What is Geospatial Data? Types, Formats, Examples

Geospatial data is information tied to a location on the Earth's surface. A store, a road, a field of crops, a storm forecast, and a satellite photo of a city are all geospatial data, because each one answers the question "where?" alongside "what?". Each record pairs a location, stored as coordinates, an address, or a shape, with attributes that describe what is there, and often a time. The US Census Bureau describes geospatial data as data that is "clearly geographic in nature," such as maps and data for GIS software.

Key takeaways

• Geospatial data combines a location, attributes, and often a timestamp.
• The two main types are vector data (points, lines, polygons) and raster data (grids of pixels).
• A coordinate reference system ties the numbers in a dataset to real places on Earth.
• Common formats include GeoJSON, Shapefile, GeoTIFF, and GeoParquet, and table formats such as Apache Iceberg now store geometry natively.
• Sources range from satellites and sensors to government agencies and open data projects such as Overture Maps.

What is geospatial data?

Every geospatial record has three parts.

1. Location. A geometry, such as a point at a longitude and latitude, a line, or a polygon, or a cell in a grid. The location only has meaning with a coordinate reference system (CRS) that says which model of the Earth the numbers refer to.
2. Attributes. The facts about the location: a building's height, a road's speed limit, a pixel's reflectance, a parcel's owner.
3. Time. When the observation was made or when it is valid. A satellite scene, a GPS ping, and a weather forecast all carry a timestamp.

Geospatial data vs spatial data vs GIS data

Spatial data is any data with a position in some space, which can include a warehouse floor plan. Geospatial data is spatial data referenced to the Earth. GIS data is geospatial data packaged for a geographic information system, usually as map layers. In practice the terms overlap, and this article uses "geospatial data" for all three.

Types of geospatial data

Geospatial data comes in two main types, vector and raster, plus a few specialized forms.

Vector data

Vector data represents discrete features with geometry. The OGC Simple Feature Access standard, also published as ISO 19125, defines the core geometry types and associates each geometry with a spatial reference system.

• Points: a single location, such as a store, a sensor, or a point of interest.
• Lines: a sequence of points, such as a road, a river, or a ship's track.
• Polygons: a closed area, such as a building footprint, a property parcel, or a census tract.

Raster data

Raster data divides an area into a regular grid of cells, and each cell holds a value. A satellite image stores reflectance in each pixel, a digital elevation model stores height, and a weather model such as HRRR stores temperature or wind speed. Cell size sets the resolution: a 10-meter pixel in Sentinel-2 imagery covers 100 square meters on the ground.

Other forms

• Point clouds: millions of 3D points from LiDAR scanners.
• Trajectories: ordered GPS points from vehicles, phones, aircraft, and ships.
• Tabular data with addresses: customer lists or claims that become geospatial once geocoded.
• Discrete global grids: systems such as H3 that assign every place on Earth to a cell with an ID, so tables can be joined on the cell.

Vector vs raster data

	Vector data	Raster data
Structure	Points, lines, and polygons with attributes	Grid of cells, each with one or more values
Best for	Discrete features with clear boundaries	Continuous surfaces and imagery
Examples	Buildings, roads, parcels, places	Satellite imagery, elevation, temperature
Resolution	Set by coordinate precision	Set by cell size
Common formats	GeoJSON, Shapefile, GeoPackage, GeoParquet	GeoTIFF, COG, NetCDF, Zarr

Most real questions use both. Counting the buildings inside a flood depth grid joins vector polygons to raster cells. For a full comparison, see raster vs vector data.

Geospatial data examples

• Satellite and aerial imagery. Earth observation data from missions such as Landsat and Sentinel-2, synthetic aperture radar that sees through clouds, and hyperspectral imagery with hundreds of narrow bands. Satellite imagery analysis turns these pixels into maps and measurements.
• Derived layers. NDVI for vegetation health, land cover classification maps, and change detection results that flag where the ground changed.
• Buildings, places, and roads. Footprints, points of interest, and road networks from open datasets such as Overture Maps.
• Boundaries. Countries, counties, census tracts, ZIP codes, and parcels.
• Hazard and risk layers. Flood risk maps, wildfire perimeters, and the event sets used in catastrophe modeling.
• Movement. Vehicle telematics, vessel positions, and aircraft tracks.

Geospatial data formats and standards

Formats differ in how they store geometry, how they compress values, and whether they suit files on a laptop or tables in cloud storage.

