Diffusion Smoothing for Spatial Point Patterns

Adrian Baddeley, Tilman M. Davies, Suman Rakshit, Gopalan Nair, Greg McSwiggan

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)123-142
Number of pages20
JournalStatistical Science
Volume37
Issue number1
DOIs
Publication statusPublished - Feb 2022

Fingerprint

Dive into the research topics of 'Diffusion Smoothing for Spatial Point Patterns'. Together they form a unique fingerprint.

Cite this