Spatial data is any information that describes the location, shape, or relationship of objects in physical space. Every time you drop a pin on a map, check traffic on your phone, or look up the nearest coffee shop, you’re using spatial data. It pairs a “where” (coordinates on the Earth’s surface) with a “what” (details about that location), and it’s the foundation of everything from GPS navigation to disease tracking to urban planning.
How Spatial Data Is Structured
Every piece of spatial data has two parts: a geometric component that describes where something is and how it’s shaped, and an attribute component that describes what it is. A hospital, for example, has a location (a point on a map defined by latitude and longitude) and attributes (its name, number of beds, specialties offered). The geometry tells a computer how to draw it; the attributes tell you something useful about it.
Real-world features get simplified into three basic shapes. Points represent single locations like a restaurant, a weather station, or a patient’s home. Lines represent things with length and direction: roads, rivers, power lines. Polygons represent areas with boundaries: a lake, a city block, a national park. Nearly every map you’ve ever looked at is built from combinations of these three shapes.
Vector Data vs. Raster Data
There are two fundamental ways to store spatial information, and they work very differently. Vector data uses precise points and line segments to represent locations. It’s ideal for things with clear boundaries: property lines, road networks, building footprints. Because it stores exact coordinates, vector data is highly accurate and works well when you need to measure distances or define borders.
Raster data, on the other hand, divides the world into a grid of cells (pixels), where each cell holds a value. Satellite imagery is raster data. So are elevation maps, temperature grids, and land-cover classifications. Raster data processes faster computationally, but it’s less precise for representing exact boundaries. There’s an old saying in the field: “raster is faster, but vector is corrector.”
Coordinate Systems: Placing Data on the Earth
For spatial data to be useful, every dataset needs to speak the same language about location. That’s what coordinate systems do. They provide a shared framework so that data from different sources can be layered together on the same map.
Geographic coordinate systems use latitude and longitude, measured in degrees, to locate any point on Earth’s curved surface. They’re great for identifying locations globally but aren’t ideal for measuring distance, because a degree of longitude covers a different physical distance depending on how far you are from the equator.
Projected coordinate systems solve this by flattening the Earth’s surface onto a two-dimensional plane using mathematical models. The Universal Transverse Mercator (UTM) system, for instance, divides the globe into zones and measures positions in meters rather than degrees. A location in UTM Zone 13N might read as 476,911 meters east, 4,429,455 meters north. These systems make it possible to calculate accurate distances and areas, which is why they’re standard for engineering, land surveying, and regional mapping.
Common File Formats
Spatial data gets stored in a range of specialized formats, each suited to different tasks:
- Shapefile: One of the most widely used formats for exchanging vector data. Developed by Esri, it’s been a standard for decades and is supported by virtually every mapping tool.
- GeoJSON: A lightweight format designed for web maps. It stores vector features in a structure that JavaScript applications can read directly, making it popular for interactive online mapping.
- KML: Originally built for Google Earth, KML can hold both vector and raster data along with styling information like colors and labels. It’s supported across many platforms beyond Google.
- GeoPackage: A newer format gaining traction as a modern alternative to the shapefile. It works across laptops, mobile devices, and various software, and can store vector features, tables, and raster tiles in a single file.
- CSV with coordinates: Sometimes spatial data is as simple as a spreadsheet with latitude and longitude columns. Many mapping tools can import these directly.
How Spatial Data Gets Collected
The raw material for spatial data comes from several sources. Satellites capture imagery and sensor readings from orbit, producing everything from weather data to land-use maps. Airborne sensors like LiDAR (which bounces laser pulses off the ground to measure elevation) can map terrain with precision down to one centimeter. GPS receivers in phones, vehicles, and survey equipment continuously generate location data. And increasingly, people contribute spatial data directly through apps that log routes, tag photos with coordinates, or report local conditions.
What You Can Do With Spatial Data
Raw locations become useful when you analyze the relationships between them. A few core operations power most spatial analysis. Buffering draws a zone of a specific distance around a feature. You could buffer every school in a city by 1,000 feet to see which liquor stores fall within that radius. Overlay analysis layers two or more datasets on top of each other to find where they intersect. Combining flood zone maps with property boundaries, for instance, reveals which homes are at risk. Proximity analysis answers “what’s nearby?” questions, which is exactly what your phone does when you search for the closest gas station.
These operations scale from the everyday to the enormous. Urban planners use spatial analysis to determine where new roads or housing developments should go. Logistics companies route deliveries through millions of possible paths to find the most efficient ones. Ecologists track wildlife movement across landscapes to identify critical habitats and migration corridors. Climate scientists monitor changes in glaciers, sea levels, and weather patterns over time.
Spatial Data in Public Health
One of the most impactful applications is in epidemiology, where spatial data helps researchers understand why disease rates vary from place to place. Disease mapping shows how health outcomes like cancer incidence or mortality from heart disease are distributed across regions. Public health agencies create maps that highlight areas with unusually high rates, which can point investigators toward environmental exposures, socioeconomic factors, or gaps in healthcare access.
Spatial data also powers disease surveillance: the ongoing, automated monitoring for clusters of illness. By analyzing patterns across both space and time, these systems can detect emerging outbreaks before anyone has formed a specific hypothesis about what’s causing them. During the early assessment of a suspected disease cluster, researchers define geographic boundaries, estimate how many cases would normally be expected in that population based on age and sex, then compare the actual count to the expected one. A statistically significant excess triggers further investigation.
Geographic correlation studies take this further by layering health data with environmental measurements (air quality, water contamination, soil composition) and demographic data (income, race, diet) to identify potential risk factors operating at a population level.
Why Spatial Data Keeps Growing
The geospatial analytics market is projected to reach $117.3 billion in 2026 and grow to roughly $310 billion by 2034, a pace of about 12.9% annually. That growth reflects how deeply spatial data has embedded itself in daily life and industry. Every ride-hailing app, precision agriculture system, autonomous vehicle, and emergency response plan depends on it. As sensors get cheaper and satellite coverage expands, the volume of spatial data being generated is accelerating, and so is the range of problems it can solve.