Journal
SUSTAINABLE CITIES AND SOCIETY
Volume 96, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.scs.2023.104692
Keywords
Social vulnerability; Sustainability; Geographically weighted regression (GWR); Risk assessment; GIS
Ask authors/readers for more resources
This study highlights social vulnerability by examining different forms of social deprivation among urban inhabitants. It proposes a quantitative approach to measure social vulnerability and associated risk in urban areas. The approach includes three dimensions of urban social vulnerability: exposure, sensitivity, and adaptive capacity, with a composite measure calculated using 15 indicators. The study analyzes the spatial heterogeneity of urban social vulnerability in 146 urban centers in Eastern India using GIS and provides insights for policymakers and urban stakeholders to address potential challenges faced by urban residents.
Social vulnerability in this study is expressed by highlighting the different forms of social deprivation experienced by the urban inhabitants. This article proposes a fundamental approach to quantify social vulnerability and associated risk in urban areas. Here, urban social vulnerability (USoV) consists of three interconnected dimensions, i.e. exposure, sensitivity, and adaptive capacity. Based on these three dimensions' the urban social vulnerability index (USoVI) was computed using 15 indicators. Therefore, using the GIS environment, a composite measure is applied to identify and map the spatial heterogeneity of USoV for the 146 urban centres in Eastern India. The results highlight that most urban locations experience moderate to high exposure, sensitivity and low adaptive capacity corresponding to high USoV. Moran's I result indicates that the adaptive capacity shows the largest spatial association, while sensitivity indicates the lowest Moran value with the least association. Also, the spatial distribution pattern of USoVI across the urban centres is assessed by using the Univariate LISA techniques. The multiscale geographically weighted regression (MGWR) model estimates 67.4% variance of the constructed model. The study findings assist policymakers and urban stakeholders in devising an effective, participatory, and geographically targeted plan for coping with potential bottlenecks experienced by urban residents.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available