In this section, the results are first evaluated and discussed. In the second step, the results are also discussed methodically.
4.1 Hotspots: Evaluation and Implications of the Results
The SVI mapping for Hamburg reveals hotspots with high SVI scores. The selected neighborhood examples show how the input variables are reflected in the overall index. The hotspots represent areas with high social vulnerability resulting from unequal distribution and social segregation processes. Focusing on absolute numbers of people, instead of the relative proportions of the population in administrative urban units, produced a picture of the city in which sparsely populated areas are predominantly classified as unproblematic.
The SVI is designed to reveal where high sensitivity and low coping capacity are concentrated, but not why. A valid explanation of SV patterns can only be derived based on a review of historical, qualitative, and quantitative data for individual neighborhoods. This means that in the case of the city of Hamburg, one has to consider the historical construction periods, the impact of disasters, migration waves, and proximity to the port of Hamburg in relation to the prevailing wind direction in order to interpret the results meaningfully. For example, in the case of the Reiherstieg quarter in Wilhelmsburg, the time of construction (Gründerzeit and the 1950s) and the building fabric (for example in terms of apartment size: maximum 3-room apartments) are particularly important and have to be considered when examining population composition (Freie und Hansestadt Hamburg, Behörde für Stadtentwicklung und Umwelt
2004). Additionally, the flood disaster of 1962 in which more than 300 people died throughout Hamburg claimed by far the most victims in this neighborhood; and the damaged buildings were often only provisionally repaired after the flooding (Paech
2008). The fabric of the buildings and the unfavorable location in the flood hazard area of the tidal Elbe River (Freie und Hansestadt Hamburg, Behörde für Umwelt, Klima, Energie und Agrarwirtschaft
2020) contributed to a negative image for the district, which in turn resulted in a negative social development. This was further reinforced by other urban planning measures (construction of expressways through Wilhelmsburg, see Markert
2008). In order to be successful, adaptation and mitigation strategies must also take these local characteristics into account. Here, educational strategies and empowering the local population are potential strategies that could improve the SVI score.
The relevant parameters used to interpret the results vary from city quarter to city quarter. In Eimsbüttel, for example, the flood of 1962 played no role in the infrastructure that can be observed today. Adequate street width and green inner and rear courtyards are rather attributable to fires (e.g. the Great Fire of 1842) and the resulting changes in building regulations (Schubert
2012, Hanke
2014). As one of the most densely populated areas of Hamburg, Eimsbüttel offers a wide range of cultural, leisure, and everyday activities (Vogelpohl
2010). This mix of living and working with a high population density at the same time has a high degree of continuity, leading to the presence of many elderly residents alongside new families looking for an apartment.
As mentioned above, St. Pauli/Neustadt is a mixture of these two city quarters. Here, housing similar to that in Eimsbüttel meets a very different neighborhood composition. St. Pauli is characterized by residents with various migration backgrounds who found cheap housing in unrenovated
Gründerzeit buildings in the 1980s—a typical development in major European cities at that time. While the majority of the children also have a migration background, the majority of the elderly singles have been rooted in the neighborhood for decades (Vogelpohl
2010). In addition to this demographic variation, there are the young students who prefer to live in this district due to its proximity to the University of Hamburg and who often contribute to the low coping capacity due to their low level of income and increased demand for housing (Pohl and Wischmann
2014). The leeward location of this district behind the port (relative to the prevailing wind direction) is a historical continuity that is expressed here in the low coping capacity, which in turn is expressed in predominantly small apartment layouts (see Reiherstieg above).
Generally, our analysis draws attention to a specific process of demographic change driving an aging of urban society in parts of the city of Hamburg and thus to an increase in SV. However, local characteristics have to be taken into account when developing new strategies to mitigate the consequences of this urban demographic shift. For example, in St. Pauli, with its relatively steep topography, measures must be designed differently than in Eimsbüttel, where people with restricted mobility can move more easily. A change in the old-age housing situation, for example through shared accommodation for the elderly, could therefore not only have positive effects on public health (Dapp et al.
2014), but also lead to a reduction in SV overall.
It is precisely the identification of areas with high SVI scores that enables a more in-depth analysis of the root causes of SV. There is a connection between our study and the program of the “social city” (Freie und Hansestadt Hamburg, Behörde für Stadtentwicklung und Wohnen
2021), which focuses on areas of Hamburg that have a low SVI-CC in our analysis. Note that our results show a spatiotemporal continuity, that is, neighborhoods with low SVI-CC have hardly changed over decades. Therefore, “spatial traps” (Pohl et al.
2010) of social inequality can also be seen as important indicators of SV.
Socio-spatial inequalities often adversely affect certain social groups, such as elderly people, people with low income, and people with lower educational backgrounds (EEA
2019). Thus, environmental hazards such as flooding not only have a physical impact on urban infrastructure, but also a social impact on those who are most marginalized. In other words, “resilient flood risk governance requires mechanisms to ensure social equity and to address ‘unfair’ distributional effects” (Driessen et al.
2018, p. 11). The neighborhood examples we chose reveal that SV, as well as coping capacity, sensitivity, and density, are unequally distributed in Hamburg, showing underlying unequal distribution and social segregation processes, which in the long term have a negative effect on the urban system and its inhabitants (Foster et al.
2019).
On a general level, however, the city has responsibilities. First, it can point out hazards and provide the (potentially affected) citizens with the necessary information (for example, Krieger et al.
