Geospatial data is any information tied to a specific location on Earth. It combines two things: a position (coordinates like latitude and longitude) and descriptive details about what exists at that position. Every time you open a map app, check a weather forecast, or track a package, you’re using geospatial data. The global geospatial analytics market was valued at roughly $114 billion in 2024 and is projected to nearly double by 2030, reflecting just how central location-based information has become across industries.
The Two Parts of Every Geospatial Record
Every piece of geospatial data has a spatial component and an attribute component. The spatial part tells you where something is, typically as a set of coordinates, a boundary outline, or a cell in a grid. The attribute part describes what’s there. A point on a geological map might mark the location of a rock formation, but the attribute table behind it stores details like the rock’s age, composition, and mineral content. The same logic applies everywhere: a dot on a city map marks a building’s location, while its attributes might include the building’s height, use type, year of construction, and owner.
This pairing is what makes geospatial data more useful than a simple list of addresses or a plain photograph. Because each record carries both position and description, you can ask spatial questions: Which schools are within a flood zone? Where are infection rates climbing fastest? What neighborhoods have the least green space per resident?
Vector vs. Raster: Two Ways to Represent Space
Geospatial data comes in two fundamental formats, and understanding the difference helps you make sense of almost any map or spatial dataset you encounter.
Vector data uses points, lines, and polygons to represent locations. A fire hydrant is a point. A river is a line. A city boundary is a polygon. Vector data is precise, making it ideal for things with clear edges: property parcels, road networks, building footprints. The most widely used vector file format is the Shapefile (.shp), which has been an industry standard for decades. Newer formats like GeoJSON are common on the web, and KML files are what you see when someone shares a custom Google Earth layer.
Raster data divides the world into a grid of cells, like pixels in a photograph. Each cell holds a value representing something at that location: elevation, temperature, land cover type, or the intensity of reflected light. Satellite imagery is raster data. So is a digital elevation model showing the height of terrain. GeoTIFF is the standard raster format, embedding location information directly into the image file’s header so software knows exactly where each pixel falls on Earth.
An old saying in the field sums it up: “Raster is faster, but vector is corrector.” Raster data is computationally simpler to process over large areas, while vector data captures shapes and boundaries with greater accuracy.
How Location Gets Assigned
Representing a round planet on a flat screen or printed map requires a coordinate reference system. This is the set of rules that translates a position on a 3D globe into coordinates you can plot in two dimensions. The most common system, WGS84, is what GPS devices and most web maps use. It defines locations in degrees of latitude and longitude, with the origin point (0,0) sitting where the equator crosses the prime meridian in the Gulf of Guinea.
Latitude and longitude work well for locating places, but they’re less useful for measuring distances because a degree of longitude covers a different physical distance depending on how far you are from the equator. For projects that require precise area or distance calculations, like surveying a construction site or measuring the size of a forest, analysts use projected coordinate systems that convert the curved surface into flat, uniform measurement units like meters or feet.
How Geospatial Data Gets Collected
Location data comes from a surprisingly wide range of sources, from satellites orbiting hundreds of miles overhead to sensors mounted on cars driving city streets.
- GPS receivers capture coordinates from satellite signals. Every smartphone has one, and higher-precision units used in surveying can pinpoint a location to within a centimeter.
- Satellite imagery provides raster data covering the entire planet at regular intervals. Some satellites capture images daily, making it possible to track changes in vegetation, ice cover, or urban growth over time.
- LiDAR (Light Detection and Ranging) fires rapid laser pulses from aircraft or ground-based scanners and measures the time it takes each pulse to bounce back. The result is a dense “point cloud” of millions of elevation measurements, detailed enough to map individual tree canopies, building facades, or subtle shifts in terrain. NOAA describes it as one of the primary methods for examining Earth’s surface with high precision.
- Drones and aerial photography capture high-resolution images that can be stitched together and georeferenced, useful for construction monitoring, agricultural assessment, and disaster damage surveys.
- Field surveys still matter. Workers with GPS-enabled equipment collect ground-truth data, verifying or supplementing what remote sensors capture from above.
Real-World Applications
City Planning and Infrastructure
Urban planners use geospatial data to analyze traffic patterns, manage waste systems, assess environmental impacts, and decide where to allow new development. By layering population density data with infrastructure maps, planners can spot areas under strain and allocate resources before problems escalate. In Scotland, high-resolution spatial analysis across the national rail network identified locations where overgrown vegetation posed safety risks. In Dammam, Saudi Arabia, remote sensing tracked the movement of sand dunes across a major development site, flagging risks to planned infrastructure before construction began.
Logistics and Supply Chains
Logistics companies rely on geospatial data to optimize delivery routes, track vehicles in real time, and reduce fuel costs. Every time a courier app shows you a driver’s position and an estimated arrival time, that’s geospatial data at work. Warehouse placement, last-mile delivery planning, and fleet management all depend on spatial analysis of road networks, traffic conditions, and customer locations.
Disaster Response
During emergencies, geospatial data provides situational awareness that can save lives. After Hurricane Maria hit Puerto Rico in 2017, the CDC used interactive maps built from open-source geospatial data to locate pharmacies, hospitals, and health facilities, then directed medical resources to areas with the greatest need. Aerial imagery helps responders assess structural damage, track population displacement, and plan safe travel routes into affected zones.
Disease Tracking and Public Health
Epidemiologists map disease cases geographically to spot clusters, track how outbreaks spread, and identify populations at risk. During the Ebola outbreak in Sierra Leone, animated maps showing household-level infection spread helped public health teams understand the epidemiologic curve and coordinate quarantine efforts. In the 2016 Flint, Michigan shigellosis outbreak, cases were mapped by census area and compared against water-quality events, revealing spatial patterns that guided deeper investigation. The CDC regularly uses geospatial analysis to determine areas of highest need and predict where problems may emerge next.
Climate and Environmental Monitoring
Researchers use geospatial data to track deforestation, measure rising sea levels, monitor shrinking glaciers, and model the effects of extreme weather. Satellite imagery captured over years or decades reveals changes in land cover that would be impossible to detect from the ground. These tools give scientists a way to connect atmospheric data, surface observations, and health outcomes, linking rising temperatures and shifting weather patterns to real impacts on food supply, water quality, and disease spread.
Common File Formats
If you ever download geospatial data from a government portal or open-data site, you’ll encounter a handful of standard formats. Shapefiles (.shp) remain the most common vector format, though they always come as a bundle of at least three files (.shp, .shx, .dbf) that must be kept together. GeoJSON is a lightweight, web-friendly alternative that stores vector features in a single text file. KML files are designed for tools like Google Earth. For raster data, GeoTIFF embeds coordinate information into standard image files. And raw LiDAR point clouds are stored as .LAS files. Geodatabases (.gdb) are used primarily within Esri’s ArcGIS platform and can hold vector, raster, and tabular data together in a single container.
AI and the Expanding Role of Location Data
Artificial intelligence is reshaping how geospatial data gets analyzed. Combining machine learning with spatial data, sometimes called GeoAI, has become one of the fastest-growing areas in data science. AI models can now automatically classify land use from satellite imagery, predict urban growth patterns, detect changes in crop health across entire regions, and identify infrastructure damage after storms. The integration spans remote sensing, urban computing, Earth system science, and cartography. As both the volume of spatial data and the power of AI models continue to grow, the ability to extract useful patterns from location-based information is accelerating across nearly every field that touches the physical world.