{"categories":[{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"spreg estimates simultaneous autoregressive spatial regression models for processes where observations interact with one another.","skillfed_tags":["spatial-analysis","econometrics","gis"],"use_cases":["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"],"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\u2014a 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.\n\nThe 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.","worth_installing":"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."},"id":"spreg","links":{"html":"https://skillfed.io/packages/spreg","md":"https://skillfed.io/packages/spreg.md","pypi":"https://pypi.org/project/spreg/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-27","license_spdx":null,"license_treatment":"permissive","name":"spreg","python_support":"supports_current","summary":"PySAL Spatial Econometric Regression in Python"},"popularity":{"monthly_downloads":137298,"position":11372,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.9.1"}