{"categories":[{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/6"},{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"Rasterstats computes summary statistics (mean, min, max, count) from raster datasets for vector geometries, and queries raster values at point locations.","skillfed_tags":["geospatial","raster-vector","gis"],"use_cases":["Calculate mean elevation or slope within each administrative boundary (county, watershed, grid cell) from a DEM.","Extract average temperature or precipitation values for polygon regions from climate raster datasets.","Query satellite imagery or land-use raster values at survey point locations for classification or validation.","Summarize vegetation indices (NDVI) or other derived rasters across field polygons for agricultural analysis.","Batch compute zonal statistics from the command line by piping GeoJSON features through rio subcommands."],"what_it_does":"Rasterstats is a geospatial analysis library that bridges raster and vector data by computing statistics from raster datasets within vector geometry boundaries. Given a vector layer (polygons, lines, or points) and a raster band (such as a digital elevation model), it calculates aggregate statistics like mean, min, max, and count for each geometry. It also supports point queries to extract raster values at specific coordinates. The package wraps rasterio for raster I/O and shapely for geometry handling, and provides both a Python API and command-line interfaces (via rio subcommands) for integration with GeoJSON workflows.\n\nThe library is designed for GIS workflows where you need to summarize continuous raster data (elevation, temperature, precipitation, satellite imagery) by administrative or analytical boundaries. It handles the geometric intersection and pixel aggregation internally, making it straightforward to compute landscape statistics for polygons or sample raster values at point locations without writing custom spatial indexing code.","worth_installing":"Yes. Rasterstats is actively maintained, has no known vulnerabilities, low install friction, and solves a common GIS task (zonal statistics) that would otherwise require custom spatial code. It is well-suited for anyone working with raster and vector data in Python, from climate and environmental analysis to remote sensing. The permissive BSD license poses no restriction."},"id":"rasterstats","links":{"html":"https://skillfed.io/packages/rasterstats","md":"https://skillfed.io/packages/rasterstats.md","pypi":"https://pypi.org/project/rasterstats/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-23","license_spdx":null,"license_treatment":"permissive","name":"rasterstats","python_support":"supports_current","summary":"Summarize geospatial raster datasets based on vector geometries"},"popularity":{"monthly_downloads":439438,"position":6655,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.21.0"}