• GeoJSON is a JSON format for vector features. RFC 7946 fixes its coordinates to WGS 84 longitude and latitude in decimal degrees.
• Shapefile is an older Esri vector format made of several files. It is widely supported and limits column names to 10 characters.
• GeoTIFF adds georeferencing tags to TIFF images. The OGC GeoTIFF standard formalized it in version 1.1. Cloud Optimized GeoTIFF arranges the file so readers can fetch only the tiles they need.
• GeoParquet adds geometry types to Apache Parquet, a columnar format. It is an incubating OGC standard and is readable by Snowflake, BigQuery, Databricks, and Wherobots.
• Apache Iceberg tables now support geometry and geography types, so vector data can live in a data lakehouse with time travel and schema evolution. Wherobots covers this in Iceberg v3 gets native geo types.
• CRS identifiers. The EPSG Dataset, maintained by IOGP, assigns codes such as EPSG:4326 to coordinate reference systems.

Where geospatial data comes from

• Satellites and aircraft. Optical, radar, thermal, and LiDAR sensors image the Earth on regular revisit schedules.
• Sensors and devices. Phones, vehicles, weather stations, and IoT devices report positions and readings.
• Government agencies. The Census Bureau's TIGER/Line Shapefiles publish legal and statistical boundaries every year. In the US, the Geospatial Data Act of 2018 set rules for how federal agencies manage geospatial data.
• Open data projects. Overture Maps Foundation releases buildings, places, transportation, addresses, divisions, and base layers, with stable GERS IDs for joining.
• Commercial providers. Parcels, high-resolution imagery, and mobility data.
• Models. Weather forecasts, climate projections, and AI outputs such as building footprints extracted from imagery.

How geospatial data is used

• Insurance: price property risk by joining buildings and parcels to hazard layers, as in probable maximum loss estimates and stochastic models.
• Energy and infrastructure: screen land for solar farms, transmission lines, and data center sites.
• Agriculture and environment: track crop health, deforestation, and water.
• Mobility and logistics: route fleets and study how people and goods move.
• AI: ground geospatial AI models and agents in real places, the foundation of physical AI.

Challenges of working with geospatial data at scale

• Volume. Imagery archives span decades of scenes, and global building and place datasets hold billions of rows.
• Two data models. Vector and raster need different storage and functions, and many tools handle only one.
• Coordinate systems. Data arrives in different CRSs and must be transformed before it lines up.
• Joins. Matching records by location requires spatial predicates and indexes. A spatial join on billions of rows needs a distributed engine.
• Analysis. Turning raw layers into answers is the job of geospatial analysis, which needs both data models in one place.

Geospatial data in Wherobots

Wherobots is a cloud platform for geospatial data, built by the creators of Apache Sedona. WherobotsDB reads and writes vector and raster data in one SQL and Python engine, and stores tables in GeoParquet and Apache Iceberg. The Havasu catalog lists the open datasets ready to query, including Overture places, Sentinel-2 imagery, and NAIP aerial imagery. The Wherobots MCP server lets AI coding tools explore the catalog and run spatial SQL.

The header image above shows one place, Lake Havasu City, Arizona, four ways: NAIP aerial imagery as raster, and Overture buildings, roads, and 2,078 places as vector polygons, lines, and points.

Example: query geospatial data with SQL

The points in the header come from one table. This query, run in Wherobots, filters Overture places to a bounding box around the island and counts them by category:

SELECT basic_category, COUNT(*) AS places
FROM wherobots_open_data.overture_maps_foundation.places_place
WHERE ST_Intersects(geometry, ST_PolygonFromEnvelope(-114.375, 34.455, -114.32, 34.49))
  AND basic_category IS NOT NULL
GROUP BY basic_category
ORDER BY places DESC
LIMIT 5

basic_category	places
real_estate_service	264
personal_or_beauty_service	156
restaurant	126
financial_service	119
home_service	76

ST_PolygonFromEnvelope builds a rectangle from minimum and maximum longitude and latitude, and ST_Intersects keeps the places that fall inside it. The same pattern works on buildings, roads, and boundaries, and raster functions such as RS_ZonalStats bring imagery and elevation into the same query. Each place also carries a geometry column, so the result can be mapped, joined to census tracts, or summarized into H3 cells.

Related terms

• Geospatial analysis: the methods that turn geospatial data into answers
• Coordinate reference system: how coordinates map to places on Earth
• H3: a hexagonal grid for indexing and joining geospatial data
• Raster vs vector data: the two main data models compared
• Spatial join: combining datasets by location
• Earth observation data: data collected about the Earth from space and the air

Read more from Wherobots

• Spatial data, GeoParquet, and Apache Sedona
• Making Overture Maps data more efficient with GeoParquet and Apache Sedona
• El Niño 2026, atmospheric rivers, and California's burn scars: one SQL engine, two data models
• The medallion architecture for geospatial data

Query vector and raster geospatial data together with a Wherobots free trial at cloud.wherobots.com.