Variable selection methods for Log-Gaussian Cox processes: A case-study on accident data

In order to prevent and/or forecast road accidents, the statistical modeling of spatial dependence and potential risk factors is a major asset. The main goal of this article is to predict the number of accidents on a certain area by considering georeferenced accident locations crossed with variables characterizing the studied geographical area such as road characteristics as well as sociodemographic and global infrastructure variables. We model the accident point pattern by a spatial log-Gaussian Cox process (LGCP). To reduce the computation burden of LGCP models in this high-dimensional setting, we suggest a two-step procedure: to perform first automatic variable selection methods based on Poisson regression, Poisson aggregation and random forest and in a second step, to use the selected variables and perform LGCP model analysis. The dataset consists in road accidents occurred between 2017 and 2019 in the CAGB (urban community of Besançon), France. Based on LGCP analysis, we are able to identify the principal risk factors of road accidents and risky areas from CAGB region.