Point process models for predicting the spatial distribution of rhino poaching activity in the Kruger National Park

Abstract

Rhino poaching in South Africa continues to threaten the existence of African rhino species. Since poachers often attack wildlife parks frequently, predictive models are essential for exploiting the availability of data to gain information about the poachers. Although a number of statistical methods have been applied to poaching prediction, they either do not take the spatial variation of observations into account, require additional observational data, depend on known priors, or result in models that are overfitted and challenging to interpret. This paper proposes the use of point process models to predict the spatial distribution of poaching activity within a wildlife park. Descriptive statistics of poaching spatial point patterns have been considered, as well as univariate non-parametric kernel density estimation. However, the focus of this work is on fitting multivariate parametric point process models, using a number of environmental factors. Since real-world poaching data could not be obtained for this work, due to the sensitivity of the data, a simulation study is performed, where numerous point patterns are generated from the same underlying point process. The method can be used when no data is available, and is based on environmental preferences of poachers, which can be obtained through expert knowledge, literature reviews, or by making intelligent assumptions. The results indicate that the point process models are able to predict the initial probabilities well, for most data generating processes. Point process models thus appear to be a promising method for predicting the spatial distribution of poaching activity.