Foreword |
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xv | |
Acknowledgments |
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xvii | |
Editors |
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xix | |
List of Contributors |
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xxi | |
I Setting the Scene |
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1 | (10) |
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1 The Role and Value of Geospatial Information and Technology in a Pandemic |
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3 | (8) |
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3 | (1) |
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1.2 Critical Role of Location Information |
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4 | (1) |
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1.3 Impact of COVID-19 on the Sustainable Development Goals (SDGs) |
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4 | (1) |
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1.4 Digital Innovation During a Pandemic |
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5 | (1) |
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1.5 Collaboration and Engagement |
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6 | (1) |
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1.6 Opportunities Emerging from the Pandemic |
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6 | (1) |
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1.7 Moving Forward from the Pandemic |
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7 | (1) |
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1.8 This Book, Objectives, Chapter Outline |
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7 | (4) |
II Technical and Techno-Social Solutions |
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11 | (216) |
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2 Land Administration and Authoritative Geospatial Information: Lessons from Disasters to Support Building Resilience to Pandemics |
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13 | (18) |
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13 | (1) |
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2.2 Emergencies - Disasters and Pandemics |
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14 | (1) |
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2.3 Economic and Financial Impacts of Disasters and Pandemics |
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14 | (2) |
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2.4 Overview of WB-FAO Partnership |
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16 | (1) |
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2.5 Resilience Enablement Through LAS and NSDI |
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17 | (3) |
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2.6 COVID-19: Specific Challenges |
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20 | (3) |
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2.7 Pragmatic Rapid Assessment of LAS and NSDI Maturity in Resilience Contexts |
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23 | (4) |
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27 | (1) |
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28 | (3) |
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3 Open Geospatial Data for Responding to the COVID-19 Challenge |
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31 | (24) |
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31 | (2) |
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3.2 What Data Is Useful for Responding to the COVID-19 Challenge? |
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33 | (2) |
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3.3 What is the Availability of such Open Data With Global Coverage? |
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35 | (12) |
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3.4 Discussion and Conclusion |
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47 | (8) |
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4 Remote Sensing and Computational Epidemiology |
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55 | (14) |
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55 | (1) |
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4.2 Remote Sensing and Health |
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56 | (2) |
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4.3 Remote Sensing Methods to Predict Health-related Outbreaks |
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58 | (3) |
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4.4 Vegetated Area Mapping |
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61 | (1) |
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61 | (1) |
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4.6 Land Surface Temperature |
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62 | (1) |
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62 | (1) |
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63 | (1) |
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63 | (1) |
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64 | (1) |
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65 | (1) |
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65 | (4) |
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5 The Potential of Drone Technology in Pandemics |
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69 | (10) |
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69 | (1) |
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5.2 Developments in Drone Technology |
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70 | (1) |
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5.3 The Impact of COVID-19 |
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71 | (4) |
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5.4 Summary and Conclusions |
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75 | (4) |
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6 The Role of Neighbourhood Social and Built Environments on Social Interactions and Community Wellbeing Through the COVID-19 Pandemic |
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79 | (8) |
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79 | (1) |
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80 | (1) |
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81 | (2) |
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83 | (1) |
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84 | (3) |
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7 Social Vulnerability to COVID-19: Preliminary Indicators and Research Agenda |
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87 | (14) |
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87 | (1) |
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7.2 Social Vulnerability and Pandemics |
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88 | (1) |
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7.3 Social Vulnerability Indicators |
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89 | (5) |
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7.4 Discussion and Conclusion Remarks |
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94 | (7) |
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8 Informal Road Detection and Uncertainty in Remote Sensing |
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101 | (22) |
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101 | (2) |
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103 | (1) |
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8.3 Uncertainty Measures in Remote Sensing |
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104 | (2) |
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8.4 Road Extraction Algorithm |
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106 | (1) |
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107 | (2) |
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109 | (8) |
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117 | (1) |
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8.8 Conclusion and Future Work |
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118 | (5) |
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9 Management and Analysis of Maritime Geospatial Data During COVID-19: Case Studies, Opportunities and Challenges |
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123 | (14) |
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123 | (7) |
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130 | (4) |
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134 | (3) |
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10 City Design and the Transmission of COVID-19 |
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137 | (8) |
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10.1 The Pandemic that is COVID-19 |
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137 | (2) |
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10.2 Using Spatial Data to Identify Global City Design |
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139 | (3) |
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10.3 Relationship Between City Design and COVID-19 |
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142 | (1) |
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143 | (2) |
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11 Sensing Community Resilience Using Social Media |
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145 | (16) |
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145 | (1) |
