Where to Buy Location Data: The Complete Buyer’s Guide

What is Location Data?

Location data, also known as geographic information or geospatial data, refers to any information that identifies the physical position of an object, device, or person in the real world. It connects digital information to a physical place and provides the ‘where’ behind every customer interaction, transaction, and movement pattern.

This data can be as simple as a city name attached to a web visit, or as precise as a GPS coordinate recorded every few seconds from a smartphone. When a user grants an app permission to access their location, that app can collect data through the device’s built-in hardware; satellites, Wi-Fi networks, cell towers, or Bluetooth beacons, and transmit it to a central provider.

Location data typically refers to the real-time or historical position of a specific object or device; think of it as the dot on a map.

Geospatial data is a broader term that includes location data but also encompasses other geographic layers: maps, satellite imagery, property boundaries, weather patterns, and demographic boundaries. Essentially, location data is a core ingredient inside the larger universe of geospatial analysis.

The Two Basic Forms

According to Tableau, location data exists in two fundamental technical forms:

• Vector Data: uses points, lines, and polygons to represent features such as cities, roads, mountains, and bodies of water. This is the dominant form used in geographic information systems (GIS).

Raster data: uses a grid of cells to represent spatial features. Satellite imagery is the most common example.

What Does It Mean to Buy Location Data?

When a business says it wants to ‘buy location data,’ it is purchasing structured information about where mobile devices, and by extension, people have been over time. This is not about tracking named individuals. Reputable providers use anonymized device IDs, not personal identifiers. The goal is to understand movement patterns, visits to specific places, and real-world behaviors at scale.

The purchased data can range from raw GPS coordinate streams tied to anonymized device IDs, to pre-processed aggregated summaries (such as ‘approximately 500 people visited this shopping mall on Saturday’), to audience segments (such as ‘device IDs that have visited a gym at least three times in the past 30 days’).

Buyers typically fall into one of two camps: data science teams seeking granular, customizable raw data, or business users seeking ready-to-use dashboards and aggregated insights. The format, freshness, and geographic coverage of the dataset determine its price and fitness for purpose.

Top 9 Types of Location Data and Where to Buy Them

Location data is not a single product, it is an ecosystem of nine distinct data categories, each describing a different layer of the physical world. Your organisation will likely need several in combination to get the full picture. Below is an in-depth breakdown of every type: what it is, what it contains, its primary use cases by industry, what to look for when buying, and the leading provider for each.

Type 1: Points of Interest (POI) Data

Points of interest data is the most foundational layer of location intelligence. A POI is any physical place on Earth, beyond private residences that people may want to visit, interact with, or reference in analysis. POI datasets describe shops, restaurants, hospitals, schools, stadiums, monuments, parks, airports, gas stations, and thousands of other place types.

Each POI record typically includes: a place name, category and sub-category, street address, latitude and longitude coordinates, phone number, website, brand affiliation, and operating hours. Premium POI datasets such as SafeGraph Places also include a unique place identifier (like Placekey), parent-child relationships (e.g., a Starbucks inside a Target), NAICS industry codes, and polygon geometry for the building footprint.

A critical property of POI data is its dynamic nature. Businesses open and close constantly,  SafeGraph estimates that POI churn is significant in most metro areas. This means stale POI data quickly becomes a liability. The best providers update their POI datasets monthly or more frequently, using a combination of machine learning, web crawling, and human verification to maintain accuracy.

Primary Use Cases by Industry

• Mapping & Navigation: Show users where things are, with supplementary attributes like hours and contact details. Core to any consumer-facing mapping application.
• Retail & CPG Site Selection: Analyse concentrations of competitive and complementary businesses around a candidate site to predict revenue potential before signing a lease.
• Real Estate Investment: Score neighbourhoods by business mix, density, and category health. Track opening/closing rates as a leading indicator of economic vitality.
• Financial Analysis: Monitor the number and type of business openings and closings across trade areas to inform equity research and lending decisions.
• Healthcare Planning: Map existing medical facilities against population density and demographic data to identify underserved areas and plan new facility placements.
• Insurance Underwriting: Assess risk based on what types of businesses occupy a building and what is nearby, e.g., a restaurant next to a nightclub carries a different liability profile.

