response) is a mandated program of the UN’s office, which aims to promote the collaboration between the space-based community and the disaster management community (UN-SPIDER, n.d.; Tomaszewski, 2015). Generally, UN- SPIDER strives to (1) provide technical advisor missions and training courses for developing countries and institutions in need, (2) expand the reach and dissemination of knowledge, and (3) foster the exchange of knowledge and promote the interactions between governments, NGOs, and academia during the international expert meetings, seminars, lectures and workshops (Tomszewski, 2015). In addition, UN-SPIDER sets up a comprehensive knowledge portal, which provides public and free access to a vast amount of space-based data sources, in-person/online training opportunities, advisory technical support, and social networking forums (UN-SPIDER, n.d.). UN-SPIDER also complied with all the available geospatial and crowdsourcing datasets to save the users from digging the web or selecting suitable data sources for research (UN-SPIDER, n.d.). In this thesis, I used the UN-SPIDER knowledge portal and learned about the various geospatial mapping efforts for the two case studies. World Bank GFDRR (https://www.gfdrr.org/en) refers to as “the Global Facility for Disaster Reduction and Recovery,” which is committed to helping the impacted countries to improve the resilience and reduce the vulnerability to the natural hazards (Tomaszewski, 2015). In this thesis, I used two published documents to learn about the impacted situations and research the efforts of the discussed V&TCs.

Crowdsourcing Communities iRevolutions (https://irevolutions.org/) is considered a pioneering crowdsourcing network developed by the internationally recognized expert Patrick Meier (iRevolutions, n.d.). This blog- based website posts a wide range of topics in crisis mapping, crowdsourcing, big data, humanitarian technology, and more (iRevolutions, n.d.). In this thesis, I reviewed multiple blogs of discussions/introductions about disaster mapping applications. It was quite interesting to get involved in this community, interact with the experts worldwide, and learn about new ideas and different opinions.

OpenStreetMap Wiki (https://wiki.openstreetmap.org/wiki/Main_Page) creates and delivers the free geographic data (OpenStreetMap Wiki, n.d.). In this thesis, I utilized the wikis as important references to study the development of OSM and the deployment details. Ushahidi blog (https://www.ushahidi.com/blog) generally posts the notifications of volunteer recruitments and summaries of Ushahidi’s deployments (Ushahidi Blog, n.d.). In this thesis, I used several blogs to learn more about Ushahidi’s actions. Sinsai.info blog (http://sinsai-info-en.blogspot.com/) works as an extensive primary data source of Sinsai.info. In this thesis, I utilized multiple blog postings to learn about Sinsai.info’s contribution details, the hardships that the team was tackling (Sinsai.info.blogpost, n.d.).

GIS Communities GIS Lounge (https://www.gislounge.com/) is a GIS knowledge-based website that shares robust information in GIS maps, data/knowledge portals regarding the case studies, blog discussions in crowdsourcing, and disaster response (GIS Lounge, n.d.). In this thesis, I used plenty of information from this useful site as an important reference to research the V&TCs of case studies.

4.3.3 Role of the Documents - Complementary or even Supplementary Data Source Therefore, the research findings will turn out to be fruitful and thorough, due to the significant contribution of 50 documents (Table 4.7) - a complementary or even supplementary data source. These documents not only fulfilled the completion of the analysis, but also did they encourage in-depth research for further details regarding the evolution of using VGI and crowdsourcing for disaster management in chapter 5.

Table 4.7 Numbers of Studies on Each V&TC. (Note: Often one document includes information on more than one V&TC; “/” refers to “Not Discussed” or “Not Applicable”.) Source: Author. Case Studies 2011 Tohoku Earthquake and 2013 Typhoon Haiyan V&TCs Tsunami OSM 13 15 Ushahidi 15 4 Safecast 3 / ALL311 3 / ESRI 1 / MicroMappers / 11 Google Crisis Response / 2

4.4 Conclusion to Results

To summarize, in this thesis, I will focus on the seven V&TCs, including OSM, Ushahidi, Safecast, ALL311, ESRI, MicroMappers and Google Crisis Response. To illustrate, research papers and/or documents will help to develop the analysis on each V&TC (Table 4.8). I will discuss the research questions in Chapter 5, and answer the questions in Chapter 6.

Table 4.8. Summary of Targeted V&TCs that Mostly Discussed on Research Papers (R) and/or Documents (D) (Note: Blank Refers to “Not Applicable”.) Source: Author. Case Studies 2011 Tohoku Earthquake and 2013 Typhoon Haiyan Tsunami Targeted V&TCs OSM D R + D Ushahidi R + D D Safecast R + D / ALL311 D / ESRI D / MicroMappers / D Google Crisis Response / D

Chapter 5: Discussion

5.1 Introduction to Discussion

In this chapter, I utilized the selected research papers and documents from Chapter 4, and adopted a Document Analysis approach, to examine the evolution of VGI and crowdsourcing use, ranging from 2007 to 2013 (Table 5.1). From this influential history, I have learned that the case studies were interrelated and represented as the significant events in this evolution. To conduct a comprehensive analysis, I divided this chapter into five sections: (1) the milestones of initiating VGI/crowdsourcing use in disaster management, including the emergence of crisis mapping, formation of Ushahidi, the 1st International Conference of Crisis Mappers (ICCM) and formation of Crisis Mappers Network; (2) efforts to facilitate the dialogues between the Formal NGOs and V&TCs, including the 2010 Haiti Earthquake as a turning point, the formation of Humanitarian OpenStreetMap Team (HOT) and Standby Task Force (SBTF); (3) VGI and crowdsourcing use in a developed country: the 1st case study of 2011 Tohoku Earthquake and Tsunami, (4) the establishment of Digital Humanitarian Network (DHN), and lastly (5) VGI and crowdsourcing use in a less developed country: the 2nd case study of 2013 Typhoon Haiyan. According to the results from chapter 4, I discussed seven V&TCs from the case studies, including OSM, Ushahidi, Safecast, ALL311, ESRI, Micro Mappers, and Google Crisis Response. In particular, I examined the advantages, challenges as well as potential solutions of each V&TC. In addition, I reviewed the technological breakthroughs such as new GPS enabled devices, coordination tools, editing tools, and software features.

Research Question 1: How has the use of VGI and crowdsourcing enhanced the coordination across all four stages of a disaster management cycle?

Research Question 2: What have been the challenges of using VGI and crowdsourcing, and what are the potential solutions?

Table 5. 1 Evolution of Using VGI/crowdsourcing from 2007-2013 Source: Shahid (2016) and Author

Year Events Examples of VGI/ New Crowdsourcing use Devices/Tools/Features

2007 ‘Crisis Mapping and Early Warning’ Project Implemented by Harvard Humanitarian Initiate (HHI)

2008 Post-Election Crisis in Kenya -Formation of Ushahidi Ushahidi

2009 the 1st International Conference of Crisis Mappers (ICCM)

Formation of Crisis Mappers Net Crisis Mappers Net Haiti Earthquake 2010 Formation of OSM

Humanitarian OpenStreetMap Team (HOT)

Formation of Standby Task Force (SBTF) SBTF

OSM QualityStreetMap (v.2) Case Study 1: JOSM 2011 Tohoku Earthquake and Tsunami Sinsai.info Safecast ‘bGeigie Nano’ Device ALL311 ESRI

2012 Formation of Digital Humanitarian Network (DHN) ‘Notes’ Feature; 2013 Case Study 2: OSM Tasking Manager (v.1); Typhoon Haiyan iD, JOSM, Potlach (v.1); OpenMapKit (OMK)

Ushahidi ‘Crowdmap’ software; ‘Tomnod’

SBTF, ESRI MicroMappers: And GIS Corps (Tweet Clicker, Image Clicker)

Google Crisis Response

5.2 Stepping into a New Era – Milestones of Initiating VGI and Crowdsourcing Projects as well as Interfaces

“Mapping Crises to Save Lives – how cutting-edge, dynamic mapping technology can help the world detect patterns of mass atrocities early, and respond faster to humanitarian catastrophes.” - Meier & Ziemke, 2009

Since 2007, the evolution of utilizing VGI and crowdsourcing in disaster management has officially begun. In this section, I briefly introduce three milestones which include: the emergence of term ‘crisis mapping’ through a historical project from Harvard Humanitarian Initiative (HHI) in 2007, the establishment of Ushahidi platform in 2008, the significance of the 1st ICCM, and formation of Crisis Mappers Network in 2009.

5.2.1 Harvard Humanitarian Initiative (HHI) Project - Emergence of ‘Crisis Mapping’ 2007 was an extraordinary year for the outset of VGI and Crisis Mapping. While Goodchild (2007) coined the term of VGI as the participatory citizen science in a voluntary approach, Harvard Humanitarian Initiative (HHI, n.d.) launched a new historical research program of ‘Crisis Mapping and Early Warning’ in 2007-2009. This two-year project “seeks to develop an evidence base to information technologies, to convene the humanitarian and technical communities, to facilitate the dialogue among humanitarian actors, and to provide new sources of data to improve understanding of conflict dynamics” (HHI, n.d.). HHI highly acknowledged the role of ICT in a disaster setting. Specifically, HHI centralizes on the impacts of geospatial and crowdsourcing technologies, importantly, on crisis mapping. However, the term ‘crisis mapping’ was still hard to define (HHI, n.d.). With the research findings in the years after 2007, scientists from HHI had determined the future research directions of crisis mapping in disaster response. Thus, in 2009, HHI identified crisis mapping as encompassing three main fields: crisis map sourcing, crisis map visualization, and crisis map analysis (Meier, 2011a). Meier explained that, on crisis map sourcing, one can utilize various methodologies and technologies for data collection, ranging from traditional paper survey methods to the modern social media postings. On crisis visualization, practitioners visually present the collected information on a dynamic map so that

66 the users can interact with the information with each other on a large scale (Meier, 2011a). Crisis Mapping analysis strives to provide real-time information to support the decision-making process, with crisis mapping platforms. The platforms enable users to query the information and assess the impacts of a disaster in an effective fashion (Meier, 2011a). In addition to the discovery of three components of crisis mapping, the rising role of open-source crisis mapping software, use of social media, online volunteers who engaged in crisis mapping activities, as well as the growing interaction between humanitarian organizations and the volunteer communities, all had brought crisis mapping into a new era (Meier, 2011a).

5.2.2 Post-Election Crisis in Kenya - Formation of Ushahidi “We will create an engine that will allow anyone to source information from the field and provide a free and open-source tool that will help in the crowdsourcing of information, focusing on crisis and early-warning information.” -- Erik Hersman, one of the creators of Ushahidi (Ushahidi, 2018)

Another milestone is the formation of the Ushahidi platform, which initiated a new stage of real-time and collaborative mapping for crises — throwing back to the post-election violence that caused 1,000 deaths in Kenya, 2008, some activists engaged in creating a live map project with nationwide crowd-sourced reports. With the Google Map of Kenya as the base map, they embedded a Web Form with an SMS number of 6607, so Kenyan citizens were able to submit any incident reports regarding any human rights violations nearby, such as riots, deaths, property loss, government forces, civilians, rape and more (Figure 5.1). In this way, as journalists were unable to be present anytime and timely document incidents, citizens filled in the information void simply with access to the Internet and a mobile phone (Meier, 2012b).

Figure 5.1 The First Ushahidi platform Launched in Kenya, 2008 Source: Meier, 2012b

This project was called “Ushahidi,” which means “testimony” or “witness” in Swahili (Ushahidi, 2018). Since 2008, the Ushahidi platform had gone through several developments, which involves: launch of its code open-source; winning the NetSquared Mashup Challenge and received initial funding to enhance Ushahidi program; start-up of Crisis Mappers group; launch of first iPhone application; launch of alpha software (Ushahidi Engine v.1); and launch of ‘Crowdmap Class’, a free cloud-based version (Ushahidi, 2018). In addition to developing software versions, Ushahidi has also incorporated additional technologies/media such as emails, mobile phone applications, Twitter, Facebook, YouTube videos, and Flickr photos (Ushahidi, 2019). Therefore, Ushahidi has evolved into an efficient platform that supports live, multimedia, importantly, collaborative mapping (Meier, 2012b).

5.2.3 The First Interface between Worldwide Experts - ICCM Conference and Formation of Crisis Mappers Network

“If a picture is worth a thousand words, then a crisis map is worth ten thousand lives.” - Meier & Ziemke, 2009 Shahid (2016) contended the significance of two different settings regarding the first interface between the international experts, including the first International Conference on Crisis Mapping (ICCM) as a physical setting, and formation of Crisis Mappers Network as an online setting, which both substantially contributed to the enhancement of idea regarding the crowdsourcing/VGI in humanitarian response, and positively impacted the coordination of disaster management (Meier & Ziemke, 2009).

5.2.3.1 Physical Setting: ICCM Conference in 2009 In recent years, Governments, the United Nations (UN), Non-governmental organizations (NGOs), and private companies have gradually begun to recognize the importance of crisis mapping, which focused on how the crisis information is documented, displayed, analyzed, and responded (Meier & Ziemke, 2009). However, there was a lack of formal interaction between these organizations to exchange insights and innovations to enhance emergency coordination (Meier & Ziemke, 2009). Therefore, the First International Conference on Crisis Mapping was held to bring the experts worldwide together and address the concerns (Figure 5.2). Hosted in Cleveland, Ohio during October 16-19 in 2009 by HHI and John Carroll University (JCU), more than 60 prevalent humanitarian organizations, crowdsourcing and volunteer communities, as well as educational institutes participated in this conference, which involves the UN-OCHA, Map Action, Ushahidi, OSM, Google Earth, the World Bank, Harvard University and more (Meier & Ziemke, 2009). ‘Openness,’ ‘Collaboration,’ and ‘Cross-disciplinary’ were considered as the goals of this event (Meier & Ziemke, 2009). During this integral conference, many emergency practitioners, research scholars, platform developers from all around the world, were able to share their projects and deploy a discussion on crisis mapping, through a variety of Ignite Talks,

Open Roundtables, Self-Organized Sessions, Keynote Address, and a Tech Fair (Meier & Ziemke, 2009).

Figure 5.2 Participants of the 1st ICCM Conference. Source: Meier & Ziemke, 2009 Importantly, this was the first time that the worldwide experts in the Crisis Mapping field gather and share innovations in the cutting edge of the domain (Meier & Ziemke, 2009), and it was the first exposure of the concept regarding crowdsourcing in humanitarian response (Shahid, 2016). Especially with this revolutionary opportunity, these organizations were able to discuss the future of this field according to the lessons learned from past crises and carry out the 70 potential research plans. Shahid (2016) explained that, in addition to offline events provided by ICCM Conference, there was also a range of online events held on Crisis Mappers Network, which both played an instrumental role in improving the dialogues between the global experts, thus the experts were able to learn new knowledge from each other and understand each other’s offerings and requirements.

5.2.3.1 Online Setting: Formation of Crisis Mappers Network Participants from ICCM launched the International Network of Crisis Mapper on October 17th (Meier & Ziemke, 2009). The purpose of this network strives to continue enhancing collaboration and sharing the knowledge and experiences in the Crisis Mapping field (Crisis Mappers Net, n.d.).

Figure 5.3 Interface of Crisis Mappers Network Web Page. Source: Crisis Mappers Net, (n.d.)

After registering a new account and getting approved, members are able to explore the website and gain professional knowledge from ‘Resources’ (e.g., Crisis Mapping Course), ‘Videos’ (e.g. Conference presentations, research projects), ‘Webinars’ (e.g. Introduction of a humanitarian technology ), ‘Photos’ (recap of ICCM conference), or simply search keywords in

71 the search engine and review blogs posted by other members (Crisis Mappers Net, n.d.) (Figure 5.3). In addition, members can post blogs of research findings or resources sharing, participate in ‘Working Group’ discussions, and register the annual ICCM conference (Crisis Mappers Net, n.d.). In sum, the Crisis Mappers Network serves as an online platform for improving communications between experts and collaboration for future research (Crisis Mappers Net, n.d.). With this network, the application of VGI and crowdsourcing has been enhanced on a global scale.

5.3 Continuing to Facilitate the Dialogues between Formal Humanitarian Organizations and Volunteer & Technical Communities (V&TCs)

In the wake of the 2010 Haiti Earthquake, the emerging V&TC efforts had significantly contributed to the coordination of disaster management. Importantly, the Haitian Crisis is considered a turning point where the Formal Humanitarian Organizations and V&TC began to interact with one another (Boccardo et a., 2012). The successful formation of HOT and SBTF helped to address some issues between two groups and enhanced the humanitarian crisis response to a new level (Palen et al., 2015; Shahid, 2016).

