Assessment of the Socioeconomic Vulnerability to Seismic Hazards in the National Capital Region of India Using Factor Analysis

Abstract

The seismicity of the National Capital Region (NCR) of India increased significantly over the last decade. Communities in the NCR face significant exposure to damaging seismic events, and the seismic risk arises not only from the region’s proximity to the Himalayan mountains, but also from the socioeconomic vulnerabilities in its communities and the current capacities of different localities to respond to and recover from any unforeseen large seismic event. GIS-based spatial distribution of exposure to seismic hazards (SH) can help decision-makers and authorities identify locations with populations at high seismic risk, and to prepare risk-mitigation plans. Socioeconomic vulnerability (SeV) studies serve as a basis for quantifying qualitative measures. For this purpose, in the present study, the hazard of place (HoP) model is used to assess SeV to seismic hazards in the NCR. Social indicators like age, gender, literacy, family size, built environment, etc., comprising a total of 36 variables, are used to assess a socioeconomic vulnerability index (SeVI) based on factor and principal component (PCA) analyses. Based on PCA, 20 variables were retained and grouped into four factors: socioeconomic status, employment status, building typology, and family size. Ground-motion parameters, estimated from probabilistic seismic hazard assessment, are integrated with the socioeconomic vulnerability index to quantify exposure to seismic hazards. The spatial distributions in the produced socioeconomic-vulnerability index and seismic–hazard–exposure maps highlight the critical areas. The results reveal that areas of low literacy, high unemployment, and poor housing condition show moderate-to-high vulnerability. The south-eastern region of the study area is assessed as a high-risk zone by an integrated SeV–SH risk matrix. The results of this study emphasize the importance of the socioeconomic vulnerability component of disaster risk–reduction programs, from a holistic perspective, for the areas with high seismicity.

A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level

“…A welldefined income source of a community improves the living standard and increases the community's coping capacity. The employment status for the Assam region is acquired from Census 2011 (Agrawal et al 2021).…”

“…A smaller household will be less vulnerable than a household with more family members. The data of households are collected from Census 2011 (Agrawal et al 2021).…”

An integrated assessment of seismic hazard exposure and its societal impact in Seven Sister States of North Eastern Region of India for sustainable disaster mitigation planning

“…The HoP model (Cutter 1996) tries to combine social and biophysical vulnerability to produce overall place vulnerability (Cutter et al 2003). This model has been used in many social vulnerability studies worldwide (Ge et al 2013;Frigerio et al 2016;Agrawal et al 2021). Details of the methodology adopted for the social vulnerability analysis, SVA are presented and discussed in the following subsections.…”

“…Worldwide, several studies have been conducted on social vulnerability in regards to seismic hazard by considering different assessment frameworks like the Hazard of Place Model, HoP (Cutter 1996), the BBC model (Brikmann 2013), Disaster of Resilience of Place model, DROP (Cutter et al 2008), and methods like multicriteria analysis, MCA (Martins et al 2012;Frigerio et al 2016;Armas and Gavris 2017;Derakhshan et al 2020;and Agrawal et al 2021). In the Indian scenario, most seismic hazard vulnerability studies are directed towards the built environment (Sarmah and Das 2018;Dutta, Halder, and Sharma 2021).…”

“…For SVA, the indicators are selected by the application of principal component analysis (PCA) and factor analysis (FA) within the framework of the HoP model for the generation of the social vulnerability index, SVI (Agrawal et al 2021). Additionally, with the help of spatial statistical and cluster analysis tools in the GIS environment, hotspot and cold spot clusters within the study area are identified.…”