O R I G I N A L P A P E R
Assessment of social vulnerability to natural disasters:
a comparative study
D. K. Yoon
Received: 11 January 2012 / Accepted: 4 April 2012 / Published online: 18 April 2012 ÓSpringer Science+Business Media B.V. 2012
Abstract The purpose of this study is to examine and compare the methodologies being developed in assessing social vulnerability to natural disasters. Existing vulnerability lit- erature shows that two methods have been used in developing social vulnerability indexes:
(1) a deductive approach based on a theoretical understanding of relationships and (2) an inductive approach based on statistical relationships (Adger et al. in New indicators of vulnerability and adaptive capacity. Tyndall Centre for Climate Change Research, Nor- wich, 2004). Two techniques were also utilized in aggregating social vulnerability indi- cators: (1) a deductive approach using standardization techniques such aszscores or linear scaling (Wu et al. in Clim Res 22:255–270,2002; Chakraborty et al. in Nat Hazards Rev 6(1):23–33,2005) and (2) an inductive approach using data-reduction techniques such as factor analysis (Clark et al. in Mitig Adapt Strateg Glob Change 3(1):59–82,1998; Cutter et al. Soc Sci Quart 84(2):242–261,2003). This study empirically compares deductive and inductive index development and indicator aggregation methods in assessing social vul- nerability to natural disasters in the Gulf of Mexico and Atlantic coastal areas. The aggregated social vulnerability index is used to examine a relationship with disaster losses in the Gulf of Mexico and Atlantic coastal areas. The results show that coastal counties with more vulnerability in terms of social achieved status are positively associated with disaster damages, while variations in the development of the index using deductive and inductive measurement approaches produce different outcomes.
Keywords Social vulnerabilityNatural disastersAssessment methods Factor analysisStandardization
D. K. Yoon (&)
Department of Emergency Management, North Dakota State University, 107 Music Hall, Dept. #2351, P.O. Box 6050, Fargo, ND 58108, USA e-mail: [email protected]
DOI 10.1007/s11069-012-0189-2
1 Introduction
Typically, the economic losses and fatalities due to natural disasters are unevenly dis- tributed among and within nations, regions, communities, and groups of individuals.
Vulnerable groups are those who are likely to suffer a disproportionate share of the effects of hazardous events. The degree to which populations and communities are vulnerable to natural disasters is determined not only by a population’s proximity to the source of risk but also by its social vulnerability status. Social vulnerability describes those character- istics of the population that influence the capacity of the community to prepare for, respond to, and recover from disasters (Cannon 1994). Socially vulnerable populations are less likely to have access to critical resources during disaster events. So, understanding social vulnerability helps to explain why different communities can experience the same hazard event differently (Morrow2008). Understanding the differential impact of hazard events is critical to reducing the negative impact of natural disasters. Consequently, over the past two decades, there have been a number of efforts, including conceptual, methodological, and practical challenges, to assess social vulnerability (McCarthy et al.2001; Birkmann 2006). There is still no consensus, however, on the quantitative methodology best suited to assess social vulnerability.
The purpose of this four-part study is to examine and compare the methodologies being developed in assessing social vulnerability to natural disasters. It also empirically examines the relationship between social vulnerability and natural disaster damage in the Gulf of Mexico and Atlantic coastal counties as a case study. First, a literature review of the methodologies of social vulnerability measurement was conducted. Second, a case study compared these two methods. Third, an empirical examination of the relationship between a social vulnerability index based on four different methodologies and disaster damages was conducted. Finally, challenges and issues were identified regarding current method- ologies of social vulnerability measurement.
2 Assessment of social vulnerability
The literature on social vulnerability has clearly established that this concept is multidi- mensional. For example, vulnerability reflects poverty (Fothergill and Peek 2004; Long 2007), race and ethnicity (Fothergill et al.1999; Peacock et al.2000), gender (Enarson and Morrow 1998; Enarson et al. 2006), and age (Bolin and Klenow 1983; Ngo 2001;
Anderson2005; Phillips and Hewett2005; Kar2009; Smith et al.2009). An assessment of vulnerability without all or most of these dimensions is likely to be inadequate; thus, this measure must be some sort of a composite measure or index (Adger et al.2004; Gall2007;
Barnett et al.2008).
Creating such an index raises two broad questions. First, how should the components of a global index be selected? Second, once selected, how should these components combine to form the index? The answers to these questions, if not designated as universal, will lead to the creation of different indexes. Unfortunately, while a great deal of effort has been expended to create social vulnerability indexes, the importance of these two major ques- tions has not been directly addressed. The consequence of multiple possible answers has never been fully assessed, leading us to question the degree to which it matters how one measures.
In this paper, I offer a framework for organizing different approaches to the two major questions identified above, selection and combination, and I offer a case study analysis
comparing the extent to which several distinct social vulnerability indexes produced similar or dissimilar results in assessing the vulnerability of counties along the Atlantic and Gulf coasts. The framework provides a useful organizational tool for both past and future research efforts. The implications for emergency management of these different approa- ches and results were discussed at the conclusion. The following sections review existing methodologies to select and combine indexes (variables) in assessing social vulnerability to natural disasters in terms of inductive and deductive approach.
3 Methodologies for selection of social vulnerability indexes
Vulnerability literature shows that two primary methods were used in assessing social vulnerability: (1) a deductive approach based on a theoretical understanding of relation- ships and (2) an inductive approach based on statistical relationships.
3.1 Deductive approach
The deductive approach selects a limited number of variables deductively to create a social vulnerability index based on a priori theory and knowledge from existing literature. There are variations in the extent to which a researcher can employ a deductive approach. A deductive approach simply uses variables identified in previous research without any additional rationale for the selection of those variables. Table1 shows a collection by researchers of smaller deductively selected sets of variables to assess social vulnerability.
Cutter et al. (2000) chose eight variables deductively to quantify social vulnerability in examining the vulnerability of populations living inside hazard zones for Georgetown County, South Carolina. Wu et al. (2002) selected nine variables to assess the social vulnerability of Cape May County, New Jersey. Chakraborty et al. (2005) chose ten variables to determine social vulnerability for evacuation in Hillsborough County, Florida.
Zahran et al. (2008) selected only three variables as proxy to assess social vulnerability.
These selective social vulnerability variables served the needs of researchers in answering their research questions and testing their concepts.
