Determining the factors affecting the disaster resilience of countries by geographical weighted regression

Abstract

The effects of natural and technological disasters on human, the environment, economy and social life can be prevented via scientific and technological developments. Especially, international scientific studies provide a better understanding of the nature, characteristics and effects of disasters. Therefore, a more resilient community can be built after disasters by reducing the damages of disasters at national and international level. The aim of this study was to investigate the factors affecting the disaster resilience of countries. In this study, by analyzing the data of 181 countries in 2018 and 2019, the rate of total population affected by disasters was employed as dependent variable and the factors indicating the development level of countries as independent variables. The data were analyzed with the help of ArcGIS 10.7 program using ordinary least squares regression analysis that reveals general relationships and geographical weighted regression analysis that shows local relationships across study area. In conclusion, in the general model, compulsory education duration in 2018 was the only variable that positively and significantly predicted the rate of total population affected by disasters. While neonatal mortality rate and unemployment predicted positively and significantly the rate of total population affected by disasters in 2019, urban population rate predicted negatively and significantly. The effect of all the independent variables on the rate of total population affected by disasters differed depending on time and region. The result of this study is expected to contribute to the national and international organizations responsible for disaster risk reduction efforts to prepare more effective and efficient disaster plans. In addition, the method and approach of this study may give an idea to scientists investigating on disasters. © 2022 Elsevier Ltd