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Coordinate Systems for GIS Professionals

GIS work involves managing multiple coordinate systems, projections, and transformations.

GIS professionals work at the intersection of geography, data science, and cartography. Understanding coordinate systems — not just formats, but the underlying projections and datums — is fundamental. This guide covers the formats used in PostGIS, Elasticsearch, and modern geospatial stacks.

WGS84 vs. Other Datums

WGS84 (EPSG:4326) is the datum used by GPS and this converter. NAD83 differs by up to 1m in North America. Web Mercator (EPSG:3857) is used by Google Maps, OpenStreetMap, and tile servers for display, but coordinates are in meters, not degrees. Always store data in WGS84 and reproject for display.

PostGIS and Geospatial SQL

PostGIS stores coordinates as geometries: ST_SetSRID(ST_MakePoint(-122.3321, 47.6062), 4326). Note GeoJSON order: longitude, latitude. The ST_Transform() function reprojects between EPSG codes. For indexing, GIST indexes work with geographic coordinates; for UTM, use a projected CRS.

Geohash in Redis and Elasticsearch

Redis: GEOADD key -122.3321 47.6062 "Seattle". Redis stores as Geohash internally and supports GEODIST, GEOSEARCH. Elasticsearch: geo_point field type natively indexes geohashes. Use geo_distance queries for proximity search. Geohash prefix sharing enables efficient bounding-box queries.

Coordinate Transformations at Scale

For bulk coordinate transformations, use PROJ (the open-source coordinate transformation library), GDAL, or pyproj in Python. For web services, the AWS Lambda pattern: receive DD, reproject to UTM for measurement, reproject to Web Mercator for rendering, store as WGS84.

Coordinate Converter

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