3.2 Inductive approach
The inductive approach to assessing social vulnerability is to create a systematic social vulnerability index (SoVI) using extensive sets of variables that influence social vulner- ability (Cutter et al.2003). Even though the inductive approach to selecting variables also draws from vulnerability literature, this approach differs from the deductive approach in that it includes all possible variables mentioned by literatures to assess social vulnerability.
Cutter et al. (2003) undertook an extensive analysis of the pool of vulnerability literature, drawing together a set of 85 variables of social vulnerability. Among these variables, Cutter et al. (2003) used a normalized set of 42 variables in a statistical analysis to measure social vulnerability for over 3,000 counties in the United States in 1990. Many other researchers have borrowed Cutter’s social vulnerability index (SoVI) to examine social vulnerability to specific climate variability hazards among areas of coastal erosion (Boruff et al. 2005), coastal inundation and storm surge (Rygel et al. 2006), hurricanes (Myers et al.2008), and flooding (Azar and Rain2007; Fekete2009).
4 Methodologies for combination of social vulnerability indexes
Several methods have been suggested to measure or aggregate social vulnerability measures on the basis of multiple variables. Two main methods were utilized in aggregating social vulnerability variables: (1) the deductive approach used standardization such aszscores or linear scaling (Wu et al.2002; Chakraborty et al.2005) and (2) the inductive approach used data-reduction technique such as principal components analysis (PCA), one of the most common multivariate factorial approaches (Clark et al.1998; Cutter et al.2003).
4.1 Deductive measurement approach—standardization
Researchers who chose selective variables deductively for social vulnerability measure- ment standardized or normalized the variables to aggregate them in creating a composite
Table 1 Selected variables for
social vulnerability assessment Authors Variables Cutter et al. (2000) Total population
Total housing units Number of females
Number of non-white residents Number of people under age 18 Number of people over age 65 Mean house value
Number of mobile homes Wu et al. (2002) Total population
Total housing units Number of females
Number of non-white residents Number of people under 18 Number of people over 60
Number of female-headed single-parent households
Number of renter-occupied housing units Median house value
Chakraborty et al. (2005) Total population Number of housing units Population age 5 years or under Population age over 85 years Number of mobile homes Poverty
Telephone availability Vehicle availability Institutionalized population Population with disabilities Zahran et al. (2008) Non-poverty (%)
White (%)
Median household income
value. Since the variables of any given social vulnerability index are often measured in different units, a standardization procedure is necessary to eliminate the unit of mea- surement by transforming the data into a small and specified range of scale.zscore nor- malization, maximum value transformation, and min–max rescaling techniques are commonly used to make variables unitless. These methods are kinds of linear aggregation techniques used to construct a final score.
4.1.1 z score transformation
zscore transformation converts all indicators to a common scale with a mean of zero and a standard deviation of one, as in the following equation:
Z ¼ ðscoremeanÞ=standard deviation:
Zahran et al. (2008) selected three variables for social vulnerability measurement and converted those values to standardized scores usingzscores and then summed all values to create a composite social vulnerability score.
4.1.2 Maximum value transformation (ratio of value)
The maximum value transformation technique is used to rescale values between zero and one. It is defined as the ratio of the value of that variable (Xi) to the maximum value (Xmax) for the variable. Higher index values indicate higher vulnerability, as in the following equation:
Ri¼ Xi
Xmax
:
Cutter et al. (2000), Wu et al. (2002), and Chakraborty et al. (2005) used a maximum value transformation technique to aggregate their selected social vulnerability index variables.
4.1.3 Min–max rescaling transformation
Min–Max rescaling is a method in which each variable is decomposed into an identical range between zero and one, with a score of 0 being the worst rank for a specific indicator and a score of 1 being the best. All other values are then scaled between the minimum and maximum values. This scaling procedure ultimately subtracts the minimum value (Xmin) and divides by the range of the indicator values (the maximum value (Xmax) subtracts the minimum value (Xmin)), as illustrated by the following equation:
Vi¼ XiXmin
XmaxXmin
:
St. Bernard (2007) and Cutter et al. (2010) used the min–max rescaling transformation method to aggregate variables and create composite scores.
4.2 Inductive measurement approach—factor analysis
Another approach to quantify the multivariate nature of a population is the use of exploratory or inductive factor analysis, a data-reduction technique that has been widely used in human geography research (Mather and Openshaw1974; Scott1975; Clark et al.
1998). Principal component analysis (PCA) is a statistical approach that uncovers the underlying dimensions of a large set of variables and mathematically transforms data into a smaller set of components (factors) based on inter-correlated variables (see Fig.1).
Researchers, including Cutter et al. (2003), Boruff et al. (2005), Boruff and Cutter (2007), Cutter and Finch (2008), and Rygel et al. (2006), used an inductive factor analytic approach to assess social vulnerability by using factor scores.
5 Study areas
This study focuses on all 196 counties in the Gulf of Mexico and Atlantic coastal areas.
The logic of the case study areas’ selections is following reasons. First, the goal of this study is to compare different methodologies for constructing social vulnerability indexes.
For inductive method of assessing social vulnerability, this study employed the original 42 SoVI variables, which were selected by Cutter et al. (2003). These original 42 variables were all county-level variables that measure various aspects of the counties’ socioeco- nomic and built environment conditions. And some of these 42 variables are not available for census-block or census-block group areas (Wood et al.2010). Because of this limitation of data availability, this study used county-level data as the unit of analysis.
Second, the second goal of this study is to examine the relationship between the aggregated social vulnerability scores and disaster property damages. The data on disaster property damages for this study were collected from the Spatial Hazard Events and Losses Database for the United States (SHELDUS) database. This database consists of a county- level inventory of 18 different natural hazard types, including tropical storms, hurricanes, floods, wildfires, tornadoes, and drought. Thus, this study used county-level data to examine the relationship between social vulnerability and disaster property damage.
Third, to control for geographical variance, this study chose coastal counties in the Gulf of Mexico and Atlantic coastal areas (similar geographical condition and disaster type) for the analysis. The Gulf of Mexico and Atlantic coastal counties are particularly susceptible to a wide range of natural hazards from hurricanes and severe storms to floods and landslides. The selection of the Gulf of Mexico and Atlantic coastal counties was based on the original USGS selection criteria, which are counties that had some portion of their land area directly exposed to the Gulf of Mexico and Atlantic Ocean.