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146 | (2) |
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148 | (1) |
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149 | (8) |
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157 | (4) |
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12 Role of the Professional Body in a Pandemic |
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161 | (10) |
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Zaffar Sadiq Mohamed-Ghouse |
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161 | (1) |
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12.2 Serving Surveying and Spatial Science Professionals |
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162 | (1) |
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12.3 COVID-19 Member Survey |
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163 | (5) |
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12.4 Moving Back to Normality |
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168 | (1) |
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168 | (3) |
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13 OpenStreetMap Data Use Cases During the Early Months of the COVID-19 Pandemic |
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171 | (16) |
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171 | (2) |
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13.2 Background and Related Work |
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173 | (1) |
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13.3 Methodology and Research Approach |
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173 | (2) |
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13.4 Use of OSM Data for COVID-19 |
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175 | (3) |
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13.5 Collection of OSM Data for COVID-19 |
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178 | (1) |
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13.6 Academic Research with OSM During the COVID-19 Response |
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179 | (1) |
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13.7 Conclusions and Future Work |
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180 | (7) |
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14 Utilization of Geospatial Network Analysis Technique for Optimal Route Planning During COVID-19 Pandemic |
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187 | (8) |
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187 | (1) |
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188 | (1) |
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14.3 Methodology and Materials |
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189 | (1) |
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14.4 Results and Discussion |
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190 | (3) |
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193 | (2) |
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15 Formalizing Informal Settlements to Empower Residents Against COVID-19 and Other Disasters |
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195 | (8) |
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195 | (1) |
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15.2 The Need for Geospatial Data and Tools to Improve Decision-making |
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196 | (2) |
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15.3 Measures Taken by Governments to Manage the Pandemic |
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198 | (1) |
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15.4 How to Formalize Informal Construction to Empower Residents |
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199 | (4) |
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16 Spatially Enabled COVID-19: A Review of Applications and Systems |
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203 | (8) |
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203 | (1) |
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204 | (1) |
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205 | (4) |
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209 | (2) |
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17 COVID-19 Spatiotemporal Hotspots and Prediction Based on Wavelet and Neural Network |
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211 | (16) |
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211 | (1) |
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17.2 Materials and Methods |
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212 | (3) |
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17.3 Results of Proposed Model |
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215 | (7) |
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222 | (2) |
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224 | (3) |
III Regional, Country and Local Applications |
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227 | (184) |
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18 London in Lockdown: Mobility in the Pandemic City |
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229 | (16) |
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18.1 The 2020 Pandemic in Britain |
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229 | (2) |
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18.2 Defining Essential Workers |
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231 | (5) |
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18.3 The Movement Patterns of Essential and Non-Essential Workers |
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236 | (4) |
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18.4 Drilling Down Into Individual Locations in London |
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240 | (3) |
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18.5 Conclusions and Next Steps: A More Integrated Analysis |
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243 | (2) |
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19 Americas' Geospatial Response to COVID-19 |
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245 | (10) |
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245 | (2) |
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19.2 Overview On the Regional Geospatial Response to COVID-19 |
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247 | (4) |
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251 | (1) |
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252 | (3) |
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20 Spatio-Temporal Information Management to Control the COVID-19 Epidemic: Country Perspectives in Europe |
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255 | (12) |
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255 | (1) |
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20.2 Spatiotemporal Spread of Infectious Diseases |
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256 | (1) |
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20.3 NUTS The European Union's Spatial Reference for Statistical Data |
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257 | (1) |
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20.4 COVID-19 Pandemic Data Using the NUTS System |
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257 | (4) |
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20.5 Shortcuts and Challenges of COVID-19 Data Provision |
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261 | (1) |
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262 | (2) |
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264 | (3) |
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21 Practicing Online Higher Education Facilitated by ICT in China: In the Context of COVID-19 Pandemic |
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267 | (10) |
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267 | (1) |
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268 | (3) |
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21.3 Practice of Online Higher Education in China |
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271 | (3) |
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274 | (3) |
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22 Time-Series Analysis of COVID-19 in Iran: A Remote Sensing Perspective |
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277 | (14) |
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277 | (1) |
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22.2 Materials and Methods |
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278 | (5) |
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22.3 Results and Discussion |
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283 | (4) |
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287 | (4) |
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23 Creating a Set of High-Resolution Vulnerability Indicators to Support the Disaster Management Response to the COVID-19 Pandemic in South Africa |
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291 | (14) |
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291 | (1) |
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23.2 Government Structures in South Africa |
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292 | (1) |
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293 | (1) |
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23.4 SARS-CoV-2 and COVID-19 in South Africa |
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293 | (1) |