5.3.1 2010 Haiti Earthquake “No one disputes the fact that the Haiti Response marked the start of something new.” -Patrick Meier, co-founder of Ushahidi (Meier,2015) Due to the unfortunate fact that Haiti is one of the world’s poorest country which is lacking standard web-based maps such as Google Maps and Bing Maps, it was difficult to provide humanitarian aid in a traditional GIS approach (Zook et al., 2010). Thus, a number of V&TCs emerged to provide real-time and efficient support, including Ushahidi, OSM, Crisis Mappers, Google Map Maker, Geo Commons, Crisis Commons/Camps, and others (Zook et al., 2010; Boccardo et al., 2012). All the efforts made the Haitian earthquake considered as a turning point in the history of disaster response (Boccardo et al., 2012). Since Haiti, the value of VGI and crowdsourcing has been widely acknowledged; importantly, the non-authoritative data sources became an important source of information during a crisis (Boccardo et al., 2012).

5.3.2 Introduction of Formal Humanitarian Organizations and Volunteer & Technical Communities (V&TCs) The worldwide experts generally are divided into two groups: Formal Humanitarian Organizations and Informal – Volunteer and Technical Community (V&TC), both have put substantial support in disaster relief with the effective humanitarian response; however, each of them encompasses different characteristics (Waldman et al., 2013). Formal Humanitarian Organizations usually refer to the United Nations Organizations and Agencies, NGOs, Government Humanitarian Agencies (Waldman et al., 2013). Humanitarian Organizations contain a range of characteristics, including: “established humanitarian principles, organizational and management structures, global footprint, and a donation-based financial system” (Waldman et al., 2013, p .12). V&TC refers to the ICT-based community consisting of volunteers who apply and leverage their technical skills in collecting, managing, and disseminating data to respond to crises as humanitarian support (Liu, 2014; Harvard Humanitarian Initiative, 2011; Capelo et al., 2012). V&TCs were considered as new and informal humanitarian communities, and they provided technical support for a few previous crises before the Haiti Earthquake. However, many V&TC had enlarged rapidly in terms of membership, capabilities, and reputation in Haiti's response (Capelo et al., 2012). Importantly, V&TCs had gained experience in 2010; therefore better preparing for future crises. Capelo et al. (2012, p.7-8) also concluded with multiple characteristics of V&TCs: “Open-Source Ideology, Flexible Structure and Hierarchy, Collaborative Workflow, Altruistic Nature, Desire to Cultivate and Disseminate Technical Skills, and Enthusiasm for Partnership.”

5.3.3 Issues between Formal Humanitarian Organizations and Volunteer & Technical Communities (V&TCs) Even though the first ICCM conference that was hosted various experts had opened a door for communication in the field of crisis mapping, there was still a lack of sufficient interfaces between two groups to respond to disasters collaboratively. Harvard Humanitarian Initiative (2011) contended with multiple issues: lack of communication channel between field and online staff, verification of V&TC data, awareness of the emergence of V&TC (i.e., some

73 formal humanitarian organizations were not aware of the V&TC), as well as reliability, professionalism, trust and brand of V&TC. Since Haitian Crisis, a number of digital humanitarian organizations (e.g., HOT and SBTF) had emerged to increase the communication between the humanitarian system and Volunteer & Technical Communities (V&TC) (Shahid, 2016), and began to address some concerns including reputation, reliability, during the interaction.

5.3.4 Addressing Some Issues: Formation of Humanitarian OpenStreetMap Team (HOT) and Standby Task Force (SBTF) The establishments of HOT and SBTF not only started to build reliability, trust, and popularity since the Haitian Earthquake, but also did they provide more opportunities for interfacing with formal humanitarian organizations. In addition, HOT and SBTF developed multiple technologies for improving data quality.

Humanitarian OpenStreetMap Team (HOT)

“We work together to provide map data which revolutionizes disaster management, reduces risks, and contributes to the achievement of the sustainable development goals.” -HOT, n.d. Established on August 20, 2010, HOT began to serve as a bridge between OSM and the UN, which strives to physically and remotely operate to supply free geo-data to international crisis preparedness and response (HOT, n.d.). The core missions of HOT involve: connecting humanitarian systems and open mapping communities, provision of remotely sensed data during a disaster and support the deployments of the field, gathering and analyzing the existing data, distribution tool of open and free geographic data, and development platform for open knowledge and tools (HOT, n.d.). Moreover, HOT offers training for the area in need of OSM, for example, if a person lives in a country with poorly existing maps, he/she will need a series of professional training on how to use a GPS unit, record the data and add the information to a map. Thus, HOT performs this role as a trainer and enables people to build geographic data with skills. In addition, HOT promotes crowdsourcing/VGI in data sharing and develop technical enhancements for OSM.

Harvard Humanitarian Initiative (2011) stated that HOT was the most successful V&TC in terms of creating reliable and consistent operation since Haiti.

Standby Task Force (SBTF) Standby Task Force (SBTF) is a global network of professionally trained volunteers devoted to online collaboration, which was launched by Crisis Mappers in late 2010 (Cavelty et al., 2011). SBTF strives to serve as an online tech-savvy volunteer community for real-time mapping and interface platforms with international humanitarian organizations, together to provide timely response to the area where a large-scale mapping workforce is needed (SBTF, n.d.). Since the 2010 Haiti earthquake, SBTF began to carry out projects in Libya, Japan, Philippines, and other countries (SBTF, n.d.). Together with HOT, SBTF was highly acknowledged for its advantages in disaster response. Soon, SBTF developed multiple technologies to support crisis management (Shahid, 2016). For instance, ‘Verify’ uses social media to verify data; ‘Artificial Intelligence for Disaster Response (AIDR)’ uses machine learning approaches to recognize information from Twitter; and importantly, MicroMappers that developed in 2012, was used to identify the most affected area by sorting the crowd-soured information in Twitter (MicroMappers, n.d.). This Platform showed its value in 2013 Typhoon Haiyan, which will be discussed later.

5.4 Case Study 1: 2011 Tohoku Earthquake and Tsunami

“This was an important response, as it showed the importance of crowd mapping even for a crisis occurring in a developed country.” -Shahid, 2016 Japan is well-known for its ‘disaster preparedness’ (The World Bank, 2011), and there was a variety of self-sufficient disaster support mechanisms provided by the country. For instance, ALL311 served as a well-known disaster knowledge portal which was particularly promoted by the local volunteers (The World Bank, 2011); ‘Sahana Japan Foundation’ served as a valuable free and open source for disaster management portal (in Japanese only) (Yoshida et al., 2011). ESRI Japan Corporation provided a Japan Incident Map for extensive use (Seto &

Nishimura, 2016). Google Crisis Response team offered the Personal Finder tool and Shelter list Sharing Service (Seto & Nishimura, 2016). Tsunami Damage Mapping team from the Association of Japanese Geographers provided maps of the impacted Sendai area (Seto & Nishimura, 2016). Even though the Japanese government emphasized emergency management planning due to being an earthquake-prone country, this historical catastrophe revealed the shortcomings of their efforts (Hong, 2014). Therefore, Japan attempted to seek help from the worldwide volunteers. For example, OSM, Sinsai.info, ESRI, as well as Safecast, which played important roles in using crowdsourcing and VGI to enhance the coordination of disaster management.

5.4.1 OSM “Crisis mapping leverages the innate desires of people to share information during the emergencies”. - Cavelty et al., 2011 OSM community has strived to contribute disaster preparedness, response, and recovery with a variety of openly shared geographic data. Originated from 2007, the Japanese OSM community began to expand at a slow pace. With the establishment of the OpenStreetMap (OSM) Foundation Japan in December 2010, the provision of OSM license by the National Land Numerical Information Data, as well as the development of Bing Maps by Microsoft Corporation (Seto & Nishimura, 2016), the reputation of OSM community body had significantly thriven. Importantly, the numbers of volunteers in/outside Japan had quickly grown during the 2011 Japanese tertiary crisis - earthquake, tsunami, and radiation leak (Hayawaka et al., 2014; Seto & Nishimura, 2016). Through a set of communication media such as Internet Relay Chat (IRC), OSM mailing lists, fieldwork as well as exhibitions, the OSM volunteers were able to exchange mapping information and actively engage in group work (Seto & Nishimura, 2016). In addition, OSM beginners utilized the official OSM wikis to implement the project more efficiently, which covers a variety of information involving mapping tutorials, useable software, and documentation of task instructions (Neis & Zielstra, 2014). With JOSM as a primary editing tool (Ellamsd, 2011), more than 200 volunteers across the globe joined this large-scale mapping project, and over 500 OSM volunteers together mapped

76 and edited more than 5000,000 roads and streets (Park & Johnson, 2019). Notably, around 100 volunteer students from the Nara University of Hokkaido participated in tracking the roads and inputting the locations of evacuation centers by using the Bing map (Yoshida et al., 2011). By extensively digitizing the roads and infrastructure, the post-earthquake OSM showed much more details than the pre-disaster map (Figure 5.4).

Figure 5.4 Pre (2009) and Post-Earthquake (May 2011) of Sendai Region in Japan on OSM Map Source: Yoshida et al., 2011

5.4.1.1 QualityStreetMap (v.2) - New Coordinator Tool In addition to using JOSM as the editing tool (Ellamsd, 2011), OSM mappers also used the second version of QualityStreetMap (QSM) as an important coordinator tool (2011 Sendai Earthquake and Tsunami, n.d.). QSM has evolved from the OSM Metrix Tool (from the 2010 Haiti Earthquake) and further influenced the formation of the Tasking Manager (History of the OSM Tasking Manager Tool, n.d.). The OSM volunteers were asked to mark the tiles after the landscape objects are completely traced or in progress, in particular (2011 Sendai Earthquake and Tsunami, n.d.): First, use CTRL to select multiple tiles; Second, add a hashtag (e.g., “building,” “highway”) and specify the traced objects, or add the hashtag “fixme” if the tile is undone; Third, use “Tiles validated for ‘tag’” on the bottom of the left to display special tags with green-colored tiles.

Figure 5.5 Interface of QualityStreetMap Source: Vanderbiest, 2010. Advantages of OSM 1) This crisis mapping project was considered as a milestone for the Japanese volunteers collaborating with the global contributors to save more lives. The mappers gathered online to exchange knowledge and learn new skills from each other. Therefore, OSM was able to build its long-lasting and global reputation, bringing more cross-broader collaboration opportunities. 2) Importantly, OSM provided an open license, which served as a significant cause for attracting more potential users. Instead of struggling with legal issues from other maps (e.g., Google Maps) in a time-critical situation, OSM allowed any registered user to contribute by using and downloading free data (Seki, 2011b; Hayakawa et al., 2014). 3) The OSM community body has increased from 854 (before March 2, 2011) to 1179 (March 30, 2011) (Hayakawa et al., 2014). This increase helped to grow the reputation and brand of OSM, as well as trust from the current and potential users. Also, this can boost positive participant motivations.

4) OSM mapping project also greatly enhanced the educational purposes. The volunteer students have gained both technical and collaborative skills. Moreover, the project enlarged the student body in OSM.

Issues and Solutions of OSM 1) Even though the numbers of OSM contributors have significantly increased during this crisis, OSM was still calling for more volunteer mappers to expand the OSM group (Miyazaki et al., 2011; Hayakawa et al., 2014). What’s worse, there is no guarantee that the current contributors would serve as long-term contributors. The constant and stable motivation of volunteers is vital to the sustainable development of the OSM community. It would be a challenge if the volunteers lost motivation before the next OSM mapping project. Thus, operators in Japan need to work on the enhancement of the OSM community body and raise the social awareness of OSM in the emergency response (Yoshida et al., 2011). 2) There was a lack of sufficient basic geographic data in the OSM software, so the mapping project was impeded to continue. Thankfully, the mappers decided to use the data from Internet Company Yahoo! (Hayakawa et al., 2014). Thus, it is crucial to enrich the OSM database for future use. 3) Due to the extremely severe impacts caused by the earthquake and tsunami, the scale of the affected area was much more extensive than expected. It was a massive challenge for mapping so many areas with insufficient data from OSM (Hayakawa et al., 2014). 4) The volunteers from OSM Japan Foundation were encountering the legal issue of unable to use Google Maps as the base map, as the Google user policies restricted them. Thanks to the development of Bing Maps, which provided aerial photos and high-resolution images in the field survey and featuring non-GIS data attributes (Seto & Nishimura, 2016). The release of the Bing imagery datasets significantly benefited the data collection (Neis & Zielstra, 2014). Thus, the mappers were able to use Bing Maps as a suitable alternative (Hayakawa et al., 2014). 5) Commitment to open data from the local government is important, and live mapping became more difficult without open data (Hayakawa et al., 2014). More, the Japanese

government should implement emergency training for future disaster preparedness and document the lessons from this crisis (Yoshida et al., 2011; Hayakawa et al., 2014). 6) “QualityStreetMap” was considered a technological development compared to Haiti; however, since it was still in the trial period, the bugs still existed while using (Sendai Earthquake and Tsunami, n.d.).

5.4.2 Sinsai.info – A Collaboration between OSM, Ushahidi and SBTF “Four Open Spirits – Open Source, Open Data, Open Collaboration, and Open to Global”. -Hal Seki, Managing Director of Sinsai.info (Seki, 2011b).

The destructive earthquake which shook the Sendai Region (仙台) of Japan on March 11, 2011, resulted in a massive tsunami that took away thousands of lives and severely interrupted the critical infrastructure, especially mobile phone communications (Meier, 2012b). SMS or voice calls through mobile phone carriers were delayed; instead, the social networks were mainly used (e.g., Twitter, Mixi – a Japanese social networking site) (Peary et al., 2012; Seto & Nishimura, 2016). Three hours after the earthquake struck (Sekimoto, 2013), a member of Japan’s OpenStreetMap Community launched a crisis map on the Ushahidi platform for collecting disaster-related information from social media such as Twitter email, site reports (McDougall, 2012; Meier, 2012b; Seto & Nishimura, 2016). Also, some Japanese students from the Fletcher School of Tufts University contacted the Japan OSM team to provide real-time crisis mapping support (McDougall, 2012). The OSM team received financial support from Amazon, Nippon Telegraph and Telephone, and Yahoo Japan (Park & Johnson, 2019), with main partners involving GeoRepublic Japan and OpenStreetMap Foundation Japan (Cavelty et al., 2011). “Sinsai” refers to “Earthquake Disaster” in Japanese (“震災”), which is a disaster information delivery service providing crisis mapping, as well as the mapping of activities centered on the disaster situation of OSM, which was built on the Ushahidi platform and supported with technical assistance from SBTF (Ota, 2012; Meier, 2012b; Seto & Nishimura, 2016). The OSM volunteers successfully managed to map an average of 3,000 tweets per week during the first month of the project (Meier, 2012b), and they were able to create topographical

80 maps with annotations in English and Japanese (the World Bank, 2011). The collected information was systematically evaluated and aggregated to visualize the situation on the ground (Cavelty et al., 2011). With this group work, the Sinsai.info map provided the most up-to-date and comprehensive information for the public (Shibuya, 2017). During the time-critical crisis, the most critical challenge was the difficulty of obtaining timely geographic data from the Japanese government, thus the role of Sinsai.info in disaster response was crucial (Hayakawa et al., 2014), as well as the role of ordinary citizens, who were able to contribute the information through Twitter, emails, and report (Meier, 2012b).

Figure 5.6a Interface of Sinsai.info. (in Japanese) Source: Seki, 2011a

Figure 5.6b Interface of Sinsai.info (Translated by Google). Source: Ushahidi Staff, 2011a

As seen in Figure 5.6b, Sinsai.info pinpointed locations where the information came from, the information includes the notices and information (from government), trusted reports (evaluated by the Platform moderators), services available (e.g. free bathing), infrastructure status (severity extent), hazard zone, transportation, locations of supplies, as well as location of disaster agencies. The red circles show the numbers and locations of requests from the Japanese citizens. The diameters of the circles are proportional to the number of requests from the location (The World Bank, 2011). In conclusion, the deployment of this crisis mapping project ascertained that VGI and crowdsourcing were able to create a direct line between the citizens and the relief agencies, to

82 help guide the emergency responders with decision making as well as supplies delivery (Cavelty et al., 2011). Japanese citizens delivered more than 12,000 requests via Sinsai.info (Park & Johnson, 2019), and 6302 reports were published on the web map from March 11 to 18 (Hong, 2014). Ushahidi Staff (2011b) stated that there are 1, 213,258 total page views, 833,399 total visors, and 430,021 total unique visitors. Further, the Japanese government presented Sinsai.info an award for a considerable contribution to such enormous efforts (Ota, 2012).

5.4.2.1 How Does Sinsai.info Work for Citizens?

Figure 5.7 Report Instructions on Sinsai.info. (Translated by Google). Source: Sinsai.info, n.d.