What to Look for When Buying POI Data

Best Place to Buy POI Data: SafeGraph

SafeGraph Places covers 78M+ POIs globally across the US, UK, Canada, and expanding international markets. Records are updated monthly by the 7th of each month, more frequently than most competitors that refresh every three to six months. Each record includes brand hierarchy, NAICS category, Placekey ID, open hours, and polygon geometry.

Type 2: Property Data (Building Footprints & Geometry)

Property data represents the accurate physical boundaries of tangible places in the real world. While POI data tells you where a place is (as a single point), property data tells you what shape it is and how big it is. It is most commonly represented as polygon geometry, the outline of a building’s footprint on the ground.

Beyond simple building outlines, the most valuable property datasets include spatial hierarchy metadata: the relationships between a building and the individual units within it. For example, a shopping mall is one property, but it contains dozens of individual tenant spaces, each of which may be a separate POI. Property data that captures this parent-child structure is far more useful for visit attribution and retail analytics.

Property data answers questions that a single POI coordinate cannot: Did someone actually walk inside this building, or did they just walk past it? How large is this store’s footprint compared to competitors? Which entrance did customers use? Without polygon geometry, these questions are unanswerable.

Primary Use Cases by Industry

• Visit Attribution: Match GPS pings from mobile devices against a building’s polygon boundary to determine whether a device actually entered the building. Critical for measuring ad-to-store conversion.
• Retail Analytics: Measure the precise square footage a customer traverses, which store zones attract the most dwell time, and how traffic flows through a physical space.
• Insurance Risk Assessment: Assess a building’s risk profile based on its size, construction type, neighbouring structures, and the types of tenants it contains. A nail salon sharing a wall with a fireworks store carries a very different risk profile than the same salon located next to a daycare.
• Mapping Accuracy: Displaying a building’s true polygon shape on a map is far more informative and visually accurate than a single pin marker, especially for large properties like hospitals, warehouses, or airports.
• Real Estate & Construction: Estimate gross leasable area, compare property sizes across candidate sites, and integrate with tax parcel data for investment analysis.

What to Look for When Buying Property Data

Best Place to Buy Property Data: SafeGraph Geometry

SafeGraph Geometry delivers building footprints and spatial hierarchy metadata pre-linked to every SafeGraph Places POI via a shared Placekey identifier. The dataset captures sub-property relationships, individual tenant spaces within malls and complexes, eliminating manual polygon-to-POI matching.

Type 3: Mobility Data (Foot Traffic & Movement Patterns)

Mobility data captures how, when, and where people move in their daily lives. It is derived from anonymized, aggregated GPS signals collected from opted-in mobile device users. Unlike raw GPS trace data, which records every coordinate of every device, mobility data is pre-processed into meaningful metrics: visit counts to specific POIs, dwell times, hourly and daily traffic patterns, trade-area origins (where visitors come from), and cross-visit behaviour (where else visitors go).

Mobility data does not expose individual mobile phone locations or identities. All records are aggregated to the POI or Census Block Group (CBG) level, ensuring that no individual’s movement can be identified. This makes it the most widely adopted and privacy-compliant form of location intelligence for business use.

The richest mobility datasets include not just visit counts but also visitor home and work CBGs (revealing trade-area catchment), median dwell times, bucketed visitor frequency (first-time vs. repeat), and cross-shopping patterns. This depth of signal enables analyses that go far beyond simple foot-traffic measurement.

Primary Use Cases by Industry

• Retail Store & Ad Placement: Understand where potential customers are concentrated and where they already shop, to optimise new store locations and out-of-home advertising placements.
• Competitive Benchmarking: Compare your store’s foot traffic trends against competitor locations on a weekly or monthly basis. Identify if a competitor is gaining share and when the trend started.
• Insurance Liability Policies: Write general liability policies calibrated to a venue’s actual visitor volume. A venue with 10,000 weekly visitors warrants a very different premium than one with 500.
• Urban & Transportation Planning: Measure commuter flows between neighbourhoods, identify transportation bottlenecks, and assess demand for new public transit routes or housing developments.
• Tourism & Economic Development: Quantify visitor inflows to attractions and districts, measure the economic impact of events, and benchmark recovery after disruptions.
• Healthcare & Public Health: Track population movement during outbreaks to model transmission risk, assess compliance with mobility restrictions, and identify healthcare access gaps.

