beita6969 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
- 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.
- 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.
- Spatial operations - Perform geoprocessing: buffer, intersect, union, clip, dissolve. For point data: spatial joins, nearest neighbor analysis. For raster: reclassification, map algebra, zonal statistics.
- 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).
- 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.
- 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.
- 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