There are generally three ways to share the crowd-sourced information on Sinsai.info. Platform. One can simply send email to [email protected], or send tweets with hashtags including “hashtagjishin” (自身, refers to “hashtag oneself” in Japanese), “j_j_helpme”, “hinan” (避難, refers to “evacuation”), “anpi” (安否, refers to “safety”), “311care”, “genpatsu” (原発, refers to “Nuclear Power Plant”) to share the surrounding situation (Figure 5.7). Also, citizens were able to fill out the report form from Sinsai.info (Figure 5.8). Specifically, contributors needed to fill out the report address, tile, explanation of the situation, select a category of situation, including “infrastructure status,” “transport,” “emergency help” and more, and insert a link if the information to be reported were from the Internet. Importantly, contributors would select the level of reliability of his information, which acts as the key to determine the reliability in such as time-sensitive situation. Next, around 40 “Moderators” (Sinsai.info management group) began to verify the reliability of the content (Mizushima et al., 2012). After a series of processes, the post would be published as “Incidents” located on the button section of the Sinsai.info webpage. McDougall (2012) reported that approximately 6.1%

83 of the total reports were verified. However, Sinsai.info could not guarantee the accuracy of such information (Sinsai.info, n.d.).

Figure 5.8 Interface of Submitting A Report (in Japanese) Source: Sinsai.info, n.d.

Then, the approved posts were published on the Sinsai.info web page as “Incident” and generally categorized into eight groups: rescue, medical, supply, information, lifeline, transportation, human resources and others (Hong, 2014). The report contents on Figure 5.9 included requests for water and food supply, locations of shelters, volunteer centers, and free bathing service, report for unavailable disaster alert acquisition and more.

Figure 5.9 Screenshot of Reports on Sinsai.info (Translated by Google) Source: Sinsai.info, n.d.

Advantages of Sinsai.info 1) The Sinsai.info team emphasized an important concept of “Openness,” referring to “open source, data, collaboration and open to global” (Ushahidi Staff, 2011b). This concept benefited the public and enabled any citizen to contribute the crowd-sourced information such as situation reports, emails, tweets. The collected crowd-sourced information significantly accelerated the government and disaster relief organizations with

implementing the rescue actions and coordinating relief supplies (Gao et al., 2011). More, the World Bank (2011) highly acknowledged the potential of crowd-sourced methods for data collection. 2) Emergency responders were able to utilize the geo-tagged information to accurately locate the particular regions that requesting urgent assistance (Gao et al., 2011). Notably, Twitter played a crucial role in reports collection as well as reputation enhancement (Ushahidi Staff, 2011b). Taichi Furushima, the vice president of the OSM Japan Foundation, contended that the most significant contribution of Sinsai.info was that it practically saved lives (Miyazaki et al., 2011). One geo-tagged tweet posted on Sinsai.info helped the Search and Rescue team to locate the accurate area and successfully find 100 isolated victims (Miyazaki et al., 2011). 3) Due to the limitation of real-time information provided by the Japanese government, Sinsai.info was extensively used as a supplementary data source (Shahid, 2016). 4) Cavelty et al. (2011) added that Sinsai.info created transparency for crisis relief, also enhanced dialogues between government, public. The Japanese government was not initially involved in this crisis mapping project, but due to the extensive use of the Sinsai.info platform, the government began to submit notices to inform and interact with citizens. 5) Seki (2011b) explained that, when the electricity and communication platform were in recovery, Ushahidi provided a reliable platform to gather the reports. Without such a platform, they could not gather so much information from the public (Ushahidi, 2018).

Challenges and Solutions of Sinsai.info 1) The language barrier was a challenge for Sinsai.info. Japan is not an English-speaking country, so a variety of reports, resources were in the Japanese version only. Also, there were merely five translators and it was a burden to the workload (Seki, 2011b). Therefore, it is necessary to mitigate the language (Yoshida et al., 2011) through enhancing the global collaborations; and expand the Sinsai.info translator volunteers. 2) There was no formal mechanism specifically designed for collaboration and coordination between different disaster relief organizations (Gao et al., 2011); thus, some valuable resources provided by volunteer-based communities were not aware by the society

(Meier, 2013). Therefore, creating protocols for connecting the volunteer-based communities with official government entities is increasing important (The World Bank, 2011). In addition, the social awareness of the V&TCs can be enhanced through advertisements (e.g., posters, audios, videos), therefore enable the adults, even the children, to be aware of the availability of these open-license geospatial resources for future disasters (Miyazaki et al., 2011). 3) Taichi Furushima also pointed out that the rapid expansion of the Sinsai.info volunteer body was unexpected, and there were not sufficient numbers of operators to direct them, especially the foreign volunteers with the language barrier (Miyazaki et al., 2011). 4) Data quality was considered a critical issue (Gao et al., 2011; Sinsai.info, n.d.). Even though the Sinsai.info moderators were well-organized, they were unable to guarantee the accuracy of data (Sinsai.info, n.d.). Irrelevant, fraud and duplicate reports all resulted in a reduction of data quality and negative impacts on the disaster relief process (Gao et al., 2011; Miyazaki et al., 2011). 5) Furushima also expressed his concerns on the long-term motivations of volunteers (Miyazaki et al., 2011). Starting in May, many volunteers left the team and went back to their daily jobs, resulting in a much more workload for the rest of the members (Miyazaki et al., 2011). 6) Safety was another concern when making geo-tagged data available in public (Gao et al., 2011). Conflicts might occur, and nefarious groups might target the relief staff. Therefore, the safety and privacy of Sinsai.info workers should be protected.

5.4.3 Safecast “Safecast’s goal essentially is… base-lining the world - crowdsourcing environmental data from every corner of the earth.” - Franken, a volunteer of Safecast (Makinen, 2016)

The earthquake and tsunami resulted in hydrogen explosions at the Fukushima Daiichi Nuclear Power Plant from March 12th to 14th. In this challenging situation, Safecast, an international online volunteer-based network, was officially established within a week of the

87 disaster, to monitor and openly share the information on environmental radiation and other harmful pollutants (Brown et al., 2016; Park & Johnston, 2019); importantly, to create ‘rapid, agile and resilience systems’ (Traganou, 2016, p. 232).

Figure 5.10 Japan Radiation Map. Source: Safecast.org, n.d. Beginning with immediate communication via Skype and emails, founders and volunteers from Safecast discussed how to deal with mapping the radiation data in the time- sensitive situation, due to the fact that there were undisclosed or unreported data by governments or monitoring agencies (McDougall, 2012; Traganou, 2016; Park & Johnson, 2019). Within the first week of the deployment, there was a total of 100 core volunteers who engaged in the Safecast project (McDougall, 2012). Thanks to the group efforts, a low-cost radiation monitoring device was successfully developed – “bGeigie Nano” (Figure 5.11). bGeigie Nano is a GPS-enabled mobile radiation sensor, which was particularly designed to collect static readings and contamination findings (Brown et al., 2016). In addition, a website called “RDTN.org” was created to aggregate and visualize the radiation data from both the official agencies and private companies (Park & Johnson, 2019). Safecare utilized bGeigie Nano and RDTN.org to measure, map, and release and radiation data (Park & Johnson, 2019); thus, an open-source, citizen-science based, collaborative radiation mapping project was carried out (Brown et al., 2016) (Figure 5.10).

Figure 5.11 Screenshot of bGeigie Nano device. Source: Safecast, n.d. Therefore, plenty of monitoring devices, including air sensors and radiation sensors, were deployed across Japan (Figure 5.10). Researchers were able to easily track the status (online or offline) and the dynamics of such sensors. This collaborative project was considered useful for emergency responders, the policymakers, and the general public since anyone was allowed to download and use the data for individual purposes (Brown et al., 2016; Park & Johnson, 2019).

Advantages of Safecast Safecast is considered as a typical VGI project, which strived to produce openly shared a collection of radiation levels by location and time, and served as a citizen-led early warning system to detect radiation leaks and hot spots (Colleti et al., 2017). As Brown et al. (2016) contended, there are three benefits: 1) Transparency and credibility are considered as the key to the successful deployment of Safecast (Brown et al., 2016). Safecast team worked to provide open-source hardware and software available for scrutiny and improvement, and the datasets came with a free license, which was available to download and use by anyone. Therefore, trust and

reputation have significantly accumulated. By December 2012, Safecast has received more than 3.5 million readings (Meier, 2013). 2) With its “open and collaborative culture,” this multi-connected network has dramatically enhanced the Safecast team’s flexibility, speed, and agility (Brown et al., 2016). 3) Incorporating social media and online discussion has primarily contributed to the Japanese radiation crisis. Since Safecast is an international organization, it strongly relied on social networks to connect with other volunteers from the different parts of the world. Social media has served as a useful communication channel that helped to reinforce the community bonds and exchange information (Brown et al., 2016).

Issues and Solutions of Safecast Data validity is considered a critical issue of Safecast (Colleti et al., 2017; Brown et al., 2016). Researchers criticized that some Safecast volunteers lack technical ability, and data were thus less accurate (Brown et al., 2016). For example, volunteers caused errors while assembling the sensor when they were inexperienced with complicated electronic equipment (Colleti et al., 2017). Worse, it is indicated that some volunteers did not follow the bGeigei Nano user instructions, terribly impacting the data quality (Colleti et al., 2017; Brown et al., 2016). To address such concern, the Safecast team initiated a set of measures on data quality control (Coletti et al., 2017): First, all the electronic products are tested before being distributed to the team. Second, some units are required to be randomly picked, disassembled, and implemented with a range of calibration tests by different research agencies in Germany, the US, and Austria. The test units are expected to match with the typical Safecast performance. Third, all the data needs to be examined by a team of experts before being put into the database. Forth, a range of educational activities (e.g., training, workshops) is encouraged to enhance the knowledge background of both new and current volunteers.

5.4.4. ALL311

Figure 5.12a Interface of ALL311 Web Page (in Japanese). Source: ALL311, n.d.

Figure 5.12b Interface of ALL311 Web Page. (Translated by Google) Source: ALL311, n.d.

In addition to Sinsai.info and Safecast, ALL311 is another example of open-source geographic data developing, sharing, and distribution for disaster response and recovery (The World Bank, 2011). ALL311 acted as a comprehensive local mapping and geospatial information platform (in Japanese only) (Yoshida et al., 2011; Ota, 2012; Yamamoto, 2013). ALL311 was launched immediately after the earthquake strike and built on an e-economy platform developed by the National Research Institute for Earth Science and Disaster Prevention (NIED) (The World Bank, 2011.; Ota, 2012). This portal site was sponsored by JAXA, OSM Japan, OSGeo Japan, and private agencies (Ota, 2012). In general, ALL311 served as a disaster data portal and communication channel for the local researcher volunteers (Figure 5.12b). On the one hand, similar to the UN-SPIDER knowledge portal, ALL311 was used to gather and disseminate sufficient geographic information containing a wide range of geospatial data, maps, and other datasets to highly encourage volunteer institutions and individual researchers to engage in new projects (ALL311, n.d.). On the other hand, similar with the Crisis Mappers Net, ALL311 served as a communication portal which enabled the interested volunteer researchers to find the suitable collaborators and projects (Yamamoto, 2013), improved dialogues between the volunteers, thus realized the long-term objectives on recovery and reconstruction for the affected communities (A3LL, n.d.). It undoubtfully facilitated the connection between the local researchers. A limitation could be the language barrier. For instance, non-Japanese researchers in/outside Japan, might not be able to interpret the information on ALL311 or collaborate with local scientists.

5.4.5 ESRI Social Media Map Another successful VGI example is a Social Media Map regarding the disaster-affected areas produced by ESRI Japan (Yamamoto, 2013). Kerski (2011) also illustrated that ESRI has often contributed to major global events with their social media maps, which are created by using ArcGIS Server. During the 311 Crisis, United States Geological Survey (USGS) provided ESRI with abundant earthquake data, and ESRI built collaborative maps with this authoritative data and real-time social media postings to support the disaster response. For example, ESRI has collected various social media information, including Ushahidi reports, YouTube videos, Twitter reports,

92 and Flickr photos, and layered this information on a base map (Figure 5.13). In general, this map shows the location of many Ushahidi reports, while YouTube and Flickr postings are relatively insufficient. ‘Streetmap’ was selected as the base map, and users were able to view the frequency of earthquakes as well as the explosion location of the nuclear plants. In addition, this ‘collective wisdom’ was able to be updated in real time (Yamamoto, 2013). Therefore, scientists were able to utilize this citizen-science collected datasets to understand the real-time disaster situation on a full scale and effectively coordinate the disaster relief operations.

Figure 5.13 ESRI Social Media Map. Source: Kerski, 2011.

5.5 Further Formalization of the Dialogues between Two Groups - Establishment of Digital Humanitarian Network (DHN)

Figure 5.14 Digital Humanitarian Network Community Interface Diagram. Source: DHN, n.d.-a

Even though the formation of HOT and SBTF facilitated the dialogues between humanitarian organizations and V&TCs, still, the communication gap between the two groups existed to no small extent. In order to raise the awareness of V&TCs for the traditional humanitarian organizations, and help the humanitarian organizations to access and collaborate with V&TCs, a critical network was implemented to provide a coordinator service and develop guidance regarding the collaboration (DHN, n.d.-b). Digital Humanitarian Network (DHN) was officially established in 2012, the core mission of such network is to leverage the ‘crowd’ in a more formal and organized fashion, through the provision of a platform connecting the formal humanitarian organizations (e.g., OCHA, UNHCR, Save the Children) and V&TCs (e.g., HOT, ESRI, Map Action), importantly, through the formalization of activation process (DHN, n.d.-a; Network, 2014). Particularly, DHN

94 provides a portal for formal humanitarian organizations to specify the disaster relief needs and request assistance from V&TCs, then DHN coordinators receive the request and distribute the task to the most suitable V&TCs, or compile the task to multiple V&TCs for necessary group work. Therefore, with the coordination role of DHN, two groups of digital humanitarians including volunteers, practitioners, researchers, software developers, policymakers are able to respond to emergencies with a range of skills such as big data analysis, crisis mapping, social media filtering, through an efficient collaboration and communication (Network, 2014). DHN was successfully activated in the 2013 Typhoon Haiyan, multiple V&TCs were called for emergency response; details are discussed in the following section.

5.6 Case Study 2: 2013 Typhoon Haiyan

“Humanitarian organizations respond to the new opportunities afforded by the Internet and other digital technologies to navigate challenges and exploit innovative solutions… This was no more evident than when Typhoon Haiyan struck the Philippines in 2013.” -Weinandy, 2016.

The 2013 Haiyan crisis offered a unique perspective on a situation in which NGOs utilized new forms of engagement to improve humanitarian response through collaboration with V&TCs (Weinandy, 2106). Primarily due to the successful establishment of the Digital Humanitarian Network (DHN), Typhoon Haiyan received the most significant disaster support from V&TCs and formal NGOs (Meier, 2015; Weinandy, 2016), up to 566 partners have worked to provide relief assistance (Chan & Comes, 2014). Generally, there are six V&TCs, which were actively responding to the Typhoon Haiyan, including OSM, Ushahidi (‘Haiyan.CrowdMap’, and ‘Philflood.Map’), SBTF, GIS Corps, ESRI (created MicroMappers) and Google Crisis Response Team (Typhoon Haiyan/Yolanda Relief and Crisis Map).

5.6.1 OSM “There are literally thousands of new mappers who I’ve never met before, and they’re by and large doing a great job.” -Dale Kunce, CEO of the American Red Cross (Dittus et al., 2017)

Requested by humanitarian aid agencies, there were two primary mapping goals for the Typhoon crisis (Typhoon Haiyan, n.d.; Silverman, 2014): (1) Mapping Disaster Response (online contribution): volunteers were required to provide a detailed and general base map of populated areas and roads by using Tasking Manager coordination tool; (2) Mapping Disaster Recovery (onsite contribution): volunteers were asked to conduct a damage assessment by using OpenMapKit (OMK) device.

Figure 5.15 Before and After the part of Tacloban City was mapped by HOT Source: The World Bank, 2014.

The priority task was to digitize the roads and infrastructure of Tacloban City. The American Red Cross played a critical role in the coordination of HOT team activation, (Roberts & Doyle, 2017), and HOT activated the OSM Haiyan crisis mapping project on November 7th, 2013, around 24 hours before Haiyan was about to cause landfalls (The World Bank, 2014). After being notified via the OSM mailing list, the OSM volunteers eagerly participated in this large-scale mapping event and worked to meet the humanitarian aid organizations’ needs (Kuo et al., 2017). As can be seen in Figure 5.15, with the successful group work, OSM volunteers

96 developed a richly detailed map of Tacloban city (Hern 2013; Meyer, 2013). The pre-disaster map was scarcely populated, in terms of the roads and infrastructure network. However, the post- disaster map provided more comprehensive information and proved its value for supporting humanitarian agencies in disaster relief. Specifically, the American Red Cross used the maps for the distribution of relief supplies including food, tents, even for water and sanitarian areas (Meyer, 2013). Overall, approximately 1679 volunteers from 82 countries had participated in this crisis mapping project (Shahid, 2016). Chapman (2013) concluded that the HOT team has successfully merged 1,240 place names from GNS data, mapped 29,845 buildings, and traced 3,036 residential land use areas.