Key Metrics to Look for in a Mobility Dataset

Best Places to Buy Mobility / Foot Traffic Data: Veraset + Unacast

Veraset and Unacast are the two leading providers of privacy-compliant foot traffic and mobility data and are frequently used together for comprehensive coverage.

Veraset offers two core products: Veraset Movement (anonymized, cleansed GPS pings from tier-1 apps and SDKs) and Veraset Visits (Movement data snapped to POIs with dwell-time and visit-count metrics). The North American panel covers 768M+ US devices and 70B+ daily pings, plus 30+ APAC countries and 42+ African markets.

Unacast transforms raw location data into actionable insights for advertising, investment strategy, and market analysis. Their global mobile location data covers 247 countries and is accessible via API or bulk S3 delivery.

Type 4: Demographic Data

Demographic data provides aggregated counts of people in a geographic area, along with structured information about who those people are. Attributes include age range, gender distribution, household income, educational attainment, employment status, marital status, housing tenure (own vs. rent), household size, and racial/ethnic composition. This data is primarily sourced from government censuses, surveys, and administrative records.

Demographic data alone shows you who lives somewhere. Its power multiplies significantly when combined with other location data types. Pair it with mobility data and you can understand not only who lives in an area but also who visits it, and whether they match your target customer profile. Pair it with POI data and you can assess whether the businesses in an area serve the resident population or attract visitors from further away.

At SafeGraph, US census demographic data is cleaned and formatted for easy integration with POI and mobility datasets, organized by Census Block Group (CBG). This pre-processing eliminates much of the data-wrangling overhead that normally comes with working with raw government data files.

Primary Use Cases by Industry

• Trade Area Profiling: Characterise the population living within 1-, 3-, and 5-mile radii of a store or candidate site; age, income, household size, and spending power.
• Store & Product Strategy: Determine which product categories, price points, and brands will resonate most with the resident demographic of each location. A luxury goods retailer needs very different site criteria than a discount grocer.
• Advertising Targeting: Match your target customer profile against geographic demographic data to identify the highest-potential postal codes, DMAs, or CBGs for local advertising investment.
• Financial Services: Assess creditworthiness, income distribution, and financial service gaps within a trade area for branch placement or community lending programmes.
• Public Health & Government: Identify underserved populations who lack access to healthcare, food, transit, or educational facilities based on income and residential density.

What to Look for When Buying Demographic Data

Best Places to Buy Demographic Data: SafeGraph Open Census + Spatial.ai + Esri

Three providers stand out for demographic data, each serving a distinct need. Use them separately or in combination depending on your use case.

SafeGraph Open Census is the recommended free starting point for US demographic data. SafeGraph pre-processes the US Census Bureau’s American Community Survey (ACS) into 7,500+ attributes organised by Census Block Group, paired with CBG polygon geometry, and formatted to join seamlessly with SafeGraph Places via a shared FIPS code, eliminating the data-engineering overhead of working with raw government files.

Spatial.ai (formerly Cuebiq Insights / SocialSpatial) goes further than census data by combining demographic attributes with psychographic and lifestyle intelligence derived from real-world mobility patterns and social signals. Rather than simply counting who lives in an area, Spatial.ai reveals how people in that area think, shop, eat, and behave, giving marketers and retailers a far richer audience profile for targeting and store positioning.

Esri provides the most comprehensive commercially enriched demographic data product available, delivered through the ArcGIS platform as Business Analyst Data. Esri’s demographic datasets combine US Census data with proprietary current-year estimates and five-year projections, going beyond what the Census Bureau publishes to provide forward-looking population and spending forecasts at the block group, ZIP code, and trade area level. Over 15,000 demographic and consumer spending variables are available.

Type 5: Address Data

Address data is the connective tissue of the entire geospatial data ecosystem. It provides navigation-related information about specific places, represented by geographic coordinate pairs associated with structured street address records. Every other geospatial data type ultimately references address data, either directly (a POI’s street address) or indirectly (a boundary that contains thousands of addresses).

