This document summarizes a study that analyzed the spatial vulnerability of urban areas to terrorism in Tokyo, Japan. It presented a methodology for identifying susceptibility factors like building population and traffic flow. A case study analyzed these factors for an area of central Tokyo using spatial data on buildings, demographics and transportation. The factors were operationalized and weighted to create vulnerability maps showing hotspots over time. The study provided insights into defining attributes that affect terrorism risk and visualized micro-scale spatial patterns of vulnerability to inform authorities and raise public awareness.

1. Spatial Methodologies for the Analysis of
Vulnerability in Urban Areas
ー A Case Study for Terrorism in Tokyo,
Japan
ー A Case Study for Terrorism in Tokyo,
Japan
Konstantin GREGER
Department for Spatial Information Science
University of Tsukuba
09.04.2013

2. Agenda
Methodology
Case Study
Susceptibility Factors
Building Population
Traffic Flow
Vulnerability Mapping
Summary
2

3. Methodology

4. Research Objective
disasters can happen everywhere
but they are more severe in some locations than in others
special case of terrorism ➞ deliberate decision: "If I were a terrorist, I would ..."
vulnerability centric, scenario based research
the unit of analysis is the geography, not the event
no investigation what outcome disaster (or an attack) at a location can cause
⇒Finding out how prone a location is to a disaster (or an attack),
as a result of the attributes that define it.
4

5. Hypotheses
① Vulnerability is not distributed equally in space.
places with high vulnerability vs. places with low vulnerability
② Factors exist that enhance or mitigate vulnerability.
attributes of objects at risk
③ Vulnerability of objects influences their spatial surroundings.
spatial influence of objects
5

6. Objects of Interest
① buildings
houses, offices, factories, warehouses, stations, ...
② infrastructures
railroads, roads, utilities
③ people
most important!
human activities define urban space
6

7. Environmental Backcloth
theory originated in the field of place-based crime analysis
“
The urban settings that create crime and fear are human constructions,
the by-product of the environments we build to support the requirements
of everyday life: homes and residential neighborhoods; shops and offices;
factories and warehouses; government buildings; parks and recreational
”
sites; sports stadia and theatres; transport systems, bus stops, roadways
and parking garages. The ways in which we assemble these large building
blocks of routine activity into the urban backcloth can have enormous
impact […] on the quantities, types and timing of the crimes we suffer.
(Brantingham and Brantingham 1995)
7

8. Susceptibility
“ ”
[m]otivated offenders will commit crime against suitable targets at certain
places according to the environmental characteristics of those places that
make it easier to complete the crime successfully.
(Caplan and Kennedy 2010a)
expert knowledge about building susceptibility:
"Reference Manual to Mitigate Potential Terrorist
Attacks Against Buildings" by U.S. Federal Emergency
Management Agency (FEMA 2003)
"Handbook for Rapid Visual Screening of Buildings
to Evaluate Terrorism Risks" by U.S. Federal Emergency
Management Agency (FEMA 2009)
8

9. Spatial Urban Vulnerability Analysis (SUVA)
Framework
susceptibility
factor 1 SI factor map 1
weight 1
spatial data
factor 2 SI factor map 2
weight 2
non-spatial data
factor 3 SI factor map 3
weight 3
expert knowledge
...
...
...
factor n SI factor map n
weight n
RTM
vulnerability
map
9

10. Spatial Influence (SI)
analysis focuses on the effect that the “crime generators” have on the object's
immediate surroundings
3-dimensional real-world objects vs. 1-dimensional point objects
“
The best way to map crime factors for the articulation of criminogenic
backcloths is to operationalize the spatial influence of each factor, acting
”
as crime generators, throughout a common landscape rather than
atheoretically mapping the factors as points, lines or polygons in a
manner that keeps them disconnected from their broader social and
environmental contexts.
(Caplan and Kennedy 2010a)
10

