Algorithm for analyzing randomness in point patterns

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.