Varimax rotation with Kaiser normalization converged in eight iterations, returning seven factors explaining 66% of the variance, as summarized in Table 2. Factor loadings observed in the rotated factor matrix indicate the patterns of association or clustering among the variables. The top-loading variable for each factor was selected and aggregated to create the ZSVI (Table 3) using Eq. 2.
The Zeeland social vulnerability index ranges from a score of 0.20 in Schore in the municipality of Kapelle, indicating low social vulnerability, to a maximum score of 0.64, indicating high vulnerability, in Oudelandse Hoeve of Terneuzen. The mean score among the districts is 0.36. District scores applied to the 2012 district boundaries for the 147 districts of Zeeland were mapped using the natural breaks (Jenks) classification method (four classes). Figure 2 illustrates the social vulnerability indices by districts in Zeeland. The scores ranged from low social vulnerability, illustrated in green, to a high level of vulnerability, shown in red. Districts with inadequate data are shown in grey.
Factors of Social Vulnerability in Zeeland
Analyzing the pattern of the data in the rotated matrix aids understanding of how the various indicators of social vulnerability are related to each other. We named each of the seven factors to reflect the common dimension of vulnerability they represent.
The highest-loading variable on the first factor is “Population Density,” followed by “Total Active Businesses.” Additional variables include more childcare facilities and more high rise residential structures. These variables cluster together to represent urban density.
Researchers have found that high-density urban areas may be particularly vulnerable after large-scale disruptive events due to higher potential for economic and commercial losses. For example, Cutter et al. (2003) cited the complications involved with evacuation in high-density urban areas and the financial burden that complex infrastructure repairs can put on recovering communities. The H. John Heinz III Center for Science, Economics, and the Environment (2002) of Washington, DC identifies the burden placed on families in a disaster situation when essential services such as childcare centers close. Centers of more dense populations may include residents who are dependent on the range of public services that may be disrupted during and after a large-scale flood.
The second factor represents sources of socioeconomic vulnerability associated with family structure and household economic resources. The top-loading variable for this factor is “% Low-Income Households.” Related attributes include a larger percentage of rental housing, more single-person households, lower per capita income and housing value, and fewer passenger automobiles.
The link between these conditions and social vulnerability to large-scale disturbances has been suggested by recent research. For example, Fekete (2010) reported that low-income households and those individuals living alone may have a reduced ability to prepare for and protect themselves during disaster situations. Moreover, households with lower incomes would face a range of difficulties in recovering following a large-scale flood, including inadequate funds to replace lost possessions and property.
Recent Population Change
The third factor relates to changes in population within a municipal district from 2003 to 2012. The highest-loading variable is the absolute change in population numbers, either an increase or decrease in recent years. Such a change is in contrast to a more consistent population of residents, wherein social networks and housing patterns may be more stable. While it is intuitive that a rapid population loss may contribute to vulnerability, researchers have noted that population increases also may create challenges to emergency planning and recovery following a large-scale disturbance (Perrow 2007). Population growth may introduce elements of vulnerability if newcomers are not familiar with the flood risks of the area (Donner and Rodriguez 2011). The analysis identified newer housing stock as another variable related to recent population change.
The analysis places female gender on the fourth factor. Females may experience a more difficult recovery process due to lower wages and family care responsibilities (Cutter et al. 2003). Dutch females have less of a presence in the full-time work place than men, possibly attributable to the responsibility of raising children. The second highest loading variable here is percentage of people aged 65 and older, indicating a more vulnerable group of residents who may face more obstacles in planning for and recovering from a large flood.
The fifth factor is characterized by “Distance to Train Station,” a variable that represents access to infrastructure necessary for daily transportation and rapid evacuation. The variables of percentage of foreign born population and fewer highly educated residents also load on this factor, indicating additional potential sources of vulnerability. With 40% of its total area comprised of water, Zeeland’s dry land area is fragmented. Further, only about 60% of residents own personal vehicles, so public rail transportation plays an important role in the lives of the residents. During an emergency evacuation, trains would have the capacity to move large numbers of residents away from the coast. Only one passenger rail line runs through the central lobe of Zeeland; To access that train, residents of the northern and southern lobes must cross water, either by tunnel, bridge, or ferry, introducing potential challenges to residents in the case of a large-scale flood.
The sixth factor is distinguished by fewer self-employed residents. The category of self-employed may include a wide range of occupations such as agriculture, small business owners, contractors, trade workers, and professional service providers (Eurofound 2017). In the Netherlands, the ranks of the self-employed are increasing and the outlook for new businesses is generally strong, with roughly 74% surviving the first 2 years (Braams 2010). Cutter et al.’s (2003) US SoVI identifies persons employed in resource-extractive industries, such as farmers, as more vulnerable after a disaster event; but this may not be the case in the Netherlands. Dutch farmers may be somewhat less vulnerable to disruptions in income due to government assistance programs.
The seventh factor is the percentage of the local workforce employed in service industries, not including workers in medical fields. The service workers in the hospitality industries including restaurants, hotels, and shops in tourist towns of the Zeeland may be particularly vulnerable to any disruption in commercial or recreational activities following a large-scale storm or flood.
These seven factors are somewhat similar to other studies. For example, Fekete (2010) derives three factors of social vulnerability, including regional conditions, socioeconomic conditions, and fragility. While “regional conditions” covers concepts of gender, access to healthcare, family/housing conditions, and so on, the highest loading variable on the first factor, population per settlement area, matches the outcome of the Zeeland SVI top-loading first factor variable Population Density. The highest loading variable of our second factor (low-income households) is found loading second highest on Fekete’s second component, socioeconomic conditions, along with the Zeeland factor four “Employment” variable. Lastly, the Zeeland index reflects Fekete’s third component (fragility) led by population over 65 years old, which is located on the fifth factor for Zeeland.
