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

The effects of natural and technological disasters on human, the environment, economy and social life can be prevented with the help of 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 is to investigate the factors affecting the disaster resilience of countries. In the study, by analyzing the data of 181 countries in 2018 and 2019, the rate of total people affected by disasters was used as dependent variable and the factors indicating the development level of the countries as independent variables. The data were analyzed with the help of ArcGIS 10.7 program using Least Squares Regression Analysis that reveals general relationships and Geographical Weighted Regression Analysis that shows local relationships. In conclusion, in the general model, the duration of compulsory education in 2018 was the only variable that positively and significantly predicted the rate of total people affected by disasters. While the neonatal mortality rate and unemployment predicted positively and significantly the rate of people affected by disasters in 2019, the urban population rate predicted negatively and significantly. The effect of all the independent variables on the rate of total people affected by disasters differ 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 make more effective and efficient disaster plans. In addition, the method and approach of the study will give an idea to scientists working on disasters.