Spatial Methodologies for the Analysis of Vulnerability in Urban Areas ー A Case Study for Terrorism in Tokyo, Japan

The presentation I gave at the IGU2013 Conference in Kyoto on August 8, 2013. It contains an introduction into my ongoing research about the use of spatial methodologies in the analysis of vulnerabilities in highly urbanized areas. It also shows the applications and usefulness, as well as some preliminary results for a case study about terrorism vulnerability in Tokyo, Japan.

Transcript

1. Spatial Methodologies for the Analysis of Vulnerability in Urban Areas ʔ A Case Study for Terrorism in Tokyo, Japan Konstantin GREGER Division of Spatial Information Science University of Tsukuba greger@geoenv.tsukuba.ac.jp @kogreger Regional Conference of the IGU, Kyoto August 8, 2013 (Session CS18-12)
2. Hypotheses 2 ᶃɹVulnerability is not distributed equally over space and time. ɹɹplaces with high vulnerability ← → places with low vulnerability ᶄɹFactors exist that enhance or mitigate vulnerability. ɹɹattributes of objects at risk ᶅɹVulnerabilities of objects influence their spatial surroundings. ɹɹspatial influence of objects
3. Research Objectives 3 Establish a methodology to quantify how prone a location is to a certain kind of terrorist attack (“scenario”), as a result of the attributes that define it. The unit of analysis should be the geographical space (“place”), not the specific outcome of a singular event (“attack”). Gain analytic insight in the definition of attributes and factors affecting terrorism vulnerability in urban areas. Create micro-scale vulnerability maps to visualize the spatial distribution of single vulnerability factors and overall vulnerability. Target audiences are the general public, involved stakeholders, and academia.
4. Research Framework
5. Spatio-Temporal Vulnerability Analysis Framework 5 factor 1 factor 2 factor n ... vulnerability spatial influence (SI) factor 1 maps factor 2 maps factor n map ... raster algebra (RTM) vulnerability maps building data address data census data movement data ... factor weights selection
6. Spatial Influence ᶃɹspatial concentration ɹɹidentification of agglomerations (hotspots) of objects with equal attributes ɹɹ→ kernel density estimation (KDE) ᶄɹspatial proximity ɹɹeach object affects the space surrounding itself by its attributes ɹɹ→ buffering (euclidian distance) dimension of proximity to be defined for each factor case study: 250m KDE bandwidth / 100m buffer (CAPLAN & KENNEDY 2010a) 6
7. Spatio-Temporal Vulnerability Analysis Framework 7 factor 1 factor 2 factor n ... vulnerability spatial influence (SI) factor 1 maps factor 2 maps factor n map ... raster algebra (RTM) vulnerability maps building data address data census data movement data ... factor weights selection
8. Case Study
9. Terrorism Vulnerability Factors 9 terrorism vulnerability factors goals ᶃɹbuilding population (stationary; [transient]) affect maximum number of people ɹɹestimated number of people within each bldg. affect maximum number of people ᶄɹmobile population affect maximum number of people ɹɹestimated number of pedestrians affect maximum number of people ɹɹ[estimated number of train passengers] affect maximum number of people ᶅɹsymbolic value gain maximum attention ɹɹbinary variable (yes/no) [→ weighted gradient] gain maximum attention ɹɹsubjective/qualitative selection & estimation gain maximum attention
10. Study Area Central Tokyo: Chiyoda-ku, Chuo-ku, Minato-ku ~ 43 km2 area ~ 92,000 buildings diverse land uses, building types and building density several iconic buildings many critical infrastructures
11. ᶃ Building Population Estimation Results
12. Building Population Estimation volumetric estimation approach (from LWIN & MURAYAMA 2009) enhanced by activity dimension ᶃɹhome ᶄɹbusiness & office ᶅɹeducation ᶆɹretail & service ᶇɹleisure ᶈɹpublic institution enhanced by temporal dimension 12
13. Spatio-Temporal Population Profile maxhome = 506 maxwork = 343 13
14. 00:00 Building Population Time Series 14
15. 01:00 Building Population Time Series 15
16. 02:00 Building Population Time Series 16
17. 03:00 Building Population Time Series 17
18. 04:00 Building Population Time Series 18
