Jennifer Bogle
Assignment 7 – Technical Paper
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. LITERATURE REVIEW
The following section provides a detailed summary literature reviewed before conducting the
assessment. While each article provides valuable information on the approach to vulnerability
assessments, there are two key points arising from the literature reviewed. First, there is no
standard for spatial vulnerability analyses, both in terms of weighting criteria and selection of the
criteria themselves. Second, weighting vulnerability criteria is a common practice but is
generally criticized for its subjectivity.
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. 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.
To measure social vulnerability, Chakraborty et al. 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 as a means to inform disaster
risk reduction and capacity building efforts after identifying the level of vulnerability and coping
capacity in communities. They also indicate that no standard measure 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 Rural-Urban Mapping Project (GRUMP) population datasets.
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 risk and vulnerability assessments. Based on a
review of a vulnerability index in Germany, he offers the following conclusions:
 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
block-level 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 (considered a factor grouping in itself, comprised of
SLOSH Zones and Population Density)
 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
 Name and Description: POPULATION DENSITY derived from the
2010 SF-1 Table H10: Total Population in Housing units
Source: US Census Bureau 2010 Census
URL:
http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refr
esh=t
Key Attributes: Total population in housing units and census tract area
a. 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.x
html?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 5Year Estimates
URL:
http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.x
html?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 5Year Estimates
URL:
http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.x
html?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/HurricaneEvacuation-Centers/ayer-cga7?
Key Attributes: Address
4. ANALYSIS STEPS
Four total factor groupings were mapped separately to show the specific cause of risk or
vulnerability in each census tract before aggregating the results of each factor grouping to
calculate total vulnerability scores. The indicators used to determine the score for each grouping
were as follows:
a. Geophysical Risk Factoring Grouping
 Factor Groupings: SLOSH Zones
o Indicator: % of Census Tract in SLOSH Zone
o Indicator: Population Density / SQ-KM
b. Socioeconomic Vulnerability Factor Groupings
 Age and Special Needs
o Indicator: % of Population > 65
o Indicator: % of Population < 5
o Nursing homes in SLOSH zones were highlighted to show highly
vulnerable points, but were not included in the score.
 Income and Material Resources
o Indicator: % of Population with Poverty Status in Past 12 Months*
o Indicator: % of Population
 Lifelines
o Indicator: Mean Distance to 911 Receiving Hospitals
o Indicator: Mean Distance Subway Entrances
o Indicator: Mean Distance Hurricane Evacuation Centers
* This is out of the number of persons for whom poverty status was determined rather than the
total population in the census tract.
GEOPHYSICAL RISK
SLOSH Zone Analysis Steps
1. Used the Select by Location Tool to select census tracts intersecting with SLOSH zones.
2. Created a new data layer depicting only tracts intersecting with SLOSH zones.
3. Clipped this new data layer to SLOSH zones to create a new layer that would include the
SLOSH area in each tract. This enabled me to calculate the tract area in the SLOSH zone.
4. Added fields for calculating the SLOSH (clipped) area and the ratio of SLOSH to total
tract area using Calculate Geometry and Field Calculator, respectively.
5. Joined this table with the TIGER census tract layer.
6. Used Polygon to Raster to convert this layer to a raster to show the calculated ratio.
Lesson Learned: Originally, I planned to use the Intersect tool for this part of the analysis.
However, the SLOSH zone was divided into four categories, which cause complex, disorganized
tables after the intersect. Using Select by Location overcame this issue.
Population Density Analysis Steps
1. Joined occupied housing unit population with TIGER census tracts.
2. Exported data to create a layer in which to use Field Calculator to calculate the total
population in housing units per square kilometer.
3. Used Polygon to Raster tool to convert this layer to a raster.
Geophysical Risk Map
1. Reclassified the SLOSH zone and density rasters to have five categories. Census tracts
outside SLOSH zones had no data as a result of the clipping to tracts intersecting with
SLOSH zones. In the SLOSH zone reclassification, no data areas were included in the 1
score.
