Meera Velu
April 19, 2013
UEP 232
Topic
The issue I have chosen to investigate through geographic visualization is vulnerability to disasters in the City of Oakland. Specifically, this vulnerability will include both physical and social vulnerability and will be related to the most present danger to Oakland, a severe earthquake. Since vulnerability will be including two different aspects, the social as well as the physical, there will be two maps showing these two elements separately, then a third map will integrate the two together to give a wider view of vulnerability.
Another aspect that this project will explore is the use of social capital in addressing vulnerability. To examine this, another map will include community resources as an element to include in a vulnerability analysis. This map will show how a current disaster management project, which integrates community resources, has potential for benefitting Oakland communities in terms of disaster mitigation, response, and recovery.
The end product of the investigation will be various maps displaying vulnerability within
Oakland. Included with the maps will be tables displaying the variables used to analyze vulnerability and the weight given to each of these variables. This product will be a basic analysis of vulnerability in the city and will provide a structure for future analyses of vulnerability.
Spatial Questions
The spatial questions at play in the investigation are various, but all look to see how social factors interact with the built environment to produce a vulnerable zone during a natural disaster.
I will go into the specific questions for this particular investigation after looking at previous geospatial research done to address the same question.
Relevant Literature
Haki, Zeynep, Zuhal Akyurek, and Sebnem Duzgun. “Assessment of Social Vulnerability Using
Geographic Information Systems: Pendik, Istanbul Case Study.”
Middle East Technical
University . http://agile.gis.geo.tu-dresden.de/web/Conference_Paper/CDs/AGILE%202004/papers/4-3-
4_Haki.pdf
The following study uses multi-criteria evaluation and multi-attribute utility theory to define and map vulnerability for an earthquake in Istanbul. The authors’ definition of vulnerability includes two sides: the external, which includes risk, shock, and stress than an individual or household is subject to; and the internal, or the defenselessness and lack of coping without damaging loss (414). The indicators used to shape vulnerability include employment status, reasons for not working, educational level, age, sex, household size, owner of household, and ownership of other dwellings. To spatially analyze using the variables, the authors find the kernel estimation application the must helpful indicator of the variation of intensity of

the data. The conclusion of the study is that income and education are important factors in defining social vulnerability.
Rashed, Tarek and John Weeks. 2003 “Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas.”
International Journal of Geographical Information
Science 17 (6): 547-576. http://geography.sdsu.edu/Research/Projects/IPC/publication/Rashed_Weeks_IJGIS.pdf
Rashed and Weeks analyze the deficiencies in social vulnerability analysis that uses GIS. In the article, the authors specify that vulnerability is an inherent spatial problem that benefits from the use of GIS. In vulnerabilities to disasters such as earthquakes, one notices that there are social and natural interrelationships that are unsustainable. Thus, analyzing social vulnerability requires looking at both social and biophysical variables. The difficulty in creating an examination of social vulnerability with GIS is how to weigh the various elements and processes that underlie it. A famous method of creating a social vulnerability index is to weight social and biophysical variables with the same weight (Cutter et al. 2000).
The authors advocate using an inductive approach based on spatial multicriteria analysis.
The criteria that they provide for vulnerability analysis are amount of debris, percentage of burned area, demand on shelter, costs for recover of buildings, functionality of highways, power utilities, hospitals, emergency services, and bridges. In looking at these criteria, one may look at previous disasters and assess risks and vulnerabilities based on the damages from that.
Bac-Bronowicz Joanna and Nobuyuki Maita. 2007. “Mapping Social Vulnerability to
Earthquake Hazards by using Analytic Hierarchy Process (AHP) and GIS in Tehran City.”
University of Tehran . http://www.gisdevelopment.net/application/natural_hazards/earthquakes/ma0777.htm
This next study performs a qualitative assessment of vulnerabilities and focuses on the built environment and place inequalities. The authors describe generally accepted indicators of vulnerability as being age, gender, race, and socioeconomic status. They detail more specific indicators that include lack of access to resources, social capital, building stock and age, frail and physically limited individuals, type and density of infrastructure and lifelines. These are summarized here:
Physical distance: adverse facilities; facilitative facilities
Socio-economic: illiteracy, unemployment, employment
Housing: households per housing unit, housing quality
Population: population density, age, number of households
When specifically addressing the variables, the authors highlight qualities of the variables that are more vulnerable. The qualities specified as more vulnerable are:
larger households
densely populated housing units
less durable housing units
the unemployed and illiterate
areas that are further from open space, hospitals, and fire stations
areas that are close to gasoline stations and danger-prone industrial establishments

