Spatial data is any data that has a geographic component, connecting information to a specific location on Earth. This includes coordinates, street addresses, administrative boundaries, postal codes, and any other reference to a place. It is estimated that up to 80% of all data has a spatial component.
What is Spatial Data?
Spatial data — also called geospatial data or location data — describes objects, events, or phenomena that have a position on the surface of the Earth. Every time you tag a photo with a location, log a delivery address, or record sensor readings from a weather station, you are creating spatial data.
Spatial data can be as simple as a pair of latitude/longitude coordinates or as complex as a 3D model of a building. What makes it “spatial” is the ability to place it on a map and relate it to other geographic features through distance, proximity, or containment.
Types of Spatial Data
There are two fundamental types of spatial data:
Vector data represents geographic features as discrete points, lines, and polygons. Examples include store locations (points), roads (lines), and country boundaries (polygons). Vector data is ideal for representing features with clear boundaries.
Raster data represents geographic information as a grid of cells (pixels), where each cell holds a value. Satellite imagery, elevation models, and temperature maps are common examples. Raster data is ideal for continuous phenomena.
Common Spatial Data Formats
Spatial data is stored and exchanged using several standard formats:
- GeoJSON — A JSON-based format widely used in web mapping and APIs
- GeoParquet — A columnar format optimized for large-scale analytics in cloud data warehouses
- Shapefile — A legacy format by Esri, still widely used in traditional GIS
- KML — An XML-based format originally developed for Google Earth
- GeoPackage — An SQLite-based format supporting both vector and raster data
How Spatial Data is Used
Organizations across industries use spatial data to make better decisions:
- Retail and CPG — Site selection, trade area analysis, and market planning based on customer locations and demographics
- Logistics — Route optimization, fleet tracking, and delivery zone planning
- Insurance — Risk assessment using proximity to flood zones, fault lines, or fire-prone areas
- Telecommunications — Network coverage planning and signal strength analysis
- Government — Urban planning, census analysis, and emergency response coordination
Spatial Data in Cloud Data Warehouses
Modern cloud data warehouses like BigQuery, Snowflake, and Databricks have native support for spatial data types (GEOGRAPHY and GEOMETRY columns). This allows organizations to store spatial data alongside their business data and query it using Spatial SQL — without needing separate GIS software or data movement.
Platforms like CARTO extend these data warehouses with advanced spatial analytics functions through the Analytics Toolbox, enabling operations like spatial indexing, isoline generation, and spatial statistics directly where the data lives.
Frequently Asked Questions
What is the difference between spatial data and geospatial data?
The terms are often used interchangeably. “Geospatial data” specifically emphasizes data tied to the Earth’s geography, while “spatial data” is a broader term that can include any data with a positional component (including non-geographic spaces). In practice, most people use both terms to mean the same thing: data with a geographic location.
How much data is spatial?
It is commonly estimated that 80% of all data has a spatial component. This includes any data that references a location — from customer addresses and IP geolocation to timestamped sensor readings and social media check-ins.
