{"categories":[{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"esda computes global and local spatial autocorrelation statistics, join-count tests, and multivariate spatial association measures to identify spatial structure and clustering patterns in geospatial data.","skillfed_tags":["spatial-statistics","gis","pysal-ecosystem"],"use_cases":["Identify spatial autocorrelation in epidemiological data (disease incidence) to detect clustering before regression modeling.","Detect hot spots and cold spots in crime or economic indicators across administrative regions using local Moran statistics.","Test for spatial randomness in ecological or environmental measurements to validate assumptions for spatial models.","Analyze multivariate spatial association between correlated geospatial variables (e.g., income and education).","Measure shape regularity and geometric characteristics of spatial features to understand spatial configuration."],"what_it_does":"esda is a Python library for exploratory spatial data analysis, part of the PySAL ecosystem. It provides methods to measure and test spatial autocorrelation\u2014the degree to which values at nearby locations are similar\u2014and to identify spatial clustering, hot spots, and cold spots in geospatial data. The library implements both global statistics (Moran's I, Geary's C, Getis-Ord G) that summarize spatial structure across an entire dataset, and local indicators (LISA) that reveal where clustering occurs.\n\nBuilt on NumPy, SciPy, GeoPandas, and libpysal, esda integrates with the broader PySAL ecosystem and supports areal and point-referenced data, binary and categorical patterns, and multivariate spatial association. It includes permutation-based inference for statistical significance testing and works directly with GeoPandas DataFrames and spatial weights objects, making it practical for researchers and practitioners who need to understand spatial structure before formal modeling.","worth_installing":"Yes. esda is actively maintained, has low install friction, carries a permissive BSD license, and provides essential spatial statistics tools for exploratory analysis. It integrates seamlessly with GeoPandas and the PySAL ecosystem. No known vulnerabilities. Suitable for research, spatial analysis, and GIS workflows. Requires Python 3.12+."},"id":"esda","links":{"html":"https://skillfed.io/packages/esda","md":"https://skillfed.io/packages/esda.md","pypi":"https://pypi.org/project/esda/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-19","license_spdx":null,"license_treatment":"permissive","name":"esda","python_support":"supports_current","summary":"Exploratory Spatial Data Analysis in PySAL"},"popularity":{"monthly_downloads":262141,"position":8379,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.10.0"}