Geospatial Data Types: 9 Key GIS Data Types, Examples & Formats

The first thing to know about geospatial data is that it comes in many different forms. Some GIS data types are better suited to certain tasks than others, and many real-world projects require more than one type to see the full picture.

Whether you’re a GIS analyst, a data scientist, or a business intelligence professional, understanding which geospatial data types exist and what each one can and can’t tell you, is essential before building any spatial workflow.

At SafeGraph, we work with organizations across retail, insurance, healthcare, and urban planning who use our location dataset alongside multiple GIS data types to answer complex spatial questions. Below, we’ve mapped the geospatial data ecosystem into 9 distinct categories, along with real-world examples and primary use cases for each.

The 9 GIS data types covered in this guide:

• Points of Interest (POI)
• Property
• Mobility
• Demographics
• Address
• Boundaries
• Environment
• Streets
• Imagery
  
  

But before diving into each type, there’s a foundational concept every GIS practitioner needs to understand: the difference between vector and raster data.

The Two Fundamental GIS Data Formats: Vector vs. Raster

All geospatial data, regardless of what it represents, is stored in one of two fundamental formats: vector or raster. Understanding this distinction matters because the two formats require different tools, storage approaches, and processing methods.

Vector Data

Vector data represents geographic features as discrete geometric shapes: points, lines, and polygons.

• A single business location is a point (defined by a latitude/longitude coordinate pair).
  
  
• A road or river is a line (a sequence of connected coordinate pairs).
  
  
• A building footprint, city boundary, or parcel of land is a polygon (a closed shape defined by coordinate pairs).
  
  

Vector formats are ideal for features with precise, well-defined boundaries and structured attributes, like addresses, property lines, or named places. Common vector file formats include GeoJSON, Shapefile (.shp), GeoPackage, and Well-Known Text (WKT) / Well-Known Binary (WKB).

Geospatial data example (vector): SafeGraph’s Places dataset is a vector dataset. Each of the locations in the dataset is represented as either a point or polygon, paired with structured attributes like address, NAICS category code, brand affiliation, and open hours.

Raster Data

Raster data represents geographic space as a continuous grid of cells (pixels), where each cell holds a value. The resolution of a raster dataset is determined by the size of each cell; a 1-meter resolution raster, for example, has one pixel for every square meter of Earth’s surface.

Raster formats are better suited to continuous phenomena that don’t have sharp boundaries, like temperature gradients, elevation, rainfall, or satellite imagery. Common raster formats include GeoTIFF, PNG tiles, and NetCDF.

Geospatial data example (raster): A GeoTIFF of NDVI (Normalized Difference Vegetation Index) satellite data assigns each pixel a value between -1 and 1 representing vegetation density across a landscape.

Which Format Do the 9 GIS Data Types Use?

Most of the data types below; POI, property, address, boundaries, streets, are vector-based. Imagery is the primary raster type. Mobility and demographic data can be either, depending on the provider and how the data is aggregated.

9 Geospatial Data Types: Ways of Representing Places, People, and Things on Earth

1. Points of Interest

Points of interest (POIs) are one of the most fundamental GIS data types. They describe physical places on Earth, any location that people may want to visit, or use as a reference point in spatial analysis. On smaller-scale maps, POI data can also abstractly represent cities or towns.

POI data is vector data; each location is represented as a point defined by geographic coordinates.

Many POI datasets also carry rich attribute data alongside the coordinates. For example, a POI record for a business might include the street address, phone number, open hours, brand affiliation, and business category code.

Geospatial data example: A SafeGraph Places record for a Target store includes the store’s latitude/longitude, its polygon building footprint, NAICS category code (452311), open hours, parent brand, and full street address, all linked by a unique place identifier.

It’s also important to note that POI data can be highly dynamic. Businesses open and close constantly. Stale POI data will direct you to locations that no longer exist, or miss places that have newly opened. That’s why SafeGraph updates our Places data every month, to keep pace with real-world changes.

Primary uses:

POI data underpins a wide range of GIS workflows. Mapping platforms use it to show people where things are and surface relevant attributes (like store hours). Real estate companies use it to assess investment opportunities based on nearby business density and predicted population growth.

Retailers and CPG brands use POI data to measure local market conditions and size their addressable customer base in a given trade area. Financial institutions track business openings and closings across sectors to guide investment decisions. And healthcare planners use it to locate existing facilities and compare coverage against surrounding population demographics.

2. Property

Property data represents the accurate physical boundaries of tangible places in the real world. Most commonly, it refers to the shapes of buildings or parcels of land. It can also capture finer-grained spatial hierarchy, individual units within a building, such as apartment units, stores in a mall, or offices in a business complex.

Property data is almost always vector polygon data. SafeGraph’s Geometry dataset is a prime example: it provides polygon building footprints with spatial hierarchy metadata that describes how places relate to each other within a building.

