{"categories":[{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"PySAL is a meta-package that bundles 29 spatial analysis libraries for geospatial data science, enabling spatial clustering detection, network analysis, spatial regression, and exploratory spatio-temporal analysis on vector data.","skillfed_tags":["geospatial-analysis","spatial-statistics","econometrics"],"use_cases":["Detect spatial clusters and hot-spots in disease incidence, crime, or economic data using local spatial autocorrelation methods.","Build and analyze transportation networks or social networks embedded in geographic space using spaghetti and network algorithms.","Estimate spatial regression models to understand how geographic proximity influences outcomes in econometric studies.","Measure urban form and morphology by analyzing building footprints and street networks with momepy.","Calculate spatial accessibility indices to evaluate service coverage or travel-time equity across regions.","Perform areal interpolation to downscale census or administrative data to finer geographic units."],"what_it_does":"PySAL is a meta-package that brings together a curated ecosystem of 29 spatial analysis libraries under one installation. Rather than a monolithic tool, it serves as an entry point to a family of specialized packages organized into four layers: foundational algorithms (libpysal), exploratory analysis (esda, giddy, pointpats, segregation, spaghetti, inequality, momepy), confirmatory modeling (spreg, mgwr, spglm, spint, spml), and applied methods (access, tobler, spopt). The package is designed for geospatial data scientists working with vector data who need to detect spatial patterns, build spatial networks, fit regression models on geographic data, or measure spatial inequality and segregation.\n\nWhen you install pysal, you gain access to methods for spatial autocorrelation analysis, network-based inference, urban morphometrics, areal interpolation, spatial optimization, and accessibility modeling. The runtime dependencies include geopandas, shapely, scipy, scikit-learn, and pandas\u2014standard tools for geospatial and statistical computing. This is a research-grade library with emphasis on econometric and statistical rigor rather than real-time GIS operations.","worth_installing":"Yes, if you are doing geospatial data science or spatial econometrics. PySAL is actively maintained, has no known vulnerabilities, and bundles a mature ecosystem of specialized tools. The permissive BSD license poses no restriction. The main constraint is the Python 3.12 requirement; if you are on an older Python version, you cannot use it. For researchers, urban planners, or data scientists working with geographic data, this is a standard reference library."},"id":"pysal","links":{"html":"https://skillfed.io/packages/pysal","md":"https://skillfed.io/packages/pysal.md","pypi":"https://pypi.org/project/pysal/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-31","license_spdx":null,"license_treatment":"permissive","name":"pysal","python_support":"supports_current","summary":"Meta Package for PySAL - A library of spatial analysis functions"},"popularity":{"monthly_downloads":108571,"position":12552,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.7"}