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

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.

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Konstantin Greger

August 08, 2013
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  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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