5.6.1.1 ‘Notes’ - New OSM Feature

“It’s the detailed local knowledge of our contributors that makes OSM so much in demand from web and app developers, by making it even easier to add to the map. we’re increasing the amount of on-the-ground knowledge we can capture – further distancing OSM from the traditional map data companies and their lack of local expertise.” -Simon Poole, Chairman of OSM (Fairhurst, 2013)

‘Notes’ was a new drop-pin annotation feature, which was developed after the Tohoku Earthquake in April 2013 (Fairhurst, 2013). It was considered as an issue-tracking feature to point out improper tagging or to suggest additional information that is added to the OSM map (Palen et al., 2015) (Figure 5.16). The Notes feature also enabled “meta-conversation that sits on top of the map and is not encoded in the database” (Palen et al., 2015, p. 4119). Importantly, this feature allowed anyone to contribute their local knowledge to the map. Simply by clicking ‘Add a Note’ in the bottom right corner of the OSM interface, OSM mappers from the Haiyan relief were able to share their knowledge by adding notes or corrections (Fairhurst, 2013). In all, the creation of Notes feature suggested that OSM had been exploring new tools to facilitate their web-based mapping system (Palen et al., 2015), thus enhanced the overall disaster relief coordination.

Figure 5.16 Interface of Notes Feature Source: Fairhurst, 2013

5.6.1.2 Tasking Manager – New Tool for Mapping Disaster Response (online contribution)

“The efforts and attention that is paid to collecting data for maps are super important. Desk jobs sometimes get short shrift, but the output we produce makes a huge difference.” -Robert Banick, GIS Officer from the Red Cross (RedCross, 2014)

The tasking manager has evolved from the OSM Matrix tool (in Haiti) to QualityStreetMap (in Japan) and eventually established as a critical micro-tasking platform for disaster map-making (Silverman, 2014; History of the OSM Tasking Manager Tool, n.d.). The first version of the Tasking Manager was launched in September 2011; however, it was not used in a destructive crisis until the Typhoon Haiyan struck the Philippines (Palen et al., 2015).

Figure 5.17 Interface of Tasking Manager. Source: Silverman, 2014

Humanitarian aid agencies assigned the mapping tasks to HOT, then HOT administrators created tasks with written instructions about the mapping features, broke down the tasks into small ‘square’ tiles, and actioned by OSM volunteers (Silverman, 2014; Palen et al., 2015). In particular, a tasking manager allowed HOT to determine the roads and buildings that needed to be mapped, and volunteers would select a mapping task, or be given one at random, then pick a suitable editing tool such as JOSM, iD or Potlatch (Figure 5.17) (Silverman, 2014). The task was required to be completed within 30 or 60 minutes (Liu, 2014). iD and Potlatch are browser-based editors that are more suitable for new mappers, while JOSM is a desktop application that requires mappers to download and configure the plugins (Silverman, 2014; Shahid, 2016). In addition, JOSM automatically validates a user’s edits before upload, warning about the improbable data, such as buildings overlapping (Silverman, 2014). Then volunteers were able to carry out one

99 specific task by utilizing Bing imageries for developing pre-disaster maps, and DigitalGlobe imageries were used to develop post-disaster maps (Typhoon Haiyan, n.d.) The tasking manager also enabled experienced contributors to undertake the validation process in which they would validate or invalidate tasks made by others (Typhoon Haiyan, n.d.). As seen in Figure 5.17, red tile implies the task is done, green donates that it is validated, grids with yellow outline means the task is still in process. Tasking Manager allowed the HOT administrators to coordinate the remote volunteers more efficiently, also this tool presented the workflow of the task and provided statistics of the mapping progress (Liu, 2014). Further, more than 1,500 volunteers contributed to this large-scale project by using this incredible tool (Shahid, 2016). Dividing each task was very useful to make the best use of effort (Silverman, 2014). Palen et al. (2015) added that the micro-tasking environment reduced some mapping collisions or disagreements between the mappers and increased the collaboration without cost to the efficiency.

5.6.1.3 OpenMapKit (OMK) – New Tool for Mapping Disaster Recovery (onsite contribution) In the aftermath of Typhoon Haiyan, the American Red Cross decided to lead a crowdsourcing project to collect geographic data of disaster-affected streets, houses, farms, and create a database of building-level damage information (Roberts & Doyle, 2017). Soon, the Red Cross identified an approach of a more efficient OSM field data collection tool that could work with structured surveys, so they created OpenMapKit with initial funding from USAID Global Development Lab (Kunce, 2015). OpenMapKit (OMK) is an Android mobile data collection device, which allows OSM volunteers to create professional-quality mobile data collection surveys for field deployments (OpenMapKit, n.d.; Westrope et al., 2014). OMK consists of five generic features for mapping damage assessment: OSM tags, Points of Interests, survey collection, and open-source contribution (OpenMapKit, n.d.). During the Typhoon Haiyan, HOT members were assigned to conduct crowd-sourced remote damage assessment by utilizing maps of OSM building data, satellite imageries from DigitalGlobe, and ODK device (Westrope et al., 2014). Specifically, volunteers were able to include standard OMK tags in the survey or create new Points of Interests and load the data into OSM, then collect survey results in OMK Server. The last step would be saving the edits, and upload the data to OSM. During the Haiyan project, volunteers

100 were particularly required to assess all the buildings, map the locations, and entered the damage assessment information (e.g., building material, coordination, numbers of floors, address number, and street number) to the mobile device (Figure 5.18). In conclusion, this project was described as “continued responsiveness and diligence of the crowd” (Roberts & Doyle, 2017, p. 126), due to the fact that the HOT team have completed the requested task of mapping and validating the Tacloban City within 48 hours.

Figure 5.18 Interface of OMK. Source: OpenMapKit, n.d.

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Advantages of OSM 1) The successful collaboration between the American Red Cross and HOT was discussed in detail in the papers of Westrope et al. (2014) and Weinandy (2016). After the significant establishment of DHN in 2012, the Red Cross played an instrumental role in the coordination of the Haiyan relief. In particular, humanitarian agencies that built technical expertise and cultivated relationships with OMS should be able to direct remote OSM contributors toward priority mapping tasks (Westrope et al., 2014). Due to their fruitful partnership, quality communication and transparency between the Red Cross and HOT have been crucial to developing mapping products, which are key to ensure the data quality (Westrope et al., 2014; Weinandy, 2016). 2) The OSM community has experienced a wide expansion over time (Palen et al., 2015). Users of OSM have significantly grown, which attracted and retained new participants, and made data more reliable and accessible (Palen et al., 2015). In particular, comparing to 2010, 1,574 mappers made 3,648,537 node edits, including adding 3,294,981 new nodes to the map (Palen et al., 2015). 3) Efforts to build broader OSM communities should be encouraged since the data collection is crucial for the initial disaster response as well as the operation of the recovery phase (Haklay et al., 2014; Westrope et al., 2014). Continuing data collection outside of the immediate usage is essential, as the long-term recovery process encourages the continued work to reconstruct the affected community more quickly (Haklay et al., 2014). 4) The legal status of OSM changed in September 2012 (Palen et al., 2015). OSM removed itself from the Creative Commons and began an Open Database License. Even though OSM carried considerable controversies regarding data protection (Palen et al., 2015), a variety of open and free data is good news for the general public. 5) Technological advancement was significant in this case study. New tools such as the Tasking Manager (Silverman, 2014) and OMK (Westrope et al., 2014), along with the new ‘Notes’ feature (Palen et al., 2015), all advanced OSM into a better web-based mapping community. 6) VGI data from the OSM projects can be an important complement data source to official data sources and used for scientific modeling purposes (Haklay et al., 2014).

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7) OSM as free software reduces or eliminates project costs (Cruz, 2013; Meyer, 2013). Dale Kunce also contended that OSM served as a foundation, base map, and data store for the Red Cross (Cruz, 2013). 8) Technological advancements have emerged. The tasking manager was developed and used for the initial mapping response (Silverman, 2014); OMK was developed for mapping damage assessment for recovery purposes (Westrope et al., 2014). 9) The expansion of the OSM community is significant (OpenStreetMap, n.d.; Shahid, 2016). Comparing to the OSM registered users from 2010, the numbers had increased from 200,000 to approximately 1.5 million. The numbers of contributors from 2010 were only around 200; however there were around 7000 mappers in Typhoon Haiyan (OpenStreetMap, n.d.; Shahid, 2016). 10) It was indicated that the majority of volunteers were non-professional mappers; however they were willing to gain more experiences because there were not many opportunities for serving a humanitarian organization (ReliefWeb-OCHA, 2013a). 11) Mapathon Activities were hosted across the globe (The World Bank, 2014; Typhoon Haiyan, n.d.), A wide range of Mapathon events were all over the world (e.g., the Philippines, UK, France, Japan, USA, Kenya, etc.). Volunteers were able to meet in person and learn new mapping techniques and experiences from each other. 12) Existing aerial photos from Aerial Vehicles (UAVs) proved their significant in decreasing the resolution concerns caused by poor satellite imageries (Westrope et al., 2014).

Issues and Possible Solutions of OSM 1) Poor/no Internet connectivity was an issue in some disaster zones (Cruz, 2013). Even though the HOT members were well-prepared with more than 20 hard-copy maps, it was difficult for the Red Cross to send prompt HOT updates (Cruz, 2013). Thus, the Red Cross decided to provide digital products such as handheld tablets, dashboards, and a local map server that allowed them to have access to real-time updates (Cruz, 2013). 2) Limited technical capacity of OSM volunteers was another challenge (Westrope et al., 2014). The OSM volunteers were mainly local university students who did not carry any knowledge background in damage assessments or technical expertise in humanitarian

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mapping (Westrope et al., 2014). The data they collected might heavily impact the overall data quality. Therefore, the Red Cross and Reach Initiate worked to offer a set of training to ensure that the volunteer students were able to collect the data correctly and consistently (Westrope et al., 2014). Importantly, improvements to the guidance and training of OSM contributors and standardized validation review procedures for contributor data would greatly boost the accuracy of data (Westrope et al., 2014). In addition, generic and disaster-specific damage assessment guidance materials will help remote volunteers to better understand the mapping tasks (e.g., YouTube tutorial video or 5-10 pages of a visual guide), therefore reduce the unnecessary errors (Westrope et al., 2014). Further, a test before contributing should be advocated for new volunteers. 3) Different criteria of classification system between field assessment and OSM was a headache (Westrope et al., 2014). It was argued that the classification system used for field assessment contains four categories: no damage, partial damage, major damage, and completely destroyed, while the classification system in OSM only includes three categories: undamaged, damaged, and destroyed or collapsed (Westrope et al., 2014). Therefore, the assessment team from the American Red Cross and REACH initiative attempted to diminish the negative effects through close management and oversight of field volunteers (Westrope et al., 2014). They used a logic model to compare both classification systems and considered “no damage” and “partial damage” categories from field assessment that equated with “no damage” from the OSM category (Westrope et al., 2014). 4) Limitations of Satellite Imagery resolution was a challenge to some volunteers (Westrope et al., 2014. It is argued that the data sources were relatively low to reliably differentiate between destroyed and merely damaged buildings (Westrope et al., 2014). For example, buildings with major damage may be mistaken for destroyed; buildings with severely damaged roofs can appear destroyed at a one square meter pixel resolution; buildings that were swept away may not appear on the imagery at all. All these issues not only confused the inexperienced OSM volunteers (Westrope et al., 2014) but also did they affect the collection of geographic data and disaster recovery efforts. 5) Delay of Satellite Imagery Delivery was a concern (Westrope et al., 2014). Some areas received the satellite imageries just a few days after the disaster struck; however some

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areas were not provided until over a week after the storm (Westrope et al., 2014). Such delay of data distribution snowballed the difficulties in the timely use of collected data, reduced the accuracy of damage assessments and affected the response as well as the recovery process. Moreover, the late distribution of satellite imageries can lead to obsolete and inaccurate within a matter of days, which turns out to be equally inaccurate damage assessments and less timely information (Westrope et al., 2014). In the Philippines crisis, HOT members even started an online petition requesting the quicker release of satellite imagery from DigitalGlobe (Shahid, 2016). Therefore, satellite imagery contributors should ensure the timely data distribution within the 24-48 hours after the disaster (American Red Cross, n.d.). Meanwhile, humanitarian organizations such as the American Red Cross should also build stronger relationships with satellite imagery providers. Especially, Dale Kunce stated that: “the Red Cross needed to make such data available to field responders and, in general, for field responders to be self- sufficient as possible by having, amongst other things, their own maps” (Shahid, 2016; Cruz, 2013). 6) Weinandy (2016, p. 30) argued that “The organizational hierarchy and donation dependency of the latter can conflict with the open-source ideology and opt-in nature of the former.” Fortunately, such issues can be mitigated by the published guidance of Digital Humanitarian Network (DHN), so the collaboration between formal humanitarian organizations and V&TC has been enhanced compared to prior 2012. For future disaster preparedness, it is important to build relationships and trust between disparate organizations. 7) Weinandy (2016) also mentioned other issues concerning V&TC, including privacy, data authorship, liability, and security. 8) A final concern would be the mental health for volunteers (Meier, 2015; Weinandy, 2016). Volunteers could be traumatized and depressed from working with damaged populations, which can result in a sense of isolation if there is no nearby peer support or effective counseling service available (Weinandy, 2016). Thus, it is necessary to state that the negative sentiments associated with their tasks are natural, and it is crucial to seek professional help (Weinandy, 2016).

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5.6.2 Ushahidi 5.6.2.1 ‘Haiyan.Crowdmap’

Figure 5.19 Interface of Haiyan.Crowdmap by using the Ushahidi Platform. Source: Haiyan.Crowdmap, 2013.

Ushahidi team developed a new tool called ‘crowdmap’ and released it on December 31, 2010 (Ushahidi, 2019). ‘Crowdmap’ provides an advantage in that the underlying technology is hosted in the cloud by ‘crowdmap,’ importantly, it requires minimal technical capacity for novel volunteers (Tomoszski, 2015). Importantly, ‘crowdmap’ allows users to set up their own deployments without having to install it on a web server (Ushahidi, 2019). New features include ‘Checkins’ – which is a geospatial add-on to ‘crowdmap’ that allows users to create a white-label option. Instead of having to fill out submission forms online, checkins allow ‘crowdmap’ users to expedite data entry to their deployment, focusing first on location and adding more information later (Ushahidi, 2019; Tomoszeski, 2015). Moreover, ‘crowdmap’ software enabled users to filter the reports by dates; thus they were free to check the information that they were looking for (Gekker et al., 2018). Interestingly, even though ‘crowdmap’ was launched before 2011, Sinsai.info did not use this tool due to the disrupted mobile phone communications (Meier, 2012b). Instead, ‘crowdmap’ was successfully deployed in the wake of Typhoon Haiyan by VISOV (Volontaires 106

Internationaux En Soutien Aux Opérations Virtuelles), and mainly used for relief requests collection via email, twitter and onsite forms (Gekker et al., 2018). VISOV is a humanitarian organization in France, which is translated as ‘Social Media in Emergency Management’. A team from VISOV put efforts in crowdsourcing images of infrastructure destruction as well as relief deployment, importantly in alleviating sufferings for the Filipino citizens (Lungati, 2013). With similar features from Sinsai.info, Haiyan. Crowdmap enabled victims to use IOS/Android smartphones to either send an email to ‘[email protected]’, send a tweet with hashtags including ‘reliefPH’ and ‘rescuePHD,’ or simply submit a report (Figure 5.19). The categories of reports varied from damage images, aid images, trusted reports. However, one difference between ‘Haiyan. Crowdmap’ and Sinsai.info is the credibility feature on the report form (Figure 5.20). Sinsai.info asked contributors to rate the report reliability before submitting them, and the value ranged from -1 to 1. If the report were convinced reliable, the number would go up; whereas the submitted report was doubted reliable, the number would go down. However, in the case of ‘Haiyan. Crowdmap’, the contributors were not required to rate their reports with a particular value. If reporters convinced that their reports were reliable, they could simply click a category called ‘Trusted Reports’ (Haiyan.Crowdmap, 2013).

