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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
[email protected]
@kogreger
Regional Conference of the IGU, Kyoto
August 8, 2013 (Session CS18-12)
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Hypotheses
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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
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Research Objectives
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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.
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Research Framework
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Spatio-Temporal Vulnerability Analysis Framework
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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
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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)
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Spatio-Temporal Vulnerability Analysis Framework
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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
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Case Study
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Terrorism Vulnerability Factors
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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
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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
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ᶃ Building Population Estimation Results
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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
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Spatio-Temporal Population Profile
maxhome = 506
maxwork = 343
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Building Population
Time Series
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Time Series
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Time Series
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Time Series
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Time Series
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Time Series
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Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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Building Population
SI Time Series
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Building Population
SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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ᶄ Mobile Population Estimation Results
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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)
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Spatio-Temporal Centrality Profile
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Building Centrality
Time Series
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Building Centrality
Time Series
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Building Centrality
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Building Centrality
SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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SI Time Series
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ᶅ Symbolic Value Evaluation Results
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Symbolic Value Identification
official buildings:
ɹɹpolice stations, fire stations, government office buildings
train stations with large passenger volume (> 500.000 per day):
ɹɹࡾాɺژڮɺɺɺେɺๅொɺඌɺ৽ڮɺ݄ౡɺ౦ژɺࣚཹɺ
ɹɹொɺాொɺਆాɺڮɺࣳɺஙɺࡔɺۜ࠲ɺ߿ொ
landmark buildings and institutions:
ɹɹeconomical (౦ژূ݊औҾॴ)
ɹɹpolitical (ࠃձٞࣄಊɺ༃ࠃਆࣾ)
ɹɹtouristic (ϑδςϨϏϏϧɺ౦ژλϫʔɺஙࢢɺ૿্ࣉ)
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Symbolic Value
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Symbolic Value
Spatial Influence
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Vulnerability Mapping
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Vulnerability Map Creation (Raster Algebra)
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building population (SI):
mobile population (SI):
symbolic value (SI):
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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
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^
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Vulnerability Map
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Vulnerability Map
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Outcome Analysis
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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
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09:00
Vulnerability Map
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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
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Total Population in Vulnerable Areas
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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
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Sensitive
Infrastructures
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Sensitive Infrastructures in Vulnerable Areas (9am)
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Conclusion
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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
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Thank you for your kind attention!
http://www.konstantingreger.net
[email protected]
@kogreger
This research is partly funded by the
2012 Sinfonica GIS & Statistics Research Grant (ฏ24 γϯϑΥχΧ౷ܭGISݚڀॿ)
of the Statistical lnformation Institute for Consulting and Analysis (ެӹࡒஂ๏ਓ౷ܭใݚڀ։ൃηϯλʔ)
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