When comparing results to the Norway SVI, we find greater similarity in the findings, possibly due to the authors’ decision to allow for a greater number of factors. While the first of the seven factors in the Zeeland SVI groups variables related to urban and high density areas, the Norwegian sorts these conditions independently as the fourth and fifth of 10 components respectively. The second Zeeland SVI factor includes similar variables of components one and seven of the Norwegian SVI, including the grouping of variables related to age and social status and renters, in addition to other variables related to personal wealth. The third factor in the Zeeland SVI includes variables related to unemployment, disability benefits, old houses, and population change. Similar variables may be found in components two and six of the Norwegian SVI, including marginal social groups and demographic instability, respectively.
Summary of Results
The factor analysis of 25 indicators of social vulnerability resulted in seven factors explaining about 66% of the total variance. The factors of social vulnerability in Zeeland are urban density, low income, population change, more females, distance to train stations, fewer self-employed, and more employed in service industries.
The index presented here considers dense commercial, industrial, and residential areas as containing the highest levels of social vulnerability. The districts of the southern lobe of Zeeland earned the highest scores due to the intersection of multiple factors associated with social vulnerability, which include limited access to lifelines (that is, evacuation routes) and greater foreign population. The six most vulnerable districts are located in the municipality of Terneuzen, bordering the Westerschelde, which is the only unfortified estuary of the Netherlands. The map in Fig. 2 shows the spatial distribution of the social vulnerability scores for the districts of Zeeland Province. Index scores range from a low social vulnerability score of 0.20 in Schore of Kapelle, to the highest social vulnerability score, 0.64 in Oudelandse Hoeve of Terneuzen.
Forty-five of the 50 districts with highest social vulnerability scores are located in South Zeeland, with the top six found in Terneuzen, the most populous municipality in Zeeland. Terneuzen is situated with Belgium to the south and the Westerschelde, a major shipping corridor, to the north. Terneuzen’s poorest-scoring district is characterized by high population density, a high proportion of female population, a relatively large number of low-income households, and limited access to lifelines, such as evacuation routes. Terneuzen is home to large-scale chemical industry, and contains several districts reserved strictly for industrial activity, which were omitted from the analysis due to the small resident population of these areas. Terneuzen does not have access to rail, but is connected to the Netherlands by a toll tunnel.
The majority of the districts with lower social vulnerability index scores are located in Zeeland’s central region and the municipality of Tholen. Much of the central lobe of Zeeland is prized for tourism and Middelburg, the historic capital of Zeeland, the central lobe and Tholen are serviced by passenger rail, providing critical lifelines to the rest of the Netherlands in the case of an evacuation.
Two districts in the municipality of Kapelle rank in the top 10 lowest social vulnerability index scores, with the third placing just behind, ranked 30. Kapelle is centrally located in the Zeeland, with access to both the Oosterschelde and the Westerschelde, characterized by low population density, stable population relative to change, and nearby access to lifelines.
When we consider the spatial distribution of these social vulnerability scores across the province, several observations can be noted. For example, a high proportion of the elderly population resides in the beach communities of central Zeeland, where towering dunes contribute some of the highest elevations in the province. As a result, the higher elevation may offer flood protection, and thus reduced physical vulnerability to an otherwise socially vulnerable subgroup of the population. Similarly, the Noord-Beveland districts received index scores indicating greater social vulnerability despite relatively high elevation. One contributing factor is due to its role as a tourist destination, and corresponding greater portion of service industry workers. Noord-Beveland does not have access to rail with road access only to the low-lying delta lobes to the north and south.
It is important to point out that factor analysis groups variables together into different factor dimensions based on the similarity in spatial distributions of the variables; it does not necessarily mean that the factors or the selected variables correspond to vulnerability. We interpret how the variables may contribute to social vulnerability within the specific study area based on the findings of related research about sources of social vulnerability. A more objective approach would require validation of the index either through examining empirical damage data (Cai et al. 2016; Lam et al. 2016) or household survey results from those who have experienced large floods as demonstrated in Fekete (2010). Since the flood damage data were not available in the study area (because the study region has not experienced large-scale flooding since 1953), we were not able to verify the derived index.
Our approach is a simplification of the traditional social vulnerability index calculation (Cutter et al. 2003). Instead of computing the combined factor scores and aggregating them into an index, we selected the top-loading variable to represent a factor, and then computed their relative importance and aggregated them into a single vulnerability index. The method can be modified to add two or more variables from each factor if desired. This simplified approach is meant to make it easier for planners and managers to identify the specific attributes of communities that may make them more vulnerable and to identify how these attributes are related to other contextual conditions.
The inclusion of proximity to public transportation is a measure unique to this social vulnerability analysis. In the case of Zeeland, where vehicle ownership is relatively low compared to bicycle ownership, residents without easy access to trains or major roadways would not be able to evacuate or recover quickly if faced with a large-scale flood. For example, traveling from Terneuzen to the provincial capital city Middelburg via public transportation requires multiple bus transfers and a ferry crossing, resulting in over an hour of travel time each way to move a relatively short distance. The fragmentation in the landscape in Zeeland Province introduces a significant potential for “bottleneck” delays during an emergency evacuation. Local planners, residents, and other stakeholders could consider investments in additional rail lines and/or other public transportation modes to help reduce the social vulnerability of these communities to future large-scale floods.