19. 05:00 Building Population Time Series 19
20. 06:00 Building Population Time Series 20
21. 07:00 Building Population Time Series 21
22. 08:00 Building Population Time Series 22
23. 09:00 Building Population Time Series 23
24. 10:00 Building Population Time Series 24
25. 11:00 Building Population Time Series 25
26. 12:00 Building Population Time Series 26
27. 13:00 Building Population Time Series 27
28. 14:00 Building Population Time Series 28
29. 15:00 Building Population Time Series 29
30. 16:00 Building Population Time Series 30
31. 17:00 Building Population Time Series 31
32. 18:00 Building Population Time Series 32
33. 19:00 Building Population Time Series 33
34. 20:00 Building Population Time Series 34
35. 21:00 Building Population Time Series 35
36. 22:00 Building Population Time Series 36
37. 23:00 Building Population Time Series 37
38. 00:00 Building Population SI Time Series 38
39. 01:00 Building Population SI Time Series 39
40. 02:00 Building Population SI Time Series 40
41. 03:00 Building Population SI Time Series 41
42. 04:00 Building Population SI Time Series 42
43. 05:00 Building Population SI Time Series 43
44. 06:00 Building Population SI Time Series 44
45. 07:00 Building Population SI Time Series 45
46. 08:00 Building Population SI Time Series 46
47. 09:00 Building Population SI Time Series 47
48. 10:00 Building Population SI Time Series 48
49. 11:00 Building Population SI Time Series 49
50. 12:00 Building Population SI Time Series 50
51. 13:00 Building Population SI Time Series 51
52. 14:00 Building Population SI Time Series 52
53. 15:00 Building Population SI Time Series 53
54. 16:00 Building Population SI Time Series 54
55. 17:00 Building Population SI Time Series 55
56. 18:00 Building Population SI Time Series 56
57. 19:00 Building Population SI Time Series 57
58. 20:00 Building Population SI Time Series 58
59. 21:00 Building Population SI Time Series 59
60. 22:00 Building Population SI Time Series 60
61. 23:00 Building Population SI Time Series 61
62. ᶄ Mobile Population Estimation Results
63. Mobile Population Estimation pedestrian volume necessary data (all including spatial and temporal information): ɹɹtrain station passengers ɹɹbuilding population ɹɹmovement profiles calculation algorithm: ɹɹbetweenness centrality index ɹɹ(BRANDES 2001; FREEMAN 1977; SEVTSUK & MEKONNEN 2012) 63
64. Spatio-Temporal Centrality Profile 64
65. 00:00 Building Centrality Time Series 65
66. 01:00 Building Centrality Time Series 66
67. 02:00 Building Centrality Time Series 67
68. 03:00 Building Centrality Time Series 68
69. 04:00 Building Centrality Time Series 69
70. 05:00 Building Centrality Time Series 70
71. 06:00 Building Centrality Time Series 71
72. 07:00 Building Centrality Time Series 72
73. 08:00 Building Centrality Time Series 73
74. 09:00 Building Centrality Time Series 74
75. 10:00 Building Centrality Time Series 75
76. 11:00 Building Centrality Time Series 76
77. 12:00 Building Centrality Time Series 77
78. 13:00 Building Centrality Time Series 78
79. 14:00 Building Centrality Time Series 79
80. 15:00 Building Centrality Time Series 80
81. 16:00 Building Centrality Time Series 81
82. 17:00 Building Centrality Time Series 82
83. 18:00 Building Centrality Time Series 83
84. 19:00 Building Centrality Time Series 84
85. 20:00 Building Centrality Time Series 85
86. 21:00 Building Centrality Time Series 86
87. 22:00 Building Centrality Time Series 87
88. 23:00 Building Centrality Time Series 88
89. 00:00 89 Building Centrality SI Time Series
90. 01:00 90 Building Centrality SI Time Series
91. 02:00 91 Building Centrality SI Time Series
92. 03:00 92 Building Centrality SI Time Series
93. 04:00 93 Building Centrality SI Time Series
94. 05:00 94 Building Centrality SI Time Series
95. 06:00 95 Building Centrality SI Time Series
96. 07:00 96 Building Centrality SI Time Series
97. 08:00 97 Building Centrality SI Time Series
98. 09:00 98 Building Centrality SI Time Series
99. 10:00 99 Building Centrality SI Time Series
100. 11:00 100 Building Centrality SI Time Series
101. 12:00 101 Building Centrality SI Time Series
102. 13:00 102 Building Centrality SI Time Series
103. 14:00 103 Building Centrality SI Time Series
104. 15:00 104 Building Centrality SI Time Series