Variable 1
Variable 2
Variable 3
Variable 4
Variable 5
Factor A
Factor B Variable 1
Variable 2
Variable 3
Variable 4
Variable 5
Factor A
Factor B Fig. 1 The model for principal
components analysis
6 Methods
This study examines and compares deductive and inductive methods in assessing social vulnerability to natural disasters in the Gulf of Mexico and Atlantic coastal counties. For the inductive measurement approach, this study replicates the method used by Cutter et al.
(2003) to select social vulnerability index variables (i.e., Social Vulnerability Index, SoVI).
For the deductive measurement approach, this study employs all social vulnerability index variables used by Cutter et al. (2003), but creates a different social vulnerability index in terms of the concepts of a people’s vulnerability and a place’s vulnerability. A people’s vulnerability variables are determined based on social achieved status (e.g., poverty, education level, employment, occupation, etc.) and social ascribed status (e.g., gender, age, race, ethnicity, etc.) for this study. A place’s vulnerability variables are determined by economic and built environmental status. These two indices are then aggregated using factor analysis (principal components analysis) and standardization methods (zscores, the ratio of value, and min–max rescaling transformation), respectively. These four aggregated social vulnerability index scores based on inductive (factor analysis) and deductive methods (three standardization methods) are compared and mapped using GIS to show spatial differences. Moreover, these aggregated social vulnerability index scores are used to examine their relationships with disaster losses in each of the 196 Gulf of Mexico and Atlantic coastal counties from Texas to Maine.
6.1 Comparison of index (variables) selection methods 6.1.1 Inductive approach
To create an index of social vulnerability for the 196 Gulf of Mexico and Atlantic coastal counties, this study replicated the methods developed by Cutter et al. (2003). Using data from the US Census (City and County Data Books for 2000) and the Hazards and Vul- nerability Research Institute (HVRI) of University of South Carolina, 42 socioeconomic variables were collected and used for the social vulnerability index (Table2). These 42 variables were normalized to percentages, per capita, or density functions.
6.1.2 Deductive approach
To create a social vulnerability index using the deductive approach, this study employed the original 42 variables used by Cutter et al. (2003), but they were categorized in terms of the concepts of people and place vulnerability. People’s vulnerability variables are chosen based on ascribed and achieved social status. Ascribed social status is assigned at birth, which is neither earned nor chosen by individuals. These assigned social status variables were selected based on gender, age, race, and ethnic backgrounds (Linton1936; Foladare 1969). Achieved social status is a social position that is acquired or earned on the basis of personal abilities and efforts. These achieved social status variables used in this study were selected based on education, occupation, and income (Linton1936; Foladare1969).
This study used 20 variables for people’s social vulnerability. Seventeen variables were employed from Cutter’s et al. (2003) original variables and four new variables were added.
The variable of language isolation, which is the percent of a population 5 years and over in linguistically isolated households, is included for ascribed social status. Two variables, the percent of householders with no vehicle available, and the percent of housing units with no
Table 2 Social vulnerability index variables for inductive approach method No. Variables Description
1 PBLACK00 Percent African American 2 PINDIAN00 Percent Native American
3 PASIAN00 Percent Asian
4 PSPANISH00 Percent Hispanic
5 MEDAGE00 Median age
6 PAGE500 Percent of population under 5 years old 7 PAGE6500 Percent of population over 65 years
8 PFEM00 Percent females
9 PFHHCH00 Percent female-headed households, no spouse present 10 PERCAP00 Per capita income (in dollars)
14 PED12LES00 Percent of population 25 years or older with no high school diploma 15 PRICH00 Percent of households earning more than $75,000
16 PPOVTY00 Percent living in poverty
17 PRENTER00 Percent renter-occupied housing units
11 HSEMDVAL00 Median dollar value of owner-occupied housing 13 AVEHSEHOLD00 Average number of people per household
12 MEDRENT00 Median rent (in dollars) for renter-occupied housing units 18 PLABOR00 Percent of the population participating in the labor force 19 PFEMLBR00 Percent females participating in civilian labor force 20 PUNEMPLOY00 Percent of civilian labor force unemployed
21 PAGRI00 Percent employed in primary extractive industries (farming, fishing, mining, and forestry)
22 PTRAN00 Percent employed in transportation, communications, and other public utilities 23 PSERV00 Percent employed in service occupations
24 PSSREC00 Per capita Social Security recipients
25 MANUDEN00 Number of manufacturing establishments per square mile 26 EARNDEN00 Earnings (in $1,000) in all industries per square mile 27 COMDEVDN00 Number of commercial establishments per square mile 28 PROPERTYDEN00 Value of all property and farm products sold per square mile 29 DEBTREV00 General local government debt to revenue ratio
30 HUDENT00 Number of housing units per square mile 31 PURBAN00 Percent urban population
32 PRURALFRM00 Percent rural farm population
33 PLANDFRM00 Land in farms as a percent of total land 34 PMOBILE00 Percent of housing units that are mobile homes
35 HUPERMITDEN00 Number of housing permits per new residential construction per square mile 36 PPOPCHG9000 Percent population change, 1990–2000
37 MIGRA00 Net international migration, 2000–2004
38 BRATE00 Birth rate (number of births per 1,000 population) 39 NURSINGPC00 Per capita residents in nursing homes
40 HOSPITALPC00 Per capita number of community hospitals 41 PHYSICN00 Number of physicians per 100,000 population
42 PVOTE00 Vote cast for president, 2000—percent voting for leading party (Republican)
telephone service available were included for achieved social status (Table3). Vulnera- bility variables of a place were selected in terms of community economic status and built environmental characteristics. Ten economic status variables include values of property and housing, government debt, business establishment, and labor force. These variables were borrowed from Cutter’s et al. (2003) social vulnerability index. Variables for com- munity built environmental characteristics included housing conditions (housing density, housing permits, and percent of mobile homes) and urban population. A variable of percent of housing units built in 1950 or earlier was included in the place’s vulnerability index.
Overall, this study selected 35 variables to develop a social vulnerability index. Of the 42 social vulnerability index variables used by Cutter et al. (2003), eleven variables were excluded and four variables are newly added. Eleven variables that were excluded were duplicated or irrelevant variables for the conceptual framework: median age, households earning more than $75,000, females participating in civilian labor force, rural population, percent population change, net international migration, birth rate, per capita residents in nursing homes, per capita number of community hospitals, number of physicians per 100,000 population, and votes cast for president. The four variables newly included for the deductive social vulnerability index are linguistic isolation, vehicle availability, telephone availability, and housing built year.
