Planning for Climate Adaptation

Abstract

While recent research has recognized the importance of considering social vulnerability, the changing patterns of social vulnerability within cities and the climate adaptation challenges these shifts pose have yet to receive much attention. In this article, we evaluate the changing patterns of social vulnerability in three coastal cities (Houston, New Orleans, and Tampa) over a thirty-year time period (1980–2010) and integrate neighborhood change theories with theories of social vulnerability to explain those patterns. Through this analysis, we highlight emerging dimensions of vulnerability that warrant attention in the future adaptation efforts of these cities.

Keywords

social vulnerability climate change adaptation land use planning neighborhood change

Introduction

Planning for climate adaptation tends to overemphasize future climate variability without considering the complex and evolving nature of social vulnerability, which can lead to nonadaptive or even maladaptive outcomes (Orlove 2009; Barnett and O’Neill 2010; Macintosh 2013). This oversight runs the risk of focusing solely on identifying the climatic hazards of a place rather than fully understanding why people are located in hazard-exposed areas in the first place. Recent research indicates that in addition to hazard exposure and physical vulnerability, social vulnerability—measured as variation in characteristics such as income, race/ethnicity, gender, and household composition—matters when predicting the impacts of coastal surge and flooding (Highfield, Peacock, and Van Zandt 2014). Adaptation approaches that ignore the social dynamics of a city can create an environment for nonadaptation when, despite efforts to climate-proof places, people are pushed into harm’s way. This becomes more problematic in a multihazard urban environment where zoning restrictions or flood-proofing policies may further the climate adaptation of certain places, but socially vulnerable populations may only be able to afford places that are more exposed. The intersection of adaptation and social vulnerability demands a new approach to land use planning that is more responsive to shifting patterns of social vulnerability and more cognizant of the housing options available to socially vulnerable households within a multihazard urban area.

Although theories of neighborhood change and social vulnerability represent two distinct literatures, when considered jointly they yield important insights into the vulnerability dynamics of a city. While social vulnerability explores what makes people vulnerable to hazards, neighborhood change theories explain, among other things, why vulnerable people move to or concentrate in certain areas of a city. Neighborhood change theories explain the underlying forces that drive shifts in the demographic composition of neighborhoods (Temkin and Rohe 1996; Peterman 2000). On the other hand, social vulnerability theories explore how factors such as poverty, race, and age reduce the capacity of marginalized population groups to withstand hazard events or delay their recovery process (Blaikie, Davis, and Wisner 1994; Peacock, Morrow, and Gladwin 1997). In this article, we integrate these two theoretical frameworks to explore changing patterns of social vulnerability in three coastal cities: Houston, New Orleans, and Tampa. Recognizing the historical path dependency of social vulnerability, we analyze three decades of data (1980–2010) to understand how the different dimensions of vulnerability have interacted with each other over time and to identify emerging dimensions of vulnerability that warrant attention in future adaptation efforts. Climate change impacts have garnered increasing attention from both planning scholars (Blanco et al. 2009) and policy makers (Goggin, Gerber, and Larson 2014), and this article makes an important contribution to the discourse of adaptation planning by highlighting key aspects of social vulnerability that need more attention and can inform policy responses.

Social Vulnerability and Adaptation Planning

Hazard impacts are a function of hazard exposure (probability of impact), physical vulnerability (ability of the built environment to withstand impact), and social vulnerability (variation in an individual, household, or community’s ability to prepare, respond, absorb, and/or recover from an impact) (Masterson et al. 2014). Including social vulnerability in our understanding of hazard impacts situates disasters and their effects within broader social contexts and processes (Wisner et al. 2004) and highlights social factors that influence or shape the susceptibility of various groups to harm (Blaikie, Davis, and Wisner 1994; Hewitt 1997). This perspective emphasizes the socioeconomic characteristics that influence a community’s ability to prepare for, respond to, cope with, and recover from a hazard event (Peacock, Morrow, and Gladwin 1997; Laska and Morrow 2006) and is most often described using individual characteristics like age, race, income, etc. Prior research has shown that minority and low-income households usually have a lower level of disaster preparedness (Mileti and Darlington 1997; Peacock 2003), are less likely to hold earthquake or flood insurance (Blanchard-Boehm 1998; Fothergill 2004), and are less likely to receive and act on official disaster warnings (Perry and Nelson 1991; Fothergill and Peek 2004). As a result of these differences, socially vulnerable populations are not only more likely to experience damage (Cutter 1996; Fothergill and Peek 2004; Zahran et al. 2008; Van Zandt et al. 2012) but are slower to recover from disaster impacts (Peacock et al. 2014), which may exacerbate preexisting inequalities and further expose them to future impacts (Zhang and Peacock 2009).