Figure 5.20 Comparison of Report Requirement on Reliability between Sinsai.info and Haiyan. Crowdmap Source: Sinsai.info, n.d.; Haiyan. Crowdmap, 2013

Unlike “Sinsai.info”, ‘Tomnod CrowdRank’ was used in this deployment (Figure 5.19), serving as a validation tool to save a considerable amount of time for the ‘Moderators’ (Barrington, 2014). To illustrate, CrowdRank was created as a geospatial censorship algorithm to

107 calculate the agreement of the reports; every click from every viewer on the Tomnod website would be analyzed by CrowdRank in two dimentions, confidence and reliability of the crowds (Barrington, 2014). Due to the more rigorous process for data validation, the verified reports were only 1.6% (Haiyan.Crowdmap, 2013). From November 10 to December 3, 2013, Haiyan. Crowdmap received a total of 246 reports, including 222 from ‘Damaged Images,’ 53 from ‘Aid Images,’ and only 4 ‘Trusted Reports’ (Figure 5.21). The contents ranged from pictures, aerial photos, videos of the description of infrastructure damages, destruction of highways/roads, and locations of relief supplies all across the country. Besides the necessity of validation on these reports, all these data acquisitions were the substantial contributions from the Filipino citizens for emergency responders to learn the impacts of the affected community remotely; therefore they were able to implement relief plans, including allocation of supplies as well as search and rescue actions.

Figure 5.21 Screenshot of Reports on Haiyan.Crowdmap Source: Haiyan.Crowdmap, 2013 The screenshot shows map data in the form of a list of reported entries, and the reports are date stamped at the right according to when they were entered into the ‘crowdmap’ database (Note: Red-denoted Section on Figure 5.21). However, Gekker et al. (2018) indicated an issue regarding the existence of time lag. For example, the first report was taken on November 10, 2013 (Figure 5.21), we could assume this was the date when the damaged occurred when this

108 photo was taken, or it could be the date when ‘crowdmap’ staff received this report. No matter how sophisticated the software is, the time lag issue remains (Gekker et al., 2018). Further, it is crucial to address the issue of how the software manages the time zones and correspondences between the time of the server and the user (Gekker et a., 2018).

5.6.2.2 ‘Philflood.Map’

Figure 5.22 Interface of Philflood. Map. Source: Philflood. Map, 2013

Besides a new tool and feature change, another difference from this Typhoon crisis to the previous earthquakes is the more widespread use of the Ushahidi platform for other purposes. For example, a GIS Research Group from the University of Heidelberg (Germany) had engaged in a crisis mapping project by employing the Ushahidi Platform, aiming to present a comprehensive map with information of population, OSM elements at risk, storm surge and Flickr images (Grekker et al., 2018). In addition, a Philippines flood map called ‘Philfood’ was developed, which aimed to collect the reports concerning the extent or height of flood across the country. Categories were ranging from ‘Gutter,’ ‘1/2 knee’, ‘1/2 Tire’, ‘Knee,’ ‘Tire,’ ‘Waist,’ and ‘Chest’ (Figure 5.22). When the water reached a certain height, the Filipino citizens would

109 send reports via email ([email protected]), or send a tweet with hashtags ‘Philipineflood’, or complete a form and submit it to the ‘crowdmap’ database. More, there is another difference from this report system design with the others. In this case, the ‘philflood map’ report system did not require citizens to determine whether it was a reliable report. Different from the discussion in Figure 5, there was not actually such an option on the report for asking citizens to decide on the reliability. At last, the ‘philflood’ map contributed with flood information during the crisis; however, this map was designed for a short period of use (Gekker et al., 2018); thus, the reports and other detailed information were no longer available for analysis.

Advantages of Ushahidi In addition to the advantages of previous deployments of Sinsai.info, there are additional benefits in this case: 1) The big technological breakthrough is the creation and implementation of the ‘crowdmap’ tool by the Ushahidi team. With the flexibility of ‘Checkins’ and filtering reports by dates, both maps that launched on ‘crowdmap’ software were significant contributions in the Typhoon emergency relief (Ushahidi, 2019). Importantly, CrowdRank was developed to validate the reports and saved the workload for the ‘Moderators’ (Barrington, 2014). 2) Increasing numbers of projects by using the same platform was demonstrated in this case study. Compared to the Tohoku Earthquake, the Ushahidi platform was widely used more than one mapping project in the case of Typhoon Haiyan. In addition to ‘Haiyan. Crowdmap’ by UISOV, there is also ‘Phiflood.Map.’ (by unknown) (Gekker et al., 2018). 3) More diverse mapping purposes emerged in this disaster event. Besides collecting reports of the damage populations and emergency requests (Haiyan.Crowdmap, 2013), the launch of ‘Philflood. Map’ was used for measuring the water height (Gekker et al., 2018).

Issues of Ushahidi 1) Gekker et al. (2018) argued that there was a time lag issue. Unfortunately, all sophisticated software contains such an issue. It was confusing to identify whether a report date was when the damaged occurred, when the attached photo was taken, or when

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‘crowdmap’ staff received this report (Gekker et al., 2018). Moreover, ‘crowdmap’ software needed to pay attention to the time zones issues (Gekker et al., 2018). 2) Gekker et al. (2018) also argued that some maps were created for a short period of use only. Thus, the reports and other information on the ‘Philflood. Map’ was not available anymore, it is difficult to discuss and analyze for future research.

5.6.3 MicroMappers – A New Tool by SBTF, GIS Corps and ESRI “MicroMappers represents an important shift in the digital humanitarian space… and democratizes digital humanitarian efforts.” -Patrick Meier, co-founder of MicroMappers (Meier, 2015)

OCHA activated the DHN just hours before the Typhoon made landfall on November 8, 2013 (Meier, 2015). In particular, OCHA required the volunteers from SBTF to collect all the tweets related to the Haiyan over 72 hours, find out which of these included links to pictures, determine the level of damage in each, and then geo-reference those that showed the most damage (Meier, 2015). A new platform for tagging tweets, images, videos, and geo-tagging was in demand. Therefore, Patrick Meier and his team decided to launch a new web-based project to create a fully customized micro-tasking platform, quickly making sense of all the user-generated as well as multi-media content that was posted on social media (Meier, 2013c), which would be on standby and available within minutes after the crisis began – MicroMappers (Meier, 2015). MicroMappers is a digital volunteer organization engaged in annotating and mapping tweets as well as other data during the Typhoon Haiyan (Vieweg et al., 2014). Furthermore, it is a well-designed collection of customized micro-tasking applications. This collection includes four Clickers: “Image Clicker,” “Image Geo Clicker,” “Tweet Clicker,” and “Tweet Geo Clicker” (Meier, 2013d; Meier, 2015). In addition, the entire mapping process was implemented in partnership with the GIS Corps and ESRI, which accelerated the successful deployment of MicroMappers (Meier, 2013c).

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Figure 5.23 Crisis Map Produced by SBTF, GIS Corps, and ESRI. Source: Meier, 2013c Meier concluded that, within 48 hours, there was a total click of 105,000 on MicroMappers (Image and Tweet Clickers) (Meier, 2013d). Eventually, a live crisis map was successfully developed (Figure 5.23) to display all the geotagged tweets and images that were processed by using Image Clicker and Tweet Clicker, and extensively used by humanitarian organizations such as FEMA, Red Cross and the UN for disaster relief coordination (Meier, 2015).

5.6.3.1 Image Clicker and Image Geo Clicker Meier and his colleague automatically-collected pictures scraped from Twitter during the crisis and then uploaded them to the Image Clicker for tagging (Meier, 2013a). Image Clicker allowed these digital humanitarians to quickly tag pictures based on the extent of the damage that was shown in the photos, and rate the level of damage ranging from ‘None,’ ‘Mild,’ to ‘Severe’ (Figure 5.24). At least five volunteers were responsible for the security verification of each image. After the first server provided by the University of Geneva crashed, due to the severe

112 traffic of data processing at the same time, Meier’s team quickly came up with a solution – they purchased a more powerful server to handle tens of thousands of volunteer tagging work simultaneously (Meier, 2015). Within 48 hours, there were around 15,000 images clicked using the Image Clicker, and approximately 180 images were verified and posted on the crisis map (Meier, 2013d).

Figure 5.24 Interface of Image Clicker. Source: Meier, 2013d. Next, Meier’s team developed and operated Image Geo Clicker so that they were able to geo-reference the images and later add them to a crisis map (Meier, 2015). If an image showed “mild” or “severe” damage, and it was voted over three times by using the Image Clicker, then it would be automatically pushed to the Image Geo Clicker together with the text of the original tweet (Meier, 2013d). Then each picture was geotagged by three different volunteers before it was posted to the crisis map. Importantly, the Image Geo Clicker contained a map that SBTF volunteers used to pinpoint where, in the Philippines, the displaced picture might have been taken (Figure 5.25). Additionally, the Filipino government served as an important role in providing local knowledge, so the landmarks from the pictures were recognized. Both the UN and the local government used this map to assess the damages caused by the Typhoon Haiyan (Meier, 2015).

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Figure 5.25 Interface of Image Geo Clicker. Source: Meier, 2013d 5.6.3.2 Tweet Clicker and Tweet Geo Clicker Other applications of MicroMappers are Tweet Clicker (Figure 5.26) and Tweet Geo Clicker (Figure 5.27), which aimed to identify tweets concerning any urgent need requests, infrastructure damage, or population displacement (Meier, 2015). As an easy-to-use and convenient tool, contributors simply needed to tag and categorize tweets based on the content of tweets (Meier, 2013d). As Figure 5.26 shows, volunteers were just required to select the second option, “Requests for Help/Needs,” according to the Tweet content. Similar to Image Clicker, each tweet was voted on by at least five different volunteers for security verification purposes (Shahid, 2016).

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Figure 5.26 Interface of Tweet Clicker Source: Meier, 2013d

Once the tweet was verified, the geo-tagging content of the tweet would be able to come into play (Meier, 2015). The blue sign of Twitter in Figure 5.27 indicates exactly where the requests came from, with precise coordination that could be actioned by humanitarian responders (Shahidi, 2016). Even though the contributors filtered out retweets and deployed an automated algorithm to identify unique tweets, the server issues remained. Meier’s team still was able to tag more than 30,000 tweets within 72 hours (Meier, 2015). Among these tweets, 38,000 were identified as relevant, and approximately 600 were added to the crisis map (Meier, 2015).

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Figure 5.27 Interface of Tweet Geo Clicker. Source: Meier, 2013d

Advantages of MicroMappers 1) The top significance is the successful collaboration between the three DHN members: SBTF, GIS Corps and ESRI, which further proved that the establishment of DHN had substantially contributed to effective communications, thus increasing the responsive coordination to a disaster strike (Meier, 2013c; Weinandy, 2016). 2) Telecommunication infrastructure is widespread in the Philippines; nearly every Filipino adult has access to a mobile phone (Vieweg et al., 2014). Also, regarding Twitter use, the Philippines was ranked 10th in the world for a number of Twitter accounts by 2014 (Veiweg et al., 2014). Therefore, the benefits of widespread internet access and high Twitter use have facilitated the launch of MicroMappers (Vieweg et al., 2014). 3) A broad benefit of MicroMappers is that it could play a critical role in data collection, filter, translation, and verification (Weinandy, 2016). Particularly, MicroMappers

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allowed online volunteers to tag messages and images based on location, level of need, population displacement, or infrastructure damage (Weinandy, 2016). With the visualization of the produced Crisis Map, the information from the map acted as valuable local knowledge for the OCHA and other humanitarian organizations to better understand the situation in real time and coordinate relief efforts in such a catastrophic disaster. 4) MicroMappers accelerated the rapid damage assessment process for the UN, shortening the process from five to seven days, into only two days (Meier, 2015). 5) The disaster response came from all over the globe (Meier, 2013d), thus Typhoon Haiyan received a variety of cross-broader attention and relief aid. 6) Meier (2015) contended that the SBTF volunteers were able to make MicroMappers as easy as a single click of the mouse, to facilitate the humanitarian aid in a real-time situation. 7) MicroMappers did not require any previous experience for new volunteers; watching a tutorial video would simply help to grasp the general idea (Meier, 2015). 8) MicroMappers also served as a free and open-source, which attracted around 300 contributors from all over the world (Meier, 2013d). 9) It was also mentioned that new tools such as Video Clicker and Translate Clicker were under development, which would be very helpful as well (Collins, 2013).

Issues and Solutions of MicroMappers 1) Meier described this project as “finding the needles in the haystack, and doing that in real time” (Collins, 2013). It brought considerable pressure to humans as well as machines. Thus, it is important to develop new tools to mitigate this issue. 2) It was mentioned several times that the server was the main issue in deploying MicroMappers (Meier, 2015). Even though the SBTF team purchased a brand new and faster server, the issue remained to intermittently interrupted the data processing (Meier, 2015). Therefore, it is crucial to get a much more powerful server to handle the load and traffic; otherwise it would delay the emergency response due to this technical issue (Meier, 2015). Also, as discussed in the first challenge, a set of new technological innovations should be developed to alleviate the server traffic.

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3) Another challenge is the geo-location for around 3,000 tweets and 5,000 images tagged using the Clickers (Meier, 2013d). Image Geo Clicker and Tweet Geo Clicker were not developed and ready for the prime time (Meier, 2013d). Meier and his team were only able to launch Geo Clickers one week after the crisis (Meier, 2013d).

5.6.4 Google Crisis Response “… When Google Maps came out in 2005, I fell in love. I thought, ‘Yes, this is the way of the future.’” -Pete Giencke, an engineer from the Google Crisis Response Team (Gordon, 2013)

Google Crisis Response is a team with Google.org that aims to make critical information more accessible around natural disasters and humanitarian crises (Google Crisis Response, n.d.). Google products generally contain Google Maps, Google Earth, and Google Person Finder (Gordon 2013; Google Crisis Response, n.d.). All these tools help gather and relay information in connection with the incredible devastation in a destructive crisis (Google.org.blog, 2013).

Figure 5.28 Typhoon Haiyan/Yolanda Crisis and Relief Map by Google Crisis Response. Source: Google Crisis Response, 2013.

“Mapping is one of Google’s most glittering talents” (Gordon, 2013). Google Crisis Response Team also launched a crisis mapping project in the wake of the Typhoon Haiyan (Figure 5.28). Volunteers on the ground regularly updated the Google Crisis Map with

118 information on evacuation centers, relief drop zone areas, road washouts and infrastructure damage (Gordon, 2013). Thus, the volunteers from the Philippines and relief workers could better navigate the crisis. With the local knowledge from Filipino residents, this crisis map provided updates on evacuation centers, hospital and health facilities, police stations, and relief goods drop-off centers (Figure 5.28). Additionally, a number of Google Crisis Response Team members with extensive technical capacities (e.g., GIS professionals, engineers, software developers) engaged in mapping damage assessment of the Tacloban City. As seen in Figure 5.29, red grids refer to the destroyed regions, orange grids refer to highly affected places, light orange means the moderately affected, and yellow grids means the possibly affected areas. The darker the color looks, the heavier the extent of damages shows on the map. This map was utilized to coordinate emergency relief and allocate relief funds and goods. The most significant advantage is the trust and brand of Google; thus, it is considered as a reliable source without carrying many controversies. Also, Google holds advantages in its significant community body and partnership. (Neils & Zielstra, 2014).

Figure 5.29 Damage Assessment on Typhoon Haiyan/Yolanda Crisis and Relief Map Source: Google Crisis Response, 2013

5.7 Conclusion to Discussion In this chapter, I examined the evolution of using VGI and crowdsourcing in disaster management from 2007 to 2013. From this, I have discovered that two case studies were

119 connected and situated as the symbolic segments of the evolution. In particular, I discussed how OSM, Ushahidi, Safecast, ALL311, ESRI, MicroMappers as well as Google Crisis Response had utilized VGI and crowdsourcing in disaster response and recovery phases. Further, I discussed their advantages as well as issues along with potential solutions. In the next chapter, I will summarize these contributions and concerns to answer the research questions.

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Chapter 6: Conclusion “These new connection technologies can extend to amplify our humanity, can translate our initial private emotions of sadness and powerlessness into public – indeed global – action to help others thousands of miles away.” -Meier, 2015 In this chapter, I answered the two research questions, discussed the limitations that I have encountered in this research, examined the potential contributions of this thesis to geography, and proposed some future research recommendations.

6.1 Answering Research Questions

Research Question 1: How has the use VGI and Crowdsourcing enhanced the coordination across the four stages of a disaster management cycle? Through the discussion from two case studies of the 2011 Tohoku Earthquake and the 2013 Typhoon Haiyan, I discovered that seven V&TCs utilized VGI and crowd-sourced information to substantially support the coordination of initial disaster response as well as the long-term recovery phase. According to the analysis of each V&TC from Chapter 5, I found that there are eleven advantages of using VGI and crowdsourcing for disaster management (Table 6.1), including: (1) facilitating fruitful partnership between the formal NGOs and V&TCs, (2) increasing collaborations between the V&TCs, (3) realizing time-efficiency during a disaster, (4) harnessing the local knowledge, (5) filling the knowledge void of authoritative data sources, (6) enhancing transparency of the dialogues between the government and the public, (8) tailoring the platform into different disasters, (8) boosting technological innovations, (9) facilitating the expansion of V&TC bodies, (10) enabling wider data dissemination through social media, and (11) promoting positive participant motivation.