6.2 Comparison of index combination methods 6.2.1 Inductive approach—factor analysis
The 42 socioeconomic variables were standardized by converting tozscores resulting in zero means and unit variances. A principal components analysis (PCA) was used to create vulnerability indices from the standardized variables. Using the varimax rotation option, 10 factors with eigenvalues greater than one were extracted. The ten factors explained most of the variation in the dataset. These factors explain 80.38 % of the variance among the Gulf of Mexico and Atlantic coastal counties (Table4). These factors were named based on the characteristics of the variables, specifically a dominant variable. For example, the first factor was named ‘‘density of the built environment’’ because the variables in this factor included the number of commercial establishment per square mile as a dominant variable, along with housing density and median owner-occupied housing value. Although general names are used to describe the ten components, more individual variables load highly onto those components than the names can represent (Rygel et al. 2006). For example, the component named ‘‘poverty’’ also includes high percentages and densities of African American people, less-educated people, and unemployment. All factors were rescaled so that positive values indicated higher levels of vulnerability, while negative values decrease vulnerability. The ten factor scores were placed in an additive model to produce the overall composite social vulnerability score.
6.2.2 Deductive approach—standardization (z score, ratio of value, and min–max rescaling)
The 35 deductively selected variables were transformed using z score, maximum value (ratio of value), and min–max rescaling techniques in terms of people’s social status including ascribed and achieved social status, place’s economic characteristics, and built environmental characteristics. To combine multiple variables in the assessment of social vulnerability, the arithmetic mean of the social vulnerability indices was calculated by
Table 3 Social vulnerability index variables for deductive approach method
Category Variables Description
People’s social vulnerability
Social ascribed status PFEM00 Percent females
PAGE500 Percent of population under 5 years old PAGE6500 Percent of population over 65 years PBLACK00 Percent African American PINDIAN00 Percent Native American PASIAN00 Percent Asian
PSPANISH00 Percent Hispanic
PLANG00 Percent of population 5 years and over in l inguistically isolated households Social achieved status PERCAP00 Per capita income (in dollars)
PPOVTY00 Percent living in poverty
PSSREC00 Per capita Social Security recipients AVEHSEHOLD00 Average number of people per household
PFHHCH00 Percent female-headed households, no spouse present, with children under 18 years
PED12LES00 Percent of population 25 years or older with no high school diploma
PUNEMPLOY00 Percent of civilian labor force unemployed PAGRI00 Percent employed in primary extractive industries
(farming, fishing, mining, and forestry)
PTRAN00 Percent employed in transportation, communications, and other public utilities
PSERV00 Percent employed in service occupations PNOVEH00 Percent of householders with no vehicle available PNOTEL00 Percent of housing units with no telephone service
available Place’s vulnerability
Economic status HSEMDVAL00 Median dollar value of owner-occupied housing
MEDRENT00 Median rent (in dollars) for renter-occupied housing units
PRENTER00 Percent renter-occupied housing units PLANDFRM00 Land in farms as a percent of total land MANUDEN00 Number of manufacturing establishments per
square mile
COMDEVDN00 Number of commercial establishments per square mile
PLABOR00 Percent of the population participating in the labor force
DEBTREV00 General local government debt to revenue ratio EARNDEN00 Earnings (in $1,000) in all industries per
square mile
PROPDEN00 Value of all property and farm products sold per square mile
dividing the sum of index values of all variables by the number of variables. When standardized in this manner, the values of the social vulnerability index range from 0 to 1 and are not affected by the number of variables included in the calculation. For example, to calculate people’s social vulnerability, the equally summed standardized value was divided by 20, where the number of social ascribed status variables is 8 and the number of social achieved status variables is 12. Finally, the overall composite social vulnerability score using standardization was calculated by the sum of the arithmetic average of people’s social vulnerability score (20 variables), and place’s vulnerability score (15 variables).
6.3 Comparison of 4 different combination methods
Four different aggregated social vulnerability scores were compared using Spearman’s rank correlation (Table5). In order to compare their social vulnerability index score, counties are rank-ordered based on their overall composite social vulnerability score.
Spearman’s rank correlation coefficient is a nonparametric rank statistic proposed as a measure of the strength of the association between two variables and ranges from a-1.00 to 1.00 (Hauke and Kossowski2011).
Table5 shows that the overall social vulnerability scores using three different stan- dardization methods are very highly correlated with each other. The coefficient of overall
Table 4 Dimensions of social vulnerability by inductive factor analysis Factor Name Percent variation
explained
Dominant variable Correlation
1 Density of the built environment
21.94 No. commercial establishments/mi2 0.99
2 Poverty 16.02 Living in poverty (%) 0.81
3 Age 12.49 Population under 5 years old (%) 0.88
4 Family structure 7.84 Female-headed households, no spouse present, with children (%)
0.72
5 Urban 5.17 Rural farm population (%) -0.86
6 Ethnicity–Hispanic 4.37 Percent Hispanic (%) 0.84
7 Unemployment 3.73 Unemployment (%) 0.77
8 Female 3.20 Female (%) 0.75
9 Occupation 2.96 Employed in transportation,
communication, and public utilities (%) 0.77 10 Social instability 2.66 Net international migration (%) 0.75 Table 3 continued
Category Variables Description
Built environmental characteristics
HUDENT00 Number of housing units per square mile PURBAN00 Percent urban population
HUPERMDEN00 Number of housing permits per new residential construction per square mile
PMOBILE00 Percent of housing units that are mobile homes PHUBLT5000 Percent of housing units that were built 1950
or earlier
social vulnerability between z score transformation and maximum value transformation (ratio of the value), betweenzscore transformation and min–max rescaling transformation, and between maximum value transformation and min–max rescaling transformation is 0.969, 0.981, and 0.993, respectively. This result indicates that there is no significant difference among standardization methods. Using the same social vulnerability index variables with different aggregation techniques does not create a difference in the overall social vulnerability index score. However, the coefficient of the overall social vulnerability score between standardization methods and factor analysis is between 0.6 and 0.8, which indicates a moderate correlation.
