Diffusion Smoothing for Spatial Point Patterns

Traditional kernel methods for estimating the spatially-varying density of points in a spatial point pattern may exhibit unrealistic artefacts, in addition to the familiar problems of bias and over- or under-smoothing. Performance can be improved by using diffusion smoothing, in which the smoothing kernel is the heat kernel on the spatial domain. This paper develops diffusion smoothing into a practical statistical methodology for two-dimensional spatial point pattern data. We clarify the advantages and disadvantages of diffusion smoothing over Gaussian kernel smoothing. Adaptive smoothing, where the smoothing bandwidth is spatially-varying, can be performed by adopting a spatially-varying diffusion rate: this avoids technical problems with adaptive Gaussian smoothing and has substantially better performance. We introduce a new form of adaptive smoothing using lagged arrival times, which has good performance and improved robustness. Applications in archaeology and epidemiology are demonstrated. The methods are implemented in open-source R code.

Automated summary

This paper develops a practical statistical methodology combining traditional kernel methods with diffusion smoothing for estimating the density of two-dimensional spatial point pattern data. Diffusion smoothing exhibits better adaptive performance and robustness compared to traditional Gaussian kernel smoothing, and it can be applied in various fields such as archaeology and epidemiology.