Chakraborty, Jayajit, Graham A. Tobin, and Burrell E. Montz. 2005. “Population Evacuation:
Assessing Spatial Variability in Geophysical Risk and Social Vulnerability to Natural Hazards.”
Natural Hazards Review 6(1 ): 23-33. http://ascelibrary.org.ezproxy.library.tufts.edu/doi/full/10.1061/%28ASCE%291527-
6988%282005%296%3A1%2823%29
The above article analyzes social vulnerability in response to hurricane zones, with an emphasis on evacuation as a priority tool for safety. The authors specify a well established claim that marginalized groups suffer the most in disasters. With this in mind, the study examines socioeconomic components and geo-physical components and the authors lay out the three following approaches.
Approach 1: population and structure
Total population
Number of housing units
Number of mobile homes
Approach 2: differential access to resources
Population below poverty level
Occupied housing units with no telephones
Occupied housing units with no vehicles
Approach 3: special evacuation needs
Population age 5 years or under
Population age over 85 years
Population (age over 5 years) with disabilities
Approach 4: All of above three
Association of Bay Area Government’s Earthquake Hazard Guide http://quake.abag.ca.gov/
Physical variables listed:
shaking
liquefaction
active faults
landslides
tsunamis
dam failure inundation
hazardous materials incidents
Included in part of physical vulnerability is housing. Housing designated as vulnerable housing are:
single-family living space over garage
single-family homes with cripple walls
soft-story wood frame multi-unit buildings
non-ductile concrete multi-unit buildings
uninforced masonry
hillside houses
mobile homes
non-structural elements: water heaters and brick chimneys

“Earthquake Vulnerability Reduction for Cities.”
Asian Disaster Preparedness Center . http://www.adpc.net/casita/Course%20Modules/Earthquake%20vulnerability%20reduction%20f or%20cities/EVRC0302A_Social_cultural_and_economic_Vulnerability.pdf
This article came up after further research on vulnerability variables. The main factors defining the vulnerability of a society in an earthquake as stated by the article are:
Demographic factors
Social stratification
Literacy rate
Family type
Cohesion among neighbors
Ebert, A. and N. Kerle. 2008. “Urban Social Vulnerability Assessment Using Object-Oriented
Analysis of Remote Sensing and GIS Data. A Case Study For Tegucigalpa, Honduras.” The
International Archives of Photogrammetry, Remote Sensing and Spatial Sciences 37(B7): 1306- http://www.isprs.org/proceedings/XXXVII/congress/7_pdf/7_WG-VII-7/02.pdf
Variables listed in the article include:
Personal data:
age
gender
employment status
literacy
Household characteristics:
household size
access to water, gas and power supply
Personal preparedness:
knowledge about hazard and risk
access to information
Housing conditions:
building stock
building construction material
Physical:
proximity to hazard
relief/slope
abundance of transport infrastructure
road conditions
building density/proportion of built area
roof material/roof size
distance to neighboring building
size and distribution of green space
commercial and industrial development

