arbiter
JournalsAccount

The Impact of R on Statistical Science for Spatial Point Processes

Adrian Baddeley et al.

Australian and New Zealand Journal of Statistics2026https://doi.org/10.1111/anzs.70037article
ABDC A
Weight
0.37

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.

1 citation

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1111/anzs.70037

Or copy a formatted citation

@article{adrian2026,
  title        = {{The Impact of R on Statistical Science for Spatial Point Processes}},
  author       = {Adrian Baddeley et al.},
  journal      = {Australian and New Zealand Journal of Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/anzs.70037},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

The Impact of R on Statistical Science for Spatial Point Processes

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.