Choosing the right spatial data format is one of the first decisions in any GIS project. Each format reflects a different era of geospatial technology and optimizes for different use cases — web delivery, desktop analysis, database storage, or archival exchange. This guide compares the most widely used spatial data formats, including GeoJSON, KML, Shapefile, GeoPackage, GeoTIFF, FlatGeobuf, and WKT/WKB. A detailed comparison table at the end helps you pick the right format for your workflow.
GeoJSON
GeoJSON is a lightweight, text-based format defined by RFC 7946. It encodes vector geometries (points, lines, polygons) and their attributes using standard JSON syntax. Because every programming language has a JSON parser, GeoJSON integrates seamlessly with web APIs, JavaScript mapping libraries like Leaflet and Mapbox GL JS, and NoSQL databases such as MongoDB.
Strengths: human-readable, zero-dependency parsing in browsers, broad library support, natively streamable over HTTP. Weaknesses: no built-in styling, no spatial indexing, verbose for large datasets, limited to WGS 84 coordinates (EPSG:4326). GeoJSON is the best choice for web applications, REST API responses, and small-to-medium datasets under a few megabytes.
KML
KML (Keyhole Markup Language) is an XML-based format standardized by the OGC. It was originally developed for Google Earth and supports rich presentation features: icon styles, line colors, polygon fills, balloon pop-ups, ground overlays, screen overlays, network links, tours, and time-based animations. KML files can be compressed into KMZ archives that bundle images and models alongside the XML.
Strengths: built-in styling and visualization, 3D terrain support, time-based features, wide consumer adoption via Google Earth and Google Maps. Weaknesses: verbose XML structure, limited support in server-side GIS tools, no spatial indexing, WGS 84 only. KML is ideal for sharing styled maps with non-technical users and for Google Earth presentations. You can convert between GeoJSON and KML using GeoDataTools converters.
Shapefile
The Esri Shapefile is the oldest widely used vector format, introduced in the early 1990s. A "shapefile" is actually a collection of at least three files: .shp (geometry), .dbf (attributes), and .shx (spatial index). Optional files include .prj (projection), .cpg (character encoding), and others.
Strengths: universal support across GIS software, spatial index via .shx, well-understood by the GIS community. Weaknesses: 2 GB file size limit, 10-character field name limit in DBF, single geometry type per file, multi-file structure is error-prone, no support for nested attributes or date-time types. While still widely used, Shapefiles are increasingly replaced by GeoPackage for new projects.
GeoPackage
GeoPackage is an OGC standard built on SQLite. A single .gpkg file can store multiple vector layers, raster tiles, attribute tables, and metadata — all in one portable, self-contained database. It supports arbitrary coordinate reference systems, large file sizes, and SQL-based querying.
Strengths: single-file container, no size limits in practice, supports vector and raster data, full CRS support, spatial indexing via R-tree, SQL querying. Weaknesses: binary format (not human-readable), limited browser support without server-side processing, less JavaScript library support compared to GeoJSON. GeoPackage is the recommended format for desktop GIS analysis and cross-platform data exchange per the OGC GeoPackage specification.
GeoTIFF
GeoTIFF extends the TIFF image format with georeferencing metadata embedded in the file header. It stores raster data — satellite imagery, elevation models, land-cover classifications — with precise coordinate information. Cloud-Optimized GeoTIFF (COG) adds internal tiling and overviews for efficient HTTP range-request access.
Strengths: industry standard for raster data, supports multi-band imagery, lossless or lossy compression, COG variant enables cloud-native workflows. Weaknesses: raster only (no vector features), file sizes can be very large, requires specialized libraries (GDAL, rasterio) to process.
FlatGeobuf
FlatGeobuf is a modern binary format designed for fast reading and HTTP streaming of vector data. It uses a Hilbert-curve spatial index that enables clients to request only features within a bounding box using HTTP range requests, without downloading the entire file.
