---
id: pointpats
version: "2.6.0"
license: BSD 3-Clause
license_treatment: permissive
maintenance: active
---

# pointpats — Methods and Functions for planar point pattern analysis

License: permissive · Maintenance: active · Downloads: 124.5K/mo

## 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 above — verify before relying.

Pointpats provides statistical methods for analyzing planar point patterns, including clustering detection, distance-based tests, and spatial distribution metrics on Cartesian coordinates.

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

pip install pointpats
uv add pointpats
poetry add pointpats

## Installing pointpats

Before you install: Low install friction with a pure-wheel distribution. Active maintenance with a recent release 52 days ago and ongoing repository activity.

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 data

Data must be in Cartesian coordinates; geographic coordinate data must be projected first. Requires Python 3.12 or later.

Verify 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_current
- Install friction: low
- Maintenance: active
- Downloads: 124.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

point pattern analysis, spatial statistics planar, clustering detection spatial, distance-based spatial tests, point distribution analysis, spatial-analysis, point-patterns, hypothesis-testing

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