Address data encompasses both business locations and residential properties, making it uniquely broad in scope. A comprehensive address dataset includes the full structured address (street number, street name, suite/unit, city, state/province, postal code, country), the corresponding latitude/longitude coordinates, and ideally a validated status flag confirming the address corresponds to a deliverable, real-world location.

One of the key technical challenges with address data is standardisation. Street addresses contain multiple components that can be expressed in countless abbreviations and formats; ‘Street’ vs ‘St’ vs ‘St.’, ‘Avenue’ vs ‘Ave’, ‘United States’ vs ‘USA’ vs ‘US’. This variability makes it easy for systems to treat two records pointing to the same physical location as entirely separate places. Standardisation layers and unique identifiers like Placekey are designed to solve this problem.

Primary Use Cases by Industry

• Geocoding & Reverse Geocoding: Convert street address strings into coordinate pairs (geocoding) or convert coordinates back into human-readable addresses (reverse geocoding). Fundamental to any location-enabled application.
• Address Validation: Verify that a user-entered or CRM-stored address corresponds to a real, deliverable location, critical for e-commerce, direct mail, and fraud prevention.
• Residential Mapping: Unlike POI data, address data includes residential properties, enabling mapping and analysis of where people live, not just where they shop.
• Data Enrichment: Use address data as a join key to append other geospatial attributes; weather history, flood risk, school district, tax jurisdiction, to any dataset that contains a location.
• Logistics & Routing: Last-mile delivery systems rely on highly accurate address geocoding to ensure drivers arrive at the correct entrance, not just the general vicinity of a large property.

The Address Standardisation Challenge

Address standardisation is one of the most underestimated data challenges in geospatial analytics. A single address can be written in dozens of legitimate ways. Systems that fail to standardise addresses before analysis will generate duplicate records, missed joins, and incorrect visit attribution. Best practice is to apply address standardisation as an early step in any data pipeline, using a tool or service that normalises abbreviations, handles international formats, and validates against a master address database.

Best Places to Buy Address Data: SafeGraph Address + GeoPostcodes

For address data, the recommended combination is SafeGraph Address for geocoded global address intelligence in hard-to-source markets, paired with GeoPostcodes for postal-code-level validation and standardisation across 247 countries.

SafeGraph Address provides standardised, geocoded address data for global regions where public records are often incomplete, including LATAM, MENA, Southeast Asia, and Eastern Europe. It is built for organisations expanding globally who need reliable address foundations for mapping, delivery routing, and fraud prevention.

GeoPostcodes provides the world’s most comprehensive international address database built from 1,500+ authoritative sources. It covers 247 countries, 233 postal systems, street-level address validation in 81 countries, and multilingual support for 299 languages. Enterprise clients include Amazon and DB Schenker.

Type 6: Boundary Data

Boundary data is effectively a large-scale version of property data. While property data outlines individual buildings, boundary data outlines larger geographic areas that typically contain many addresses, properties, and POIs. Like property data, boundaries are almost always represented as polygon geometry.

Boundaries come in many forms: political boundaries (countries, states, provinces, counties, municipalities), administrative boundaries (postal codes, census tracts, census block groups), service area boundaries (school districts, hospital catchment areas, utility service territories, emergency response zones), and custom business boundaries (trade areas, sales territories, franchise zones).

The analytical value of boundary data is its role as an organisational framework. By assigning every POI, address, and mobility ping to a named geographic unit, boundary data enables aggregation, comparison, and attribution at any geographic scale. Without it, location data is just a cloud of coordinates with no structural context.

Primary Use Cases by Industry

• Political & Organisational Mapping: Designate country, state, county, and municipal boundaries for administrative mapping, electoral analysis, and regulatory compliance monitoring.
• Catchment Area Analysis: Define which populations fall within the service area of a school, hospital, or emergency service. Identify coverage gaps and plan new facilities accordingly.
• Retail Territory Management: Assign stores or sales representatives to exclusive geographic territories. Analyse performance by territory and identify overlaps or gaps in coverage.
• Real Estate Development: Understand which tax jurisdictions, zoning categories, and school districts a property falls within all of which affect its value and development potential.
• Custom Trade Area Definition: Define a store’s trade area using drive-time polygons or custom shapes based on actual visitor home locations from mobility data.