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23.5 The COVID-19 Vulnerability Dashboard |
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294 | (6) |
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300 | (1) |
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23.7 Conclusions and the Way Forward |
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301 | (4) |
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24 Rapid Development of Location-based Apps: Saving Lives during a Pandemic - the South Korean Experience |
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305 | (16) |
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305 | (2) |
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307 | (6) |
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24.3 Real-time Data Processing Systems |
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313 | (3) |
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24.4 COVID-19 Response Success Factors |
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316 | (1) |
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317 | (4) |
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25 Spatial Analysis of Urban Parks and COVID-19: City of Whittlesea, Victoria, Australia |
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321 | (14) |
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321 | (1) |
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322 | (1) |
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323 | (1) |
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323 | (8) |
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25.5 Results Discussion and Limitations |
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331 | (1) |
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331 | (4) |
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26 The Economic Impact of COVID-19 in Pacific Island Countries and Territories |
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335 | (12) |
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335 | (1) |
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26.2 Socio-economic Context |
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336 | (1) |
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26.3 Coming of COVID-19 and How It Is Reported in the Pacific Region |
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336 | (1) |
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26.4 Mapping COVID-19 in the Pacific |
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337 | (3) |
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26.5 What Is Being done to Monitor the Impact of COVID-19 via Economic Statistics? |
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340 | (3) |
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26.6 What We Can Learn from COVID-19 for Future Pandemics or Other Disasters? |
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343 | (1) |
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26.7 Building Preparedness Through Better Data |
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344 | (3) |
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27 Promoting Resilience While Mitigating Disease Transmission: An Australian COVID-19 Study |
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347 | (16) |
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Katitza Marinkovic Chavez |
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347 | (1) |
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27.2 Early Phase of the Australian Epidemic and the Public Health Response |
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348 | (1) |
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27.3 Understanding the Response of Australians to COVID-19 |
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349 | (1) |
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27.4 Overview of Data Collection and Analysis |
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349 | (1) |
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27.5 Geographic Variation in COVID-19 Epidemiology and Public Health Response |
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350 | (1) |
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351 | (6) |
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27.7 Discussion and conclusions |
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357 | (2) |
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27.8 COVID-19 Developments and Further Research |
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359 | (4) |
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28 Impacts of COVID-19 Lockdown Restrictions on Housing and Public Space Use and Adaptation: Urban Proximity, Public Health, and Vulnerability in Three Latin American Cities |
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363 | (22) |
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363 | (2) |
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28.2 Case Studies Context Summary |
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365 | (2) |
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28.3 Research Methodology |
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367 | (2) |
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369 | (6) |
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375 | (4) |
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28.6 Conclusions and Future Work |
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379 | (6) |
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29 Use of Geospatial Information and Technologies in Understanding the COVID-19 Pandemic in Canada: Examples and Critical Discussion |
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385 | (8) |
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385 | (1) |
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386 | (2) |
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29.3 Institutional and Technical Responses |
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388 | (2) |
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390 | (1) |
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390 | (3) |
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30 Geospatial Intelligence in Dealing with COVID-19 Challenges in Czechia |
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393 | (6) |
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393 | (1) |
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30.2 Visual Analytics of COVID-19-related Health Statistics in Czechia |
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394 | (1) |
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30.3 Tracking and Analysis of People's Movement |
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395 | (1) |
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30.4 Decision Support Systems for Public Administration |
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395 | (2) |
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30.5 Conclusions and Discussion |
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397 | (2) |
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31 COVID-19 in France: A Multiphase and Multidimensional Approach to a Complex Societal Imbalance |
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399 | (12) |
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399 | (1) |
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399 | (4) |
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31.3 Multidimensional Analysis |
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403 | (6) |
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409 | (2) |
IV Stakeholder Perspectives |
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411 | (94) |
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32 Digital Earth: A World Infrastructure for Sustaining Resilience in Complex Pandemic Scenarios |
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413 | (4) |
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32.1 Spatial Information During a Pandemic |
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413 | (1) |
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32.2 A New Paradigm of Thinking |
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414 | (1) |
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415 | (1) |
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416 | (1) |
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33 COVID-19: The Open Data Pandemic |
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417 | (2) |
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33.1 Unlocking the Value of Data |
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417 | (1) |
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33.2 From Data Sharing to Open Science |
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417 | (1) |
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418 | (1) |
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34 The Challenge of Mapping COVID-19 Data |
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419 | (4) |
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34.1 The Mapping Challenge |
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419 | (1) |
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419 | (1) |
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420 | (1) |
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34.4 From Data to Insights...to Actions |
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421 | (2) |
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35 Better Engagement to Build Smarter, Resilient Communities |
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423 | (4) |
