Jennifer Bogle
Assignment 6
1. PROJECT GOALS
The purpose of this project is to complete a census tract-level vulnerability assessment for the borough
of Manhattan based on an analysis of geophysical risk and social vulnerability.
2. ANNOTATED EXAMPLES
The following section provides a detailed summary literature reviewed to inform the preparation of this
project plan. While each article provides valuable information on the approach to vulnerability
assessments, there are two key points that will inform my analysis. First, there is no standard for spatial
vulnerability analyses, both in terms of weighting criteria and selection of the criteria themselves.
Second, weighting social vulnerability criteria is a common practice but is generally criticized for its
subjectivity. I have chosen to still use the weighting system, but will note the limitations in the final
paper.
Chakraborty, J., Tobin, G.A., & Montz, B.E. (2005). Population Evacuation: Assessing Spatial
Variability in Geophysical Risk and Social Vulnerability to Natural Hazards. Natural
Hazards Review, 6(1), 23-33.
Chakraborty et al. (2005) indicate that GIS-based analysis of geophysical risk and social vulnerability
is beneficial for three primary reasons, as it (1) allows integration of multiple data sources, such as
hazardous areas and vulnerable populations, (2) shows a geographic representation of complex data,
and (3) allows for spatial analysis such as buffering and overlays.
The study points to the importance of integrating geophysical conditions and social systems in
measuring spatial vulnerability. However, their GIS-based analysis of vulnerability demonstrates that
that data gaps, inaccurate data, and selection of vulnerability criteria can significantly influence
measures of vulnerability. For instance, depending on the selected set of variables, between 4 and 15%
of the population was found to be in an area with high evacuation assistance needs.
It is clear from their review of literature, that no standardized indices of social and geophysical risk
may be applied to all locations. Furthermore, identifying which are the most significant determinants
of vulnerability is also problematic. They also noted that geophysical risk is a relatively static measure
as compared to social risk, which will change over time based on population change.
The study uses hurricanes and floods in the analysis of geophysical risk, since their probability of
occurrence varies significantly across the U.S. They used the National Hurricane Center Risk Analysis
Program (HURISK) to determine hurricane risk and flood insurance maps to determining the spatial
extent of flood hazards. Note that Sea, Lake and Overland Surges from Hurricanes (SLOSH) is the
standard used in New York City.
To measure social vulnerability, they used three characteristics, including the general population and
structural attributes (e.g., housing units), access to resources (e.g., population below poverty level and
houses without vehicles), and populations with special evacuation needs (e.g., institutionalized
population, population under 5 years, over 85 years, or with disabilities).
They used a specialized weighting system to calculate composite vulnerability, noting the availability
of several methods, including weighting systems to show relative contributions of each variable.
Roy, D.C., & Blaschke, T. (No Date). A Grid-Based Approach for Spatial Vulnerability Assessment
to Floods: A Case Study on the Coastal Area of Bangladesh. Retrieved from
http://ispace.researchstudio.at/sites/ispace.researchstudio.at/files/238_full.pdf
Roy and Blaschke state the importance of vulnerability assessments a means to inform disaster risk
reduction and capacity building efforts after identifying the extent of level of vulnerability and coping
capacity in communities. They also indicate that no standard measures of vulnerability exists.
They point to the Hyogo Framework for Action for 2005-2015, which was developed in 2005 in the
World Conference on Disaster Reduction, which highlighted the social, economic, and environmental
impacts of disasters. While stressing the importance of vulnerability assessments, given the
vulnerability of coastal areas to sea level rise, erosion, and extreme natural events, Roy and Blaschke
acknowledge that these assessments are complicated as a result of social, economic, political, and
institutional distinctions among societies. There is currently no standard measure of vulnerability, and
challenges exists with regards to development of vulnerability assessments that include physical,
social, economic, ecological, and other key factors.