What to Look for When Buying Boundary Data

Best Places to Buy Boundary Data: Mapbox + CARTO

Mapbox and CARTO are the two strongest commercial boundary data providers, each with distinct strengths. Mapbox excels at rendered, cartographically-matched boundaries optimised for mapping and BI applications; CARTO excels at analytical boundary datasets for spatial intelligence and enterprise data workflows.

Mapbox Boundaries is a curated set of 4 million global boundaries covering administrative, legislative, locality, postal, and statistical layers. Polygons are fully detailed, edge-matched at all zoom levels, and maintained with regular updates. Major BI platforms including Tableau and Microstrategy use Mapbox Boundaries as the underlying data source for their geospatial analysis.

CARTO’s Spatial Data Catalog includes nearly 10,000 geospatial datasets with 600+ dedicated boundary layers, covering all major global markets. Backed by $92M in total funding, CARTO is the leading cloud-native spatial analytics platform.

Type 7: Environmental Data

Environmental data covers natural geographic phenomena: weather and temperature patterns, land elevation and terrain models, seismic and volcanic activity, tidal patterns and flood zones, soil composition, air quality, wildfire risk, hurricane and storm tracks, and the habitats or migration routes of plant and animal species. It is almost always in raster format, stored as a continuous grid of cell values rather than discrete points or polygons.

Environmental data has historically been the domain of scientists, conservationists, and government agencies. However, its commercial relevance has grown enormously as climate risk has become a material concern for businesses, investors, and insurers. A property’s proximity to a floodplain, its historical wildfire exposure, or its projected heat-wave frequency over the next 30 years are all quantifiable with modern environmental data, and all are increasingly relevant to lending, insurance pricing, and real estate valuation.

Primary Use Cases by Industry

• Insurance Risk Assessment: Quantify the probability of natural disaster damage to a specific property by overlaying it against flood zone maps, wildfire risk scores, wind-speed models, and seismic hazard data. Climate risk scores are now routinely incorporated into property insurance pricing.
• Climate Risk for Real Estate: Lenders and real estate investors increasingly require climate risk assessments as part of due diligence. Datasets that score properties on flood, fire, heat, drought, and storm risk over 30-year horizons are becoming industry standard.
• Business Continuity & Crisis Planning: Overlay supply chain nodes, distribution centres, and retail locations against natural disaster risk models to identify vulnerabilities and plan contingency routes.
• Environmental Conservation: Track deforestation, coral reef bleaching, ice sheet extent, species habitat loss, and water quality changes over time using satellite-derived environmental data.
• Agriculture & Land Use: Monitor soil moisture, crop health (via NDVI indices), frost risk, and precipitation patterns to optimise planting, irrigation, and harvest decisions.

What to Look for When Buying Environmental Data

Best Place to Buy Environmental Data: Ambee

Ambee is a global climate technology company delivering high-resolution real-time, historical, and forecast environmental intelligence. Headquartered in Bangalore,India, with a presence in Boston and New York, Ambee unifies satellite feeds, ground-based sensors, radar, and best-in-class climate models into harmonised, analytics-grade datasets validated for global accuracy and consistency.

Unlike providers that rely on a single input source such as airport weather stations or isolated sensor networks, Ambee combines all available environmental inputs into a statistically consistent dataset that flows from historical archive through real-time data into forecast, covering any point on Earth.

Type 8: Streets Data (Transportation Networks)

Streets data provides structured information about road transportation networks, the backbone of physical-world connectivity. A streets dataset describes every road segment as a geometric line with associated attributes: road name, road class (motorway, arterial, local), speed limit, direction of travel, turn restrictions, and access permissions for different vehicle types.

Advanced streets datasets go beyond static road geometry to include dynamic traffic data: real-time and historical traffic speeds, congestion levels, incident reports (accidents, roadworks, weather closures), and estimated travel times by time of day. This dynamic layer transforms streets data from a simple map backdrop into a live operational tool.

Streets data is integral to geocoding, translating address strings into map coordinates requires knowing where streets are and how they are numbered. It is also the foundation for routing algorithms, which calculate optimal paths between two points taking into account distance, speed limits, turn restrictions, and current traffic conditions.