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423 | (1) |
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35.2 Learning from Experience |
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424 | (1) |
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35.3 Extending Anonymisation to "Big" Geospatial Data |
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424 | (1) |
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35.4 Building Trust for Future Resilience |
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425 | (2) |
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36 How the Coronavirus Could Change Urban Planning |
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427 | (8) |
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427 | (1) |
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36.2 Present: Urban Development in Corona Times |
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428 | (1) |
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36.3 Future: The Smart, Participatory and Resilient City |
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428 | (3) |
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36.4 Rethinking urban planning |
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431 | (2) |
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433 | (2) |
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37 Toward Agile Strategies for Enhancing Community Resilience Following the COVID-19 Pandemic: An Interview Study |
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435 | (4) |
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435 | (1) |
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436 | (1) |
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436 | (1) |
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37.4 Discussion and Conclusion |
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437 | (2) |
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38 COVID-19 Pandemic in Finland: Converting a Forced Digitalisation into an Opportunity |
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439 | (4) |
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38.1 Many Dimensions of Resilience |
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439 | (1) |
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38.2 The Importance of Open Geographic Data and Social Inclusion |
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440 | (1) |
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38.3 Lessons Learnt from Finland |
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441 | (2) |
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39 What's the Future of Greek Cities in the Post-COVID-19 Period? New Perspectives on Urban Resilience and Sustainable Mobility |
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443 | (12) |
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39.1 Introduction: A Brief Review of the Pandemic |
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443 | (1) |
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39.2 Initial Ideas About an "Anti-social" Planning Policy |
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444 | (2) |
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446 | (1) |
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39.4 What's Happening in Greece? The Case Study of Athens |
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447 | (3) |
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450 | (1) |
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450 | (5) |
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40 COVID-19 Pandemic Challenges and Impacts on the SDGs 2030: Indian Perspective |
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455 | (14) |
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455 | (1) |
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40.2 COVID-19 Impact on SDGs |
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456 | (7) |
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40.3 Analysis and Interpretation |
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463 | (3) |
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40.4 Summary and Conclusion |
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466 | (3) |
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41 The Value of a Policy-Responsive Research Funding Model: The Geohealth Laboratory Collaboration in New Zealand |
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469 | (6) |
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41.1 What Is the GeoHealth Laboratory? |
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469 | (1) |
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470 | (1) |
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470 | (2) |
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472 | (3) |
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42 Pandemic and the City: A Melbourne Perspective for Community Resilience |
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475 | (6) |
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475 | (1) |
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42.2 Growth of Inner-City Melbourne |
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476 | (1) |
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476 | (1) |
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42.4 Melbourne's Response to COVID-19 |
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476 | (1) |
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42.5 Impacts of COVID-19 on Central Melbourne's Liveability |
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477 | (1) |
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42.6 Planning to Co-Exist With COVID-19 |
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477 | (4) |
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43 Spatial Modelling Concepts for Controlling COVID-19 Risk in Saudi Arabia |
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481 | (6) |
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481 | (1) |
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43.2 GIS-based Mapping and Modelling |
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482 | (1) |
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43.3 The Current Spatial Distribution of COVID-19 in Saudi Arabia (SA) |
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482 | (3) |
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485 | (2) |
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44 COVID-19 in Spain and the Use of Geospatial Information |
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487 | (4) |
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44.1 COVID-19 and the State of Emergency in Spain |
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487 | (1) |
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44.2 Geospatial Information Use |
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488 | (2) |
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490 | (1) |
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45 Lessons Learned from COVIDSafe: Understanding Conditions for Successful Implementation of Track and Trace Technologies |
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491 | (4) |
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491 | (1) |
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45.2 Do track and Trace Mechanisms Work? |
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491 | (1) |
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45.3 The Failures of COVIDSafe: Technology or User? |
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492 | (1) |
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45.4 Enhancing Implementation Through Education |
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493 | (1) |
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45.5 Lessons from Australia: Enhancing Contact Tracing |
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493 | (2) |
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46 Sustainable Transport as a Key Pillar to Community Resilience During the COVID-19 Pandemic |
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495 | (10) |
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495 | (1) |
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46.2 Sustainable Transport and the Call for a Green Recovery |
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496 | (1) |
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46.3 Providing Safe Mobility to Those Who Need It |
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497 | (3) |
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500 | (5) |
V The Future Direction |
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505 | (10) |
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47 Preparing for the Next Pandemic: Geospatial Information for Enhanced Community Resilience |
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507 | (8) |
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507 | (1) |
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47.2 Key Lessons from COVID-19 |
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508 | (2) |
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47.3 The Road Ahead: The Only Certainty in the Future is Change |
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510 | (1) |
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47.4 Strategies to Face the Next Crisis and Build Community Resilience |
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511 | (4) |
Index |
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515 | |