Meera Velu
UEP 232, Intro to GIS
May 7, 2013
Oakland, CA Disaster Vulnerability Analysis
Preparation for an Earthquake Disaster
Background
Learn ♦ Lead ♦ Lift is a disaster justice pilot project lead by the City of Oakland Fire
Department’s Emergency Management Services Division (EMSD). The project seeks to engage
and integrate all Oakland neighborhoods in disaster planning, preparedness, and mitigation
efforts regardless of social, cultural, religious, and economic status.
All communities need to plan and prepare for what to do when disasters strike to be able to care
for themselves and their loved ones. Through Learn ♦ Lead ♦ Lift, the City is focused on
fostering working relationships with community resources and building effective coordination in
order to increase disaster readiness.
Oakland, CA is at risk for a series of disasters, including earthquakes, floods, and fires. This
vulnerability analysis focuses on the city’s present danger of a major earthquake.
The spatial questions at play in the investigation look to see how social factors interact with the
built environment to produce a vulnerable zone during a natural disaster.
The maps presented demonstrate vulnerability through a series of variables including:
Physical Vulnerability
Vulnerability with the inclusion of resource accessibility
Vulnerability including resource accessibility and social vulnerability
Annotated Bibliography: Literature Review
The following articles and studies examine disaster vulnerability in the context of an earthquake
and provide factors to take into account when developing a vulnerability analysis.
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/4-34_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%2915276988%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): 1306http://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
Methodology
In order to produce a map of vulnerability for Oakland, one must analyze a series of variables.
As noted in the background, the variables included within the presented maps deal with three
main categories of vulnerability. The variables included in each category are as follows:
Physical vulnerability variables
- Hayward fault line
- Slope
- Impervious surfaces
- Active and referred superfund sites
- Land cover
- Liquefaction zones
- Landslide regions
Resources
- Parks
- Hospitals (Highland Hospital, Kaiser, Alta Bates)
- Schools designated as shelters
Social vulnerability variables
- Age < 10
- Age > 65
- Population density
A number of tools came into play when analyzing the variables using ArcGIS; these tools
include raster conversion, Euclidian Distance, Feature to Point, Kernel Density, Reclassify, and
Raster Calculator.
First, the layers of slope, impervious surfaces, and land cover were converted from .tiff images to
raster.
Next, for both physical vulnerability and resource vulnerability variables, the Euclidian Distance
tool was used. A close distance to the following factors led to increased vulnerability: fault line,
landslide regions, liquefaction zones, and active and referred superfund sites. Close distance to
these factors led to decreased vulnerability: hospitals, schools, and parks.
Block census data for the City of Oakland went into analysis of social vulnerability factors. In
order to analyze the census data, the data was first joined with census block polygons then
converted to points using the Feature to Point tool. Next, Kernel Density was used to show
population densities.
After rasters, distances, and densities were determined, each variable was assigned a
classification from 1 – 6, with 1 standing as the least vulnerable and 6 as the most vulnerable.
Each variable was added together using Raster Calculator to create a comprehensive
vulnerability score, with a maximum vulnerability score of 51.
The following table provides each variable’s classification:
Reclassifications
Layer
1 (least
2
3
4
vulnerable)
Impervious
0-12%
12-32
32-51
51-67
Land
Oakland
3+ mile
2 - 3 miles 1 - 2 miles ½ mile - 1
Liquefaction distance
mile
Oakland
3+ mile
2 - 3 miles 1 - 2 miles ½ mile - 1
Landslide
distance
mile
Hayward
3+ mile
2 - 3 miles 1 - 2 miles ½ mile - 1
Fault
distance
mile
Slope
0 -5%
5 - 10
10 - 15
15 - 25
Land Cover
Open
Water (11, Barren
Developed,
Space,
12, 90, 95) Land (31)
Low
Forest (21,
Intensity
41, 42, 43,
(22)
51, 52, 71,
81, 82)
Superfund
3+ mile
2 - 3 miles 1 - 2 miles ½ mile - 1
Sites
distance
mile
Hospitals
0 - ¼ mile ¼ - ½ mile ½ - 1 mile 1 mile - 2
distance
miles
Schools
0 - ¼ mile ¼ - ½ mile ½ - 1 mile 1 mile - 2
distance
miles
Parks
0 - ¼ mile
¼ - ½ mile ½ - 1 mile 1 – 1 ½
mile
Residents 65 0 - 500
500 - 1,000 1,000 2,500 years and
individuals
2,500
4,000
older
Children
0 - 200
200 - 600
600 - 1,500 1,500 ages 9 and
individuals
2,500
younger
Population
0 - 2,500
2,500 5,000 12,000 individuals 5,000
12,000
18,000
Notes on Data Layers
Superfund sites: selected by attribute for active and referred sites
5
67-81
6 (most
vulnerable)
81-100
¼ - ½ mile
¼ mile
¼ - ½ mile
¼ mile
¼ - ½ mile
¼ mile
25 - 40
Developed,
Medium
Intensity
(23)
40+
Developed,
High
Intensity
(24)
¼ - ½ mile
¼ mile
2 - 3 miles
3+ miles
2 - 3 miles
3+ miles
1 ½ mile –
2 miles
4,000 5,500
2+ miles
2,500 3,500
3,500 5,425
18,000 25,000
25,000 35,500
5,500 7,463
Oakland schools: selected by attribute for the schools working with the city as shelter sites
Hospitals: hospitals included are Highland Hospital, Kaiser, Children’s Hospital, and Alta Bates
Landslide regions and liquefaction zones: data includes known landslide incident areas and
known liquefaction areas (there is no differentiation between sites; data is categorized as number
of sites)
Slope: hazardous slope angles derived from the following study
http://www.conservation.ca.gov/cgs/information/publications/ms/documents/ms58.pdf
Data layers were derived from the following sources:
USGS
Census 2010
California Department of Conservation
City of Oakland
Results
The maps presented show high physical vulnerability within the Oakland hills. Resource
availability alleviates vulnerability within East and Downtown Oakland, however the hills
remain fairly vulnerable. With the addition of social variables that take into account population
density and density of individuals over the age of 65 and under the age of 10, vulnerability
increases strongly in East, Southeast Oakland, as well as Downtown Oakland.
2010 Census Data, used to project median yearly income between $6000 - $35, 000, reveals a
high percentage of Oakland’s low-income inhabiting the highest vulnerability zones of the city.
These maps provide a relatively basic outline of the City of Oakland’s vulnerability in the event
of a major earthquake.
As shown in an additional map, the focus neighborhoods of Learn ♦ Lead ♦ Lift (Havenscourt,
Elmhurst, and Acorn) are located within fairly vulnerable regions of the city. This map indicatest
he importance of the project and its goal of directly preparing the community for disasters.
The inclusion of income data lying over vulnerability zones is important to notice as well. Lowincome and low-education are two vital social vulnerability factors to deal with in disaster
situations. The high vulnerability zones with low-income residents provide additional
neighborhood targets for Learn ♦ Lead ♦ Lift.
Next Steps
To create a deeper analysis of vulnerability, one can include in the map additional physical and
social variables. Such variables would be multi-unit housing, flood zones in proximity to dams,
and educational attainment. Additionally, each variable included in an analysis can be weighted
according to a designated level of hazard. For example, proximity to a fault line may have a
higher hazard risk than access to a shelter site. This analysis has not weighted any variable,
because such analysis would require a more thorough examination and knowledge of possible
risk.
Maps