ABSTRACT A spatial point pattern is a dataset representing the observed locations of things or events, such as disease cases, distant galaxies, trees, crimes, earthquakes or road accidents. The stochastic mechanism that generated the data is called a spatial point process. The statistical analysis of such data has been completely transformed by the availability of R . The R environment has enabled fundamental methodological research to proceed hand in hand with software development, leading to substantial advances in statistical methodology for spatial point processes which were immediately applicable to real data. This article is a broad historical account of the breakthroughs in statistical methodology for spatial point processes that were facilitated by R software, specifically the authors' package spatstat . It focuses on spatial point process modelling, model‐fitting, model diagnostics, challenges to statistical inference, lessons to be learned and new challenges for future research.
Baddeley et al. (2026) studied this question.