Currently, climate change mitigation planning and practice focus primarily on reducing emissions through land use and transportation decision making (Lutsey and Sperling 2008; Kashem, Irawan, and Wilson 2014), while adaptation planning attempts to reduce exposure through structural and nonstructural mitigation techniques (Masterson et al. 2014; Perry and Lindell 1997). Only recently have planning scholars begun incorporating an understanding of social vulnerability into their assessments of mitigation and adaptation planning efforts in the United States (Berke 2015). Planners who study social vulnerability recommend that mitigation and adaptation policy include comprehensive vulnerability assessments that identify socially vulnerable areas and target both policies and resources toward addressing the needs of these populations to reduce overall impacts and increase resilience (Masterson et al. 2014; Van Zandt et al. 2012).

Although studies of social vulnerability contribute important insights for better understanding the differential outcomes of disasters, with few exceptions, they fail to explore the generative process of social vulnerability in a place—specifically, why vulnerable population groups move into hazardous areas and how present patterns of social vulnerability have evolved over time. Understanding these dynamics will help us to monitor and better manage these forces to increase resilience. In the next section, we examine how socially vulnerable populations come to be concentrated in areas that may expose them to greater hazards through the lens of neighborhood change theories.

Neighborhood Change Theories and Social Vulnerability

Neighborhood change theories explain the macro- and micro-level socioeconomic, political, and institutional forces that drive changes in neighborhood characteristics (Temkin and Rohe 1996) and have been used to understand the implications of change for urban planning and policy (Peterman 2000; Li and Morrow-Jones 2010). Within these theories there are three major schools of thought (Temkin and Rohe 1996)—ecological, subcultural, and political economy. Ecological change theories treat neighborhood change as a natural evolution process (Downs 1981; Burgess 2008). Subcultural models (Gans 1962; Suttles 1972) reject the economic determinism of the ecological models and stress the attachment of residents to their neighborhoods as a key determinant of why and how residents live in certain parts of the city. Political economy models (Harvey 1973; Castells 1983; Logan and Molotch 1987), on the other hand, highlight the institutional forces that influence neighborhood change.

Theories of neighborhood change offer an important precedent and parallel for attempts to conceptualize social vulnerability as multidimensional and place-specific. According to the Hazards of Place (HOP) model of vulnerability (Cutter 1996), interactions between people and their environment occur within a particular place, and places have a unique hazard potential, which arises from the interaction between hazard risk and socially determined mitigation activities (or lack thereof). Geographic characteristics within the study area, such as elevation and proximity, work to modify hazard potential across space. This modified, spatially differentiated, hazard potential is called biophysical vulnerability. The hazard potential is also modified by the social fabric of the area, which in turn modifies and differentiates the hazard potential across space. The social fabric consists of those characteristics that describe the distribution and composition of the population measured, for example, by sociodemographic, economic, and welfare variables. The social contributions to the spatial differences are called social vulnerability. Biophysical and social vulnerability interact to create an overall place vulnerability, but place vulnerability can, in turn, modify both the event risk of an area as well as the mitigation approaches used in the area. The linkages identified in the model communicate an understanding of the dynamic nature of vulnerability, namely, that changes in the physical and social setting of an area may result in changes in vulnerability, risk, and mitigation.

An alternative conceptualization, the Vulnerability Framework for Sustainability Science (VFSS), was developed to bridge the risk hazards and the political economy and political ecology traditions as different ways of conceptualizing vulnerability (Turner et al. 2003). It aims to capitalize on the explicit inclusion of vulnerability stemming from biophysical subsystems seen in the risk hazards approaches and reconcile this with the multiscaled and structural explanations of the political economy approaches. Like the HOP model, it employs the geographic concept of place as the lens through which the interactions of social and physical systems are analyzed. In this framework, vulnerability occurs within a specific place, but is influenced by human and environmental influences at regional and global levels.

These conceptual models of vulnerability share some important characteristics with the integrated model of neighborhood change articulated by Temkin and Rohe (1996) where neighborhood stability, analogous to the notion of resilience in the hazards literature, depends on internal factors as well as relationships with institutions and actors at the regional, national, and global levels. Similarly, the HOP and VFSS models of vulnerability emphasize the importance of understanding how the existing pattern of vulnerability emerges from and is produced by biophysical, social, and political forces that exist outside the borders of a given place (Dooling and Simon 2012).