They use a raster or grid-based approach to their assessment to help address issues regarding data
availability and to increase transferability of the assessment, noting that most methodologies use
administrative units and boundaries as the basis for the assessment. In their view, a grid-based
approach incorporates detailed spatial variation that the administrative boundaries approach overlooks,
and also facilitates incorporation of new indicators. While population data is typically organized into
vector census tracts, more recent efforts have been made to transform vector population data into raster
data. For instance, the Centre for International Earth Science Information Network (CIESIN) at
Columbia University has developed the Gridded Population of the World (GPW) and Global RuralUrban Mapping Project (GRUMP) population datasets. However, my analysis will use artificial
boundaries, recommended the use of raster-based assessments in future analyses.
To conduct their spatial vulnerability assessment, Roy and Blaschke used 12 vulnerability domains.
This included nine sensitivity domains with more detailed indictors: population and age, livelihood and
poverty, health, water and sanitation, housing and shelter, roads and other infrastructure, land use and
over, environment, gender; and three coping capacity domains: assets, education and human resource
capacity, and economic alternatives. They assigned relative weights to each of the 12 vulnerability
domains based on the Analytic Hierarchy Process (AHP) model, weighting livelihoods highest and
gender lowest.
Fekete, A. (2012). Spatial disaster vulnerability and risk assessments: challenges in their quality
and acceptance. Natural Hazards, 61(3), 1161-1178.
Fekete (2012) documents the challenges of spatial risks and vulnerability assessments to determine
areas for improvement. Based on a review of a vulnerability index in Germany, he provides the
following key findings:
 There is a significant range of analytical approaches to vulnerability assessments.
 Socioeconomic indicators are often associated with issues pertaining to data quality, gaps,
currency, and normalization to facilitate cross-regional comparisons.
 There is a challenge in selecting indicators that are minimal and applicable but also able to
sufficiently explain the issue in question.
 Weighting was avoided because social vulnerability could not be adequately determined.
Experts often do not feel comfortable weighting social vulnerability factors for a specific area
based on generalized knowledge.
 There are concerns regarding the consequent stereotyping that results from social vulnerability
indices. It is for this reason that I have opted not to include race as a vulnerability factor.
Clark, G.E., Moser, S.C., Ratrick, S.J., Dow, K., Meyes, W.B., Emani, S., Jin, W., Kasperson,
J.X., Kasperson, R.E., & Schwarz, H.E. (1998). Assessing the Vulnerability of Coastal
Communities to Extreme Storms: the Case of Reverse, MA., USA. Mitigation and
Adaptation Strategies for Global Change, 3, 59-82.
Clark et al. (1998) conducted a vulnerability assessment of Revere, Massachusetts. They used blocklevel census variables based on commonly referenced determinants of vulnerability, including age,
disabilities, family structure and social networks, housing and built environment, income and material
resources, lifelines (e.g., transportation), occupation, race and ethnicity. Census data was selected due
to both its availability and familiarity among local emergency managers. While recognizing varying
vulnerabilities among households, Clark et al. determined that the block group was the most practical
unit to use in aiding local officials with resource allocation.
They used a factor analysis to cluster variables into five thematic sets of measures or factor groupings,
which include poverty, transience, disabilities, immigrants, and young families.
The factor scores for each individual factor and its composite variables were then mapped according to
block groups in Revere. The researchers then combined each separate multidimensional factor map
using two different methods: (1) averaging to provide an absolute index and (2) data envelopment
analysis (DEA) to provide a relative measure. They noted that a weighted average is generally the most
common way to combine factors, but that this method is not ideal given the subjectivity of weights.
DEA index is conceptually similar to the weighted average but uses an optimization model.
Clark et al. then looked how social vulnerability interacted with physical exposure by adapting a
FEMA Insurance Rate Map of flood zones to Revere and identifying areas that are physically high-risk
and socioeconomically vulnerable.
One important consideration for hazard mitigation and response planning is that it is important to look
back at what makes a particular area more vulnerable in order to tailor strategies accordingly.
