The partial <i>K</i> function
Authors
Paper Information
-
Journal:
Journal of the Royal Statistical Society Series B -
DOI:
10.1093/jrsssb/qkag123 -
Published:
September 10, 2026 -
Added to Tracker:
Sep 11, 2026
Abstract
Abstract The K function and its related statistics have been an enduring tool in the analysis of spatial point processes, providing an easy to compute and interpret summary statistic for characterising the interactions between points of one type, or between two different types of points. In this paper, we introduce a partial K function, enabling us to account for some of the effects of the other point types when analysing point-point interactions. The partial K function we introduce reduces to the usual K function when the other points are independent of the points of interest and has a similar interpretation. Using examples, we demonstrate how the partial K function can unpick dependence between point types that would otherwise be hidden in the usual K function. We also discuss important bias correction steps and hyperparameter selection. In addition, we introduce an extension to account for other spatial covariates, and demonstrate the methodology on the Lansing Woods dataset.
Author Details
Jake P Grainger
AuthorTuomas A Rajala
AuthorDavid J Murrell
AuthorSofia C Olhede
AuthorCitation Information
APA Format
Jake P Grainger
,
Tuomas A Rajala
,
David J Murrell
&
Sofia C Olhede
(2026)
.
The partial
<i>K</i>
function.
Journal of the Royal Statistical Society Series B
, 10.1093/jrsssb/qkag123.
BibTeX Format
@article{paper1671,
title = { The partial
<i>K</i>
function },
author = {
Jake P Grainger
and Tuomas A Rajala
and David J Murrell
and Sofia C Olhede
},
journal = { Journal of the Royal Statistical Society Series B },
year = { 2026 },
doi = { 10.1093/jrsssb/qkag123 },
url = { https://doi.org/10.1093/jrsssb/qkag123 }
}