Primary Use Cases by Industry

• Navigation & Consumer Mapping: Power turn-by-turn navigation in consumer apps. Streets data quality directly determines whether drivers are routed correctly or sent down a road that no longer exists.
• Last-Mile Logistics: Optimise delivery routes across hundreds of stops, accounting for vehicle type restrictions, time windows, and real-time traffic. Every minute saved per route scales across an entire fleet.
• Retail Drive-Time Analysis: Calculate drive-time catchment areas around store locations. A store accessible within a 10-minute drive to 500,000 people is fundamentally different from one accessible to 50,000.
• Detour & Incident Planning: Reroute vehicles in real time when roads are blocked by accidents, construction, or weather events.
• Urban & Transport Planning: Analyse traffic flow patterns, identify congestion hotspots, and model the impact of new road infrastructure on city-wide mobility.

What to Look for When Buying Streets Data

Best Places to Buy Streets / Transport Data: StreetLight Data + OpenStreetMap

StreetLight Data is the most widely adopted transportation analytics platform in North America, trusted for over 1 million transportation projects by public agencies and private consultancies. Its proprietary Route Science engine processes hundreds of data sources into on-demand metrics for vehicles, bicycles, pedestrians, trucks, and transit across virtually every road and Census Block Group.

OpenStreetMap (OSM) is the world’s largest free, open-source street network database, maintained by a global volunteer community and used as the base road geometry layer by Mapbox, HERE, and countless GIS tools. For teams that need the base road network at no cost, OSM is the standard starting point.

Type 9: Imagery Data (Aerial & Satellite)

Imagery data provides true-to-life photographic representations of what places look like in the physical world, from sweeping continental satellite views down to sub-metre aerial photography of individual buildings. Unlike every other location data type we have covered, imagery is always stored in raster format: a continuous grid of pixel values rather than discrete points, lines, or polygons.

Imagery data comes in several forms: optical satellite imagery (RGB photographs taken from orbit), multispectral imagery (capturing wavelengths beyond visible light, enabling vegetation and soil analysis), synthetic aperture radar (SAR) imagery (which can penetrate clouds and capture terrain at night), aerial photography (taken from aircraft at lower altitudes for higher resolution), and street-level imagery (horizontal ground-level photography, as seen in Google Street View).

The resolution of imagery data ranges enormously, from 30-metre pixels in freely available Landsat data to sub-30-centimetre pixels in commercial very high resolution (VHR) satellite imagery. The appropriate resolution depends entirely on the use case: monitoring global deforestation requires far less resolution than assessing damage to a specific building after a storm.

Primary Use Cases by Industry

• Basemap Rendering: Provide the photographic background layer on which all other geospatial data types are displayed. The satellite imagery background in Google Maps, Apple Maps, and ArcGIS is imagery data.
• Environmental Monitoring: Track changes in forest cover, water bodies, ice extent, and land use over time by comparing imagery from different dates. Detect illegal deforestation, measure urban heat island effects, and monitor coastal erosion.
• Construction & Development Monitoring: Detects building activity, parking lot occupancy, and industrial output by analysing imagery at regular intervals. Used by hedge funds and real estate firms to generate proprietary intelligence.
• Disaster Assessment: Rapidly assess the extent of structural damage after earthquakes, floods, or wildfires by comparing pre- and post-event imagery. Critical for emergency response planning and insurance claims processing.
• Agricultural Intelligence: Measure crop health using Normalised Difference Vegetation Index (NDVI) derived from multispectral imagery. Monitor irrigation patterns and forecast yields.
• Retail Car Park Analysis: Count vehicles in retailer car parks using aerial or satellite imagery to estimate store traffic before official sales figures are released, a technique widely used in alternative investment research.

What to Look for When Buying Imagery Data

Best Places to Buy Imagery Data: Planet Labs + Albedo

Planet Labs and Albedo represent two complementary tiers of the commercial satellite imagery market: Planet for high-frequency medium-resolution global monitoring, and Albedo for the highest commercially available resolution.

Planet Labs operates 200+ Dove satellites capturing near-daily imagery of the entire Earth at 3-5 metre resolution. Their SkySat constellation provides 50 cm high-resolution on-demand tasking. Planet’s imagery archive is used by agricultural, energy, utilities, forestry, and intelligence teams globally.