11. Risk Terrain Modeling (RTM)
“
Risk terrain modeling (RTM) is an approach to risk assessment in which
separate map layers representing the influence and intensity of a crime
risk factor at every place throughout a geography is created in a
geographic information system (GIS). Then all map layers are combined
to produce a composite “risk terrain” map with values that account for all
risk factors at every place throughout the geography. RTM builds upon
principles of hotspot mapping […] to produce maps that show where
”
conditions are ideal or conducive for crimes to occur in the future given
the existing environmental contexts. It offers a [...] statistically valid way to
articulate and communicate crime-prone areas at the micro-level
according to the spatial influence of criminogenic features.
(Caplan and Kennedy 2010a)
identification of factors ➞ operationalization (SI) ➞ weighting ➞ mapping
11

12. 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

13. Three Susceptibility Factors
① building population
estimated number of people within each building
② traffic flow
estimated number of pedestrians, train passengers
③ symbolic value
dichotomic variable; qualitative estimation
13

14. Susceptibility Factor ①: Building Population

15. Three Population Types
① permanent / long-term population
residents; employees
volumetric estimation
② temporary / short-term population
customers; visitors; guests
customer-floorspace ratio
③ dynamic / moving / mobile population
➞ susceptibility factor ② traffic flow
15

16. Spatio-Temporal Categorical Permanent /
Long-Term Volumetric Building Population
Estimation
where PBPi,c,t is the permanent building population in category c of building i at
time t, APAi,c,t is the total population of category c at time t in area A, which
contains building i, A is the set of areas, BA is the building footprint area, BF is
the number of floors, k is an index
16

17. Necessary Datasets
① Building data, containing the footprint area and the number of floors
➞ Zmap-TOWNII by Zenrin Co., Ltd. ( 株式会社ゼンリン )
② Census data, both for residential and employment populations
➞ population census ( 国勢調査 ) from the Statistics Bureau at the
Ministry of Internal Affairs and Communications ( 総務省統計局 )
➞ employment census ( 経済センサス ) from the Statistical Information
Inst. for Consulting & Analysis ( 公益財団法人統計情報研究開発センタ
ー)
③ Address point data, containing categorical information
➞ テレポイント Pack! by Zenrin Co., Ltd. ( 株式会社ゼンリン )
④ Population movement profiles
➞ person trip data ( 東京都市圏人の流れデータ ) by the University of
Tokyo
Center for Spatial Inf. Science ( 東京大学空間情報科学研究センター ) 17

18. Usage Categories
① home
② business & office
③ education
④ retail & service
⑤ hotel
⑥ leisure
⑦ public institution
⑨ other
18

19. Mixed Building Usage Categories
shortcoming of previous simplified
2-dimensional approach (residents/employees)
(cf. Lwin & Murayama 2009)
many buildings comprise more than one
usage category
no detailed usage ratio information available
approximation using number of address
points per category per building
Building ① ② ③ ④ ⑤ ⑥ ⑦ ∑
大手町ビルヂング 113 3 25 1 36 3 181
新丸の内ビルディング 13 47 44 3 107
六本木ヒルズ森タワー 73 7 53 32 16 181
ファミール月島グランスイートタワー 18 1 19 19

23. 100 m 22
100 m2 2 1 0 0 m2
4 floors 3 floors 1 0 floor s
→ 400 m 22
→ 300 m 22 → 1 , 0 0 0 m2
→ 13.3% → 10.0% → 33.3%
⇒ 200 ppl. ⇒ 150 ppl. ⇒ 5 0 0 p p l.
2 0 0 m2 2
4 f loor s 100 m 2
→ 8 0 0 m2 2 5 floors
6
→ 26.7% → 500 m2
⇒ 4 0 0 pp l. → 16.7%
⇒ 250 ppl.
Cumulative floor space: 3,000 m 2
Total area population: 1,500 ppl.

25. Spatio-Temporal Categorical Permanent /
Long-Term Volumetric Building Population
Estimation
where PBPi,c,t is the permanent building population in category c of building i at
time t, APAi,c,t is the total population of category c at time t in area A, which
contains building i, A is the set of areas, BA is the building footprint area, BF is
the number of floors, k is an index
25

26. Persontrip Data
Time Place
Start End Start End Means Purpose
07:14 07:19 Home Sta. A walk for work
07:19 07:32 Sta. A Sta. B train for work
07:32 07:37 Sta. B Sta. C train for work
07:37 07:45 Sta. C Office walk for work
17:30 17:40 Office Restaurant walk for leisure
21:00 21:07 Restaurant Sta. C walk for home
21:07 21:12 Sta. C Sta. B train for home
21:12 21:25 Sta. B Sta. A train for home
21:25 21:30 Sta. A Home walk for home
26