Political economy theorists explain neighborhood change through two broad streams of thought—the “urban growth machine” thesis (Molotch 1976; Logan and Molotch 1987), and the “urban restructuring” or “globalization” thesis (Borja and Castells 1997; Sassen 2000). While urban growth machine theorists argue that neighborhood change occurs through active exploitation of the real estate market and political process by local elites, theories of urban restructuring focus on how capital and labor restructuring at the global scale influences urban growth and movement within cities. One of the basic tenets of growth machine theorists is that the local growth coalition, driven by their fixation on economic growth, can “bend the policy priorities of localities toward developmental rather than redistributional goals” (Logan, Whaley, and Crowder 1997, 75). This can be particularly problematic for vulnerable populations in poor neighborhoods who may face displacement and hardship, as happened through the 1960s “urban renewal” projects and now as a result of the gentrification process. Economic and labor restructuring, as argued by the urban restructuring theorists, also makes it harder for vulnerable populations to find better paying jobs or affordable housing (Sassen 2000; Galster, Mincy, and Tobin 1997).

Globalization of the economy coupled with local economic restructuring is rapidly changing the demographic composition of cities, particularly in the growing coastal cities of the United States. Demand for low-wage workers fueled an influx of immigrant workers from Latin America and Asia, and thereby is breaking down the dominant black–white race paradigm within the cities, as Soja, Morales, and Wolff (1983) showed for Los Angeles. These forces are also changing the patterns of social vulnerability in urban neighborhoods, and this reality needs to be accounted for in climate adaptation initiatives. In this article, we explore the evolving and increasingly heterogeneous nature of socially vulnerable groups within the three coastal urban counties through the lens of neighborhood change theories discussed above.

Study Methodology

Case Study Cities

Similarity of geographic location and variation in population trends were the primary criteria for selecting the case study cities. All three case study cities are located in the US Gulf Coast region, but within different state jurisdictions—Houston in Texas, New Orleans in Louisiana, and Tampa in Florida. All of these cities are considered highly exposed to climate change impacts (Nicholls et al. 2008), but have varying levels of population and assets exposure (Table 1).

Table 1.

Case Study Cities.

Case Study Cities	Population Growth, 2000–2010 (%)	Current Climate Exposed Population (Thousands)^a	Current Climate Exposed Assets (Billion $)^a
Houston, TX (Harris County)	7.46	59	12.21
New Orleans, LA (Orleans Parish)	−29.06	1,124	233.69
Tampa, FL (Hillsborough County)	10.63	415	86.26

Estimates from Nicholls et al. (2008), Appendix 3: City Data and Rankings.

Since the boundary of the cities has changed over time, their respective counties¹ are used to spatially delineate the study cities for this research. These cities also had different trajectories of population change over time. While Houston has experienced consistent growth since 1960, Tampa was characterized by relatively moderate growth in recent years, and New Orleans experienced consistent decline.

Variables and Data Collection

A variety of methods have been employed to measure social vulnerability at many different scales, but the one constant has been a consensus on the multidimensionality of this concept. For example, vulnerability is often understood to include poverty (Fothergill and Peek 2004; Long 2007), race and ethnicity (Fothergill, Maestas, and Darlington 1999), gender (Enarson, Fothergill, and Peek 2007), and age (Smith et al. 2009). In order to evaluate social vulnerability dynamics in the study cities, we calculated the Social Vulnerability Index (SoVI) decennially at the census tract level over a thirty-year time period (1980–2010). This was accomplished by using the inductive approach for measuring social vulnerability developed by Cutter, Boruff, and Shirley (2003) with some modifications, considering data availability and suggested refinements to this foundational method by subsequent studies (Finch, Emrich, and Cutter 2010; Tate 2012). Given our interest in understanding the social construction of vulnerability and the availability of required data at census tract level, we selected twenty-six variables following the approach of Finch, Emrich, and Cutter (2010). The list of variables used for SoVI calculation is presented in Table 2. For 2010, these variables were collected from US Census American Community Survey (ACS) 2008–2012,² while for 1980, 1990, and 2000, data were drawn from the decadal census reports of the US Census Bureau.³ Census tracts with high group quarters populations (e.g., jails, university campuses) or too few residents in any time period were excluded from the analysis.⁴ For the purposes of longitudinal comparison, all the data (1980 to 2010) were converted to 2010 census geography using the Longitudinal Tract Data Base (LTDB) approach developed by Logan, Xu, and Stults (2014). These data were then normalized to percent, per capita, or density functions given the differential population and land area of census tracts.

