Modeling the Influence of Places on Crime Risk Through a Non-Linear Effects Model: a Comparison with Risk Terrain Modeling

Analyzing how the proximity to certain features or particular places in a city increases or decreases crime risk across space is a fundamental issue in quantitative criminology from both explanatory and predictive perspectives. Regarding the latter, the detection of high-risk cells is of special interest for practical reasons. There are several statistical modeling approaches that can be implemented in order to fulfil these two main objectives. The purpose of this study is to compare risk terrain modeling (RTM), a method widely used among quantitative criminologists, with a non-linear effects model that considers a non-linear function of distances to the selected places. To this end, a dataset containing crime events recorded in Valencia (Spain) along four years was used to perform the comparison. Several socio-demographic covariates and a selection of places in the city were considered for modeling crime counts with both the RTM and the non-linear approaches. The two modeling techniques were moderately coherent with regard to detecting certain types of places as responsible of higher (or lower) crime rates, but several differences arose. Furthermore, the non-linear model was more accurate than RTM to predict future crime occurrences for each of the three crime types that were considered for the analysis. In conclusion, the possibility of modeling the effect of a place on crime risk through non-linear functions appears as one competitive alternative or at least complement to RTM that may deserve further consideration.