Every smart business decision today has a location attached to it. Whether you’re expanding a retail chain, planning infrastructure, or optimizing a supply network, types of location data are at the heart of your analysis. However, not all location data works the same way.
Many teams make costly mistakes because they confuse GPS location data with POI data, or treat mobility data and foot traffic data as interchangeable. They are not. Each type answers a different question. Therefore, choosing the wrong one leads to incomplete or misleading insights.
This guide breaks down each type clearly. By the end, you’ll know exactly which location data type fits your use case and how to combine them for maximum intelligence.
What Is Location Data?
Location data is structured or raw information that identifies where people, businesses, vehicles, or other entities are situated in the physical world. It serves as the foundation for analytics systems, AI model training, logistics operations, site selection workflows, and market research at scale.
The term itself is broad by nature. It covers several distinct data types, each collected through different methods, each suited to a different analytical purpose. The four primary categories are:
- GPS location data captures device coordinates in real or near real time
- POI data describes the attributes of fixed business and landmark locations
- Mobility data reflects aggregated population movement patterns across geography and time
- Foot traffic data measures visit volume and dwell time at specific physical venues
Knowing which category fits a given question is table stakes for anyone building location intelligence into business decisions seriously.
GPS Location Data
What Is GPS Data?
GPS location data consists of latitude and longitude coordinates captured from mobile devices, vehicle sensors, delivery scanners, wearables, and IoT equipment. The signal updates continuously or at set intervals and is typically accurate within a few meters under open sky conditions.
What separates GPS from other location data types is its precision and its real time nature. No other category tells you where something is right now with comparable accuracy across such a wide range of hardware.
What GPS Data Is Used For?
GPS data performs best in operational contexts where physical position in the current moment is what matters:
- Navigation and dynamic route recalculation for drivers and field workers
- Fleet tracking across delivery, transport, and construction operations
- Asset location monitoring for high value or mobile equipment
- Geofence triggers for arrival, departure, and zone entry events
- Last mile delivery coordination and live estimated arrival calculation
Limitations of GPS Data
GPS data answers the question of where. It does not answer what. A coordinate tells you a device stopped at a specific point. It does not tell you whether that location is a competitor’s flagship store, a distribution hub, or a parking structure.
Raw GPS is also granular enough to raise privacy concerns at the individual level. Most B2B location data providers anonymize and aggregate it before it enters any product. That step is necessary, but it also reduces some of the precision. For market research, competitive benchmarking, or site evaluation, GPS coordinates alone give you almost nothing commercially useful.
POI (Point of Interest) Data
What Is POI Data?
POI data is a structured record set that describes fixed physical locations. Each record represents a place. Each place carries attributes: name, category, address, geographic coordinates, operating hours, phone number, website, and sometimes ratings or review volume.
A coffee shop on Fifth Avenue is a POI. So is a hospital in Mumbai, a rail station in Singapore, or a fast food location in Lagos. POI datasets organize these records consistently across geographies and verticals, making them queryable, filterable, and ready to feed into analytics systems without heavy preprocessing.
What POI Data Is Used For?
POI data is the entry point for most commercial geography analysis. Teams use it to understand what exists in a market before making any decisions about entering it:
- Competitive landscape mapping across cities, regions, or countries
- Site selection for retail stores, franchise locations, distribution centers, and clinics
- Trade area analysis to evaluate demand within a defined catchment
- Real estate investment screening based on neighborhood business density
- Franchise network gap analysis identifying underrepresented categories
- Local search infrastructure for consumer apps and business directory platforms
Why POI Data Is Foundational for B2B Intelligence?
B2B location data strategies almost always start with POI. The reason is structural. POI records follow a consistent schema across sources and geographies. Unlike GPS traces, which vary by device type, sampling rate, and collection method, POI datasets are stable and straightforward to integrate into GIS tools, analytics platforms, and machine learning pipelines.
LocationsCloud builds and maintains POI datasets at global scale across millions of business locations and hundreds of industry verticals. The datasets are delivered cleaned, normalized, and in formats analytics teams can use without additional preparation work.
Mobility Data
What Is Mobility Data?
Mobility data captures aggregated, anonymized movement of people or vehicles across geography and time. It is assembled from signals including GPS traces, cell tower handoffs, and app activity, then processed to strip individual identifiers and correct for sampling bias.