Figure2 shows the overall social vulnerability of the Gulf of Mexico and Atlantic coastal counties using four different indicator aggregation methods. In order to map the geographic patterns of social vulnerability scores in each county, I classified the visuali- zation of mapped scores using standard deviations from the mean since the computed social vulnerability scores do not itself have any absolute interpretation. I define high and low social vulnerability counties as those counties with social vulnerability scores greater than one standard deviations from the mean (high vulnerability[1 standard deviation; low vulnerability\-1 standard deviation).
Mapping the overall social vulnerability scores for the study areas shows that the most vulnerable counties appear in the Gulf of Mexico counties, particularly Texas, Louisiana, and counties in West Central Florida with some notable exceptions. Using factor analysis, the highest levels of socioeconomic vulnerability are found in counties in southern Texas and in Louisiana coastal counties. In Texas and Louisiana, high social vulnerability index scores are due to large percentages of Hispanic population, and high levels of poverty and unemployment rate. Using standardization methods, however, the highest levels of socioeconomic vulnerability are found in southern Texas and in counties in West Central Florida. Along the Florida coast, high social vulnerability index scores are attributed to residents’ ascribed social status such as, race and age. For example, communities with higher proportion of Hispanic populations and elderly populations display higher social vulnerability score. Also contributing to high social vulnerability index is the built envi- ronmental characteristic such as high percentage of mobile homes.
Tables6and7show the 10 least and most vulnerable counties in terms of four different social vulnerability index aggregation methods. Table6 shows the least vulnerable counties in the Gulf of Mexico and Atlantic coastal counties. Counties labeled as the least vulnerable using the different standardization methods are clustered in New England, from Virginia through New Hampshire to Maine. These counties are all relatively homoge- nous—wealthy, white, and highly educated—characteristics that lower the level of social vulnerability. Counties’ rank order for the 10 least vulnerable counties produced using factor analysis is similar to that produced by standardization methods. Table6indicates that Kenedy County in Texas is ranked as the least vulnerable county using factor analysis.
Table 5 Comparison of the overall social vulnerability scores using Spearman’s rank correlation zscore Ratio of the value Min–max rescaling Factor analysis
zscore 1
Ratio of the value 0.969** 1
Min–max rescaling 0.981** 0.993** 1
Factor analysis 0.714** 0.748** 0.737** 1
** Correlation is significant at the 0.01 level (2-tailed)
The low social vulnerability score for Kenedy County is based primarily on its small total population that has little ethnic diversity. In 2000, there were only around 400 residents living in the county.
Table7shows that the most socially vulnerable county in the study area is New York County based on standardization methods and Bronx County based on factor analysis. The outcome is largely based on the density of the built environment. The overall vulnerability
Fig. 2 Overall social vulnerability of the Gulf of Mexico and Atlantic coastal counties based on four different methods
score accounts for the placement of King County and Queens County among the top five most vulnerable counties as well.
7 Relationship between social vulnerability and disaster damages
Using an OLS regression analysis, this study examines the influence of social vulnerability on disaster damages. For this analysis, county-level property damage from natural disasters from 1990 to 2010 was used as the dependent variable, and social vulnerability indices calculated by four different methods were used as independent variables. Four different models were developed to examine the association between social vulnerability indices using factor analysis and three different standardization methods and disaster damages.
The dependent variable, total property damages per capita in a county, was measured as the total dollar loss (in 2009 dollars) from all types of natural disasters from 1990 to 2010 within a county divided by the total county population in 2000. These data were collected
Table 6 The 10 least vulnerable counties
Rank Standardization Factor analysis
zscore Ratio of the value Min–max rescaling
1 MA, Nantucket MA, Dukes MA, Nantucket TX, Kenedy
2 VA, James City MA, Nantucket MA, Dukes VA, Poquoson city
3 VA, Poquoson city NC, Dare NC, Dare VA, Northumberland
4 MA, Dukes VA, Stafford VA, Stafford FL, Franklin
5 VA, Stafford MD, Calvert MD, Calvert NC, Hyde
6 NC, Dare ME, Lincoln NH, Rockingham VA, James City
7 NH, Rockingham VA, James City VA, James City FL, Jefferson
8 MD, Calvert ME, Hancock VA, King George ME, Lincoln
9 CT, Middlesex NH, Rockingham VA, Poquoson city VA, Mathews
10 VA, Fairfax VA, King George ME, Lincoln VA, York
Table 7 The 10 most vulnerable counties
Rank Standardization Factor analysis
zscore Ratio of the value Min–max rescaling
187 LA, Orleans NJ, Essex NJ, Essex FL, Miami-Dade
188 VA, Northampton LA, Orleans LA, Orleans MD, Baltimore city 189 MD, Baltimore city TX, Cameron TX, Cameron TX, Matagorda
190 TX, Cameron TX, Willacy TX, Willacy NJ, Hudson
191 TX, Willacy MD, Baltimore city NJ, Hudson NY, Queens
192 NJ, Hudson NJ, Hudson MD, Baltimore city VA, Williamsburg city
193 NY, Queens NY, Queens NY, Queens TX, Cameron
194 NY, Kings NY, New York NY, New York NY, Kings
195 NY, Bronx NY, Kings NY, Kings NY, New York
196 NY, New York NY, Bronx NY, Bronx NY, Bronx
from the SHELDUS database at the Hazard Research Lab at the University of South Carolina. However, the limitation of SHELDUS data collection method is that the observed value of the dependant variable (property damage) at a county is not spatially independent of the values of the property damages at neighboring counties. The SHELDUS data assigned the estimated property damage evenly across all afflicted counties. So the dependent variable exhibits spatial autocorrelation. In order to control for spatial auto- correlation in the dependent variable, this study added an adjacent property damage var- iable, calculated as the total property damage in all adjacent counties per capita as an independent variable. The dependent variable and adjacent property damage variable were log-transformed to approximate a normal distribution.
Sixteen variables including the adjacent property damage variable as a control variable for spatial autocorrelation were used as independent variables. Among those sixteen variables, ten variables, which were factor scores derived from factor analysis, and the adjacent property damage variable were included in Model 1. Model 1 examined the association between social vulnerability using factor analysis and property damages. These variables include the density of built environment, poverty, age, family structure, urban, ethnicity-Hispanic, unemployment, female, occupation, and social instability. Five vari- ables created for the deductive social vulnerability index were used for Model 2, Model 3, and Model 4, all of which were designed to examine the association between property damages and three different standardization methods:z score, maximum value transfor- mation, and min–max rescaling transformation, respectively. These five independent variables were two people’s social vulnerability including ascribed and achieved social status, two place’s vulnerability including economic status and built environmental char- acteristics, and the adjacent property damage variable.
