ArticleMethodsX2025
Algorithm for analyzing randomness in point patterns.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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Abstract
Various tests can be used to assess whether the spatial distribution pattern of a set of points is random, dispersed, or clustered. These tests typically compare the expected and observed distances among points, assuming no barriers. However, what is deemed ``random'' in point spatial patterns may be influenced by socio-environmental factors such as wetlands or transportation networks. This tool introduces a sequence of spatial analysis procedure and a statistical testing to evaluate the correlation between observed point patterns and potential spatial determinants (polygons). If a determinant influences the observed point pattern, the classification of the distribution as random must be reconsidered. We implemented this algorithm in Python as a QGIS script with two main steps: the first handles overlay operations and preliminary calculations for the chi-square goodness-of-fit test with and without Bonferroni correction in the second step.•A detailed step-by-step procedure for analyzing randomness in point patterns in a processing toolbox Python script for integration into the open-source software QGIS.•Automated scripts for structuring data, calculating expected and observed values, and applying the chi-square goodness-of-fit test for statistical analysis.•Advanced spatial analysis using chi-square goodness of fit coupled with and without Bonferroni correction, providing deeper insight into the study of the effect of spatial phenomena on the distribution of point events.
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