abundance of medical facilities
abundance of educational facilities
building codes
The following articles formulate a social support theory behind coping with disasters:
Beggs, John J. 1996. “Situational Contingencies Surrounding the Receipt of Informal
Support.”
Social Forces 75(1): 201-222. http://sf.oxfordjournals.org/content/75/1/201.short
Lichterman, J D. 2000. “A ‘community as resource’ strategy for disaster response.”
Public Health Reports 115(2-3): 262-265. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1308723/
Patterson, Olivia, Frederick Weil, and Kavita Patel. 2010. “The Role of Community in
Disaster Response: Conceptual Models.”
Population Research and Policy Review 29(2):
127-141. http://link.springer.com/article/10.1007%2Fs11113-009-9133-x?LI=true#page-1
Nonspecific disaster related social vulnerability mapping:
Montz, Burrell E. and Thomas A. Evans. 2001. “Gis and Social Vulnerability Analysis.”
NATO Science Partnership Sub-Series 77(2): 37-48. http://link.springer.com/chapter/10.1007/978-94-010-0918-8_5#page-1
Morrow, Beatty Hearn. 1999. “Identifying and Mapping Community Vulnerability.”
Disasters 23(1): 1-18. http://onlinelibrary.wiley.com.ezproxy.library.tufts.edu/doi/10.1111/1467-
7717.00102/pdf
Schmidtlein, Matthew C, Ronald C. Deutsch, Walter W. Piegorsch, and Susan L. Cutter.
2008. “A Sensitivity Analysis of the Social Vulnerability Index.”
Risk Analysis 28(4):
1099-1114. http://onlinelibrary.wiley.com.ezproxy.library.tufts.edu/doi/10.1111/j.1539-
6924.2008.01072.x/pdf
GIS Data Layers
In order to begin a vulnerability analysis, I must first choose the factors that comprise physical and social vulnerability.
In order to guide the variables to use, I will use those listed in the Association of Bay Area
Governments’ (ABAG) earthquake-related hazards guide and in the various articles above.
[Arrows indicate relationship of variable to high vulnerability status]
Physical vulnerability variables:
proximity to active fault lines (  -  )
relief/slope to judge shaking potential and landslides (

-

)
liquefaction zones (

-

)
building density/proportion of built area (

-

)

multi-unit buildings (

-

)
hillside homes (

-

)
proximity to open space (

-

)
proximity to hazardous material sites (

-

)
proximity to medical facilities (

-

)
proximity to educational facilities (   )
Social vulnerability variables:
age (ages <10 and 65+

)
education level (

-

)
household size (

-

)
population density (

-

)
To include community resources in the vulnerability analysis, I will need a list of the resources.
The resources I will include are churches and known community based organizations.
Data Layer
Active fault lines
Source
USGS
Key Attribute
Variables
Link to Source
Liquefaction zones
Parks
Superfund sites
Hospitals
USGS
Shaking amplification USGS
TIGER 2012
TIGER 2012
Childrens Hospital,
Highland, Kaiser http://pubs.usgs.gov/of/2002/of0
2-296/
“ ”
“ ”
Schools Middle schools, high schools
Toxics Release
Inventory
Landslide Incidence and Susceptibility
Dams
Seismic Hazard
Hazard Events 2001-
2009
Landcover 1 km resolution
EPA http://www.epa.gov/enviro/geo_ data.html
National Atlas High susceptibility, http://nationalatlas.gov/atlasftp.h
high incidence
National Atlas Water Supply, tml?openChapters=#chpclim
“ ”
Irrigation
National Atlas Acceleration level
“ ”
National Atlas Property Damage
National Atlas Urban and Built up
Land
National Atlas High relief
“ ”
“ ”
“ ”
Shaded relief land
(bw)
Brownfield properties EPA http://www.epa.gov/enviro/geo_ data.html

Analysis Process
The basic first step in analysis is to clip each data layer to the City of Oakland city line as provided by the Alameda County City Line data layer. Then each layer will be projected in
California State Plane III.
Second, I would select by attribute in the data layers hospitals, schools, and superfund sites to create data layers that show the main hospitals; middle and high schools, which are the official shelter resources; and active superfund sites.
In order to create vulnerability maps, I will use kernel density analysis to calculate magnitudes for a unit area from the point and polyline features of the data layers shown above.
Analysis questions:
1) How would I overlay a kernel density over another to create a map that shows
2) differentiated magnitudes of vulnerability in the city based on the total of variables?
Which data layers would I have to convert into raster data?