Strengths: extremely fast read performance, built-in spatial index, HTTP range-request support for cloud storage, compact binary size. Weaknesses: less tooling support than Shapefile or GeoJSON, binary format is not human-readable, relatively new and still gaining adoption.
WKT and WKB
Well-Known Text (WKT) and Well-Known Binary (WKB) are geometry serialization standards defined by the OGC and ISO. They encode geometries as text strings or binary sequences, respectively, and are the native geometry interchange formats for spatial databases like PostGIS and SQL Server. WKT is human-readable (POINT(-73.98 40.74)); WKB is compact and efficient for database storage and transfer.
WKT and WKB are not full-featured spatial file formats — they encode geometry only, without attributes, CRS metadata, or styling. They serve as building blocks inside databases, SQL queries, and interchange protocols like WFS.
Comparison Table
| Format | Type | Encoding | Max Size | Web Support | Styling | CRS Support | Spatial Index |
|---|---|---|---|---|---|---|---|
| GeoJSON | Vector | Text (JSON) | No limit* | Excellent | No | WGS 84 only | No |
| KML | Vector | Text (XML) | No limit* | Good | Yes | WGS 84 only | No |
| Shapefile | Vector | Binary + text | 2 GB | Poor | Via SLD | Any (.prj) | Yes (.shx) |
| GeoPackage | Vector + Raster | Binary (SQLite) | No limit | Poor | Via extensions | Any | Yes (R-tree) |
| GeoTIFF | Raster | Binary | 4 GB (classic) | COG support | N/A | Any | Overviews |
| FlatGeobuf | Vector | Binary | No limit | Good | No | Any | Yes (Hilbert) |
| WKT / WKB | Geometry only | Text / Binary | N/A | Indirect | No | Context-dependent | No |
* Text formats have no hard size limit, but performance degrades with large files because the entire file must be parsed.
Choosing the Right Format
There is no universally "best" spatial data format. The right choice depends on your use case:
- Web mapping and APIs: Use GeoJSON. Every front-end mapping library supports it natively, and it is trivial to generate from any server-side language. Use GeoDataTools to inspect, filter, and convert GeoJSON files in the browser.
- Google Earth and styled sharing: Use KML or KMZ. The built-in styling, 3D views, and consumer familiarity make it the best choice for presentation-oriented maps.
- Desktop GIS analysis: Use GeoPackage. It replaces Shapefile with a modern, single-file container that supports multiple layers, full CRS flexibility, and large datasets.
- Satellite imagery and raster analysis: Use GeoTIFF (or COG for cloud workflows). It is the universal raster interchange format.
- High-performance vector serving: Use FlatGeobuf. Its spatial index and HTTP range-request support make it ideal for serving large vector datasets from cloud storage.
- Database interchange: Use WKT or WKB inside SQL queries and spatial database columns.
If you need to move data between formats, tools like GeoDataTools KML-to-GeoJSON converter and GDAL/OGR make the process straightforward.
FAQ
Which spatial format is best for web applications?
GeoJSON is the standard for web applications. It is natively supported by Leaflet, Mapbox GL JS, OpenLayers, D3.js, and Turf.js. For very large datasets, consider FlatGeobuf with HTTP range requests or server-side vector tiles to avoid loading the entire file into the browser.
Is Shapefile still relevant?
Yes, Shapefile remains widely used in government agencies, academic research, and legacy enterprise systems. However, for new projects the OGC recommends GeoPackage as a modern replacement that eliminates Shapefile limitations such as the 2 GB size cap and 10-character field name restriction.
Can I convert between these formats?
Most spatial formats can be converted using GDAL/OGR (ogr2ogr), QGIS, or Python libraries like GeoPandas and Fiona. For quick GeoJSON and KML conversions without installing software, use the GeoDataTools browser-based converter. Keep in mind that format-specific features (KML styling, Shapefile spatial index) may not transfer between formats.