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Table 6.1 Eleven Advantages of Using VGI/Crowdsourcing in Disaster Management. (Note: “J” refers to Japan, “P” refers to the Philippines. Blank refers to “Not Discussed” or “Not Applicable”.) Source: Author. No. Discussed V&TCs OSM Ushahidi Safecast ALL311 ESRI Micro Google Mappers Crisis Advantages Response 1 Facilitating Fruitful Partnership between Formal Humanitarian Organizations and V&TCs P P P 2 Increasing Collaborations J J J J J between V&TCs P P P P 2.1 Local Collaboration Within a J Particular V&TC 2.2 Global Collaboration within a J J J J Particular V&TC P P P P 2.3 Global Collaborations between J Different V&TCs P 3 Realizing Time-Efficiency J J J J J J During a Disaster P P P P 4 Harnessing the Local Knowledge J J J J J P P P P 5 Filling the Knowledge Void of J J J Authoritative Data Sources P P P 6 Enhancing Transparency of the J Dialogues between the Government and Citizens P 7 Tailoring the Platform into J J Different Disasters P P 8 Boosting Technological J J Innovations P P P 9 Facilitating the Expansion of J J J V&TC Bodies P P P 9.1 Major Cause – J J J J J Open License, Open Data P P P 9.2 Positive Impacts – J J J J J

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Reputation, Brand, Trust, More Collaboration Opportunities P P P P 10 Enabling Wider Data J J J J J Dissemination Through Social Media P P P 11 Promoting Positive Participant J J J J J J Motivation P P P P

11.1 Providing Learning J Opportunities for Student P Volunteers

Facilitating a Fruitful Partnership between Formal Humanitarian Organizations and V&TCs The most important benefit of using VGI and crowd-sourced information is increasing the dialogues and collaborations between the Formal Humanitarian Organizations and V&TCs. After the 2010 Haiti Earthquake, the Harvard Humanitarian Initiative (2011) identified the main issue between two groups of the world experts was the insufficient interfaces to respond to the emergencies collaboratively. Thus, a number of digital humanitarian organizations including HOT and SBTF were formulized to mitigate such issues and provide additional communication channels (Shahid, 2016). However, the communication gap between the two sides still largely existed until 2012. Thankfully, the Digital Humanitarian Network (DHN) was officially established. This network was implemented to provide a coordinator tool between two groups and develop the guidance for fruitful partnerships (DHN, n.d.- b). Take the 2013 Typhoon Haiyan as an example, which has been proved to be a successful deployment because the DHN was activated promptly, and this network provided a unique perspective for formal NGOs on how to utilize new forms of engagement and improve humanitarian response through collaboration with V&TCs (Weinandy, 2016). For instance, The American Red Cross and OCHA played an important role in supporting and navigating V&TCs. The quality of communication and transparency between them also ensured the quality of collected VGI data (Westrope et al., 2014).

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Increasing Collaborations of V&TCs Increasing Local Collaboration within a Particular V&TC First of all, the use of VGI and crowdsourcing facilitated the local research collaborations. Take ALL311 as an example, ALL311 (n.d.) was designed to collect a variety of relevant geographic datasets (similar to UN-SPIDER Knowledge Portal) and to encourage local volunteer institutions and individual researchers to engage in new projects (similar to Crisis Mappers Net). This network had improved the communication between local researchers, brought innovations for potential research opportunities and mitigated the long-term recovery challenges for the severely affected communities.

Increasing Global Collaboration within a Particular V&TC More, the use of VGI increased collaborations among worldwide volunteers within a particular V&TC. Take Safecast as an example, and this multi-connected network enhanced the team’s flexibility, efficiency as well as agility (Brown et al., 2016). OSM, Ushahidi, Google Crisis Response, ESRI, MicroMappers showed the same significance.

Increasing Global Collaboration between Different V&TCs Further, the use of VGI/crowdsourcing has facilitated the large-scale collaborative efforts between different V&TCs. For example, during the Japanese crisis, Sinsai.info was considered as a collaborative project of OSM, Ushahidi, and SBTF (Meier, 2012b; Seto & Nishimura, 2016). Specifically, a majority of OSM volunteers, along with some Ushahidi volunteers were operating the Sinsai.info website, which was built on a Ushahidi platform (v.2). Moreover, the volunteers received technical assistance from SBTF (Meier, 2012b). Hal Seki, the managing director of Sinsai.info, emphasized its openness of source, data, collaboration (Seki, 2011a). Therefore, the dedicated worldwide support from the three V&TCs has significantly advanced Sinsai.info. Ushahidi Staff (2011b) reported that Sinsai.info had received a total of 1,213, 258 page-views, 833,399 total visitors, and 430,021 total unique visitors. Importantly, the Japanese government presented Sinsai.info with an award for such a considerable contribution to this historical crisis (Ota, 2012). Another example is the MicroMappers tool from the Typhoon Haiyan, which was also teamwork of SBTF, GIS Corps, and ESRI (Meier, 2013c). After OCHA activated the DHN, they

124 assigned the mapping task to a number of SBTF volunteers, GIS Corps and ESRI practitioners. The partnership of the three V&TCs accelerated the new mapping project. Patrick Meier, the co- founder of MicroMappers, contended that there was a total of 105,000 clicks on this platform within 48 hours (Meier, 2013d), and a live crisis map was developed to display all the disaster- related tweets and images. Importantly, some humanitarian organizations such as FEMA, Red Cross, and the UN used this crisis map for efficient disaster relief navigation (Meier, 2015).

Realizing Time-Efficiency During a Disaster Time is key to the coordination of large-scale disaster management. However, some authoritative data usually takes a longer validation process before the distribution of the data; this delay of official data sources results in more casualties and economic losses. From my research findings, the OSM and Ushahidi projects were ideal solutions in these time-sensitive situations. They have not only met the urgent demand for abundant geographic information, but they also have realized the goals of enhancing disaster relief in an effective and timely fashion. Sinsai.info was recognized for its diverse geographic information, including reports, emails, tweets, which accelerates decision making, implementation of relief actions, and allocation of relief supplies by relief agencies (Gao et al., 2011). During the Typhoon Haiyan, the launch of MicroMappers accelerated the rapid damage assessment progress for OCHA, and significantly shortened the time of data distribution into only two days, instead of five to seven days (Meier, 2015). By utilizing user-friendly software, these platforms and devices increased the ability to respond to emergencies quickly. There was no technical requirement for contributing to Ushahidi and Google Crisis Response, while there was some technical capacity for using OSM, OMK device, Safecast, and MicroMappers. Both the concise video tutorials and hard-copy guidance helped the volunteers understand the task objective and allowed them to carry out the relief efforts quickly.

Harnessing the Local Knowledge Goodchild’s argument (2007) was proven in this thesis, for citizens were able to act as a unique sensor to provide contextual and filtered information, since they were able to synthesize the surrounding observations with their local knowledge, and interpret the phenomenon more accurately than non-locals. This was also validated in the V&TCs, including Ushahidi,

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MicroMappers and Google Crisis Response project. In particular, the Filipino volunteers on the ground regularly updated Google Crisis Map, with their local information on evacuation centers, relief drop zone areas, road washouts and infrastructure damage (Gordon, 2013). Also, on the ‘Haiyan.Crowdmap,’ the contributors identified the damaged buildings and shared the information with the public, which enabled the remote non-Filipino emergency responders to learn about the impacts on the affected community accurately. Moreover, the Japanese OSM volunteers were able to correctly trace the damaged roads because their local knowledge served as a critical information management tool.

Filling the Information Void of Authoritative Data Sources My research findings not only validated the viewpoint from Poser and Dransch (2010) that VGI did play a supplementary role in filling the knowledge void of authoritative data sources, but also the findings showed the complementary role of VGI use. Both a developed country as Japan and a less developed country as the Philippines did not have maps after the crisis took place. During the operation of Sinsai.info, the potential of crowdsourced approaches for data collection was acknowledged (The World Bank, 2011). Due to the limitation of real-time information provided by the Japanese Government, Sinsai.info was extensively used as a supplementary data source (Hayakawa et al., 2014). During the deployment of OSM projects, the Red Cross pointed out that OSM served as a foundation, base map, and data store for their humanitarian organization (Cruz, 2013). In other words, VGI/crowdsourced information enriched authoritative data. MicroMappers was important in disaster data collection, filter, translation as well as verification (Weinandy, 2016). As Goodchild and Glennon (2010) contended, VGI did act as an alternative to the official data sources, showing its significance in time efficiency for information collection and open sharing. Moreover, Haklay et al. (2014) added that VGI was also used for scientific modeling. However, I contend that VGI functions as a complementary data source based on my analysis from Chapter 5.

Enhancing Transparency of the Dialogues between Government and Citizens Additionally, the use of VGI and crowd-sourced information did help to increase social awareness and facilitate the dialogues between the citizens and the government (Meier, 2012b ; Haworth & Bruce, 2015). During the operation of Sinsai.info, the public actively contributed to

126 the observed disaster-related information via emails, Twitter and reports on the platform. Moreover, the citizens were able to exchange information with each other regarding the evacuation shelters openly. Interestingly, the Japanese government was not involved with this project at first (Cavelty et al., 2011). However, due to the growing numbers of users, the authorities decided to join this project, announced disaster information on this platform, and interacted with the public. In conclusion, the use of VGI and crowdsourcing significantly enhanced public participation in a disaster event and improved the communication between the authorities and the public.

Tailoring the Platform into Different Disasters OSM and Ushahidi have been discussed in both case studies; thus, it will be interesting to compare them and examine their flexibility and adaptability in different disasters. Example 1: OSM Table 6.2 Comparison of the Characteristics of OSM in Two Case Studies. Source: Author. Case Studies 2011 Tohoku Earthquake 2013 Typhoon Haiyan Characteristics Project Purpose Online Contribution: Online Contribution: Digitizing roads and infrastructure Digitizing roads and infrastructure Onsite Contribution: Damage Assessment Focused Mapping Area Sendai Region Tacloban City Project Implementation Time Within 3 days Within 24 hours Project Initiator Japan OSM Foundation American Red Cross License Change National Land Numerical Open Database License Information Data Main Partners HOT, Microsoft (Bing Maps) HOT, Microsoft (Bing Maps) Numbers of Contributors 1179 1679 Users of the Map The Japanese Government; The Philippines Government; Disaster relief agencies; etc. Disaster relief agencies; etc. Accomplishment Mapped over 5,000,000 roads and Merged 1,240 place names from streets on the map GNS data; mapped 29,845 buildings; traced 3,036 residential land use areas

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As Table 6.2 shows, the most significant differences are the increased project tasks, more applied disaster phases, growing efficiency of responding to an emergency, license change, expansion of OSM community body, and status of being self-sufficient or more dependent on the international humanitarian organization (e.g., the American Red Cross).

Example 2: Ushahidi Table 6.3 Comparison of the Characteristics of Ushahidi in Two Case Studies (Note: “/” refers to “Not Discussed” or “Not Specified”). Source: Author. Case Studies 2011 Tohoku Earthquake 2013 Typhoon Haiyan Characteristics (e.g., Sinsai.info) (e.g., ‘Haiyan.CrowdMap’) Main Project Purpose Collect real-time information from Collect real-time information from the public; geo-locate the reports; the public; geo-locate the reports; coordinate search and rescue coordinate search and rescue Type of Platform Web Web and Mobile App Site Establishment Time 3 hours / Project Initiator Japanese Students from Turfs VISOV University; Japan OSM Foundation Main Partners GeoRepublic Japan; HOT; etc. DigitalGlobe; HOT; etc. Categories of Published Reports 18 3 Validation “Moderators” “Tomnod” Reported Quality 6.1% 1.6% Used Media Twitter, Email, Web Report SMS, Twitter, Email, Web Report Users of the Map Citizens; Media; Citizens; Media; The Japanese Government; The Philippines Government; Humanitarian organizations, etc. Humanitarian organizations, etc. Availability of Site Shut Down Still Available Total Received Reports Approximately 12,600 245 Total Team Volunteers More than 200 / Translation Staff Members 5 / Accomplishment 1,213,258 total page views, 833,399 / total visitors, and 430,021 total unique visitors; Award by the Japanese Government

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As Table 6.3 shows, the most significant changes are the launch of the mobile application, the use of “Tomnod” as a new validation tool, more rigorous reported quality, availability of the website. Similar to OSM, “HaiyanCrowd.Map” project was also initiated by the international NGO - VISOV (HaiyanCrowd.Map, 2013). In addition, the Ushahidi platform was also tailored to “Philflood.Map” project to measure the water heights (Gekker et al., 2018). However, due to the limited documentation regarding the Typhoon Haiyan, some indicators are unable to be compared.

Boosting Technological Innovations In my research, I have discovered that the use of VGI and crowd-sourced information have boosted technological developments, mainly focusing on two V&TCs: OSM and Ushahidi. Because OSM and Ushahidi were both discussed in the two case studies, it would be interesting to examine the new changes throughout the years. As seen in Table 6.4, there are four types of developments on OSM: coordination tool, editing tool, data collection tool, and a new feature. During the Tohoku Earthquake, the OSM volunteers used JOSM as their essential editing tool to trace the roads, buildings, infrastructure, and create a comprehensive base map (Sendai Earthquake and Tsunami, n.d.). They also used the second version of QualityStreetMap as their OSM coordination tool to add missing data and details (Sendai Earthquake and Tsunami, 2011). However, this tool was still in the trial period, so sometimes, the bugs might have impeded the mapping process in such a time-critical situation. Thanks to the technological improvements in 2013, the Tasking Manager has evolved from and replaced QualityStreetMap and was established as a crucial micro-tasking coordination tool (Silverman, 2014). Importantly, this new tool helped to divide a cumbersome task into smaller ones and make the best use of volunteers’ efforts (Silverman, 2014; Shahid, 2016). In addition to JOSM, there are two editing tools including iD and Potlatch, suitable for new volunteers. Speaking of new features, ‘Notes’ allowed mappers to share their local knowledge online. In addition, ‘Notes’ served as a quality assessment tool that allowed users to correct an error simply by clicking ‘Add a Note’ (Neis & Zielstra, 2014; Palen et al., 2015). When it comes to mapping damage assessment, the Tsunami Damage Mapping Team from the Association of Japanese Geographers was responsible for this project (Seto & Nishimura, 2016). However, in

129 the Haiyan case, the Red Cross helped to create the OpenMapKit (OMK) as a new field data collection tool and assigned the task to the HOT (Westrope et al., 2014). This easy-to-use mobile device enabled the HOT volunteers to carry out a successful damage assessment mapping project.

Table 6.4 Technological Advancements of OSM and Ushahidi in Two Case Studies. (Note: “/” refers to “Not Discussed” or “No Developments Discovered.”) Source: Author.

Case Study OSM Ushahidi Coordination Editing Data New Software New Tool Tool Collection Feature Version Feature Tool 2011 QualityStreetMap JOSM / / Ushahidi (v.2) / Tohoku (v.2) Earthquake and Tsunami 2013 Tasking Manager JOSM; OpenMapKit “Notes” ‘Crowdmap’ - ‘Checkins’; Typhoon (v.1) iD; (OMK) software - Filter reports Haiyan Potlatch by date; - Elimination of unnecessary feature

Further, I have found some technological developments on the Ushahidi platform (Table 6.1). During the Japanese crisis, the OSM volunteers launched Sinsai.info on the second version of Ushahidi. Then in 2013, the Ushahidi team developed a new tool called ‘crowdmap’ and released it in December 2010 (Ushahidi, 2019). Even though this new tool was not particularly used in Japan, it has been widely used in the Philippines. ‘Crowdmap’ allowed users to set up their own deployments without having to install it on a web server. New features include ‘Check- ins,’ reports filter by dates, and elimination of reliability rate by the public (Ushahidi, 2019). The new tool and features have significantly contributed to information sharing during disaster relief. In addition to the technological developments of OSM and Ushahidi, MicroMappers is considered an extraordinary technology created by SBTF, GIS Corps, and ESRI. Many

130 humanitarian organizations highly acknowledged the innovations of both Tweet Clickers and Image Clickers. Lastly, the Safecast team developed the GPS enabled device bGeigie Nano to trace and map the radiation impacts (Park & Johnson, 2019).