The output is not a record of where one person went. It is a statistical picture of how populations move. How many people traveled from one district to another between 7am and 9am on weekdays? Which corridors carry the highest pedestrian volume on Saturday afternoons? Mobility data is built to answer those questions at scale.
What Mobility Data Is Used For?
- Transit and road infrastructure planning at city and regional scale
- Origin to destination flow analysis for logistics network design
- Trade area definition based on observed travel patterns rather than radius assumptions
- Tourism volume measurement for destination marketing and capacity planning
- Population behavior analysis for public health, emergency response, and urban development
Key Considerations
Mobility data requires careful quality assessment before use. The underlying signals come from heterogeneous sources with different sampling rates, geographic coverage gaps, and demographic skews. A dataset that over represents urban smartphone users produces misleading results when applied to rural or older demographics.
The data becomes substantially more useful when joined with POI data. Movement volume through a district is a raw number until you understand what businesses and infrastructure occupy that space. Combining the two turns a headcount into context.
Foot Traffic Data
What Is Foot Traffic Data?
Foot traffic data measures how many people visit a specific physical location, how long they stay, and how often they return. The granularity operates at the venue level, not the city or corridor level. That distinction is what separates it from mobility data.
Visit counts, median dwell times, peak hour distributions, repeat visitor rates, and catchment area profiles are all standard outputs of foot traffic analysis. Retailers, real estate investors, and commercial landlords rely on this data to evaluate location performance in concrete, measurable terms.
What Foot Traffic Data Is Used For?
- Store performance benchmarking against competitors and category averages
- Closure and consolidation decisions backed by actual visitor evidence rather than manager estimates
- New location scouting validated by visit density at comparable existing sites
- Marketing attribution connecting campaign spend to measurable physical visit increases
- Tenant mix evaluation for shopping centers and mixed use commercial developments
Why Foot Traffic Data Is Powerful?
Foot traffic data records actual consumer behavior. Surveys ask people what they plan to do. Foot traffic shows what they did. That distinction matters enormously when a decision involves a multi million dollar lease commitment or a major infrastructure investment.
The data gets sharper when layered on POI datasets. POI tells you where businesses are. Foot traffic tells you which of those businesses are drawing real visitor volume. A location with dense business coverage but low visit counts is a fundamentally different investment thesis than one with sparse coverage and strong demand signals.
Comparing Types of Location Data
The table below summarizes each data type across four evaluation dimensions:
| Data Type | What It Measures | Best For | Key Limitation |
| GPS Data | Precise device coordinates in real time | Fleet tracking, navigation, asset monitoring, logistics | No business or place context |
| POI Data | Attributes of fixed business and landmark locations | Market mapping, site selection, competitive intelligence, real estate | Static; reflects what exists, not what performs |
| Mobility Data | Aggregated population movement flows | Urban planning, transport network design, infrastructure investment | Requires heavy aggregation; prone to sampling bias |
| Foot Traffic Data | Visit volume and dwell time at individual venues | Retail benchmarking, site validation, campaign attribution | Accuracy depends on normalization methodology |
How These Location Data Types Work Together?
No single type of location data answers every question. Analysts who rely on just one category hit a ceiling fast. The strongest location intelligence frameworks pull from multiple data types and let each one do what it is built for.
A practical example from retail expansion planning:
- Start with POI data to map competitors, anchor tenants, and complementary businesses in the target market
- Layer mobility data to identify which corridors and districts carry the highest pedestrian and vehicle volumes
- Apply foot traffic data to confirm which competitor and anchor locations are actually generating visits, not just occupying space
- Use GPS data operationally once a site is selected, to manage deliveries, field teams, or last mile logistics
Each of these geospatial data types contributes something the others cannot provide. Remove any layer and the analysis carries a blind spot. Together they build a picture accurate enough to act on.
Industry Use Cases by Location Data Type
Retail and Franchise Expansion
POI data maps what already exists in a target market. Foot traffic data reveals which of those locations are pulling real customer volume. Franchise teams use both together to identify territories where demand is present but existing supply is limited, then validate shortlisted sites before committing to leases.
