Learning from Accidents: Spatial Intelligence Applied to Road Accidents with Insights from a Case Study in Setúbal District, Portugal

Abstract

Road traffic accidents are a major concern for modern society with a high toll on human life and involve hard to account economic consequences. New knowledge can be obtained from combining GIS tools with machine learning and artificial intelligence, developing what is, in this work, identified as spatial intelligence. This approach is tested in a case study of Setúbal district, Portugal, for the period of 2016 to 2019. Departing from a heatmap analysis, and applying kernel density estimation, new spatial approaches were used, namely DBSCAN and Getis-Ord. The results obtained allowed the identification of novel meaningful locations of road traffic accidents. Consequently, the knowledge built from the underlying patterns is considered the key to developing new strategies to solve this modern social curse. The methodology proposed in this study demonstrates that the combination of expertise built from the different spatial analyses can provide a better understanding of the determinants of road traffic accidents. This approach is expected to be valuable for data analysts and decision-makers, contributing to diminishing human losses related to road traffic accidents.