Albedo is a VLEO (Very Low Earth Orbit) imaging company operating its Clarity-1 satellite at roughly half the altitude of traditional EO satellites (~200-300 km vs ~500 km), enabling 10 cm optical and 2 m thermal infrared imagery, the highest resolution commercially available. Albedo has partnered with SkyFi (global) and European Space Imaging / EUSI (Europe and North Africa) for accessible ordering and distribution.

Buyer’s Decision Matrix

Use this matrix to quickly identify which data type fits your business goal, what to expect to pay, and where to start your search.

Exact pricing varies by volume, geography, and use-case licence terms. Always request a sample before committing to a purchase.

Common Use Cases in Action

Data Quality and Privacy Considerations

Evaluating Data Quality

Not all location datasets are created equal. Before purchasing, evaluate suppliers across three core dimensions:

1. Accuracy

How close are the reported coordinates to the device’s true position? GPS is typically accurate to within 5–10 metres outdoors; bidstream or cell-tower data can be off by 100–300 metres. Always ask vendors what signal sources they use, and request accuracy documentation or independent audits.

2. Freshness

How recently was the data collected, and how frequently is it updated? Some providers refresh daily; others update monthly. For time-sensitive use cases, campaign measurement, operations optimisation, competitive monitoring, you need the most current data available.

3. Coverage

What share of the population or geographic area is represented in the dataset? Some datasets cover as little as 10% of the US population in a given week. Ensure the sample is both large enough to be statistically meaningful and representative of your target audience demographics and geography.

Pro tip: Always request a sample dataset before committing to a purchase. Cross-reference visit counts or movement patterns against your own known sales or in-store data to verify quality.

Navigating Privacy Regulations

Location data is classified as sensitive personal information under several major regulatory frameworks. Compliance is non-negotiable.

Buyer’s Privacy Compliance Checklist

1. Choose vendors with documented, transparent privacy practices and consent mechanisms.
2. Confirm that data subjects have given informed, opt-in consent for location collection.
3. Purchase only the data granularity you actually need; aggregated data if individual paths are unnecessary.
4. Ensure the data is stored securely and access is role-limited within your organisation.
5. Include data-use restrictions and liability clauses in your vendor contracts.
6. Establish a data-deletion protocol for when records are no longer needed.

Common Limitations of Off-the-Shelf Location Data

Even high-quality purchased datasets have inherent limitations that buyers should anticipate:

• Generic datasets lack industry-specific context, a foot-traffic spike might reflect a nearby event rather than genuine customer interest.
• Fixed data schemas can make it difficult to answer unique or emerging business questions.
• Panel under-sampling may skew results for specific demographic groups or rural geographies.
• Monthly or quarterly update cycles leave gaps for time-sensitive decision-making.

Supplementing purchased data with real-time contextual signals; event calendars, competitor announcements, local news, helps bridge these gaps and explains the ‘why’ behind the numbers.

Conclusion

Buying location data is not a single decision; it is a series of decisions. The right dataset for a retail site selection project looks very different from the right dataset for a geo-targeted ad campaign or an insurance underwriting model. The 9 types covered in this guide each answer a different question about the physical world, and the best buyers approach the market with that specificity in mind.

Start by defining your use case precisely. Are you trying to understand where your customers come from? Measure competitor foot traffic? Validate a new store location? Score properties for climate risk? The answer to that question will point you directly to the data type and the provider that fits.

From there, apply the same discipline you would to any significant data purchase: request a free sample, validate it against something you already know, evaluate the provider’s privacy and consent practices, and negotiate delivery terms that match your technical infrastructure. The providers recommended in this guide; SafeGraph, Veraset, Unacast, Ambee, StreetLight Data, Planet Labs, Albedo, Mapbox, CARTO, and GeoPostcodes, all offer samples or free tiers specifically to help you do this.

Location intelligence has become a foundational business capability, not a specialised research tool. The organisations that act on it effectively, combining the right data types, applying them to specific decisions, and updating their models as the data refreshes, consistently outperform those that rely on intuition alone. The data is available, the providers are accessible, and the business case is clear.