Point Pattern Analysis (pointpats)¶
Statistical analysis of planar point patterns in Python.
pointpats is an open-source library for the analysis of planar point patterns and a subpackage of the Python Spatial Analysis Library, PySAL.
Key workflows (notebooks)¶
Explore the main workflows through executable notebooks:
Quickstart¶
Install the latest release:
pip install pointpats
or using conda-forge:
conda install -c conda-forge pointpats
A minimal example:
import numpy as np
from pointpats import PointPattern
# toy coordinates
coords = np.random.random((100, 2))
pp = PointPattern(coords)
print("n:", pp.n)
print("mean center:", pp.mean_center)
print("average nearest-neighbor distance:", pp.nnd.mean())
What you can do with pointpats¶
Build and summarize point pattern objects from coordinate data.
Compute centrographic measures (mean center, standard distance, ellipses).
Perform quadrat statistics for tests of complete spatial randomness.
Use distance-based functions (G, K, L) and simulation envelopes.
Work with marked patterns and simulated point processes.
User Guide¶
The full user guide is organized around executable notebooks:
User Guide
Part of the PySAL ecosystem¶
pointpats is part of the PySAL family of spatial analysis libraries,
alongside components for spatial weights, regression, clustering, and more.
Source code: https://github.com/pysal/pointpats
Bug reports and feature requests: https://github.com/pysal/pointpats/issues
Citation¶
If you use pointpats in your work, please cite the Zenodo record:
@software{wei_kang_2023_7706219,
author = {Wei Kang and Levi John Wolf and Sergio Rey and Hu Shao
and Mridul Seth and Martin Fleischmann and Sugam Srivastava
and James Gaboardi and Giovanni Palla and Dani Arribas-Bel
and Qiusheng Wu},
title = {pysal/pointpats: pointpats 2.3.0},
year = {2023},
publisher = {Zenodo},
version = {v2.3.0},
doi = {10.5281/zenodo.7706219},
url = {https://doi.org/10.5281/zenodo.7706219}
}