Diagnostics for model misspecification, multicollinearity, and autocorrelation did not yield any major violations. Produced factor scores using the principal components analysis (PCA) with varimax rotation, which used independent variables for Model 1, were uncorrelated with other factor scores (Clark et al.1998; Rygel et al.2006). Also, there was no strong correlation among the independent variables for Model 2, Model 3, and Model 4.
The largest pairwise coefficient among the independent variable indicators was a modest 0.60, a value smaller than the typical cutoff of 0.85 for multicollinearity (Munro2001).
I did, however, detect heteroskedacticity in the data, leading this study to analyze regression equations with robust standard errors.
Table8shows that ten social vulnerability index variables using factor analysis (Model 1) explain 56.8 % of the variance in property damage from natural disasters along the Gulf of Mexico and Atlantic coastal counties. The Model 1 result indicates that poverty is the most powerful variable in the model. Counties with higher percentages of poverty are positively associated with disaster property damage per capita in the Gulf of Mexico and Atlantic coastal counties during the study period (1990–2010). This result also demon- strates that ‘‘urban,’’ and ‘‘female’’ as factors have statistically significant association with property damages. Counties with more urban populations and more female populations are associated with higher disaster property losses during the study period (1990–2010). The result indicates that the effect of property damage was particularly strong in counties adjacent to a disaster event (b=0.680, p\0.001). This result indicates that disasters including floods and hurricanes often affects multiple counties, and that these adjacency effects must be considered when examining hazard-related phenomena at a county scale.
Table8also presents the three regression analyses that examined the impacts of social vulnerability in terms of two people’s vulnerability (ascribed and achieved social status), two place’s vulnerability (economic status and built environmental characteristics), and
Table 8 Relationship between Impact of social vulnerability and property damages
Variables Model 1 Model 2 Model 3 Model 4
Factor analysis zscore Ratio of the value Min–max rescaling
b t b t b t b t
Built environment 0.048 (0.02555)
1.89
Poverty 0.130*
(0.06088) 2.13
Age 0.091
(0.05081) 1.79 Family structure 0.080
(0.05648) 1.42
Urban 0.404**
(0.04818) 8.38 Ethnicity–Hispanic 0.070
(0.07779) 0.90
Unemployment -0.154
(0.05400) -2.85
Female 0.116**
(0.03763) 3.08
Occupation 0.031
(0.04082) 0.76 Social instability 0.039
(0.03522) 1.11
Social ascribed 0.517**
(0.23115)
2.24 2.792 (1.7700)
1.58 2.448 (1.4610)
1.68
Social achieved 0.549*
(0.12867)
4.27 3.585**
(0.84567)
4.24 2.907**
(0.71221) 4.08
Economic status 0.355
(0.30156)
1.18 -0.113 (2.7446)
-0.04 0.726 (2.3615)
0.31
Built environment 0.258
(0.20090)
1.29 3.181**
(1.1425)
2.78 2.997**
(1.0891) 2.75
Adjacent property damage
0.680**
(0.08783)
7.74 0.680**
(0.07965)
8.54 0.720**
(0.08508)
8.47 0.722**
(0.08338) 8.66
Constant 5.6490
(0.29723)
5.6477 (0.26195)
5.1662 (1.0467)
4.6275 (0.9221)
Number 196 196 196 196
Probability[F 0.0000 0.0000 0.0000 0.0000
R-squared 0.5682 0.3872 0.4191 0.4145
Root mean squared Error 0.60202 0.70571 0.68711 0.68983
Note: Robust standard errors are in parentheses
*p\0.05, **p\0.01
control variable (the adjacent property damage) on disaster property damages for three different standardization methods (Model 2 forzscore, Model 3 for Ratio of the value, and Model 4 for min–max rescaling, respectively). The results indicate that there are no dif- ferences among standardization methods in examining the association between the overall social vulnerability and disaster damages.
Table8 shows that five social vulnerability index variables using standardization methods explain around 40 % of variance in property damage (38.7 % for Model 2, 41.9 % for Model 3, and 41.5 % for Model 4, respectively). Like Model 1, disaster property damages in adjacent counties are highly associated with property damages in a county. Two variables, including social achieved status and built environment character- istics, display a statistically significant association with disaster damages (except built environment variable for Model 2). The results indicate that an increase in socially vul- nerable populations and housing density leads to a significant increase in property damages associated with natural disasters. Achieved social status is the most powerful predictor of property damage (b=0.549 for Model 2, 3.585 for Model 3, and 2.907 for Model 4, respectively) in the models. Counties with high percentage of vulnerable population in terms of poverty, unemployment, disability, education level, occupation, and their resource availability like telephone and vehicle had more property damages from natural disasters in coastal counties. These four regression analyses indicate that coastal counties with more social vulnerability in terms of achieved social status (poverty) are positively associated with disaster damages, while variations in the development of the index using deductive and inductive measurement approaches produced different outcomes.
8 Discussion and conclusion
An understanding of procedures and various measurement methodologies of social vul- nerability and outcomes of social vulnerability assessment could help planners and emergency managers in making priority choices in reducing vulnerability and disaster damages (Adger et al. 2004). This study reviews the methodologies that are used to a construct social vulnerability index and the methodologies that are used to assess the social vulnerability index. This study found that there are two main approaches in constructing and measuring social vulnerability: the inductive (or exploratory) and the deductive approach. The inductive or exploratory approach to constructing a social vulnerability index selects extensive variables (more than 40 demographic and economic variables) and uses factor analysis to calculate the overall aggregated social vulnerability score (Cutter et al.2003; Boruff et al.2005; Boruff and Cutter2007; Cutter and Finch2008; Rygel et al.
2006). On the other hand, researchers like Wu et al. (2002), Chakraborty et al. (2005), St.
Bernard (2007), Zahran et al. (2008), Cutter et al. (2010) inductively selected smaller sets of variables based on their conceptual framework to construct a social vulnerability index.