Table 2.

Variables Used to Assess Social Vulnerability.

No.	Variables	Normalized Variable
1	Black or African American population	% of black population
2	Hispanic population	% of Hispanic population
3	Asian population	% of Asian population
4	Native American population	% of Native American population
5	Population under 5 years old	% of population under 5 years old
6	Population 65 years or older	% of population 65 years or older
7	Group quarters population	% of population living in group quarters
8	Foreign-born population	% of foreign-born population
9	Household size	Average number of people per household
10	Female population	% of female population
11	Female-headed households	% of families with female-headed households with no spouse present
12	Female labor force participation	% of female population in civilian labor force
13	Public transportation dependence	% of workers (Civilian pop. 16+ and employed) using public transport
14	Education attainment	% of population over 25 years old with less than 12 years of education
15	Unemployment rate	% of the civilian labor force unemployed
16	Manufacturing employment	% of persons (16+ years old) employed in manufacturing, transportation, and public administration
17	Employment in service occupations	% of persons (16+ years old) employed in service occupations
18	Poverty rate	% of population in poverty
19	Social security recipients	% of population who are social security recipients
20	Average household income	Average household income last year ($)
21	Renter-occupied housing units	% of renter-occupied housing units in total occupied housing units
22	Number of mobile homes	% of housing units that are mobile homes
23	Average gross rent	Average gross rent ($) for renter-occupied housing units
24	Average home value	Average dollar value of owner occupied housing units
25	Population in civilian labor force	% of population in civilian labor force
26	Housing density	Number of houses per sq. mile

The SoVI and Its Spatial Distribution

We calculated the Social Vulnerability Index (SoVI) at the census tract level to quantify relative levels of vulnerability between and within the three study areas. To derive the SoVI, we applied principal components analysis (PCA), which identifies a smaller set of independent factors that account for a majority of the overall variance within the original data, then interpreted and assigned a general socioeconomic or demographic interpretation to these components based on the factor loadings. One of the key steps in this process is to choose the optimum number of factors to retain from the PCA. Cutter, Boruff, and Shirley (2003) applied the Kaiser criterion⁵ for selecting the number of factors, but recent studies have shown that use of the Kaiser criterion overestimates the number of factors to retain (O’Connor 2000; Patil et al. 2008). Considering this limitation of the original approach of Cutter, Boruff, and Shirley (2003), in this study we applied parallel analysis⁶ for determining the optimum number of factors as suggested by Tate (2012).

We rescaled all the identified factors from the PCA so that positive values indicate higher levels of vulnerability, while negative values are consistent with a decrease in vulnerability. We then aggregated the factor scores in an additive model to derive the overall composite social vulnerability score. We applied this process of SoVI calculation to each of the study area (Houston, New Orleans, and Tampa) and for all time periods (1980, 1990, 2000, and 2010) separately. To determine the patterns of similarity and dissimilarity in the clustering of social vulnerability, we evaluated the degree of spatial autocorrelation among the census tracts. The degree of spatial autocorrelation of the SoVI evaluates the pattern of change in social vulnerability throughout the study cities and how it varies in different city contexts.

Discussion of Results

Results of PCA

The PCA results indicate how the prominent dimensions of vulnerability have evolved over time. Among the three study areas, one common trend from the PCA was that the percentage of total variance explained by the SoVI components gradually decreased over time. Further, the parallel analysis approach retained a different number of components for each of the counties (and even in different years) and the variance explained by the individual components also changed across time periods. This suggests that substantive changes in the composition and significance of the underlying dimensions of social vulnerability occurred at different time periods. For census tracts in Harris County (Figure 1), the overall variance explained by the selected components decreased from 74.4 percent in 1990 to 72.5 percent in 2000 and 65.5 percent in 2010. In all four years, five components were consistently retained by the parallel analysis with Race and socio-economic status (i.e., percent black, poverty, unemployment, income, etc.) as the leading component in all years. While Housing and Household size was the second most important component in 1980 and 1990 (explaining 16.5 percent and 15.1 percent variance), it was replaced by the Hispanic and Immigrant component in 2000 and 2010 (explaining 17.6 percent and 13.9 percent variance), which can be attributed to the rapid growth of the Hispanic and immigrant population during this time period.

Figure 1.

Principal components analysis results for Harris County (Houston).