105. 16:00 105 Building Centrality SI Time Series
106. 17:00 106 Building Centrality SI Time Series
107. 18:00 107 Building Centrality SI Time Series
108. 19:00 108 Building Centrality SI Time Series
109. 20:00 109 Building Centrality SI Time Series
110. 21:00 110 Building Centrality SI Time Series
111. 22:00 111 Building Centrality SI Time Series
112. 23:00 112 Building Centrality SI Time Series
113. ᶅ Symbolic Value Evaluation Results
114. Symbolic Value Identification official buildings: ɹɹpolice stations, fire stations, government office buildings train stations with large passenger volume (> 500.000 per day): ɹɹࡾాɺژڮɺ୆৔ɺ඼઒ɺେ໳ɺๅொɺ޿ඌɺ৽ڮɺ݄ౡɺ౦ژɺࣚཹɺ ɹɹ඿ொɺాொɺਆాɺ஛ڮɺ஛ࣳɺங஍ɺ੺ࡔɺۜ࠲ɺ߿ொ landmark buildings and institutions: ɹɹeconomical (౦ژূ݊औҾॴ) ɹɹpolitical (ࠃձٞࣄಊɺ༃ࠃਆࣾ) ɹɹtouristic (ϑδςϨϏϏϧɺ౦ژλϫʔɺங஍ࢢ৔ɺ૿্ࣉ) 114
115. 115 Symbolic Value
116. 116 Symbolic Value Spatial Influence
117. Vulnerability Mapping
118. Vulnerability Map Creation (Raster Algebra) 118 building population (SI): mobile population (SI): symbolic value (SI):
119. for each raster cell (5m×5m) calculate , where vt is the total vulnerability at time t, F is the set of vulnerability factors, wi is the weight of factor i, fi,t is the value of factor i at time t on a normalized scale (1: low; 2: medium; 3: high; 4: very high) Vulnerability Map Creation (Raster Algebra) 20% 40% 40% building population mobile population symbolic value 119 ^
120. 00:00 Vulnerability Map 120
121. 01:00 Vulnerability Map 121
122. 02:00 Vulnerability Map 122
123. 03:00 Vulnerability Map 123
124. 04:00 Vulnerability Map 124
125. 05:00 Vulnerability Map 125
126. 06:00 Vulnerability Map 126
127. 07:00 Vulnerability Map 127
128. 08:00 Vulnerability Map 128
129. 09:00 Vulnerability Map 129
130. 10:00 Vulnerability Map 130
131. 11:00 Vulnerability Map 131
132. 12:00 Vulnerability Map 132
133. 13:00 Vulnerability Map 133
134. 14:00 Vulnerability Map 134
135. 15:00 Vulnerability Map 135
136. 16:00 Vulnerability Map 136
137. 17:00 Vulnerability Map 137
138. 18:00 Vulnerability Map 138
139. 19:00 Vulnerability Map 139
140. 20:00 Vulnerability Map 140
141. 21:00 Vulnerability Map 141
142. 22:00 Vulnerability Map 142
143. 23:00 Vulnerability Map 143
144. Outcome Analysis
145. Vulnerability Analysis vulnerability maps allow for quick and easy visual identification of vulnerable places and times spatio-temporal investigation of highly vulnerable places: ᶃɹtotal population in vulnerable areas ᶄɹsensitive infrastructures in vulnerable areas ɹɹ❶ɹschools & kindergartens ɹɹ❷ɹhospitals & homes for the elderly 145
146. 09:00 Vulnerability Map 146
147. Vulnerability Analysis vulnerability maps allow for quick and easy visual identification of vulnerable places and times spatio-temporal investigation of highly vulnerable places: ᶃɹtotal population in vulnerable areas ᶄɹsensitive infrastructures in vulnerable areas ɹɹ❶ɹschools & kindergartens ɹɹ❷ɹhospitals & homes for the elderly 147
148. Total Population in Vulnerable Areas 148
149. Vulnerability Analysis vulnerability maps allow for quick and easy visual identification of vulnerable places and times spatio-temporal investigation of highly vulnerable places: ᶃɹtotal population in vulnerable areas ᶄɹsensitive infrastructures in vulnerable areas ɹɹ❶ɹschools & kindergartens ɹɹ❷ɹhospitals & homes for the elderly 149
150. 150 Sensitive Infrastructures
151. Sensitive Infrastructures in Vulnerable Areas (9am) 151
152. Conclusion
153. Summary introduction of spatio-temporal vulnerability analysis methodology: ɹɹtheoretical foundation → operationalization → calculation → analysis operationalization of abstract concept (“vulnerability”) and human behavior (“terrorist decision-making”) into mathematical calculations: ɹɹvulnerability factors ɹɹspatial influence ɹɹfactor weights proven usefulness and applicability in case study 153
154. Thank you for your kind attention! http://www.konstantingreger.net greger@geoenv.tsukuba.ac.jp @kogreger This research is partly funded by the 2012 Sinfonica GIS & Statistics Research Grant (ฏ੒24೥౓ γϯϑΥχΧ౷ܭGISݚڀॿ੒) of the Statistical lnformation Institute for Consulting and Analysis (ެӹࡒஂ๏ਓ౷ܭ৘ใݚڀ։ൃηϯλʔ)
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