Facilitating the Expansion of V&TC Bodies – Cause and Impacts Major Cause – Open License, Open Data From 2011 to 2013, the bodies of V&TC have significantly grown. The primary reason is the benefit of “openness” – open license and free data (Palen et al., 2015). OSM was generally described as an ideal and cost-effective open mapping software. Due to a variety of free data available to the public, more and more people were attracted to register the membership and use the data for any purposes. The registered users on OSM have approximately increased from 400,000 in May 2011 to 1,500,000 in January 2014 (Shahid, 2016). Within that time frame, the population of registered members increased by threefold! Notably, the volunteers from the Japanese OSM risen from 854 to 1179 in April 2011 following the earthquake (Hayakawa et al., 2014). This 1.5 times of growth in membership has helped with the Japanese OSM reputation. In both case studies, a wide range of OSM mapping activities were organized and enabled the increasing numbers of participants to meet in person and learn new mapping techniques from each other. Especially in the Philippines case, these activities were referred to as ‘Mapathon’ events, which were hosted all across the world (Typhoon Haiyan, n.d.). Ushahidi is another popular open-source in which the public was able to share the reports; meanwhile, the researchers and emergency responders were able to view and download the reports and use them for disaster analysis and decision-making. With this convenience, the Ushahidi platform was even tailored to different mapping purposes during the Typhoon Haiyan. In addition to “Haiyan. Crowdmap” project conducted by UISOV, there was also ‘Philflood.Map.’ (conducted by unknown), and ‘Elements at Risk & Population Distribution’ initiated by a GIS Research Group from the University of Heidelberg (Gekker et al., 2018). Safecast and MicroMappers are also examples of growing V&TCs. Safecast was viewed as a special V&TC, for it was developed in the Japanese emergency to map the radiation impacts particularly. Also, this open-source software has received more than 3.5 million readings (Meier, 2013d), and enhanced its international reputation. MicroMappers served as another free and

131 open-source, which has attracted 300 contributors from all over the world (Meier, 2013d; Shahid, 2016).

Positive Impacts – Reputation, Brand, Trust, and More Collaboration Opportunities Google and ESRI have already obtained a long-lasting reputation and a global brand, and importantly, they were broadly viewed as reliable data sources without many controversies. Different from Google and ESRI, the expansion of V&TCs such as OSM, SBTF, Ushahidi, and Safecast brings three significant impacts: building reputation and brand, gaining trust from (potential) users as well as organizations, and opportunities for taking important mapping tasks. Linus’ Law posed a positive impact on VGI (Neis & Zielstra, 2014). The assumption was that, with the numbers of contributors, the quality of the product increases (Raymond, 2002). With the enlargement of V&TCs, the social awareness of such communities has been raised quickly; the unique brands of V&TCs were gradually built in the field of digital humanitarian organizations. Thus, these popular V&TCs have gained trust from both old and new members, as well as from the Formal Humanitarian Organizations. Take the Typhoon Haiyan as an instance; the Red Cross highly acknowledged the contributions of OSM projects; also, the usefulness of MicroMappers was recognized by OCHA, FEMA, and other humanitarian organizations. Till 2014, OSM was considered as one of the most successful VGI projects (Neils & Zielstra, 2014). Therefore, these V&TCs would receive more opportunities for taking charge of large-scale disaster mapping tasks and continue in building their global brand, and continue in mitigating the disaster relief.

Enabling Wider Data Dissemination Through Social Media As Gao et al. (2011), as well as Haworth and Bruce (2015), indicated, the use of VGI and crowdsourcing enabled a more thorough broadcast of information, through the widespread propagation approach of social media. Twitter played a crucial role in reports collection as well as raising social awareness (Ushahidi Staff, 2011b). In particular, the emergency responders were able to use the geo-tagged tweets collected from the Ushahidi platform to accurately locate the specific regions that requested urgent assistance (Gao et al., 2011). For instance, one geotagged tweet posted on Sinsai.info was caught attention by the Moderators that one hundred isolated victims were calling for help (Miyazaki et al., 2011). Thus, the Search and Rescue team was able to locate the area accurately and successfully save all of the trapped citizens (Miyazaki

132 et al., 2011). Further, in the ESRI social media map, the authorities were able to learn the most up-to-date and real-time information on a full scale, which accelerated their decision-making on the allocation of relief goods (Kerski, 2011). In addition, Safecast acknowledged the advantage of incorporating social networks and online discussions in their deployment (Brown et al., 2016). The Safecast project enabled the volunteers to remotely connect with volunteers who were situated on the other side of the planet. Social media served as a critical portal that helped reinforce the community bond and remotely exchange information (Park & Johnson, 2019).

Promoting Positive Participant Motivation As Haworth and Bruce (2015) contended, the motivation of contributing or withholding did shape the dynamics of VGI evolution and impact the data contents. Regarding the OSM project in 2013, a majority of volunteers were actually non-professional mappers, but still, they were eager to gain more experience and skills. As they said, there were not many opportunities for serving a humanitarian organization like the Red Cross (ReliefWeb-OCHA, 2013a). With this passion, the volunteers put their best efforts into the OSM projects, secured the data quality, and accelerated the data release.

Providing Learning Opportunities for Student Volunteers In this thesis, both OSM projects from the case studies have included college student volunteers to contribute with their mapping skills. During the Japanese crisis, approximately 100 college students from the Nara University actively participated in tracing the roads, inputting the locations of evacuation centers, and creating a base map (Yoshida et al., 2011). During the Typhoon crisis, HOT administrators assigned a number of college students to use OMK and collect the field data (Westrope et al., 2014). In my point of view, these college students not only learned abundant mapping skills by using web-based mapping software and GPS-enabled devices but also built their professional networks while working with experts. With this experience, college students possibly have more advantages than other candidates when it comes to applying for grad school and job recruitments.

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Research Question 2: What have been the challenges of using VGI and crowdsourcing, and what are the potential solutions? For this question, I have discovered six challenges regarding the use the VGI and crowdsourced information in disaster relief (Table 6.5), including (1) delay of data distribution (2) lack of communications and collaborations between the government, the formal NGOs as well as the V&TCs, (3) factors impacting data quality, (4) lack of long-term participant motivation, (5) internet connection and technical concerns, (6) concern on volunteers’ privacy, safety and mental health. Some authors and I provided insights on potential solutions.

Table 6.5 Six Challenges of Using VGI and Crowdsourcing in Disaster Management. (Note: “J” refers to Japan, “P” refers to the Philippines, Blank Area refers to “Not Discussed” or “Not Applicable”). Source: Author. No. Discussed V&TCs OSM Ushahidi Safecast ALL311 ESRI Micro Google Mappers Crisis Issues Response 1 Delay of Data Distribution J J P 1.1 Caused by the Language Barrier J J 1.2 Caused by Insufficient Base Data J J 2 Lack of Communications and J J Collaboration P P 2.1 Lack of Communication between J V&TCs and Humanitarian Organizations P P 2.2 Lack of Collaboration between J the Government and V&TCs

3 Factors Impacting Data Quality J J P P P 3.1 Data Unreliability J P P 3.2 Insufficient Technical Capacities J of Volunteers P

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3.3 Different Criteria of Classification System P 4 Lack of Long-Term Participant J Motivation 5 Internet Connection and Technical Challenges P P P 5.1 Poor or Non-exist Internet Connectivity P 5.2 Time Lag of Software P 5.3 More Innovations to Reduce the J Workload in Need P 5.4 Repeatedly Crashed Server P

6 Privacy, Safety and Mental J J Health of Volunteers P P 6.1 Privacy and Safety J

6.2 Mental Health P P

Delay of Data Distribution Caused by the Language Barrier The 2011 Tohoku Earthquake and Tsunami was considered a milestone for successful cross-border collaboration for Japan (e.g., OSM, Sinsai.info, Safecast). Even though the Ushahidi team helped to translate the Verification Guide and User Manuals documents from English to Japanese as soon as possible (Ushahidi Staff, 2011a), the delay caused by translation might result in late dissemination of urgent information as well as last-minute decisions of emergency responders. Sinsai.info had its translation team with five members (Seki, 2011b), but they were still calling for translator volunteers for mapping the Japanese reports (Ushahidi Staff, 2011a). During the time-critical situation, it is important to avoid spending considerable time and labor handling the language issue. Solution: Yoshida et al. (2011) contended in his conference presentation that it was crucial to mitigate the language barrier and collaborate more with other countries. However, he did not

135 specifically discuss how to carry out strategic plans. From my perspective, the mitigation actions might refer to preparing the important documents, maps, and data translated and stored for future disasters, expanding the translation teams of Sinsai.info and the OSM Japan Foundation, or improving the professional level of English communication for their emergency responders. All these efforts will increase global collaboration, and benefit the timely and efficient response for future emergencies.

Caused by Insufficient Base Data During the Japanese crisis, the OSM mappers found that there was a lack of basic geographic data on OSM software (Hayakawa et al., 2014), which impeded the real-time mapping and caused the delay of data distribution. What’s more, due to the severe impacts caused by the primary and secondary hazards, the scale of the affected area was much more extensive than expected (Hayakawa et al., 2014). It was a big challenge when the volunteers had insufficient data to map such a large-scale disaster. Thankfully, they came up with a solution to use Yahoo! Data as an alternative. Even though the compatibility and accuracy of two different datasets were not discussed, it is vital to address this concern in live mapping. Solution: Hayakawa et al. (2014) contended that enriching the OSM database was essential. Particularly, the OSM staff should work to ensure the database with integrity, accuracy, availability, as well as long-term storage. Therefore, the mappers will be able to focus on live mapping and saving lives instead of spending critical time on the alternative base data.

Caused by Data Distributor During the Typhoon crisis in the Philippines, some remote or poor regions were not provided with the satellite images until a week after the typhoon (Westrope et al., 2014). Such delay of data distribution severely affected the urgent data use and slowed down the disaster relief efforts. Solution: Therefore, the satellite imagery contributor (e.g., Digital Globe) should ensure the prompt data distribution within 24-48 hours after the disaster strikes (Westrope et al., 2014). In addition, the formal humanitarian organizations (e.g., the Red Cross) should work to reinforce the bond with data distributors and ensure data availability in a time-critical situation (Cruz, 2013).

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Lack of Communication and Collaboration between V&TCs, Formal Humanitarian Organizations, and the Government Lack of Communication between V&TCs and Formal Humanitarian Organizations It was argued that there was no formal mechanism that particularly designed for collaboration and coordination between different disaster relief organizations (Gao et al., 2011). In addition, Weinandy contended that “the organizational hierarchy and donation dependency of the latter can conflict with the open-source ideology and opt-in nature of the former” (2016, p. 30). Thus, it might lead to the circumstance that the formal NGOs and V&TCs were unable to collaborate or contribute to a disaster through legitimate guidance. Also, some valuable resources provided by V&TCs were not aware by other relief agencies or the general public (Meier, 2015). This data, which could have been of substantial use, might have been overlooked and wasted. Solution: Digital Humanitarian Network (DHN, n.d. -b) was officially established to mitigate this issue. In addition, the V&TCs need to continue working on the credibility and raising their social awareness more quickly.

Lack of Collaboration between the Government and V&TCs – Uncooperative Concerning the Japanese crisis, Park and Johnson (2019) described the relation between the government, the humanitarian organizations, and V&TCs were uncooperative, which means the two groups of experts were fully aware of the existence of each other; however they chose not to initiate the collaboration. Hayakawa et al. (2014) also indicated that there was no available open data from the Japanese government, which affected the live mapping project. Some state officials might have reacted defensively to the emergence of VGI since this data challenged the dominance of the government (Calvety et al., 2011). Lack of authoritative data could cause difficulties in identifying important landscape objects, including the damaged roads and infrastructure, or determining the closest evacuation routes and shelter locations. Solution: The Japanese Government should agree with a commitment to abundant open data and supporting the OSM real-time mapping (Hayakawa et a., 2014). In addition, creating protocols for connecting the V&TCs and government entities is necessary (The World Bank, 2011). Therefore, collaborative mapping will help save more lives in this time-critical disaster.

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Factors Impacting Data Quality Data Unreliability The data quality was a concern during the Japanese emergency (Gao et al., 2011; Sinsai.info, n.d.). Even if the moderators from the Sinsai.info team worked to examine the reliability of citizens’ submitted reports, there was still no guarantee that the reports were accurate (Sinsai.info, n.d.). Irrelevant, fraud-ridden, and duplicate reports severely impacted the data quality and impeded relief actions (Gao et al., 2011; Miyazaki et al., 2011). Weinandy (2016) also expressed her concern regarding data liability. Solution: The authors in the case studies did not discuss how to deal with this concern. However, Sinsai.info can learn from MicroMappers to handle the duplicate data. In 2013, MicroMappers developed automated an algorithm to filter out the retweets and identify the unique data, which significantly reduced volunteers’ work and saved a considerable amount of time (Meier, 2015).

Insufficient Technical Capacities of Volunteers Example 1: Safecast In the relief of the Tohoku Earthquake, the data validity of Safecast was considered a critical issue, as some volunteers had insufficient technical ability, thus impacting the data quality (Colleti et al., 2017; Brown et al., 2016). In particular, some volunteers might cause errors while assembling the sensor if they were inexperienced with sophisticated electronic devices (Colleti et al., 2017). Even worse, some members did not follow the User Manuals. Solution: The Safecast team adopted three measures on data quality control (Coletti et al., 2017). First, electronic products go through a systematic inspection before being distributed to the team. In particular, some units are required to be chosen randomly, then implemented by calibration tests by a variety of research groups from Germany, the US, and Austria. All the test units are expected to meet the requirements with Safecast performance. Second, all the collected data is required to be examined by experts before being transformed into the database. Third, training and workshops should be encouraged to enhance the professional expertise of the volunteers. Example 2: OSM A majority of the HOT volunteers in the Typhoon Haiyan were mainly college students who did not carry any knowledge in damage assessments or technical capabilities in the

138 humanitarian mapping (Westrope et al., 2014). Particularly, inexperienced volunteers were having difficulties in identifying landscape objects on the low-resolution satellite images, which impeded them from accurate data analysis. Thus, the lack of field experiences might have led to data inaccuracy and extra work on data validation and distribution. Solution: The Red Cross and REACH Initiative offered a series of guidance and training for the volunteers (Westrope et al., 2014). For example, they provided 5-10-page damage assessment guidance materials and a 5-minute YouTube Tutorial. Both ways would help the mappers efficiently understand the tasks, and ensure them to collect the data correctly and consistently. Further, a basic knowledge test is encouraged on the recently joined volunteers. In addition, Neis and Zielstra (2014) suggested a set of free and online quality assessment and assurance tools, such as OSM Bugs, Keep Right, Osmose, or OSM Inspector, which can visualize the detected errors on the map.

Different Criteria of Classification Systems in Damage Assessment Mapping When the Red Cross and HOT were conducting a damage assessment project in the Philippines, the classification systems of the field assessment and OSM had different criteria (Westrope et al., 2014), with field assessment having four categories and OSM having just three. Solution: The assessment team from the Red Cross and REACH Initiative worked to reduce negative impacts by using a scientific model (Westrope et al., 2014). Particularly, they analyzed both classification systems, and integrated two categories from field assessment into one, thus allowing the categories from both systems to be even and comparable.

Lack of Long-Term Participant Motivation Whether the participants are anonymous or identified (Goodchild, 2007), or the participants' motivations are one-way information flow or two-way (See et al., 2016), as Haworth & Bruce (2015) indicated, their motivation of contributing or withholding shaped the dynamics of VGI evolution and impacted the data contents. On the Japanese crisis, Hayakawa et al. (2014) expressed their concerns on the OSM contributors’ long-term motivations for mapping efforts. In addition, Taichi Fushiruma also expressed the same issue and said since May, many volunteers left the team and went back to their daily jobs (Miyazaki et al., 2011). There was indeed no guarantee that the currently-joined volunteers would serve the OSM and Sinsai.info

139 communities in the long run. It was a headache when the volunteers lost their motivation during the unfinished mapping tasks and left OSM as well as Sinsai.info short of hands. Solution: Both Yoshida et al. (2011) and Hayakawa et al. (2014) pointed out that enlarging the community bodies of OSM and Sinsai.info was essential. Moreover, the key to community expansion is enhancing reputation, brand, and trust. With increasing popularity and credibility, more and more volunteers will be attracted and engage in getting more technical experiences at OSM and Sinsai.info. Furushima even came up with a plan to advertise the OSM and Sinsai.info through posters, audio media, and video media (Miyazaki et al., 2011). Therefore, more and more people would be able to be aware of such a convenient open-license community, and social awareness would be greatly enhanced. In this way, both experienced and new volunteers will continue staying in these well-known and reliable V&TCs.

Internet Connection and Technical Concerns Poor or Non-existent Internet Connectivity When operating the OSM mapping project in the Philippines, the poor connection to the internet was an issue (Cruz, 2013). Even though the HOT volunteers were well-prepared and they brought more than 20 printed maps to the field, it was difficult for the Red Cross to deliver the task updates remotely. Solution: The Red Cross worked on this issue by providing the field mappers with digital products, including handheld tablets, dashboards, local map servers that allowed them to have access to the updates and receive timely instructions (Cruz, 2013).

