Identifying urban–rural differences in social vulnerability to natural hazards: a case study of China

2021 ◽  
Author(s):  
Yi Ge ◽  
Wen Dou ◽  
Xiaotao Wang ◽  
Yi Chen ◽  
Ziyuan Zhang
Keyword(s):  
Natural Hazards ◽  
Social Vulnerability ◽  
Urban Rural

scholarly journals Assessment of Spatial Distribution Model of Urban-Social Vulnerability in Natural Hazards (A case study of Seven Municipality of Tehran)

2019 ◽  
Vol 6 (3) ◽  
pp. 49-70
Author(s):  
ABOLFAZL MESHKINI ◽  
ali mohammad mansourzadeh ◽  
zeynab shahrokhy far ◽  
شهربانو موسوی ◽  
◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Natural Hazards ◽  
Social Vulnerability ◽  
Distribution Model ◽  
Spatial Distribution Model

scholarly journals A review of multivariate social vulnerability methodologies: a case study of the River Parrett catchment, UK

2016 ◽  
Vol 16 (6) ◽  
pp. 1387-1399 ◽  
Author(s):  
I. Willis ◽  
J. Fitton
Keyword(s):  
Natural Hazards ◽  
Social Vulnerability ◽  
Disaster Risk Reduction ◽  
Census Data ◽  
Disaster Risk ◽  
Flood Risk Management ◽  
Large Uncertainty ◽  
Highly Sensitive ◽  
Source Data

Abstract. In the field of disaster risk reduction (DRR), there exists a proliferation of research into different ways to measure, represent, and ultimately quantify a population's differential social vulnerability to natural hazards. Empirical decisions such as the choice of source data, variable selection, and weighting methodology can lead to large differences in the classification and understanding of the "at risk" population. This study demonstrates how three different quantitative methodologies (based on Cutter et al., 2003; Rygel et al., 2006; Willis et al., 2010) applied to the same England and Wales 2011 census data variables in the geographical setting of the 2013/2014 floods of the River Parrett catchment, UK, lead to notable differences in vulnerability classification. Both the quantification of multivariate census data and resultant spatial patterns of vulnerability are shown to be highly sensitive to the weighting techniques employed in each method. The findings of such research highlight the complexity of quantifying social vulnerability to natural hazards as well as the large uncertainty around communicating such findings to stakeholders in flood risk management and DRR practitioners.

scholarly journals A review of multivariate social vulnerability methodologies; a case study of the River Parrett catchment, Somerset

2016 ◽  
Author(s):  
I. Willis ◽  
J. Fitton
Keyword(s):  
Natural Hazards ◽  
Social Vulnerability ◽  
Disaster Risk Reduction ◽  
Census Data ◽  
Disaster Risk ◽  
Large Uncertainty ◽  
River Catchment ◽  
Highly Sensitive ◽  
Source Data

Abstract. In the field of Disaster Risk Reduction (DRR), there exists a proliferation of research into different ways to measure, represent, and ultimately quantify a population’s differential social vulnerability to natural hazards. Empirical decisions such as the choice of source data, variable selection, and weighting methodology can lead to large differences in the classification and understanding of the 'at risk' population. This study demonstrates how three different quantitative methodologies (based on Cutter et al. (2003), Rygel et al. (2006), and Willis et al. (2010)) applied to the same England and Wales 2011 Census data variables in the geographical setting of the 2013/2014 floods of the Parrett river catchment, UK, lead to notable differences in vulnerability classification. Both the quantification of multivariate census data and resultant spatial patterns of vulnerability are shown to be highly sensitive to the weighting techniques employed in each method. The findings of such research highlight the complexity of quantifying social vulnerability to natural hazards as well as the large uncertainty around communicating such findings to DRR practitioners.

scholarly journals Social Vulnerability across the Great Lakes Basin: A County-Level Comparative and Spatial Analysis

2021 ◽  
Vol 13 (13) ◽  
pp. 7274
Author(s):  
Joshua T. Fergen ◽  
Ryan D. Bergstrom
Keyword(s):  
Great Lakes ◽  
Spatial Patterns ◽  
Social Vulnerability ◽  
Vulnerability Index ◽  
Social Vulnerability Index ◽  
Great Lakes Basin ◽  
Empirical Examination ◽  
Regional Patterns ◽  
Urban Rural ◽  
Social Vulnerabilities

Social vulnerability refers to how social positions affect the ability to access resources during a disaster or disturbance, but there is limited empirical examination of its spatial patterns in the Great Lakes Basin (GLB) region of North America. In this study, we map four themes of social vulnerability for the GLB by using the Center for Disease Control’s Social Vulnerability Index (CDC SVI) for every county in the basin and compare mean scores for each sub-basin to assess inter-basin differences. Additionally, we map LISA results to identify clusters of high and low social vulnerability along with the outliers across the region. Results show the spatial patterns depend on the social vulnerability theme selected, with some overlapping clusters of high vulnerability existing in Northern and Central Michigan, and clusters of low vulnerability in Eastern Wisconsin along with outliers across the basins. Differences in these patterns also indicate the existence of an urban–rural dimension to the variance in social vulnerabilities measured in this study. Understanding regional patterns of social vulnerability help identify the most vulnerable people, and this paper presents a framework for policymakers and researchers to address the unique social vulnerabilities across heterogeneous regions.

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