2013; Freie und Hansestadt Hamburg, Behörde für Umwelt, Klima, Energie und Agrarwirtschaft
2020; Freie und Hansestadt Hamburg, Behörde für Umwelt, Klima, Energie und Agrarwirtschaft
2021). Second, it can increase coping capacity if the city provides targeted subsidies for private homeowners who live in potential hazard areas, in order to strengthen private risk prevention (Mees et al.
2014).
To conclude, the SV concept helps identify hotspots of vulnerable groups that might need additional assistance in case of emergency. Social vulnerability index can also be used for further urban flood risk monitoring, which can guide flood risk communication. For instance, the SVI can be coupled with hazard and exposure data as well as data on socioeconomic losses. A map incorporating data on the structural quality of buildings with the SVI and its underlying sensitivity indicators (young children, elderly singles), alongside a map of flood zones, could help tailor emergency plans within the floodplain to people who would otherwise not reach safe areas on their own. As another example, advance warnings in case of heat waves can be targeted to areas with high SV in the city’s heat island, based on age structure and coping capacity. Therefore, working at a detailed scale is worthwhile not only for emergency response, but also for improving prevention planning and community resilience in the long term. Nonetheless, a comprehensive analysis of combining SVI with hazards and exposure maps is beyond the scope of this study.
4.2 Methodological Results and Summary
When comparing the results of the SV analysis with population density, it has been shown that population density can function as a proxy for SVI, despite the heterogeneous settlement structure of the city of Hamburg. However, population density’s explanatory power in this regard decreases massively if the segregation of cities increases, whereas in a very mixed, homogeneous city the differences between population density and SVI will only be small. Although Hamburg is structurally very heterogeneous, it is much more mixed overall than is often the case in large cities in the United States, for example. Due to the destruction in the Second World War, there are extensive rows of buildings from the 1950s in the east and south of the city (Freie und Hansestadt Hamburg, Behörde für Bau und Verkehr
2003). Many of the
Gründerzeit buildings were rebuilt and parts of the city plan were reconfigured to be car-friendly. However, neither the distance to the city center nor the type of building provide reliable information about the social composition of the districts. Our study showed that differences in detail in Hamburg, especially in potential flood hazard areas (compare the Reiherstieg quarter in Wilhelmsburg), are obscured by any analysis that only takes population density into account.
For the aggregation of the factors into a final index, variables are usually summed up, even if there is no intercorrelation between the individual variables or factors (compare for example Tapsell et al.
2002; Preston et al.
2008). This is done even if a factor analysis was previously carried out and the factors determined are orthogonal to each other (for example varimax rotation, see Fekete
2009), although an addition of variables of different dimensions is mathematically invalid (Kaiser
1958). The widely used social vulnerability index SoVI, which was originally developed by Cutter et al. (
2003), shares this problematic approach, too. This is the primary methodological reason for using the AHP model for variable selection and weighting in this study. In addition, our SVI is much easier to reproduce.
To summarize, the following points especially characterize our index:
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Using the city of Hamburg as a case study, the SVI reveals the spatial distribution and small-scale variability of social vulnerability, and particularly vulnerable population groups, with high resolution.
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For the calculation of the input variables, the actually populated areas of the city of Hamburg were identified using satellite information. These areas were blended with the survey units of the social data and thus form a unique spatial reference.
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The spatial calculation of the variables and indices derived from methodological considerations show that using population density as a proxy for social vulnerability is appropriate for a rough overview. However, if detailed small-scale urban differences in urban social vulnerability are of interest, the selected variables help to identify hotspot areas.
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We have constructed our index as precisely as necessary and as simply as possible. Despite the simplicity of the index, the proxy variables selected adequately demonstrate the range of the underlying drivers of social vulnerability (social welfare, education, and age).
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The variables chosen are consistent with a variety of studies that have examined social vulnerability to climate-related hazards. They form a basis for climate-related risk analysis for the city of Hamburg.
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Our detailed SVI mapping presents a simple and easily understandable starting point for developing guidance for local flood adaptation and prevention planning as well as emergency planning.
For new urban infrastructure projects, the map locates where special attention should be paid to vulnerable populations to mitigate unequal distribution and dynamics of social segregation. Adaptation measures are necessary to counter the vulnerability of these groups in order to prepare for future climate change. Effectively addressing social vulnerability reduces both the human suffering and economic losses associated with providing social services and public assistance after a disaster (Flanagan et al.
2011; Abebe et al.
2020).
Limitations of this study can be summarized as follows:
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The maps show the current state only and do not include dynamics such as changes in population structure. The SVI can always be recalculated to create a timeline. However, a forecast of demographic developments is only possible on the basis of further assumptions. Such a timeline would be associated with great uncertainties, but is fundamentally conceivable.
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Some variables had to be ignored due to the inaccessibility of data (for example, insurance status) because of the strict data protection regulations in Germany.
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The input variables for coping capacity—welfare recipients and school leavers with no/low educational qualification—are in a sense country-specific, as such data are rarely collected in a uniform way within Europe. Therefore, only a comparison to other German cities is strictly possible.
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In this study, local knowledge was incorporated by the expert-guided selection of variables. Therefore, our selection does not claim to be of general validity.
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Our study does not claim to be comparable with current indices with a global scope (Davino et al.
2021), which, however, work with significantly lower spatial resolutions.