pointpats
Methods and Functions for planar point pattern analysis
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Data must be in Cartesian coordinates; geographic coordinate data must be projected first.
- Requires Python 3.12 or later.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
BSD 3-Clause (permissive) — BSD 3-Clause is permissive; you can use this package in commercial and proprietary projects with minimal restrictions beyond retaining the license notice.
last release 2026-06-23 (52 days) · last repo commit 2026-08-08 · 93 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 124,469 downloads/mo, #11,867 on PyPI
Alternatives
Verify before relying
pip install pointpats
import pointpats
from pointpats import PoissonPointProcess
# Analyze a point pattern for clustering
pattern = pointpats.PoissonPointProcess(...) # your point data- Specific statistical methods and tests available in version 2.6.0 beyond what the description excerpt covers
- Whether the package handles edge cases or boundary conditions in point pattern analysis
- Performance characteristics or scalability limits for large point datasets
What it is and 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.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
pointpats on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with a recent release 52 days ago and ongoing repository activity.
Data must be in Cartesian coordinates; geographic coordinate data must be projected first. Requires Python 3.12 or later.
License in practice
BSD 3-Clause is permissive; you can use this package in commercial and proprietary projects with minimal restrictions beyond retaining the license notice.
Quickstart
pip install pointpats
import pointpats
from pointpats import PoissonPointProcess
# Analyze a point pattern for clustering
pattern = pointpats.PoissonPointProcess(...) # your point dataVerify before relying
- Specific statistical methods and tests available in version 2.6.0 beyond what the description excerpt covers
- Whether the package handles edge cases or boundary conditions in point pattern analysis
- Performance characteristics or scalability limits for large point datasets
Package facts
| License | BSD 3-Clause permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packageslibpysalgeopandasmatplotlibnumpypandasscipyshapely |
| Maintenance | Actively maintained 52 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 124,469 / month, #11,867 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: GIS |
Evidence: pointpats-2.6.0-py3-none-any.whl
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