geospatial-analysis

To compute NDVI from multispectral raster satellite imagery, use rasterio to process multispectral bands and calculate the vegetation index. This workflow transforms raw Earth observation data into actionable geospatial insights reproducibly.

The best way to align vector and raster data is by reprojecting all inputs to a common Coordinate Reference System (CRS). CRS validation ensures raster-vector interoperability and prevents silent spatial errors during overlay operations.

Yes, you can run spatial autocorrelation analysis like Moran's I using geopandas alongside pysal. This combination tests for spatial dependence, evaluating cluster versus dispersion patterns with significance evaluation on your attribute data.

To create publication-ready maps with basemaps from vector GIS data, apply matplotlib and contextily for cartographic rendering. This produces basemap-backed visualizations with publication-quality labeling suitable for professional reporting.

Yes, this geospatial workflow supports spatial joins and buffer operations on vector data using shapely and geopandas. It performs vector overlay and joins to integrate Earth observation data with administrative boundary datasets.

You need to validate coordinate reference systems before running spatial autocorrelation or overlay analysis. CRS alignment ensures accurate raster-vector interoperability and prevents silent spatial errors in distance and area calculations.