Comparision of Kernel Functions in Geographically Weighted Regression Model: Suicide Data as an Application

Abstract

The traumatic traces of suicide in a society and the emotional devastation due to these losses make it very important to determine the causes of suicide. In this study, the number of suicides data was used for Turkey's 81 provinces in 2019.The effects of factors affecting suicide and spatial differences on suicide were analyzed and predicted with geographically weighted regression models (GWR). GWR models were applied with different kernel functions, and the best GWR model was found with the bisquare kernel function. Factors affecting suicide numbers were established as human development index, proportion of internet users, and numbers of unemployment. When the results were examined, it was seen that the number of suicides in the provinces was affected by different factors. In addition, the 2019 suicide numbers and predicted values were mapped, and the results were found to be quite similar. The province with the highest number of suicides across the country was Istanbul.