3. GIS LAYERS
a. Geophysical Risk (Factor Groupings: SLOSH Zones)
 Name and Description: SLOSH Model Hurricane Inundation Zones
According to the metadata, hurricane storm surge zones are based on NOAA
SLOSH model projections of vertical surge heights associated with Saffir Simpson scale category 1 - 4 storms. The model used multiple storm landfall
locations to generate worst case flooding. New York States uses inundation
zones to determine evacuation areas and target hurricane preparedness
activities.
Source: New York State Emergency Management Office
URL: http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1043
Key Attributes: Polygon SLOSH Zone areas
b. Socioeconomic Vulnerability (Factor Groupings: Age and Special Needs, Income
and Material Resources, Lifelines)
Age
 Name and Description: YOUTH AND ELDERLY POPULATION derived
from the 2010 SF-1 Table DP-1: Profile of General Population and Housing
Characteristics: 2010
Source: US Census Bureau 2010 Census
URL:
http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?
pid=DEC_10_SF1_SF1DP1&prodType=table
Key Attributes: Total population, total population < 5 years, and total
population > 65 years
 Name and Description: NURSING HOMES
Source: NYC Open Data—NYS Department of Health
URL: https://data.cityofnewyork.us/Health/Nursing-Homes/9tqc-rnkr
Key Attributes: Address
Income and Material Resources
 Name and Description: POVERTY STATUS derived from Table S1701
Poverty Status in the Past 12 Months
Source: US Census Bureau 2007-2011 American Community Survey 5-Year
Estimates
URL:
http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?
pid=ACS_11_5YR_S1701&prodType=table
Key Attributes: Population below poverty level as a percentage of total
 Name and Description: VEHICLE ACCESS, specifically housing units
with One or More Vehicle, derived from Table DP04: Selected Housing Units
Source: US Census Bureau 2007-2011 American Community Survey 5-Year
Estimates
URL:
http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?
pid=ACS_11_5YR_DP04&prodType=table
Key Attributes: Percentage of housing units with one or more vehicle
Lifeline Variables
 Name and Description: 911 RECEIVING HOSPITALS shows the location
of 911 receiving hospitals in New York City. According to NYC Open Data,
“911 receiving hospitals are those that receive patients from ambulances
dispatched by EMS. 911 receiving hospitals must fulfill criteria set by EMS,
including an emergency room staffed by experienced emergency physicians,
specialists on call 24 hours a day; an intensive care unit; and various levels of
staffing and equipment availability”.
Source: NYC Open Data
URL: https://data.cityofnewyork.us/Public-Safety/911-ReceivingHospitals/tzvj-yhh2
Key Attributes: Addresses
 Name and Description: SUBWAY ENTRANCES
Source: NYC Open Data--DOITT as derived from MTA
URL: https://nycopendata.socrata.com/Transportation/SubwayEntrances/drex-xx56?
Key Attributes: Addresses
 Name and Description: HURRICANE EVACUATION CENTERS People requiring shelter during a hurricane are processed at a hurricane
evacuation center and transported to a hurricane shelter.
Source: NYC Open Data—NYC Office of Emergency Management (OEM)
URL: https://nycopendata.socrata.com/Public-Safety/Hurricane-EvacuationCenters/ayer-cga7?
Key Attributes: Address
4. Analysis Steps
The analysis will follow the sequence of steps used in similar vulnerability or suitability analyses. The
figure below shows the analytical sequence, which will be described in further detail in this section.
There will be four total factor groupings, each of which will be mapped separately to demonstrate the
“source” of risk or vulnerability for each tract before being integrated into an overall vulnerability
score for each tract that combines each of these groupings. The factor groupings and indicators that
will be used to determine the score for each grouping are as follows:
a. Geophysical Risk Factoring Grouping
 Factor Groupings: SLOSH Zones
o Indicator: SLOSH Zones
b. Socioeconomic Vulnerability Factor Groupings
 Age and Special Needs
o Population > 65
o Population < 5
o Nursing Homes
 Income and Material Resources
o Poverty
o Vehicle Access
 Lifelines
o 911 Receiving Hospitals
o Subway Entrances
o Hurricane Evacuation Centers
The following is an outline of the specific analysis to be completed for each indicator.
