{"categories":[{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"Pointpats provides statistical methods for analyzing planar point patterns, including clustering detection, distance-based tests, and spatial distribution metrics on Cartesian coordinates.","skillfed_tags":["spatial-analysis","point-patterns","hypothesis-testing"],"use_cases":["Detect whether disease cases or crime incidents cluster spatially or occur randomly across a region","Test for spatial randomness in ecological survey data such as tree or plant locations","Analyze clustering of retail locations or service points to inform business planning","Validate whether observed point patterns differ significantly from null models of random distribution"],"what_it_does":"Pointpats is a spatial statistics library for analyzing the distribution and clustering of points on a 2D plane. It is part of the PySAL ecosystem and provides methods to detect non-random patterns, measure spatial clustering, and test hypotheses about point distributions. The package works with Cartesian coordinates and depends on standard scientific Python tools: numpy for numerical computation, scipy for statistical functions, pandas for data handling, geopandas and shapely for geometric operations, and matplotlib for visualization. It is designed for researchers and analysts working with spatial data who need to move beyond simple descriptive statistics to formal hypothesis testing on point patterns.\n\nThe package requires Python 3.12 or later and is actively maintained. Users with geographic coordinate data must project their data to Cartesian space before analysis. The library integrates naturally with the broader PySAL ecosystem, making it suitable for workflows that combine point pattern analysis with other spatial methods.","worth_installing":"Yes. Pointpats is actively maintained, has no known vulnerabilities, uses a permissive license, and installs with low friction. It fills a specific niche in spatial statistics with a focused API. Install it if you need formal statistical tests on planar point patterns; skip it if your analysis is limited to simple distance or density calculations."},"id":"pointpats","links":{"html":"https://skillfed.io/packages/pointpats","md":"https://skillfed.io/packages/pointpats.md","pypi":"https://pypi.org/project/pointpats/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-23","license_spdx":null,"license_treatment":"permissive","name":"pointpats","python_support":"supports_current","summary":"Methods and Functions for planar point pattern analysis"},"popularity":{"monthly_downloads":124469,"position":11867,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.6.0"}