Analyzing the relationships between crime and socio-economic and spatial factors using random forest: a case study of Tallinn

Abstract

The spatial factors of crime and its socioeconomic background are important topics in crime research. This study uses a grid framework to represent various spatial, environmental, and socioeconomic factors across Tallinn in 500-meter grids. The study aims to predict the number of crimes in each grid cell through a random forest machine learning model and identify the main contributing factors. Machine learning models do not explain causal relationships between variables but highlight possible correlations, so crime factors need to be discussed within Tallinn's context. Among various types of crime, the factor of commercial locations shows the strongest relationship with the number of crimes. These reflect the concentration of economic activities, assets, and the gathering of people, which are important conditions for crime motivations. Secondly, factors such as the number of renters and the population with low socioeconomic status are associated with the number of crimes against public order.