Shapefile

The Shapefile is a geospatial vector dataVector DataVector data represents geographic features as discrete points, lines, and polygons with associated attribute informat... format introduced by Esri in the early 1990s that has become one of the most ubiquitous standards for storing and exchanging geographic information. Its simplicity, broad software support, and open specification have made it a de facto standard for sharing vector data between GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation... platforms, government agencies, and data providers worldwide, despite the emergence of more modern alternatives.

A Shapefile is actually a collection of related files that must be stored together, with at least three mandatory components sharing the same base filename. The .shp file stores the feature geometry (points, lines, or polygons). The .shx file contains the spatial index that enables efficient access to geometric records. The .dbf file stores attribute data in dBASE format. Optional files include the .prj file defining the coordinate system and projection, the .sbn/.sbx files providing a spatial index for faster queries, and the .cpg file specifying character encoding for attribute text. Each Shapefile can contain only one geometry type, so a dataset with both point and polygon features requires separate Shapefiles.

Shapefiles are used across virtually every domain that works with geospatial dataGeospatial DataGeospatial data encompasses information about the location, shape, and relationships of physical features on Earth. I.... Government agencies distribute census boundaries, administrative regions, and infrastructure data as Shapefiles. Environmental organizations share habitat maps, protected area boundaries, and monitoring data in Shapefile format. Transportation departments publish road networks and transit routes as Shapefiles. Open data portals frequently offer Shapefile downloads as a standard format alongside GeoJSONGeoJSONGeoJSON is an open standard format for encoding geographic data structures using JavaScript Object Notation (JSON). I... and KML. Data conversion between GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation... platforms often uses Shapefile as a common intermediate format due to its universal support.

The Shapefile's greatest strength is its near-universal compatibility; virtually every GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation... application, database, and mapping library can read and write Shapefiles. The format is well-documented and openly specified, enabling implementation without licensing restrictions. Its multi-file structure allows individual components to be updated independently. The format's simplicity makes it straightforward to parse programmatically, and extensive libraries exist in every major programming language.

Shapefiles have significant technical limitations that increasingly limit their utility. The .dbf attribute format restricts field names to 10 characters and imposes a 2 GB file size limit. The format does not support null values in attribute fields, stores date fields without time components, and has limited text encoding support. Shapefiles cannot store multiple geometry types in a single file, do not support topological relationships, and lack built-in support for coordinate system transformations. The multi-file structure creates data management challenges, as missing component files render the dataset unusable.

Modern alternatives like GeoPackage, GeoJSONGeoJSONGeoJSON is an open standard format for encoding geographic data structures using JavaScript Object Notation (JSON). I..., GeoParquet, and FlatGeobuf address many Shapefile limitations while maintaining broad compatibility. Organizations are increasingly migrating to these formats for new projects while maintaining Shapefile support for legacy data. Cloud-native formats optimized for streaming and partial reading are gaining adoption for large-scale data distribution.