The PCA components explained 71 percent of the overall variance among the census tracts of Orleans Parish in 1980, 70.5 percent in 1990, 69.6 percent in 2000, and 67.7 percent in 2010 (Figure 2). While four components were retained by the parallel analysis in 1980, 1990, and 2000, five components were retained in 2010. Race and socioeconomic variables consistently emerged as the leading component, although the percentage of variance it explained decreased significantly in 2010. This can be attributed to the massive loss of population in Orleans Parish between 2000 and 2010 as a result of Hurricane Katrina, which may also have contributed to the retention of more components in 2010 (i.e., previously insignificant components became more prominent in the wake of population loss).

Figure 2.

Principal components analysis results for Orleans Parish (New Orleans).

In the case of Hillsborough County (Figure 3), the PCA did not indicate any consistent trend (in terms of the percentage of variance explained or for the number of components retained) unlike Harris County or Orleans Parish. In 1990, 73.6 percent of the variance among the census tracts of Hillsborough County were explained by five components, by 2000 74.3 percent was explained by six components, and in 2010 only 65.8 percent was explained by the five components retained. Race and socio-economic variables also comprised the leading component for all four decades here. A growing concentration of elderly population in certain census tracts contributed to the significantly higher percentage of variance explained by the Age component in 2010 (increased from 13.3 percent in 2000 to 16.2 percent).

Figure 3.

Principal components analysis results for Hillsborough County (Tampa).

Cluster Analysis of the SoVI

We aggregated the component scores calculated through PCA to derive the overall composite social vulnerability index (SoVI). To identify the patterns of similarity and dissimilarity in the clustering of social vulnerability, we evaluated spatial autocorrelation of SoVI among the census tracts. Both global and local measures of spatial autocorrelation were calculated and when considered jointly, they reveal two distinct but complementary spatial characteristics of vulnerability. One important trend that emerged from the global analysis of SoVI is that the Moran’s I value decreased over time for all of the counties. This suggests that despite experiencing different patterns of population growth and different compositions of vulnerability (as revealed through the PCA earlier), in all three study areas, the overall concentration of social vulnerability has decreased over time. Although the positive values of Moran’s I (all of which were statistically significant at p < 0.05) indicate that social vulnerability is highly concentrated in all of the counties, their decreasing values indicate that the level of concentration of vulnerability has lessened.

In Harris County, Texas (Table 3), the global Moran’s I was 0.63 in 1980, but gradually decreased to 0.46 in 2010 and the number of census tracts in high-high clusters (indicating concentration of high values of the SoVI) also decreased from eighty-eight (12.34 percent of tracts) in 1980 to seventy (9 percent of tracts) in 2010. In the case of Orleans Parish, Louisiana (New Orleans), the global Moran’s I was lower in earlier years (0.40 in 1980 and 0.37 1990) but increased in 2000 (0.47) and then decreased in 2010 (0.44). This suggests an overall decrease in the concentration of social vulnerability in recent years, but the number of tracts in high-high clusters barely changed. There were ten census tracts in high-high clusters in 1980, which decreased in 1990 and 2000 only to climb back to ten in 2010. For Hillsborough County in Florida (Tampa), the concentration of vulnerability followed a consistent downward trend, similar to Harris County. Although Hillsborough County had a low concentration of vulnerability in 1980 (0.53), it increased in 1990 (0.62) before gradually decreasing in 2000 (0.57) and 2010 (0.42). For the number of tracts in high vulnerability clusters (i.e., high-high spatial clusters), however, it did not follow the downward trend of the global Moran’s I. While there were forty-four census tracts located in high vulnerability clusters in 1990, this number decreased to thirty-three in 2000, then increased to forty-six in 2010.

Table 3.

Spatial Autocorrelation Measures for SoVI in All Study Cities.

	1980	1990	2000	2010
Harris County, TX (Houston)
Global Moran’s I	0.63	0.62	0.58	0.46
No. of tracts in high-high LISA category	88	100	81	70
Orleans Parish, LA (New Orleans)
Global Moran’s I	0.40	0.37	0.47	0.44
No. of tracts in high-high LISA category	10	8	9	10
Hillsborough County, FL (Tampa)
Global Moran’s I	0.53	0.62	0.57	0.42
No. of tracts in high-high LISA category	24	44	33	46

Note: LISA = local indicators of spatial association.

