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