Time Lag of Software Regarding the ‘Haiyan.CrowdMap’ and ‘Philflood. Map,’ the time lag was discussed as an issue (Gekker et al., 2018). It was confusing whether a report date was published when the damages took place, when the embedded picture was taken, or when the Ushahidi staff received the report. The misleading information might cause rescue delay or late release of important notices to the public. Solution: Gekker et al. (2018) stated that all software has this particular issue. There were no solutions discussed in his paper. Moreover, ‘crowdmap’ software needed to take care of the time zone issues.

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More Innovations to Reduce Workload in Need Meier (2015) described the MicroMappers project as “finding the needles in the haystack and doing that in real time” (Collins, 2013). The extremely heavy workload definitely needs more technological innovations to alleviate stress. Solution: To address this issue, the MicroMappers team developed an automated algorithm to filter out the retweets and identify the unique data, which significantly reduced volunteers’ work and saved a significant amount of time (Meier, 2015). However, there are more tools needed to reduce the workload.

Repeatedly Crashed Server There was a constant server issue when operating MicroMappers (Meier, 2015). Although the SBTF team purchased a new and faster server, the problem still existed and intermittently interrupted the data processing. Solution: Purchasing a more powerful server to handle the data traffic is the solution (Meier, 2015); otherwise, it will delay the data distribution, fail the trust of members and potential clients, as well as impact its reputation and brand. In addition, insights on mitigating the extra workload are encouraged.

Privacy, Safety and Mental Health of Volunteers Privacy and Safety of Volunteers Due to the fact that the geo-tagged information (e.g., tweets) was available in public once published, the conflicts regarding the data privacy might occur. Some nefarious citizens could target disaster relief volunteers and disrupt their personal lives. Solution: It is important to take measures and resolve this issue — both the data privacy of citizens and the safety of volunteers’ matter. Thus, certain regulations regarding data privacy should be implemented; the security and privacy of contributors should be protected (Gao et al., 2011; Weinandy, 2016).

Mental Health of Volunteers Volunteers can learn new knowledge, gain technical experiences, and build professional networks while contributing to the V&TCs. However, volunteers can also be traumatized from

141 working with destructed populations, which can particularly cause a sense of isolation if there is no timely peer support or effective counseling service nearby (Meier, 2015; Weinandy, 2016). Solution: Thus, it is important to explain that the negative sentiments caused by their volunteer tasks are natural, and it is crucial to seek professional assistance timely (Meier, 2015; Weinandy, 2016). Meier (2015) shared his experience after the Haiti Earthquake, and he invited an experienced trauma counselor to provide counseling services to any volunteer in need. Importantly, the counselor taught the volunteers how to recognize the signs of secondary trauma, and she provided them with useful self-help guides to help them work through painful emotions (Meier, 2015).

6.2 Challenges of This Research

I have generally encountered three types of challenges in this research, including the language barrier, unavailable data sources, and closed data sources. 6.2.1 Language Barrier When searching the research papers and documents for the Tohoku Earthquake and Tsunami, I found that the language barrier became an issue. Most literature was in Japanese, and only a few papers were in English. As a non-Japanese speaker, it was difficult to examine the literature and resume the research. Even though I used translator tools to mitigate the barrier and grasp the general idea, the research would definitely need more accuracy in interpreting the content. Thus, I relied on many English language conference presentations slides, blog posts as an alternative. The loss of comprehending the original data sources might result in less sufficient data analysis. Also, when I was studying the Typhoon Haiyan case, I found that a GIS Research Group from the University of Heidelberg conducted an important mapping project. However, the limited documentation was all in German, I used Google Translate again to understand the general idea but unable to analyze it in this thesis.

6.2.2 Unavailable Data Sources The second obstacle is the unavailable data sources. When I was examining the ‘Sinsai.info,’ ‘ALL311’, and other sources, I found them already unavailable to be used. One reason included insufficient funding for continuous website operation. Thus, I used the Internet

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Archive Wayback Machine to retrieve the sources and examine the snaps of websites. However, the Archive did not allow me to click the specific features and explore the details. I had to conduct the analysis based on what I have observed and synthesize the findings with other authors’ opinions. My research was able to present a comprehensive selection of findings but probably was not able to provide more details.

6.2.3 Closed Data Sources The third challenge is the closed data source. One example is the “Japan Incident Map” produced by the ESRI. This map was important for the research; however, the original website was permanently deleted and not even retrievable in the Web Archive. Thus, I was unable to use or analyze this map. Another example is ‘Philflood. Map’ from the Typhoon case. Gekker et al. (2018) argued that some maps were created for a short period of use only, so the reports and other information from this map were not available anymore. This challenge had me continue analysis only using the available data.

6.3 Contributions to Geography

This review analyzed and summarized the existing findings of literature within and outside academia for both practice and scholarship. Practice can benefit from a better understanding of how VGI and crowdsourcing can be used for enhancing the coordination of disaster management, also from better solutions that develop more advanced V&TCs, disaster mapping tools or devices, in order to address the discussed issues. Scholarship can benefit from this comprehensive set of data collection as a useful reference for their research. Notably, the most appropriate and suitable collection of research papers (see Appendix 1 and 2) and documents (see Appendix 3 and 4) were demonstrated in this thesis, which can be of use for researchers who work on the long-term disaster recovery projects on Japan and the Philippines. In addition, I found the language barrier impeded some researchers, so they did not conduct much analysis regarding the Tohoku Earthquake and Tsunami. Therefore, my research findings can serve as valuable data sources for non-Japanese researchers, which will save them time from digging around on the web.

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6.4 Recommendations for Future Research

For the future, I would recommend researchers to consider three project directions. First, researchers can explore the evolution of the discussed V&TCs and examine any interesting innovations from Typhoon Haiyan to the current disaster events. Second, scientists can analyze the new V&TC that has emerged since 2013, particularly researching how they have developed new platforms or serves to respond better when a disaster strike. Third, for myself, I would like to carry out the project in two sub-directions, involving (1) explore more details in chapter 5, and employ new methods such as surveys or interviews with OSM, Ushahidi, Safecast, SBTF, ESRI, Google Crisis Response Team, even OCHA as well as the American Red Cross, to learn about the unpublished work; (2) compare the efficiency of emergency response between the recent and the past disasters, researching the technological development of V&TCs (e.g., OSM) by adopting participatory observation and interview methodologies.

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Appendix 1: Selected Research Papers on the 2011 Tohoku Earthquake and Tsunami

Author Title Discussed Database Journal/ V&TCs Conference

Brown et al., Safecast: Successful citizen-science Safecast Web of Science Journal of 2016 for radiation measurement and Radiological communication after Fukushima Protection

Colleti et al., Validating Safecast by comparison Safecast Engineering Village Journal of 2017 to a U.S. Department of Energy Environmental Fukushima Prefecture aerial survey Radioactivity

Gao et al., Harnessing the crowdsourcing Sinsai.info Web of Science Journal of IEEE 2011 power of social media in disaster Computer Society relief

Hayakawa et Towards a sustainable volunteer OSM Google Scholar The 14th PICMET al., 2014 community: an analysis of Conference: OpenStreetMap in Japan and its infrastructure activity after the 2011 Tohoku Service and Earthquake Integration

Meier, 2012 Crisis mapping in action: How open- Sinsai.info Engineering Village Journal of Map and sourced software and global SBTF Geography volunteers are changing the world, Libraries one map at a time

Mizushima Knowledge Management in a OSM Engineering Village The 12th PICMET: et al., 2012 volunteer community at the time of Sinsai.info Technology a disaster Management for Emergency Technologies

Park & Intentionally building relationship Sinsai.info Google Scholar Journal of Disasters Johnson, between participatory online groups Safecast 2019 and formal organizations for effective response

Sekimoto, Relationship between people and Sinsai.info Google Scholar Journal of Japan 2013 infrastructure with information Society of Civil technology as mediating channel Engineers (JSCE)

Shibuya, Impact of ICT tools on disaster Sinsai.info EBSCO International 2017 logics issues: a case study of the Journal of Business Great East Japan Earthquake and and Information Tsunami of 2011

Yamamoto, Volunteer activities in time of the Sinsai.info Google Scholar Journal of Earth 2013 disaster in Japan’s highly ALL311 Science and information-oriented society ESRI Engineering

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Appendix 2: Selected Research Papers on the 2013 Typhoon Haiyan

Author Title Discussed Database Journal/ V&TCs Conference

Chan & Innovative Research Design – A OSM Engineering Village Procedia Comes, journey into the information typhoon SBTF Engineering 78 2014 (2014) 52-58

Dittus et Mass participation during emergency OSM Engineering Village the 2017 ACM al., 2017 response: Event-centric crowdsourcing Conference on in humanitarian mapping Computer Supported Cooperative Work and Social Computing

Kuo et al., Towards using volunteered geographic OSM Engineering Village the 14th 2017 information to monitor post-disaster international recovery in tourist destinations conference on information systems for crisis response and management (ISCRAM)

Liu, 2014 Crisis crowdsourcing framework: OSM Engineering Village Journal of Designing strategic configurations of Computer crowdsourcing for the emergency Supported management domain Cooperative Work (CSCW)

Mejri et Crisis information to support spatial OSM Web of Science International al., 2017 planning in post-disaster recovery Ushahidi Journal of Disaster Risk Reduction

Muto & Strengthening Community Resilience OSM Google Scholar Council of Kohtake, by Remote and Citizen Sensing: Google Engineering 2017 Designing the interactive and Crisis Systems integrated disaster risk information Response Universities Global system by macro and microdata. Conference (CESUN 2018)

Palen et Success & scale in a data-producing OSM Web of Science the 33rd annual al., 2015 organization: The socio-technical ACM conference evolution of OpenStreetMap in on human factors in response to humanitarian events computing systems

Roberts & Understanding crowdsourcing and OSM Web of Science Journal of Flood Doyle, volunteer engagement: case studies for Damage Survey 2017 hurricanes, data processing, and floods and Assessment

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Vieweg et Integrating social media Micro Engineering Village International al., 2014 communications into the rapid Mappers Conference on assessment of sudden onset disasters Social Informatics

Westrope Groundtruthing OpenStreetMap OSM Engineering Village Procedia et al., Building Damage Assessment Engineering 2014

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Appendix 3: Selected Documents on the 2011 Tohoku Earthquake and Tsunami

Author Title Discussed Document Source Document V&TCs Type

Abe, 2013 Why Safecast matters: A case study Safecast Science and Newsletter in collective risk assessment Technology in Society (STS) Fortum on the East Japan Disaster

Appleby, Connecting the last mile: the role of OSM Europe Report 2012 communications in the Great East Sinsai.info Interviews Japan Earthquake ALL311 Safecast

Cavelty et Crisis Mapping: A Phenomenon and Sinsai.info Center for Security Report al., 2011 Tool in Emergencies OSM Studies in Germany

Dempsey, Sendai (Japan) Earthquake and OSM GIS Lounge Knowledge 2011 Tsunami Mapping Response ESRI Portal Safecast

Ellamsd, Humanitarian Mapping: Tracing OSM OSM Wiki Tutorial 2011 Roads in Japan using JOSM Video

Furuhashi, Sinsai.info Sinsai.info Sinsai.info.blogpost Conference 2011 Slides

Hong, 2014 Utilization of crowdsourced maps in Sinsai.info / Master’s catastrophic disasters Thesis

Kerski, Understanding Japan's Earthquakes ESRI Social ESRI Blog 2011 from a Geospatial Perspective Media Map

Makinen, Citizen Science Takes on Japan’s Safecast The LATimes Newsletter 2016 Nuclear Establishment

McDougall, An Assessment of the Contribution of Sinsai.info / Book 2012 Volunteered Geographic Information Safecast Chapter During Recent Natural Hazards

Meier, Humanitarian Technology and the Sinsai.info iRevolutions Blog 2011 Japan Earthquake (Updated) OSM

Meier, Live Crisis Mapping: Update on Sinsai.info iRevolutions Blog 2011 Libya and Japan OSM

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Miyazaki et Great contributions to the aid against Sinsai.info ISPRS Student Newsletter al., 2011 East Japan Great Earthquake: OSM Consortium Interview with Taichi Furuhashi

--, n.d. 2011 Sendai Earthquake and OSM OSM Wiki Web Page Tsunami

--, n.d. 2011 Sendai Earthquake and OSM OSM Wiki Web Page Tsunami. Mapping coordination and data sources

Ota, 2012 Standards Helped Response to the Sinsai.info OpenGeospatial.org Blog Great East Japan Earthquake ALL311

Seki, 2011 Sinsai.info – How Open Sinsai.info Sinsai.info.blog Conference Collaboration Helps Disaster-affected OSM Slides People

Seto & Crisis Mapping Project and Counter- OSM / Book Nishimura, Mapping by Neo-Geographers Sinsai.info Chapter 2016

Shahid, The Impact of Crowdmapping on OSM / Dissertation 2016 Humanitarian Response: A Sinsai.info Structurational Analysis SBTF

--, 2011. Risk and Damage Information OSM The World Bank Report Management Sinsai.info ALL311

Traganou, Citizens Radiation Mapping after the Safecast / Book 2016 Tsunami Chapter

--, 2011 Earthquake in Japan Sinsai.info UN-SPDIER Data Portal

--, 2011 Crisis Mapping Japan Earthquake Sinsai.info Ushahidi Blog and How You Can Help

--, 2011 Crisis Mapping Japan Sinsai.info Ushahidi Blog

Yoshida et Response of OSGeo Japan with Other OSM / Conference al., 2011 Communities to the Great East Japan Sinsai.info Slides Earthquake Sahana ALL311

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Appendix 4: Selected Documents on the 2013 Typhoon Haiyan

Author Title Discussed Document Document V&TCs Source Type

--, n.d. OpenStreetMap Damage Assessment OSM American Red Report Review Cross

--, 2014 Q&A: Mapping the Effects of OSM American Red Newsletter Typhoon Haiyan Cross

Capineri et European Handbook of Crowdsourced OSM / Book Chapter al., 2016 Geographic Information

Chapman, Remote HOT Activation in the OSM HOT Newsletter 2011 Philippines for the Typhoon Haiyan (Yolanda)

Collins, How AI, Twitter, and Digital Micro Wired Newsletter 2013 Volunteers are Transforming Mappers Humanitarian Disaster Response

Crisis ICCM 2013: Jus MacKinnon: Media Micro Crisis Conference Mappers Monitoring in support of UN-OCHA’s Mappers Mappers Video Net, 2014 response to Typhoon Yolanda

Crisis ICCM 2014: Dale Kunce, OSM Crisis Conference Mappers Groundtruthing OSM During Disaster Mappers Video Net, 2015 Response

Cruz, 2013 Digitizing Disaster: Red Cross Taps OSM CISCO Newsletter Online Map Makers to Help the Newsroom Philippines

Gekker et Time for Mapping: Cartographic Ushahidi / Book Chapter al., 2018 Temporalities Google Crisis Response OSM

Gordon, How Google is Transforming Disaster Google Crisis Aljazeera Blog 2013 Relief Response America

Hern, 2013 Online Volunteers Map Philippines OSM The Guardian Newsletter after Typhoon Haiyan

Lacucci, Typhoon Yolanda Update #1 SBTF Standby Task Blog 2013 Force

Lungati, Weekly: Typhoon Yolanda Relief: Ushahidi Ushahidi Blog 2013 Hackathons and Software Releases

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Meier, Digital Humanitarian: From Haiti Micro iRevolutions Blog 2013 Earthquake to Typhoon Yolanda Mappers

Meier, Early Results of Micro Mapper Micro iRevolutions Blog 2013 Response to Typhoon Yolanda Mappers

Meier, Typhoon Yolanda: UN Needs Your Micro iRevolutions Blog 2013 Help Tagging Crisis Tweets for Mappers Disaster Response (Updated)

Meier, Live Crisis Map of Disaster Damage Micro iRevolutions Blog 2013 Reported on Social Media Mappers

Meier, Digital Humanitarian Micro / Book Chapter 2015 Mappers

Meyer, How Online Mapmakers Are Helping OSM The Atlantic Newsletter 2013 the Red Cross Save Lives in the Philippines

Muhlbauer, Typhoon Haiyan: How Crisis OSM Geo Blog 2013 Mapping Works! Ushahidi awesomeness

--, n.d. Typhoon Haiyan OSM OSM Wiki Web Page

Pitts, 2013 Typhoon Haiyan (Yolanda): Mapping Ushahidi GIS Lounge Blog Response from the Tech Community

Shahid, The Impact of Crowdmapping on Micro / Dissertation 2016 Humanitarian Response: A Mappers Structurational Analysis OSM

--, 2014 Republic of the Philippines Support to OSM The World Report the Post-Yolanda (Haiyan) for Bank Reconstruction and Recovery Planning

--, 2013 201311 Typhoon Haiyan/Super OSM UN-SPIDER Knowledge Typhoon Yolanda Data Sources SBTF Portal ESRI

Weinandy, Volunteer and Technical OSM UN Chronicle Newsletter 2016 Communities in Humanitarian Micro Response Mappers

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