27. Persontrip Data
1 2 3 4 1 1 2 3 4
1 2 3
home work leisure home
time
27

28. Usage Categories vs. Activities
① home
home
① → being at home
→ being at home
② business & office
business & office
② → working
→ working permanent /
long-term
③ education
③ education → studying
→ studying population
④ retail & service
④ retail & service → shopping
→ shopping
⑤ hotel & leisure
⑤ hotel & leisure → entertaining
→ entertaining
⑦ public institution
⑦ public institution → running errands
→ running errands
temporary /
short-term
population
28

29. Spatio-Temporal Population Estimation
Result
maxhome = 506maxwork = 343
29

30. Population
Timeseries
Timeseries
00:00
23:00
22:00
21:00
20:00
19:00
18:00
17:00
16:00
15:00
14:00
13:00
12:00
11:00
10:00
09:00
08:00
07:00
06:00
05:00
04:00
03:00
02:00
01:00
30

31. Three Population Types
① permanent / long-term population
residents; employees
② temporary / short-term population
customers; visitors; guests
③ dynamic / moving / mobile population
➞ susceptibility factor ② traffic flow
31

32. Spatio-Temporal Categorical Temporary /
Short-Term Building Population Estimation
where TBPi,c,t is the temporary building population in category c of building i at
time t, FSc,i is the cumulative floorspace of category c in building i, γc is the
customer floorspace ratio of category c, βc,t is a binary variable (0 / 1) showing
whether category c is in operation at time t.
values for γc are taken from empirical data (cf. Bosserhoff 2005)
values for βc,t are taken from clustered movement profiles (cf. Gonzalez et al.
2008; Horanont 2012; Jiang et al. 2012)
32

33. Susceptibility Factor ②: Traffic Flow

34. Spatio-Temporal Categorical Temporary /
Short-Term Building Population Estimation
① motorized traffic → traffic census data
② railway traffic → traffic census data
③ pedestrian traffic → betweenness centrality measure (network theory)
based on passenger volume per station ( 国道交通省 )
based on OD data ( 東京都市圏交通計画協議会事務局 )
based on mobile phone tracking data ( 株式会社ゼンリンデータコム混雑統計)
no absolute traffic volume, but volume index
34

35. Vulnerability Mapping

36. Spatial Influence (SI)
Total Permanent Population Betwenness Centrality 36

37. Operationalization of SI
① spatial concentration
identification of hotspots
② spatial proximity
each object affects the space
(Caplan and Kennedy 2010a)
surrounding itself by its
criminogenic attributes
dimension of proximity has to be
defined for each factor
37

38. Total Permanent Population

39. Betweenness Centrality

40. Vulnerability Mapping
where Vtot,t is the total vulnerability at time t, SF is the set of susceptibility
factors, wi is the factor weight of factor i, F^i,t is the factor value on a normalized
scale (1: very low, 4 very high), i is an index.
40

41. Total Vulnerability

42. Summary

43. Summary
① analytic insight
definition of attributes and factors affecting terrorism vulnerability
new approach (terrorism + vulnerability + GIS)
② visualization
creation of a micro-scale vulnerability map of a study area
in a Japanese urban area
spatial distribution of vulnerability factors
43

44. Target Audiences
① public
visual way to communicate the complex topic of vulnerability
raising awareness for terrorism risk in Japan
② involved authorities / stakeholders
effective channeling of limited funding for mitigation measures
③ academia
44

