geospatial-analysis

## When to Trigger Activate this skill when the user mentions: - GIS, geographic information systems, spatial data - Coordinates, latitude/longitude, projections, CRS - Spatial statistics, spatial autocorrelation, hotspot analysis - Remote sensing, satellite imagery, NDVI, land cover classification - Mapping, cartography, choropleth, heatmaps - Geocoding, reverse geocoding, routing, network analysis - Shapefiles, GeoJSON, raster data, vector data ## Step-by-Step Methodology 1. **Data acquisition and format assessment** - Identify data types: vector (points, lines, polygons in shapefile/GeoJSON/GeoPackage) or raster (GeoTIFF, NetCDF). Determine coordinate reference system (CRS). Check for common issues: mixed CRS, topology errors, missing geometries. 2. **Projection and transformation** - Ensure all layers share the same CRS. Use geographic CRS (WGS84/EPSG:4326) for global data, projected CRS (UTM, state plane) for area/distance calculations. Apply appropriate datum transformation. 3. **Spatial operations** - Perform geoprocessing: buffer, intersect, union, clip, dissolve. For point data: spatial joins, nearest neighbor analysis. For raster: reclassification, map algebra, zonal statistics. 4. **Spatial statistics** - Test for spatial autocorrelation (Global Moran's I). Identify clusters and hotspots (Local Moran's I / LISA, Getis-Ord Gi*). For point patterns: kernel density estimation, Ripley's K function. For regression: spatial lag or spatial error models (GWR for non-stationarity). 5. **Remote sensing analysis** - Atmospheric correction and preprocessing. Compute indices (NDVI, NDWI, NDBI). Supervised classification (random forest, SVM) or unsupervised (K-means, ISODATA). Accuracy assessment with confusion matrix and Kappa statistic. 6. **Visualization and cartography** - Create maps with proper elements: title, scale bar, north arrow, legend, data source. Use appropriate color schemes (sequential for magnitude, diverging for deviation, qualitative for categories). Consider colorblind-safe palettes. 7. **Validation** - Verify spatial operations with visual inspection and area/count checks. Cross-validate classification accuracy. Assess edge effects in spatial statistics. Report spatial resolution and positional accuracy. ## Key Databases and Tools - **OpenStreetMap** - Open geographic data - **USGS Earth Explorer / Copernicus Open Access Hub** - Satellite imagery - **Natural Earth** - Public domain map data - **Census TIGER/Line** - US geographic boundaries - **QGIS / ArcGIS** - GIS desktop software - **GeoPandas / Rasterio / Folium** - Python geospatial libraries - **Google Earth Engine** - Cloud-based remote sensing platform ## Output Format - Maps with standard cartographic elements (title, legend, scale bar, north arrow, CRS noted). - Spatial statistics results with test statistic, p-value, and interpretation. - Classification accuracy as confusion matrix with overall accuracy, Kappa, and per-class metrics. - Coordinate data in standard formats (decimal degrees for geographic, meters for projected). - GeoJSON or shapefile outputs for derived spatial data. ## Quality Checklist - [ ] CRS explicitly stated for all datasets and outputs - [ ] Projection appropriate for the analysis (equal-area for density, conformal for shape) - [ ] Spatial resolution and positional accuracy documented - [ ] Topology errors checked and cleaned - [ ] Color scheme appropriate for data type and accessible to colorblind viewers - [ ] Scale bar and north arrow included on all maps - [ ] Edge effects and modifiable areal unit problem (MAUP) considered - [ ] Data sources and vintage documented