2023
DOI: 10.1016/j.ijdrr.2023.103883
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Comparing spatially explicit approaches to assess social vulnerability dynamics to flooding

L.G. Meijer,
L. Reimann,
J.C.J.H. Aerts
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Cited by 4 publications
(2 citation statements)
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“…The five key drivers of social vulnerability and their effect (i.e., increasing or decreasing) on social vulnerability confirm the relationships assumed in the literature (Table S1 in Supporting Information S1). An interesting insight of this study is that while per capita income, which is often used as an indicator of vulnerability (e.g., Cutter et al, 2003;Meijer et al, 2023;Rufat et al, 2019;Tate, 2013;Yoon, 2012), has been statistically significant in explaining flood fatalities (see Model 2 in Table 2), income inequality between genders seems to play a more important role in driving vulnerability. This insight confirms previous work (e.g., Rufat et al, 2015) that discusses income as a potential proxy for other vulnerability variables that reflect socioeconomic status such as education levels, which we establish as the most influential driver of social vulnerability globally.…”
Section: Drivers Of Social Vulnerabilitymentioning
confidence: 78%
See 1 more Smart Citation
“…The five key drivers of social vulnerability and their effect (i.e., increasing or decreasing) on social vulnerability confirm the relationships assumed in the literature (Table S1 in Supporting Information S1). An interesting insight of this study is that while per capita income, which is often used as an indicator of vulnerability (e.g., Cutter et al, 2003;Meijer et al, 2023;Rufat et al, 2019;Tate, 2013;Yoon, 2012), has been statistically significant in explaining flood fatalities (see Model 2 in Table 2), income inequality between genders seems to play a more important role in driving vulnerability. This insight confirms previous work (e.g., Rufat et al, 2015) that discusses income as a potential proxy for other vulnerability variables that reflect socioeconomic status such as education levels, which we establish as the most influential driver of social vulnerability globally.…”
Section: Drivers Of Social Vulnerabilitymentioning
confidence: 78%
“…To remove outliers, we established the 98th percent confidence interval (following Tate, 2013) by replacing all Vul j values below (above) the 1st (99th) percentile with the respective percentile values that we calculated based on the GlobE‐SoVI map (“winsorization”) (Hagenlocher & Castro, 2015; Hagenlocher et al., 2018). Finally, we normalized the GlobE‐SoVI to values ranging from 1 (low vulnerability) to 10 (high vulnerability) by scaling the data linearly, which is commonly done in social vulnerability assessments (Anderson et al., 2019; Hagenlocher et al., 2018; Meijer et al., 2023; Tate, 2013). We used a minimum of 1 based on the assumption that people are vulnerable to flooding to a certain degree and a maximum of 10 to prevent misconception of the GlobE‐SoVI as reflecting percentages.…”
Section: Methodsmentioning
confidence: 99%