Here’s a quick overview of common geospatial data formats, each suited for different types of geographic data and use cases:
1. Shapefile (.shp, .shx, .dbf, etc.)
- Type: Vector
- Use: Represent points, lines, and polygons (e.g., cities, roads, boundaries)
- Details:
- Composed of multiple files (.shp, .shx, .dbf, etc.)
- Widely supported in GIS software
- Lacks support for complex attributes and topologies
- Limited to 2 GB size and ASCII encoding for attributes
2. GeoJSON (.geojson or .json)
- Type: Vector
- Use: Web mapping and API integration (e.g., Leaflet, Mapbox)
- Details:
- Based on JSON format
- Human-readable
- Supports points, lines, polygons, and their collections
- Easier to parse in modern programming environments
3. KML (Keyhole Markup Language) (.kml, .kmz)
- Type: Vector
- Use: Google Earth, 3D visualization
- Details:
- XML-based format
- .kmz is a zipped version of .kml
- Can include styling, time-based features, and embedded images
4. TIFF / GeoTIFF (.tif, .tiff)
- Type: Raster
- Use: Satellite imagery, aerial photos, elevation data
- Details:
- Stores pixel-based data with georeferencing
- GeoTIFF is TIFF + embedded coordinate system info
- Large file sizes, can be multiband (e.g., RGB, infrared)
5. GPKG (GeoPackage) (.gpkg)
- Type: Vector & Raster
- Use: Portable spatial data in one file
- Details:
- SQLite-based open standard
- Stores multiple layers and types in a single file
- More efficient and flexible than shapefiles
6. CSV with Coordinates (.csv)
- Type: Tabular (pseudo-vector)
- Use: Simple point data (e.g., latitude/longitude)
- Details:
- Easy to create and edit
- Often used in data science workflows
- Must be georeferenced with coordinate columns
A comparison table or suggestions on which format is best for your specific use case in Data formats
Here’s a comparison table of commonly used geospatial data formats, including suggestions for when to use each one based on your use case:
Geospatial Data Format Comparison Table
| Format | Type | Common Use Cases | Advantages | Limitations | Best Use Case Suggestion |
|---|---|---|---|---|---|
| Shapefile (.shp) | Vector | GIS analysis, legacy systems | Widely supported, fast for small datasets | Multiple files, limited attribute support | Use when compatibility with older GIS tools is needed |
| GeoJSON (.geojson) | Vector | Web mapping, APIs (Leaflet, Mapbox) | Human-readable, easy to parse | Larger file size, limited to WGS84 projection | Use for web apps or lightweight mapping |
| KML/KMZ (.kml, .kmz) | Vector | Google Earth visualizations | Supports 3D, styling, animation | Less efficient, not ideal for analysis | Use for sharing geographic stories/visualizations |
| GeoTIFF (.tif) | Raster | Remote sensing, DEMs, land cover | High precision, supports projections | Large file sizes | Use for imagery, elevation, land cover |
| GPKG (.gpkg) | Vector & Raster | All-purpose spatial storage | Single file, efficient, supports SQL | Less widespread than shapefiles | Use for complex, multi-layer GIS projects |
| CSV with Coordinates (.csv) | Tabular | Simple point data, quick imports | Very simple, editable in spreadsheets | No geometry types beyond points | Use for importing/exporting tabular spatial data |
Suggestions by Use Case
| Use Case | Recommended Format(s) | Why |
|---|---|---|
| Web mapping | GeoJSON | Lightweight, browser-friendly, JavaScript-ready |
| Sharing maps with non-GIS users | KML/KMZ | Easily viewable in Google Earth, supports visuals |
| Spatial analysis in GIS (e.g., QGIS) | Shapefile, GeoPackage | Shapefile for legacy compatibility, GPKG for modern workflows |
| Handling large raster imagery | GeoTIFF | Maintains georeferencing, supports large datasets |
| Portable project storage (all-in-one) | GeoPackage | Compact, stores multiple layers/types |
| Data science workflows (e.g., Python, R) | CSV with coordinates, GeoJSON | Easily handled in pandas/GeoPandas, readable |
| Publishing data online (open data portals) | GeoJSON, Shapefile, CSV | Widely recognized and supported by web and GIS platforms |