Moreover, they used standardization techniques such aszscore transformation, maximum value transformation, and min–max rescale transformation to quantify social vulnerability and calculated the overall aggregated social vulnerability index score. This study also applied these two main methodologies to assess social vulnerability in the Gulf of Mexico and Atlantic coastal counties. This study compared their differences and similarities.
Through the case study, this study found that the selection of variables chosen for developing a social vulnerability index is important in assessing the overall social vul- nerability value. The overall social vulnerability scores varied and were affected by the variables used to construct the social vulnerability index.
This study found that while there are no differences in the comparisons of the overall social vulnerability scores using three different standardization techniques, the overall social vulnerability scores calculated by factor analysis and by standardization are dif- ferent. This study showed that the selection of social vulnerability variables and combi- nation of multiple methods are important in social vulnerability assessment. Social vulnerability is often hidden and complex, nested in various human aspects, and place- sensitive (Barnett et al.2008; Fekete2009). There is no single deductive approach nor is there a single inductive approach. Thus, one must be cautious to determine which method is more robust or better in social vulnerability assessment. The factors produced in the inductive statistical analysis are consistent with the hazards literature and the method works very well, explaining about 80 percent of the statistical variance in the study area.
The selection of variables, the methods of aggregation including weighting assignment, the choice of the scale of analysis, and the extent of the research area all had implications on deciding the appropriate methods of social vulnerability assessment (Fekete et al.2010).
Subjectivity in the choice of variables is also an important consideration, because the outcomes of a social vulnerability assessment are mainly decided by the selected variable regardless of assessment techniques. Data availability is one of the most crucial factors influencing indicator selection (Tapsell et al.2010). So researchers, planners, and emer- gency managers should carefully select social vulnerability indicators in order to under- stand and identify residents’ and communities’ real social vulnerability to natural disasters.
Developing a defensible weighting scheme is necessary to assess resident and community social vulnerability to natural disasters. Most vulnerability studies including this study do not apply weights to vulnerability indicators and the indicators are generally considered to be independent and equally important variables. However, not all vulnerability indicators are necessarily equal. So, some researchers attempted to allocate weighting to vulnerability indicators (Haki et al.2004; Rygel et al.2006; Cox et al.2007; Meyer et al.2007). Rygel et al. (2006) employ similar methods to those published by Cutter et al. (2003), with the main deviation being the assignment of weights for aggregation, based on a ranking of the factors. Cox et al. (2007) applied a weighting scheme based on the percent variance explained by each factor. But still the weighting of vulnerability indicators remains in the realm of subjectivity.
The scale (or unit) of analysis is also important element to consider in social vulner- ability assessment since vulnerability indicators vary with place (Jones and Andrey2007;
Rygel et al.2006), and cross-scale interaction exerts a significant impact on outcomes at a given scale (MEA2003). The present study focused on social vulnerability at county level.
In order to appreciate the findings of the study on the comparison of social vulnerability assessment methods, further study needs to be done to assess and compare census-block- level or block group social vulnerability index scores within the context of the original data. The local-level vulnerability assessment demonstrates advantages in data mining and in capturing the roots of social vulnerability (Fekete et al.2010).
Even though there is no significant difference on the outcomes of aggregated social vulnerability index scores between inductive and deductive methods, each method has strengths and weaknesses that should be recognized. The inductive method (e.g., factor analysis) developed by Cutter et al. (2003) is widely used and cited in disaster research.
This method helps understand the multivariate characteristics of individuals and com- munities by revealing the underlying dimensions of a large set of variables and mathe- matically transforms data into a smaller set of components based on intercorrelated variables (Wood et al.2010). However, this inductive method based on a principal com- ponent analysis presents some challenges for use in a public planning context. This method
is complicated and uses a statistical procedure that is not easily communicated (Dunning and Durden2011). The deductive method (e.g., standardization), on the other hand, pro- vides a simple and direct method of focusing on a smaller set of vulnerability variables.
The social vulnerability assessment procedure of this method is relatively easier to select variables and interpret outcomes with only simple conversion of selected variables to zscores (Dunning and Durden2011).
Comparing different methods of social vulnerability measurement gives emergency managers a meaningful and practical guide to emergency management and planning. The findings of the present study suggest that emergency managers could select either inductive (e.g., factor analysis) or deductive (standardization method) method to assess social vul- nerability based on their various conditions including their knowledge and analytical ability on vulnerability assessment, vulnerability data availability, and emergency man- agement strategies.
The findings through the simple regression analysis in examining what factors are significantly associated with disaster damages also offer an insight into the emergency management and mitigation planning. The present study found that achieved social status of residents, including poverty (e.g., percent living in poverty), is the most statistically significant indicator of vulnerability to disasters in the Gulf of Mexico and Atlantic coastal counties. So, emergency managers should take care to specifically consider poor popula- tions lacking access to critical resources, like emergency information, and materials in their emergency management and planning.
References
Adger WN, Brooks N, Bentham G, Agnew M, Eriksen S (2004) New indicators of vulnerability and adaptive capacity. Tyndall Centre for Climate Change Research, Norwich, Report 7
Anderson WA (2005) Bringing children into focus on the social science disaster research agenda. Int J Mass Emerg Disasters 23(3):159–175
Azar D, Rain D (2007) Identifying population vulnerability to hydrological hazards in San Juan, Puerto Rico. GeoJournal 69(1):23–43
Barnett J, Lambert S, Fry I (2008) The hazards of indicators: insights from the environmental vulnerability index. Ann As Am Geogr 98(1):102–119
Birkmann J (2006) Measuring vulnerability to natural hazards: towards disaster resilient societies. United Nations Publications, New York
Bolin R, Klenow D (1983) Response of the elderly to disaster: an age-stratified analysis. Int J Aging Hum Dev 16:283–296
Boruff BJ, Cutter SL (2007) The environmental vulnerability of Caribbean island nations. Geogr Rev 97(1):24–45
Boruff BJ, Emrich C, Cutter SL (2005) Erosion hazard vulnerability of US Coastal Counties. J Coast Res 21(5):932–942
Cannon T (1994) Vulnerability analysis and the explanation of ‘‘Natural’’ Disasters. In: Varley A (ed) Disasters. Development and Development, John Wiley, Chichester, pp 13–30
Chakraborty J, Montz BE, Tobin GA (2005) Population evacuation: Assessing spatial variability in geo- physical risk and social vulnerability to natural hazards. Nat Hazards Rev 6(1):23–33
Clark G, Moser S, Ratick S, Dow K, Meyer W, Emani S, Jin W, Kasperson J, Kasperson R, Schwartz H (1998) Assessing the vulnerability of coastal communities to extreme storms: the case of Revere, MA, USA. Mitig Adapt Strat Glob Change 3(1):59–82
Cox, JR, Rosenzweig C, Solecki WD, Goldberg R, Kinney PL (2007) Social vulnerability to climate change:
A neighborhood analysis of the northeast US Megaregion. Technical paper prepared for the Northeast Climate Impacts Assessment. Cambridge, MA
Cutter SL, Finch C (2008) Temporal and spatial changes in social vulnerability to natural hazards. PNAS 105(7):2301–2306
Cutter SL, Mitchell JT, Scott MS (2000) Revealing the vulnerability of people and places: a case study of Georgetown County, South Carolina. Ann As Am Geogr 90(4):713–737
Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards. Soc Sci Quart 84(2):242–261
Cutter SL, Burton C, Emrich C (2010) Disaster resilience indicators for benchmarking baseline conditions.
