Examining Vulnerability Factors to Natural Disasters with a Spatial Autoregressive Model: The Case of South Korea

Seunghoo Jeong,D Yoon

doi:10.3390/su10051651

Abstract

Socially and economically marginalized people and environmentally vulnerable areas are disproportionately affected by natural hazards. Identifying populations and places vulnerable to disasters is important for disaster management, and crucial for mitigating their economic consequences. From the fields of geography, emergency management, and urban planning, several approaches and methodologies have been used to identify significant vulnerability factors affecting the incidence and impact of disasters. This study performs a regression analysis to examine several factors associated with disaster damage in 230 local communities in South Korea, using ten vulnerability indicators for social, economic, and environmental aspects, and a single indicator for disaster characteristics. A Lagrange Multiplier diagnostic test-based spatial autoregressive model (SAM) was applied to assess the potential spatial autocorrelation in the ordinary least squares (OLS) residuals. This study compared the OLS regression results with those of a spatial autoregressive model, for both presence of spatial autocorrelation, and model performance. The conclusion of this study is that Korean communities with a higher vulnerability to disasters, as a result of their socioeconomic and environmental characteristics, are more likely to experience economic losses from natural disasters.

Highlights

Despite similar magnitudes, the impact of natural disasters is unevenly distributed among communities with different social and physical aspects [1,2]
As a multicollinearity problem occurs when the multicollinearity condition number is over 30 [16], the ordinary least squares (OLS) regression results showed no multicollinearity among vulnerability indicators (Table 3)
The final test is conducted to check the existence of spatial autocorrelation in the regression residuals as one assumption of the OLS regression

Summary

Introduction

The impact of natural disasters is unevenly distributed among communities with different social and physical aspects [1,2]. Vulnerability factors related to people and communities have been examined, and their relationship with disaster damages identified, using traditional regression methods such as the ordinary least squares (OLS) regression model [1,9]. If the index is spatially autocorrelated, a Local Indicators of Spatial Association (LISA) analysis can be used to identify significant clusters of regions with similar characteristics of vulnerability and damage [12,13]. An OLS regression is performed to examine the relationship between economic losses from a natural disaster, and vulnerability indicators representing social, economic, and environmental features in South Korea in 230 local communities. As vulnerability indicators are collected using spatial data, this study performs a Global Moran’s I test to examine whether there is a spatial autocorrelation in the model residuals. This study outlines some important implications for vulnerability studies and policy makers who are responsible for disaster management in local communities

Literature Review

Independent Variables

Results