Temporal Changes of Social Vulnerability

Measuring temporal change in social vulnerability is a challenging task because of shifts in the composition of vulnerability at the different time periods. Still, if we consider the SoVI as a relative measure of vulnerability within a county, it can be useful in examining how the degree of social vulnerability among all census tracts within the county has changed over time and for identifying areas that have consistently experienced an increase or decrease in social vulnerability. Keeping this in mind, we applied the approach taken by Cutter and Finch (2008) to identify historical trends of social vulnerability within the three study areas. Specifically, we converted individual SoVI scores of census tracts to z-scores (based on county mean score per decade) in order to improve their longitudinal comparability. Applying simple linear regression, we calculated a line of best fit from 1980 to 2010 for each of the census tracts. The slope of the line of best fit indicates the directionality of vulnerability over time. A positive slope of the best fit line indicates an increasing trend of social vulnerability and a negative slope indicates decreasing social vulnerability in a census tract. This is an inherently aspatial approach to evaluating longitudinal trends of social vulnerability since each of the census tracts is analyzed as a separate entity without considering changes in vulnerability in surrounding regions, but it still helps to visually locate areas that experienced a consistent increase or decrease in social vulnerability over time.⁷ SoVI trend maps for the three study counties are presented in Figures 4 to 6 highlighting the tracts for which the trends were found to be statistically significant (at the 0.05 alpha level).

Figure 4.

Temporal trends of SoVI in Harris County, TX (1980–2010).

Figure 5.

Temporal trends of SoVI in Orleans Parish, LA (1980–2010).

Figure 6.

Temporal trends of SoVI in Hillsborough County, FL (1980–2010).

When interpreting Figures 4 to 6, the fundamental question is what is the mechanism behind the observed change—were the fundamental components of vulnerability mitigated (i.e., reduced poverty, less linguistic isolation, and decreased racial and ethnic segregation) or did vulnerable populations simply relocate in space in response to some combination of push and pull factors. In New Orleans, the massive displacement caused by Hurricane Katrina is an important and obvious lens through which to analyze changes in the spatial distribution of social vulnerability, but gentrification and suburbanization processes are capable of producing similar outcomes. In fact, suburbanization and gentrification are two neighborhood change processes that could help to explain the overall trend toward less spatial concentration of social vulnerability.

Houston’s economy has exhibited significant growth over the past decade with more than six hundred thousand new jobs added since 2005 (Greater Houston Partnership 2015). Job creation in Tampa has been more modest than in Houston, while New Orleans ranks near the bottom of US metropolitan areas in the quantity and quality of employment in recent years (Ng and Shearer 2015). Both Houston and Tampa are among the more sprawling metropolitan areas between 2000 and 2010, while New Orleans ranks among the most compact (Hamidi and Ewing 2014). Finally, Houston and Tampa also rank among the US metropolitan areas exhibiting the highest rates of core upgrading and gentrification between 1990 and 2010 (Landis 2015). The spatial distribution of poverty (Kneebone and Berube 2015) is the result of the confluence of these and other factors and has fundamentally altered the geography of social vulnerability in many cities. According to data compiled by Kneebone and Berube (2015), the poverty rate for the Tampa and Houston metropolitan areas increased by 94.9 and 89 percent between 2000 and 2012, with 25.2 and 10.5 percent of the poor population living in suburban areas. During the same period, the poverty rate in the New Orleans metropolitan area increased by a comparatively modest 20.4 percent, but 25 percent of the poor population also lived in suburban areas.

The pattern of change in the SoVI in Houston is consistent with gentrification (Smith 1979) and the suburbanization of poverty (Kneebone and Berube 2013). Of the fifty-one tracts that experienced a statistically significant decline in SoVI, twenty-five are within or adjoin the Interstate 610 loop and nine of these are located within one-half mile of a light rail line. The remaining tracts where the SoVI trend line declined are primarily located beyond the city limits along the periphery of Harris County. In contrast, the SoVI trend line increased along the Richmond Corridor and the area of northeast Houston south of Interstate 610 and east of Highway 59, as well as between Interstate 610 and Beltway 8. The median year of construction for housing units in tracts where the SoVI trend line increased ranges from 1956 to 1999, with nineteen of the thirty-two tracts having a median year built of 1975 or earlier. Tracts outside of Beltway 8 where the SoVI trend line increased are typically more affluent (e.g., Pasadena, Taylor Lake, Deer Park) underscoring that vulnerability is a multidimensional concept, extending beyond income and poverty (e.g., aged population in Taylor Lake). Further, the finding that tracts located roughly midway between the central city and fringe are where the SoVI trend line increased most frequently highlights the importance of better understanding of vulnerability within suburban areas given that congestion and lower connectivity in more affluent, fringe areas could increase evacuation times during an emergency (Berg and Wilson 2013).