J Home Secur Emerg. doi:10.2202/1547-7355.1732
Dunning CM, Durden S (2011) Social vulnerability analysis methods for Corps planning. US Army Corps of Engineers
Enarson E, Morrow BH (1998) The gendered terrain of disaster: through women’s eyes. Praeger, Westport Enarson E, Fothergill A, Peek L (2006) Gender and disaster: Foundations and directions. In: Quarantelli EL,
Dynes R, Rodriguez H (eds) Handbook of Disaster Research. Springer, New York, pp 130–146 Fekete A (2009) Validation of a social vulnerability index in context to river-floods in Germany. Nat Hazard
Earth Syst 9:393–403
Fekete A, Damm M, Birkmann J (2010) Scales as a challenge for vulnerability assessment. Nat Hazards 55:729–747
Foladare IS (1969) A clarification of ‘‘ascribed status’’ and ‘‘achieved status’’. Sociol Quart 10(1):53–61 Fothergill A, Peek LA (2004) Poverty and disasters in the United States: a review of recent sociological
findings. Nat Hazards 32(1):89–110
Fothergill A, Maestras E, Darlington J (1999) Race, ethnicity, and disasters in the United States: a review of the literature. Disasters 23(2):156–173
Gall M (2007) Indices of social vulnerability to natural hazards: a comparative evaluation. University of South Carolina, Dissertation
Haki Z, Akyurek Z, Du¨zgu¨n S (2004) Assessment of Social Vulnerability Using Geographic Information Systems: Pendik, Istanbul Case Study. Paper presented at the 7th AGILE Conference on Geographic Information Science, Heraklion, Crete
Hauke J, Kossowski T (2011) Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae 30(2):87–93
Jones B, Andrey J (2007) Vulnerability index construction: methodological choices and their influences on identifying vulnerable neighborhoods. Int J Emerg Manag 4(2):269–295
Kar N (2009) Psychological impact of disasters on children: review of assessment and interventions. World J Pediatr 5(1):5–11
Linton R (1936) The study of man. Appleton-Century-Crofts, Inc., New York
Long AP (2007) Poverty is the new prostitution: race, poverty, and public housing in post-Katrina New Orleans. J Am Hist 94:795–803
Mather PM, Openshaw S (1974) Multivariate methods and geographical data. Statistician 23:283–308 McCarthy J, Canziani O, Leary N, Dokken D, White K (2001) Climate change 2001: impacts, Adaptation
and Vulnerability. Contribution of Working Group II to the Third Assessment Report of Intergov- ernmental Panel on Climate Change. Cambridge University Press, Cambridge
MEA (2003) Ecosystems and human well-being. A framework for Assessment. Island Press, World Resources Institute, Washington
Meyer V, Haase D, Scheuer S (2007) GIS-based Multicriteria Analysis as Decision Support in Flood Risk Management. Integrated Project FLOODsite, Milestone report T10-07-06
Morrow BH (2008) Community Resilience: a social justice perspective. The community and regional resilience initiative research report 4
Munro BH (2001) Statistical methods for health care research, 4th edn. JB Lippincott Co, Philadelphia, PA Myers CA, Slack T, Singlemann J (2008) Social vulnerability and migration in the wake of disaster: the case
of Hurricanes Katrina and Rita. Popul Environ 29:271–291
Ngo EB (2001) When disasters and age collide: reviewing vulnerability of the elderly. Nat Hazards Rev 2(2):80–89
Peacock WG, Morrow BH, Gladwin H (2000) Hurricane Andrew and the reshaping of Miami: ethnicity, gender, and the socio-political ecology of disasters. Florida International University, International Hurricane Center, Miami, FL
Phillips BD, Hewett PL (2005) Home alone: disasters, mass emergencies and children in self care. J Emerg Manag 3(2):31–35
Rygel L, O’Sullivan D, Yarnal B (2006) A method for constructing a social vulnerability index: an application to hurricane storm surges in a developed country. Mitig Adapt Strat Glob Change 11(3):741–764
Scott J (1975) Multivariate analysis in geography—some comments. Statistician 24(3):211–216
Smith S, Tremethick MJ, Johnson P, Gorski J (2009) Disaster planning and response: considering the needs of the frail elderly. Int J Emerg Manag 6(1):1–13
St. Bernard G (2007) Measuring social vulnerability in Caribbean States, Paper presented at 8th SALISES Annual Conference Crisis, Chaos and Change: Caribbean Development Challenges in the 21st Cen- tury, Trinidad and Tobago
Tapsell S, McCarthy S, Faulkner H, Alexander M (2010) Social vulnerability and natural hazards. Flood Hazard Research Centre—FHRC, CapHaz-Net WP4 Report
Wood NJ, Burton CG, Cutter SL (2010) Community variations in social vulnerability to Cascadia-related tsunamis in the US Pacific Northwest. Nat Hazards 52:369–389
Wu SY, Yarnal B, Fisher A (2002) Vulnerability of coastal communities to sea-level rise: a case study of Cape May County, New Jersey, USA. Clim Res 22:255–270
Zahran S, Brody SD, Peacock WG, Vedlitz A, Grover H (2008) Social vulnerability and the natural and built environment: a model of flood casualties in Texas, 1997–2001. Disasters 32(4):537–560
