Algorithm for analyzing randomness in point patterns

Area of Science:

• Spatial analysis
• Geographic Information Systems (GIS)
• Statistical modeling

Background:

• Traditional spatial point pattern analysis often assumes no barriers, overlooking socio-environmental influences.
• The definition of 'random' in point distributions can be skewed by factors like wetlands or transport networks.

Purpose of the Study:

• To develop and implement a spatial analysis procedure and statistical test to evaluate the correlation between observed point patterns and potential spatial determinants.
• To provide a method for reconsidering the randomness classification of point distributions when spatial determinants are present.

Main Methods:

• Implementation of a Python script for QGIS, involving overlay operations and preliminary calculations.
• Application of the chi-square goodness-of-fit test, with and without Bonferroni correction, for statistical analysis.
• Automated data structuring, calculation of expected and observed values, and statistical testing.

Main Results:

• A detailed procedure for analyzing point pattern randomness within QGIS.
• Automated scripts for data handling and chi-square goodness-of-fit testing.
• Advanced spatial analysis insights using chi-square with and without Bonferroni correction.

Conclusions:

• The developed tool offers a robust method for analyzing spatial point patterns and their relationship with environmental factors.
• The integration of spatial determinants into randomness assessment refines ecological and geographical distribution studies.
• The Python-QGIS script provides an accessible and automated solution for complex spatial data analysis.