The distribution of SoVI in New Orleans is inextricably linked to the displacement caused by Hurricane Katrina (Cutter and Emrich 2006), but even there social vulnerability has become less spatially concentrated and less dominated by race and socioeconomic indicators. Census tracts comprising the West End and portions of the Lakeview neighborhoods (northwest of downtown) exhibit a negative trend in SoVI because of the scale of damage to these areas as a result of the 17th Street Canal levee breach (Pistrika and Jonkman 2010). Other tracts in New Orleans experiencing a negative trend in the SoVI since 1980 tend to be more centrally located with higher population densities. These include portions of the French Quarter and Central City neighborhoods as well as the St. Thomas and St. Bernard Housing Developments. Perhaps one of the most notable patterns observable in New Orleans is the juxtaposition of tracts exhibiting a positive and negative trend while sharing a border such as the Fillmore and St. Bernard Housing Development north of downtown or the contrast offered by Village de L’est and Read Boulevard East near the intersection of Interstates 10 and 510 northeast of the city center. With only one example of this type of spatial juxtaposition observed in Houston (see Figure 4), this phenomenon could be interpreted as a reflection not only of the uneven distribution of damage from the storm throughout New Orleans, but also significant variation in the capacity to rebuild even when moving very short distances in space.

In Tampa, there are far fewer tracts exhibiting a significant positive trend in SoVI between 1980 and 2010 than in Houston or New Orleans. With the exception of Bayside West on Old Tampa Bay and portions of Lowery Park Central north of the city center, social vulnerability increased in tracts located in the unincorporated areas of Hillsborough County. This relatively even distribution of tracts where social vulnerability declined is consistent with processes of suburban growth (Hamidi and Ewing 2014) as well as core upgrading and gentrification (Landis 2015) that have been documented in the Tampa metropolitan area.

Challenges for Adaptation Planning

The above analysis identified several commonalities in the observed pattern of social vulnerability among the study counties. Despite having drastically different population growth trajectories and being located in different political and economic settings, in recent decades the spatial concentration of social vulnerability has gradually decreased in all of them. Our finding that the spatial concentration of social vulnerability at the census tract level is decreasing is consistent with earlier research by Cutter and Finch (2008), which concluded that a similar deconcentration trend is evident at the county scale. It is also consistent with a general deconcentration of poverty observed over the past two decades (Galster 2005). One could argue that if vulnerability becomes less concentrated in space by virtue of policy interventions or as a result of other processes, this is a desirable outcome because it reduces the scale of damage and loss of life that could occur in the event that a hurricane makes landfall or a toxic release occurs. However, it may not mean that the overall incidence of social vulnerability is decreased; it may just be more diffused. The implications of greater dispersal in vulnerable households differs depending on the threat considered with the provision of cooling centers during extreme heat events as one example where a target population that is more concentrated in space may be easier to reach and relief services might be delivered more effectively (Semenza et al. 1996).

However, our finding also shows that the composition of social vulnerability was different in each of the counties and changed in a nonuniform way over time. For Harris County (Houston) in Texas, the high growth of the Hispanic and immigrant populations in recent decades elevated the importance of ethnicity and citizenship status in the SoVI, along with the percentage of black or African American population and the poverty rate. This trend is consistent with the “urban restructuring” or “globalization” thesis of neighborhood change (Borja and Castells 1997; Sassen 2000) that attributes these demographic changes to capital and labor restructuring both at the global and local level. Demand for cheap labor has attracted sizeable immigrant and minority populations, which will make adaptation planning efforts more challenging because of the tendency of these groups to exhibit higher vulnerability. Ensuring equity and the legitimacy of adaptation efforts (Adger, Arnell, and Tompkins 2005) would be problematic in this changed context when these new ethnic minority and immigrant population lack adequate political participation to have their voices heard in the planning process.

In Orleans Parish (New Orleans), Louisiana, displacement by Hurricane Katrina has significantly influenced the pattern of social vulnerability, which has become less concentrated and less dominated by race and socioeconomic indicators. Although this pattern of displacement was documented by prior studies (Cutter and Emrich 2006), political economy models of neighborhood change (Harvey 1973; Castells 1983; Logan and Molotch 1987) can explain how institutional forces influenced this pattern and how distribution of resources (as highlighted by growth machine theorists) may influence the rebuilding capacity of vulnerable communities. As the Katrina recovery process moves forward and planning for climate adaptation continues, this changed vulnerability pattern should be kept in mind both to ensure equitable outcomes from the planning process and to identify effective ways for avoiding future disasters.