SEA, LAKE AND OVERLAND SURGE FROM HURRICANES (SLOSH) MODEL
HURRICANE INUNDATION ZONES
Census tracts with land in a SLOSH Zone.
Data Processing and Analysis:
1. Use Intersect tool to identify census tracts in SLOSH zones.
2. Use field calculator to determine the percentage of the tract in the SLOSH zone. I will refer to
the clip fraction example used in class.
Percentages divided into 3 categories. Lowest percentage receives a score of 1. The highest percentage
receives a score of 3.
YOUTH
Percentage of youth < 5 per census tract.
Data Processing and Analysis:
3. Join census table with census tracts.
4. Depict percentage of the population < 5 in symbology.
5. No normalization is required. Data is expressed as a percentage of the census tract population.
Percentages divided into 3 categories. Lowest percentage receives a score of 1. The highest percentage
receives a score of 3.
ELDERLY
Percentage of population > 65 per census tract.
Percentages divided into 3 categories. Lowest percentage receives a score of 1. The highest percentage
receives a score of 3.
Data Processing and Analysis:
1. Join census table with census tracts.
2. Depict percentage of the population > 65 in symbology.
3. No normalization is required. Data is expressed as a percentage of the census tract population.
NURSING HOMES
Nursing homes will not be incorporated into the vulnerability scoring system. Instead, they will be
highlighted on the map showing age and special needs-related vulnerability. The map will highlight the
location of all nursing homes, also indicating those which are in SLOSH Zones and thus more
vulnerable during a storm event.
POVERTY
Percentage of the population below the poverty level in the past 12 months per census tract.
Percentages divided into 3 categories. Lowest percentage receives a score of 1. The highest percentage
receives a score of 3.
Data Processing and Analysis:
1. Join census table with census tracts.
2. Depict percentage of the below poverty line in symbology.
3. No normalization is required. Data is expressed as a percentage of the census tract population.
VEHICLE ACCESS
Percentage of population with one or more vehicle.
Percentages divided into 3 categories. Lowest percentage receives a score of 1. The highest percentage
receives a score of 3.
Data Processing and Analysis:
4. Join census table with census tracts.
5. Depict percentage of the population with one or more vehicle in symbology.
6. No normalization is required. Data is expressed as a percentage of the census tract population.
911 RECEIVING HOSPITALS
Mean distance of each census tract to a 911 receiving hospital.
There will be 3 distance categories. Closest tracts receive a score of 1. Farthest tracts receive a score of
3.
Data Processing and Analysis:
1. Calculate Euclidean distance. Note that Euclidean distance buffers will be smaller to account
for the fact that network analysis is not being used.
2. Use Zonal Overlay as Table and calculate mean distance for each census tract.
3. Join with census tracts.
SUBWAY ENTRANCES
Mean distance to a subway entrance.
There will be 3 distance categories. Closest tracts receive a score of 1. Farthest tracts receive a score of
3.
Data Processing and Analysis:
1. Calculate Euclidean distance.
2. Use Zonal Overlay as Table and calculate mean distance for each census tract.
3. Join with census tracts.
HURRICANE EVACUATION CENTERS
Mean distance to hurricane evacuation center.
There will be 3 distance categories. Closest tracts receive a score of 1. Farthest tracts receive a score of
3.
Data Processing and Analysis:
1. Calculate Euclidean distance.
2. Use Zonal Overlay as Table and calculate mean distance for each census tract.
3. Join with census tracts.
5. Products
The final posters will include a map showing the vulnerability results for each factor grouping
distributed by tract, as well as a map depicting cumulative vulnerability scores.