Real Estate and Investment Analysis
Commercial real estate analysts layer POI data for neighborhood composition, mobility data for corridor traffic volume, and foot traffic data for tenant specific visit patterns. The combination reduces underwriting risk on acquisitions and development projects by grounding assumptions in observed behavior rather than projections alone.
Logistics and Supply Chain
GPS location data drives real time fleet visibility and delivery tracking. Mobility data informs strategic decisions about hub placement and route network design. Together they let logistics operators cut transit times and lower the cost per delivery across large networks.
Smart Cities and Urban Planning
Mobility data shows planners where populations actually move, which frequently differs from where infrastructure currently sits. Combined with POI data on service locations, transit stops, and civic facilities, it supports evidence based zoning, transit expansion, and public amenity placement decisions.
AI and Analytics Platforms
AI systems handling demand forecasting, trade area modeling, and competitive scoring need structured, verified inputs. POI data and foot traffic data are among the most commonly used feature sets in location aware machine learning. The quality of those inputs directly affects model reliability, which is why B2B location data sourcing decisions matter as much as model architecture choices.
Choosing the Right Location Data for Your Use Case
Before sourcing data, clarify what your analysis actually needs to answer. These questions point you to the right category:
- Are you analyzing what exists in a market or how people move through it? Inventory questions point to POI. Movement questions point to mobility or foot traffic.
- Do you need current operational data or historical behavioral patterns? Operations use GPS. Pattern analysis uses mobility or foot traffic records.
- Is business context important or just raw position? Business context requires POI. Raw positioning is GPS territory.
- Are you evaluating performance at specific venues? Foot traffic data is the right layer for that measurement.
For the widest range of commercial B2B location intelligence work, pairing POI data with foot traffic data covers site selection, competitive benchmarking, and market sizing without requiring additional data sources in most cases.
How LocationsCloud Supports Location Data Intelligence?
LocationsCloud produces and delivers location datasets purpose-built for analytics teams, real estate firms, logistics operators, retailers, and AI developers. The platform addresses the full range of location intelligence data needs:
- POI and business location datasets covering millions of global records across hundreds of industry categories
- Custom data enrichment and extraction for teams that need location records from specific markets or verticals
- Analytics ready delivery formats including cleaned, normalized files that integrate directly into BI tools, GIS platforms, and ML pipelines
- API access and bulk export options for teams that need location data integrated into live applications or automated data workflows
LocationsCloud structures its datasets for immediate application to B2B location data workflows at enterprise scale. Enterprises do not have capacity to spend weeks cleaning raw feeds before analysis can begin.
Location Intelligence Starts with the Right Data
GPS location data, POI data, mobility data, and foot traffic data each occupy a distinct role in location analytics. They are not interchangeable. Applying the wrong type to a business question produces analysis that looks complete but rests on the wrong foundation.
GPS delivers position. POI delivers place context. Mobility delivers population level movement patterns. Foot traffic data delivers venue level demand evidence. The most reliable location intelligence strategies use all four in combination, with each layer contributing what it was built for.
Enterprises that invest in the right B2B location data and apply it systematically across planning, site selection, logistics, and market analysis hold a structural advantage over teams that work from assumptions or incomplete datasets.
LocationsCloud provides the geospatial data types and delivery infrastructure to support that approach at enterprise scale.
FAQs
What are the main types of location data?
The four core types of location data are GPS data, POI data, mobility data, and foot traffic data. Each measures a different aspect of physical world activity and suits different analytical use cases.
What is the difference between GPS data and POI data?
GPS data captures device coordinates in real time. POI data is structured information describing fixed locations, including business name, category, address, and operating attributes.
How is foot traffic data different from mobility data?
Foot traffic data measures visits and dwellings at individual venues. Mobility data tracks aggregated population movement across broader areas and corridors over time.
Which location data is best for site selection?
POI data paired with foot traffic data is the standard combination. POI maps the competitive landscape; foot traffic validates actual demand at comparable locations.
Can location data be used for AI and analytics models?
Yes. Structured location intelligence data, especially verified POI records, is widely used as feature input for machine learning models covering demand forecasting, market sizing, and competitive scoring.
Does LocationsCloud provide POI and location data via API?
LocationsCloud offers both API access and bulk delivery. Teams integrate B2B location data directly into applications, dashboards, or automated data pipelines using either delivery method.
