Geospatial Analysis for Identifying Socially Vulnerable Areas to Natural Disasters.

Abstract

Objectives: Disaster risk reduction measures are now being developed based on social vulnerability. This study aimed to identify socially vulnerable areas to disasters in Razavi Khorasan Province, Iran.

Methods: The research utilized a mixed method approach conducted in 2 stages. First, a vulnerability index was created using 8 sub-indices, and the value of the index was calculated for each of the 91 rural districts in the study area. In the second stage, spatial analysis using Anselin's Local Moran's I was performed to identify the most vulnerable districts.

Results: Results indicated that 40 of 91 districts, covering 49% of the total area, had high social vulnerability to disasters. Anselin's Local Moran's I analysis identified 2 high-high clusters consisting of 5 districts. The study also found that areas with higher social vulnerability were more susceptible to natural hazards such as floods and earthquakes.

Conclusions: Nearly half of the studied areas exhibited a high level of social vulnerability and were at risk of natural disasters. Implementing general measures to improve the socio-economic status of the population, such as increasing education and income levels, along with specific actions like assisting vulnerable populations in relocating to safer areas, can help mitigate disaster risks.