Geospatial data example: A SafeGraph Geometry record for a shopping mall contains the mall’s outer polygon boundary, plus individual polygons for each tenant store inside it, allowing analysts to distinguish between a visit to a specific retailer vs. the mall at large.

Primary uses:

Property data is used in mapping as a more accurate representation of what a place looks like in the physical world than a point alone. This is especially important for visit attribution, determining not just whether a person was near a POI, but whether they actually entered it and how long they stayed.

Insurance companies use property data to assess a building’s risk profile based on visit volume, neighboring businesses, and building geometry. A nail salon sharing a wall with a fireworks store has a very different risk profile than one located next to a daycare. Accurate polygons make those distinctions possible.

3. Mobility

Mobility data refers to aggregated and anonymized data about where and when people move throughout their daily lives. It is typically derived from GPS signals emitted by mobile devices. Because privacy is paramount, mobility data does not track individual devices; instead, it aggregates movement patterns at the POI level or the Census Block Group (CBG) level to reveal volume and frequency of visits.

Geospatial data example: A mobility dataset might show that a particular Whole Foods location received 4,200 visits during a given week, with peak traffic between 11am–1pm on weekdays, and that 34% of visitors also visited a gym within the same week.

Primary uses:

Businesses use mobility data to decide where to locate stores, which brands to carry, and where to invest in advertising, based on where consumers actually go, not just where they live. Insurers use visit-volume data to develop general liability policies, accounting for seasonal traffic patterns (a ski resort in January carries different liability than in July). Urban planners use mobility data to measure commuter flows and identify underserved corridors in need of better transit options or housing supply.

4. Demographics

Demographic data provides aggregated population counts alongside attributes of the people within them; age, gender, income, housing costs, educational attainment, and more. In the United States, this data is primarily collected through the US Census Bureau’s decennial census and American Community Survey (ACS), which are authoritative sources for GIS practitioners.

Demographic data is typically associated with geographic boundary units; Census tracts, block groups, ZIP codes, or counties, making it inherently spatial.

Geospatial data example: An ACS 5-year estimate for a Census Block Group might show median household income of $67,400, median age of 38, and 72% owner-occupied housing, all attached to a specific polygon boundary that can be layered with POI or mobility data.

SafeGraph provides cleaned and prepped Census data formatted for easy integration with our POI and mobility datasets, reducing the data wrangling typically required when working with raw Census files.

Primary uses:

Demographic data is most powerful when combined with mobility data. Together, they answer not just “who lives here?” but “who actually shows up?” Retailers use this combination to profile trade areas and tailor product assortment. Healthcare organizations use demographic data to identify underserved populations and plan facility placement. Government agencies use it for equitable resource allocation and infrastructure planning.

5. Address

Address data is the foundation of nearly every GIS workflow. It pairs geographic coordinates with street addresses, providing a human-readable label for a location alongside the machine-readable coordinates needed for spatial analysis.

Address data is vector point data, but it differs from POI data in one important way: it can represent residential locations, not just commercial or public places.

Geospatial data example: An address record might contain: 1600 Pennsylvania Ave NW, Washington DC 20500, paired with coordinates 38.8977° N, 77.0366° W, plus metadata indicating it falls within the DC Ward 2 boundary and the District of Columbia school district.

Primary uses:

Address data powers geocoding, the process of translating a street address into geographic coordinates, and its inverse, reverse geocoding. It’s also used for address validation (confirming that an address corresponds to a real, deliverable location), which is critical for logistics, insurance underwriting, and customer data management.

Address data is notoriously difficult to standardize. A single location can be represented dozens of ways (“St.” vs. “Street,” “USA” vs. “US,” unit numbers in different positions). This is why unique identifiers like Placekey are valuable; they give each address a canonical, joinable key that eliminates deduplication headaches when combining address data with other GIS datasets.

6. Boundaries

Boundary data defines the limits of geographic areas that typically contain multiple addresses, properties, and points of interest. Like property data, it uses vector polygons, but at a larger, more administrative scale.

Boundary datasets range from national borders down to granular local units: states, counties, ZIP codes, Census tracts, school districts, legislative districts, flood zones, and tax jurisdictions.

Geospatial data example: The US Census Bureau’s TIGER/Line Shapefiles provide polygon boundaries for every state, county, Census tract, and block group in the United States, a standard reference dataset used in virtually every GIS project that involves US spatial analysis.

Primary uses:

Boundary data provides the organizational framework for spatial analysis. Real estate investors use it to understand which tax jurisdictions or zoning regulations apply to a given parcel. School planners use catchment area polygons to balance enrollment across facilities. Businesses use boundary overlays to determine which advertising rules or regulatory requirements apply in each market they operate in.