45. Bibliography
Abbott, Andrew. 1997. “Of Time and Space: The Contemporary Relevance of the Chicago School.” Social Forces 75(4):1149–1182.
Apostolakis, George E., and Douglas M. Lemon. 2005. “A Screening Methodology for the Identification and Ranking of Infrastructure Vulnerabilities Due to Terrorism.” Risk
Analysis 25(2):361–376.
Bosserhoff, Dietmar. 2005. Integration von Verkehrsplanung und räumlicher Planung Teil 1: Grundsätze und Umsetzung. 2nd ed. Wiesbaden: Hessisches Landesamt für
Straßen- und Verkehrswesen (http://www.hessen.de/irj/hessen_Internet?rid=HStK_15/hessen_Internet/presse.jsp).
Brantingham, Patricia, and Paul Brantingham. 1995. “Criminality of place.” European Journal on Criminal Policy and Research 3(3):5–26.
Brown, Gerald G., and Louis Anthony Tony Cox Jr. 2011. “How Probabilistic Risk Assessment Can Mislead Terrorism Risk Analysts.” Risk Analysis 31(2):196–204.
Caplan, Joel M., and Leslie W. Kennedy. 2010a. Risk Terrain Modeling Compendium. Newark, NJ: Rutgers Center on Public Security.
Caplan, Joel M., and Leslie W. Kennedy. 2010b. Risk Terrain Modeling Manual: Theoretical Framework and Technical Steps of Spatial Risk Assessment. Newark, NJ: Rutgers
Center on Public Security.
FEMA Federal Emergency Management Agency. 2003. “Reference Manual to Mitigate Potential Terrorist Attacks Against Buildings.” (
http://www.fema.gov/library/viewRecord.do?id=1559).
FEMA Federal Emergency Management Agency. 2009. “Handbook for Rapid Visual Screening of Buildings to Evaluate Terrorism Risks.” (
http://www.fema.gov/library/viewRecord.do?fromSearch=fromsearch&id=1567).
González, Marta C., César A. Hidalgo, and Albert-László Barabási. 2008. “Understanding individual human mobility patterns.” Nature 453(7196):779–782.
Horanont, Teerayut. 2012. “A Study on Urban Mobility and Dynamic Population Estimation by Using Aggregate Mobile Phone Sources.” (
http://www.csis.u-tokyo.ac.jp/dp/115.pdf).
Jiang, Shan, Joseph Ferreira, and Marta C. González. 2012. “Clustering daily patterns of human activities in the city.” Data Mining and Knowledge Discovery 25(3):478–510.
Kaplan, Stanley, and B. John Garrick. 1981. “On The Quantitative Definition of Risk.” Risk Analysis 1(1):11–27.
Karydas, D.M., and J.F. Gifun. 2006. “A method for the efficient prioritization of infrastructure renewal projects.” Reliability Engineering & System Safety 91(1):84–99.
Lemon, Douglas M. 2004. “A Methodology for the Identification of Critical Locations in Infrastructures.”
Lwin, KoKo, and Yuji Murayama. 2009. “A GIS Approach to Estimation of Building Population for Micro-spatial Analysis.” Transactions in GIS 13(4):401–414.
Michaud, David. 2005. “Risk Analysis of Infrastructure Systems Screening Vulnerabilities in Water Supply Networks.”
National Consortium for the Study of Terrorism and Responses to Terrorism (START). 2011. “Global Terrorism Database: Variables & Inclusion Criteria.” (
http://www.start.umd.edu/gtd/downloads/Codebook.pdf).
45
Paté-Cornell, Elisabeth, and Seth Guikema. 2002. “Probabilistic Modeling of Terrorist Threats: A Systems Approach to Setting Priorities Among Countermeasures.” Military

46. Thank you for your attention
greger@geoenv.tsukuba.ac.jp
http://www.konstantingreger.net
@kogreger

47. Vulnerability ≠ Risk
risk
(loss / probability)
hazard
vulnerability
47

48. Building Usage Categories Distribution
① ② ③ ④
Categories
① home
② business & office
③ education
④ retail & service
⑤ hotel
⑥ leisure
⑦ public institution
⑤ ⑥ ⑦ 48

49. Building Usage Categories Density
Distribution
① ② ③ ④
Categories
① home② business &
office③ education④
retail & service⑤ hotel⑥
leisure⑦ public
institution
⑤ ⑥ ⑦ 49

50. Mixed Building Usage Categories
50

54. 01:00 02:00 03:00 04:00 05:00
06:00 07:00 08:00 09:00 10:00 11:00
12:00 13:00 14:00 15:00 16:00 17:00
18:00 19:00 20:00 21:00 22:00 23:00

55. Spatio-Temporal Population Estimation
Result
maxhome = 8maxwork = 2,568
55