Hillsborough County (Tampa) in Florida exhibits two notable trends in its social vulnerability patterns, along with the decreased concentration of vulnerability found in the other two counties. In this county, gentrification in the inner city areas is pushing socially vulnerable populations (primarily minority and low-income groups) to suburban and coastal census tracts and at the same time, some of the coastal locations are experiencing high growth of elderly populations because of the development of retirement communities there. These trends can be explained by urban “growth machine” theories of neighborhood change (Molotch 1976; Logan and Molotch 1987) that emphasize how the economic interests of local elites drive policy decisions, which are in turn changing the pattern of social vulnerability on the ground. Both of these trends will make adaptation planning more challenging in the future and reveal two priorities—first evaluating the climatic risks of census tracts being populated by low-income and minority population and finding safer places for them, and second ensuring that climatic uncertainties are considered when developing retirement communities and that steps are taken to prevent displacement of other vulnerable groups.

Conclusion

This article offers important insight for adaptation planning by clearly demonstrating that social vulnerability is not a static phenomenon. Not only does the spatial distribution of socially vulnerable populations change over time and vary from location to location, but the relative importance of indicators like age, race, ethnicity, and gender also fluctuates. This recognition not only invites further longitudinal analysis but also suggests that just as adaptation strategies must be tailored to the local context, measures like the SoVI may also need to be adjusted. For example, Van Zandt and her colleagues (2012) use an alternative measure of social vulnerability that provides first-, second-, and third-order indices that permit the identification of type of need (e.g., child care needs, transportation needs, or shelter needs) to aid in the targeting of efforts. Because vulnerable populations are increasingly mobile, monitoring where these individuals and households are located with respect to hazards, which may themselves shift position over time, is critically important for urban planners. Any climate adaptation effort, whether focusing on impacts of sea level rise (prompting improved flood control), urban heat islands (prompting changes in land use and building designs), or any other climatic uncertainty should consider the prevailing patterns of social vulnerability to ensure more equitable and just outcomes from the planning process. The “network of plans” (Berke and Lyles 2013) created by local governments to respond to climate change, such as the hazard mitigation plan, the comprehensive plan, the emergency management plan, etc. should include local plans that address not only residential land use (comprehensive plans, general plans, or land use plans) but also housing, such as the consolidated housing plan (“Con plans”) prepared by local governments that receive funding from the US Department of Housing & Urban Development. Housing plans are often completed in isolation from other planning efforts but are a primary mechanism by which housing opportunities for socially vulnerable populations are addressed. While design solutions for climate change adaptation have already gained much attention (Stone et al. 2014; Masson et al. 2014; Neumann et al. 2015), few consider social vulnerability or the spatial distribution of housing, which may create an environment for nonadaptation. Existing planning products must better situate the concerns of social vulnerability within the land use planning process.

Despite exhibiting vastly different population growth trajectories, social vulnerability within each of the three cities considered here has become less spatially concentrated over recent decades, which is encouraging. Future research in this area should test the extent to which the trends in social vulnerability noted in these three cities are generalizable to coastal cities in other parts of the United States. Further, research that explores social vulnerability using census block groups or other sub-tract geographies would build upon the work presented here while providing a more detailed picture of social vulnerability. Finally, it is important for planners to realize that social vulnerability is not exogenous to planning processes, but instead is socially produced with policy decisions and planning interventions shaping both the composition and spatial distribution of vulnerable populations.

Footnotes

Acknowledgements

We appreciate the three anonymous reviewers for their thoughtful comments and suggestions that helped us to improve this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Shakil Bin Kashem is a teaching assistant professor in the Department of Geography and Geographic Information Science at the University of Illinois at Urbana-Champaign. He is interested in exploring how plans and policies, particularly within the realm of climate change concerns, affect socially vulnerable populations and in devising strategies to ensure more just and equitable outcomes from these initiatives.

Bev Wilson is an assistant professor in the Department of Urban and Regional Planning at the University of Illinois at Urbana-Champaign. His research is grounded in land use planning and informed by the use of geographic information systems (GIS) and spatial data analysis. He is interested in better understanding the spatial and temporal aspects of development, as well as its implications for the environment and society.

Shannon Van Zandt is an associate professor in the Department of Landscape Architecture & Urban Planning and Director of Center for Housing & Urban Development in Texas A&M University. Her research addresses equity issues related to the spatial distribution of housing opportunities for low-income and minority populations. She examines the forces that form residential land use patterns and thus determine access to community goods and services, or alternatively, exposure to environmental or social hazards.

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