2. Used Raster Calculator to add SLOSH zone area and population density calculated
above.
Lesson Learned: The tracts with no data affected the total vulnerability score. Tracts with no
data in the SLOSH layer later showed up as blank in the total vulnerability map. The
reclassification of no data tracts resolved this issue.
SOCIAL VULNERABILITY
Age Analysis Steps
1. Joined age census table data with TIGER census tracts.
2. Exported data in this joined layer and re-imported it as a new layer to then be able to
work with the data using Field Calculator.
3. Used the Field Calculator to calculate the percentage of the population under age 5 and
the percentage of the population over age 65.
4. Used Polygon to Raster to create separate rasters for each of these age categories.
5. Used Raster Calculator to add the percentage of the population in each census tract that is
either under 5 or above 65.
6. Reclassified the new raster to include five vulnerability categories.
Income and Material Resource Analysis Steps
1. Joined income and material resource table data with TIGER census tracts.
2. Exported data in this joined layer and re-imported it as a new layer to then be able to
work with the data using Field Calculator.
3. In each layer, used the Field Calculator to calculate the percentage of the population
reporting poverty status in the past 12 months and the percentage of households without a
vehicle.
4. Used Polygon to Raster to create separate rasters for the two indicators.
5. Used Raster Calculator to add the rasters.
6. Reclassified the new raster to include five vulnerability categories.
Lesson Learned: Classifying the raster data of each category into 5 classes was useful for
reference after reclassifying the new rasters using a parallel 5-class grouping. It enabled me to be
able to refer back to the specific characteristics of the population for high risk areas (e.g., the
percentage of the population over age 65 for a tract that scored 5 in age-related vulnerability.
Without this step, it would have been difficult to explain the cause behind the high vulnerability
score without redoing the steps.
Lifelines Steps
1. Calculated the Euclidean Distance for 911 receiving hospitals, subway entrances, and
hurricane evacuation centers (30-meter cell size).
2. Reclassified each to quantiles.
3. Completed a Raster Calculator to add the reclassified Euclidean distances for each of the
three lifeline categories.
4. Calculated the mean score for each tract based on the summation of the three lifeline
categories based on the step above using Zonal Statistics as Table.
5. Joined the Zonal Statistics table with the census tracts layer.
6. Converted the polygon to a raster based on the mean.
TOTAL VULNERABILITY
1. Used the Raster Calculator to add the raster layers for each of the four factor groupings. I
weighted each factor equally in the calculation.
2. Reclassified the raster into five categories to provide the overall vulnerability score for
each tract.
5. CONCLUSIONS
The methods used in this analysis seemed to work well. The results are logical, as the
factor grouping scores were in line with the demographic characteristics of Manhattan, and total
vulnerability aligned with the scores of each factor grouping. One interesting aspect of the
analysis was that it showed that income and material resources are a major source of
vulnerability across tracts. In a sense, Manhattan cannot escape being highly vulnerable in this
category, as it is typical to not own a vehicle in Manhattan regardless of income.
While the technical approach in GIS worked well, conducting the vulnerability
assessment highlighted the fact that there are major constraints to the process. The two common
criticisms with vulnerability assessments are the selection and weighting of indicators and factor
groupings. These are also two of the most critical components of the analysis, which may lead
one to question its value. The selection of indicators was problematic in this analysis, particularly
given that there is no recent data on disabled populations in Manhattan, which is a very
important indicator of vulnerability. Future analyses could also be improved by incorporating
data that reflects prior storm impacts. For instance, building storm damage assessments,
averaged over time would help show areas where income vulnerability is of greater concern.
Despite these constraints, there are two points to bear in mind. First, collecting input from
communities on which indicators to use and how they should be weighted would help generate a
more useful analysis. Second, this type of analysis can be considered as a first step in gaining a
general sense of potentially highly vulnerable areas before initiating further data collection in the
areas of concern.