Spatial data examples: How location insights support enterprise workflows

Spatial data gives real-world context to decisions and actions. It describes the location and relationships of objects and events on the Earth’s surface. Companies put these features on interactive maps, where people can compare and analyze multiple datasets in a collaborative environment.

That’s why spatial data shows up in enterprise workflows, even if teams don’t see it through a geographic lens. Delivery stops, flood zones, and retail stores all become more useful when you can see their location on a map and how they relate to happenings around them. Spatial data shows you boundaries, distances, and historical details.

In this guide, discover common spatial data examples for enterprise organizations in a range of industries.

What’s spatial data?

Spatial data is information that describes where an object or event is located. It can represent a fixed place, like a building, or something that changes over time, like a vehicle’s position or the movement of a storm.

Most spatial datasets include two primary components:

• Spatial information: Where something is located, often represented through latitude and longitude coordinates, boundaries, lines, or pixels on a map
• Attribute information: Details or other data linked to a feature, like a road name, plot owner, or asset condition

Attributes make spatial data different from traditional business data, where location is usually a cell in a spreadsheet. What makes geospatial data unique is the information that gives it meaning and context. For example, a customer record can tell you who bought a product, but a customer record with geographic context can show where demand is growing and which regions need support.

Spatial data is traditionally used in geographic information systems (GIS), but it doesn’t stop there. Enterprise companies analyze location-based data in geospatial databases as a part of everyday decision making.

Where does spatial data come from?

Here are the most common sources of spatial data:

• GPS coordinates and mobile devices: These record location from vehicles, cell phone location data, and field apps. For example, logistics companies use GPS data from delivery trucks to understand where delays occur along a route.
• Satellite imagery and remote sensing: These sources capture information using sensors that observe the Earth’s surface from a distance, like satellites, aircrafts, and drones. For instance, an insurance company could use satellite imagery after a storm to identify damaged areas for property inspections.
• Government and open data sources: Public agencies and open data projects publish datasets to use as a baseline for spatial data analysis in GIS. City planners can use OpenStreetMap or census datasets to understand population distribution and the way services cover different neighborhoods.
• Sensors and IoT devices: These sources collect location-based readings from physical infrastructure like traffic cameras or weather stations and attach those readings to a specific place. For example, a utility company can use pressure sensors across a water network to see where a leak may be forming.

The best insights come from layering multiple data sources. A single dataset answers one question, but connected data lets you compare conditions and make decisions with more context. For instance, a retail team might use satellite imagery of a town with GPS traffic information to plan the optimal trade area.

Types of spatial data

Most spatial data falls into two categories: vector data and raster data. Both describe geography, but in different ways.

Vector data

Vector data represents individual features with defined locations and shapes. It’s represented as points, polylines, and polygons. Points mark individual places, polylines connect points along a path, and polygons show enclosed areas.

If you can point to something, trace its path, or draw its boundary on a map, it usually works well as vector data. Common vector data examples include:

• Addresses: Helps you locate people, facilities, and delivery stops or service requests
• Roads: Show how people and vehicles move through a road network
• Land parcels: Define property lines, often used for urban planning and development, taxation, or asset management
• Administrative boundaries: Organize boundaries by regions like counties, cities, or districts

Vector data is ideal for comparing specific places or assets. Instead of looking at records in isolation, you can see how each feature relates to the surrounding geography.

Raster data

Raster data represents geography as a grid of cells or pixels. Each cell stores a value, which makes raster practical for conditions that continuously change across space rather than features with clear boundaries. Common raster data examples include:

• Satellite imagery: Helps you monitor land, infrastructure, and environmental change
• Elevation models: Shows terrain, slope, and flood exposure
• Weather data: Supports weather forecasting and risk planning
• Land cover maps: Classify surfaces as forests, farmland, bodies of water, or developed areas

Raster data helps you analyze broad areas and detect change over time. With platforms like Felt, you can work with vector and raster data in one workflow. You can upload and visualize large raster datasets directly in Felt, including GeoTIFF and satellite imagery, without relying on separate raster tools.

Common spatial data examples across enterprise workflows

Countless enterprise decisions are tied to locations, so spatial data appears across nearly every industry. The following examples show everyday geospatial data applications for different companies.

Transportation and logistics

Transportation companies use spatial data to understand how services move through delivery stops along a road network. They use GPS data from:

• Vehicles
• Road networks
• Delivery addresses
• Traffic conditions
• Depot and warehouse locations

When these layers come together on a map, teams can compare route convenience across regions and adjust service areas when demand changes. For last-mile delivery, these companies can make quick decisions to assign drivers or reroute networks.

Environmental monitoring and risk assessment

Agriculture and environmental companies use spatial data to understand field conditions across large areas. For example, Leaf standardizes agricultural data so customers can analyze field activity across millions of acres. 

Location data connects on-ground realities to crop records and machine activity. And in a platform like Felt, stakeholders can explore the data and get their questions answered in an intuitive interactive interface. They not only see vegetation data on a map, but they can also investigate spatial problems on their own without rebuilding the analysis every time.

Infrastructure and asset management

Local governments, utility companies, and telecom providers use spatial data to manage assets spread across a service area, including:

• Power lines
• Water pipes
• Substations
• Cell towers
• Traffic signals

For example, a utility company can map power outage reports against transformer locations to see where crews should go first. Ops teams have more context, and dispatch is easier to justify because maps show where problematic devices are and what depends on them.

Market expansion and site selection

Companies, especially in retail and real estate, use spatial data to inspect potential sites for new locations or service expansion. Retailers combine income data with competitor locations to find the best locations for new stores, while real estate companies measure census datasets against parcel boundaries.

It helps them look beyond markets that look attractive on the surface and make specific decisions on where growth is more likely to occur.

Business planning and territory management

Sales, revenue, and customer-centric operations use spatial data to understand how accounts and opportunities spread across regions. Common inputs include:

• Customer addresses
• Service areas
• Administrative boundaries
• Travel times
• Account value

These sources help company leaders design territories that reflect real market conditions. Account count alone may suggest territories are balanced, but geography can tell a different story. Say one area is larger and has extended travel time, while another has clusters of activity. While they both have 20 accounts, one has much more field work.

Turn spatial data into expert enterprise insights with Felt

Spatial data can only fuel informed decisions if it’s accessible and intuitive. The right platform lets you import location data and turn it into an interactive map in moments. Felt allows teams to plot data on a map, analyze it, and share the results with stakeholders using a simple link.

Felt gives you a more connected way to work with spatial data. As a modern enterprise GIS platform, Felt is built for the entire team, not only the analysts doing highly technical work. You can integrate large datasets and cloud data sources, from Snowflake and BigQuery to Databricks, Redshift, Postgres, and Amazon S3. That makes it easier to combine spatial and business data without building complex pipelines or duplicating data across systems.

Explore our map gallery to see how real companies use geospatial data analysis workflows. Better maps lead to better decisions, and Felt gets you there faster.

FAQ

What are common examples of spatial data?

Common examples of spatial data include:

• Addresses
• Land parcels
• Points of interest
• Property lines
• Building footprints
• Road maps
• Highways
• Subway lines
• Country borders
• Flight paths

These examples usually work as vector data. Other types of data are raster examples, which describe conditions across a broader area, like:

• Digital elevation models
• Digital elevation maps (DEMs)
• Topographic maps
• Aerial photographs
• Precipitation maps
• Temperature maps

What are types of spatial analysis?

There are many different types of spatial analysis depending on your use case and data type. When examining location-based information, you can analyze:

• Proximity and distance
• Multiple data overlays
• Topology
• Statistics and patterns
• Terrain and surface