spreg
PySAL Spatial Econometric Regression in Python
Decision gist · record as of 2026-08-14
Yes, if you are modeling spatial data with autocorrelation. The package is actively maintained, has low install friction, carries a permissive BSD 3-Clause license, and integrates cleanly with numpy, pandas, scikit-learn, and scipy. No known vulnerabilities. Requires Python 3.12 or later, which may constrain legacy environments. Suitable for academic and applied spatial econometric work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later.
- Low friction installation with a pure-Python wheel.
- Active maintenance: last commit 2026-07-27, released 18 days ago.
License · maintenance · safety
BSD 3-Clause (permissive) — BSD 3-Clause permissive license allows commercial and private use with attribution and liability disclaimer.
last release 2026-07-27 (18 days) · last repo commit 2026-07-27 · 90 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 137,298 downloads/mo, #11,372 on PyPI
Alternatives
Verify before relying
pip install spreg
import spreg
from libpysal import weights
from spreg import OLS
# model = OLS(y, X, w=weights_matrix)- Whether the package supports spatial lag and spatial error models specifically, or other SAR variants
- Performance characteristics or scalability limits for large spatial datasets
- Availability and completeness of documentation beyond the repository README
What it is and what it does
spreg is a Python package for estimating simultaneous autoregressive spatial regression models, part of the PySAL ecosystem. It addresses the statistical modeling of spatial processes where observations are not independent but interact with neighboring observations—a common scenario in geography, economics, and environmental science. The package builds on established scientific Python libraries (numpy, pandas, scikit-learn, scipy) and integrates with libpysal for spatial data structures.
The package is designed for researchers and practitioners who need to move beyond standard regression when spatial autocorrelation is present in their data. It handles the computational complexity of fitting models where the outcome at one location depends on outcomes at nearby locations, which violates the independence assumption of ordinary regression and requires specialized estimation techniques.
Use it for
- Model house prices or property values where neighboring properties influence each other through spatial spillover effects
- Estimate regional economic growth where neighboring regions' economies interact and influence each other
- Analyze disease or pollution incidence across geographic areas accounting for spatial clustering and diffusion
- Study crime rates in neighborhoods where crime in adjacent areas affects local crime patterns
- Model agricultural yields or environmental variables across spatial fields with spatial correlation
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are modeling spatial data with autocorrelation.
The package is actively maintained, has low install friction, carries a permissive BSD 3-Clause license, and integrates cleanly with numpy, pandas, scikit-learn, and scipy. No known vulnerabilities. Requires Python 3.12 or later, which may constrain legacy environments. Suitable for academic and applied spatial econometric work.
Install
spreg on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance: last commit 2026-07-27, released 18 days ago. Requires Python 3.12 or later.
Requires Python 3.12 or later.
License in practice
BSD 3-Clause permissive license allows commercial and private use with attribution and liability disclaimer.
Quickstart
pip install spreg
import spreg
from libpysal import weights
from spreg import OLS
# model = OLS(y, X, w=weights_matrix)Verify before relying
- Whether the package supports spatial lag and spatial error models specifically, or other SAR variants
- Performance characteristics or scalability limits for large spatial datasets
- Availability and completeness of documentation beyond the repository README
Package facts
| License | BSD 3-Clause permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packageslibpysalnumpypandasscikit-learnscipy |
| Maintenance | Actively maintained 18 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 137,298 / month, #11,372 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: spreg-1.9.1-py3-none-any.whl
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