7. Environment

Environmental GIS data relates to natural geographic phenomena: climate patterns, weather events, temperature gradients, elevation, seismic activity, tidal ranges, and flora/fauna habitat ranges or migration corridors. This category encompasses both vector data (like hurricane track lines or wildlife habitat polygons) and raster data (like elevation DEMs or temperature grids from the USGS and NOAA).

Geospatial data example: A 30-meter Digital Elevation Model (DEM) raster from the USGS assigns each pixel an elevation value in meters above sea level, allowing analysts to identify flood-prone areas by finding all pixels below a certain elevation threshold within a given watershed boundary.

Primary uses:

Environmental data is essential for conservation biology, climate science, and land management. But it also has significant commercial applications. Insurance underwriters use flood zone maps, wildfire risk rasters, and wind speed models to assess catastrophic risk and price policies accordingly. Energy companies use solar irradiance and wind speed data to identify optimal sites for renewable infrastructure. Logistics and agricultural companies use temperature and precipitation data to plan operations and mitigate weather-related risk.

8. Streets

Street data describes road transportation networks. It is vector line data, each road segment is represented as a line with attributes like road name, speed limit, road class, and directionality. Advanced street datasets also include real-time or historical traffic volume data, construction zones, and incident information.

Geospatial data example: An OpenStreetMap road segment record for a US highway might contain: road name, highway classification (motorway), maximum speed (65 mph), direction of travel, and the sequence of coordinate pairs tracing the road’s geometry.

Primary uses:

Street data is integral to any GIS workflow involving routing, navigation, or transportation analysis. It powers consumer mapping apps (Google Maps, Apple Maps) and is an essential input for logistics optimization, delivery route planning, and emergency response systems. Urban planners use street network analysis to evaluate walkability, transit accessibility, and the impact of new road infrastructure on surrounding areas.

9. Imagery

Imagery data provides true-to-life visual representations of the Earth’s surface, capturing both natural land and water features and man-made structures like buildings and roads. It is collected via aerial photography or satellite imaging. Unlike the other eight data types on this list, imagery is always raster data, it stores information as a grid of pixels, not as discrete points, lines, or polygons.

Geospatial data example: A high-resolution satellite image captured at 50cm resolution means each pixel represents a 50cm × 50cm patch of ground. At this resolution, individual cars in a parking lot are clearly visible, useful for estimating retail foot traffic or construction activity without any ground-level data collection.

Primary uses:

Imagery data most commonly serves as a visual basemap, a contextual backdrop on which vector layers like POI points, road lines, or boundary polygons are overlaid. Beyond visualization, imagery analysis supports environmental monitoring (tracking wildfire spread, deforestation, or coastal erosion over time), urban growth modeling, precision agriculture, and disaster damage assessment.

How GIS Data Types Work Together

Few real-world analyses rely on a single GIS data type in isolation. The power of geospatial data comes from combining multiple types to answer layered questions.

Example workflow: retail site selection:

1. POI data identifies existing competitor locations within a target market.
   
   
2. Boundary data defines the trade area polygon around a potential site.
   
   
3. Demographic data profiles the population living within that trade area.
   
   
4. Mobility data reveals actual foot traffic patterns, who visits the area and when.
   
   
5. Address data validates that the candidate site address resolves to the correct coordinates.
   
   
6. Street data confirms road access and proximity to transit routes.
   
   

This kind of multi-layer GIS analysis is what SafeGraph’s data is purpose-built to support, providing high-quality POI, geometry, mobility, and address data that integrates cleanly with demographic and boundary layers from public sources.

Conclusion

Geospatial data is not a single thing, it’s an ecosystem of nine distinct GIS data types, each capturing a different dimension of the physical world. POI data tells you where places are. Mobility data tells you who goes there and when. Demographic data tells you who lives nearby. Property data tells you what the space actually looks like. And formats like vector and raster determine how all of that information is stored and processed.

The organizations getting the most value from GIS today are not those using the most sophisticated tools, they’re the ones who understand which data types answer which questions, and who combine them deliberately. A retailer who overlays POI data with mobility patterns and demographic boundaries makes better site decisions than one working from any single source alone. An insurer who pairs property polygons with environmental risk rasters prices risk more accurately than one relying on address points alone.

That’s the real value proposition of understanding GIS data types: not just knowing what each one is, but knowing when to use it, what to pair it with, and where to get high-quality data that integrates cleanly across all nine categories.

SafeGraph’s Places, Geometry, and Address datasets are built specifically to function as the commercial-location layer in these multi-type GIS stacks, updated monthly, globally scaled, and formatted for direct integration with the platforms and pipelines your team already uses.

If you’re ready to go deeper, read the next chapter: Geospatial Data Sources: Where to Get the Data You Need.

Or go back to basics with Geospatial Data: A Comprehensive Guide.