Geospatial Data Sources: Where to Find and Buy the Data You Need

Once you understand what kind of geospatial data your organization needs, the next challenge is knowing where to find and buy it. Geospatial data isn’t a single product, it’s an ecosystem of nine distinct categories, each describing a different layer of the physical world: points of interest, property, mobility, demographics, address, boundary, environmental, streets, and imagery. Most organizations need several of these in combination to build a complete picture.

Collecting this data yourself is time-consuming and expensive, especially if data collection isn’t your core business. And settling for the wrong source can backfire just as easily: incomplete, outdated, or mismatched data can do more harm than good to whatever decision you’re building on top of it.

What is Geospatial Data?

Geospatial data refers to any information that identifies the location, shape, or characteristics of something on Earth, tied to a specific place rather than floating free of context. It connects information to a physical location, providing the “where” behind a business, a property, a population, or a natural feature.

This data can be as simple as a single coordinate pinpointing a store’s entrance, or as complex as a satellite image showing land cover change across an entire region. The term is broader than “location data,” which typically refers to the real-time or historical position of a specific device or person, think of it as the dot on a map. Geospatial data includes that, but also extends to everything around the dot: property boundaries, demographic patterns, road networks, weather conditions, and more.

The Two Basic Forms

Geospatial data exists in two fundamental technical forms:

Vector data uses points, lines, and polygons to represent discrete features, a store’s location (point), a road (line), or a property boundary (polygon). This is the dominant form for most business applications, POI data, property data, and boundary data are all vector-based.

Raster data uses a grid of cells to represent continuous spatial features, like satellite imagery or weather patterns, where every cell in the grid carries a value (a pixel’s color, a temperature reading). Environmental and imagery data are typically raster-based.

Most organizations end up working with both forms together: a vector polygon defining a property, overlaid with raster imagery showing what’s actually on it, for example.

What Does It Mean to Buy Geospatial Data?

When a business says it wants to “buy geospatial data,” it’s purchasing structured information about a layer of the physical world, places, properties, people, or conditions, that it doesn’t have the resources or expertise to compile itself. This isn’t about building your own satellite, running your own GPS panel, or knocking on doors to verify addresses. It’s about accessing data that’s already been collected, cleaned, and structured by a provider who specializes in exactly that.

What you’re buying varies a lot depending on the category. For POI or property data, you’re typically getting structured records, place names, categories, building footprints, delivered as a flat file or via API. For demographic or environmental data, you’re often getting pre-aggregated statistics tied to a geography, income by Census Block Group, flood risk by parcel, rather than raw inputs. For mobility data, you’re getting anonymized, aggregated patterns derived from device signals, not anything that identifies a specific person.

Buyers generally fall into one of two camps: technical teams who want raw, customizable data they can integrate into their own pipelines, or business teams who want ready-to-use dashboards and pre-built insights. The format, update frequency, and geographic coverage you need will shape both the provider you choose and what you end up paying.

Below, we break down each of the nine categories: what it is, how it’s commonly used, what to evaluate before buying, and where to source it.

1. Points of Interest (POI) Data

Points of interest data is the foundational layer of geospatial intelligence. A POI is any physical place, beyond a private residence, that people might visit or reference: stores, restaurants, hospitals, schools, stadiums, parks, and thousands of other place types. A typical POI record includes a place’s name, category, street address, coordinates, phone number, and brand affiliation. Richer datasets add a unique identifier (like Placekey), parent-child relationships (a Starbucks inside a Target, for instance), and building footprint geometry. POI data is inherently perishable, businesses open and close constantly, so leading providers refresh monthly or more often using a mix of automated monitoring and human verification.

Common Uses

• Consumer mapping and navigation
• Retail site selection, gauging competitive density around a candidate location
• Real estate investment scoring by business mix and category health
• Healthcare facility planning against population and access gaps
• Insurance underwriting based on what businesses occupy or neighbor a property
  
  

What to Evaluate Before Buying

• Coverage depth: does it include independent businesses, or only major chains?
• Update frequency: monthly refreshes are the baseline
• Attribute richness: brand hierarchy, parent-child relationships, category codes
• Geographic consistency: does schema and quality hold up across borders?
  
  

Where to Source It: SafeGraph Places

SafeGraph Places covers 80M+ points of interest globally, updated monthly to reflect openings, closures, and moves. Each record includes brand hierarchy, category, a Placekey identifier, hours, and building footprint geometry. Free samples are available for evaluation before purchase.

2. Property Data

Property data captures the physical boundaries of buildings, most often as polygon geometry outlining a structure’s footprint. Where POI data tells you a place exists at a point, property data tells you its shape and size. The most valuable property datasets also capture spatial hierarchy, the relationship between a building and the individual units inside it. A shopping mall is one property but contains dozens of separate tenant spaces, each potentially its own POI, capturing that parent-child structure is essential for accurate visit attribution and retail analytics.

Common Uses

• Visit attribution, matching device activity against a building’s actual footprint
• Retail analytics measuring store square footage and traffic flow
• Insurance risk assessment based on building size and neighboring structures
• More accurate map rendering for large properties like hospitals or airports
  
  

What to Evaluate Before Buying

• Spatial hierarchy depth: does it capture sub-properties within larger buildings?
• Footprint accuracy: derived from aerial imagery, cadastral records, or both?
• POI alignment: are footprints pre-joined to POI records via a shared identifier?
• Update cadence: how often are new construction and demolitions reflected?
  
  

Where to Source It: SafeGraph Geometry

SafeGraph Geometry delivers building footprints and spatial hierarchy metadata for millions of POIs across the US, UK, and Canada, pre-linked to SafeGraph Places via Placekey. This eliminates manual polygon-to-POI matching for teams already working with Places data.

3. Mobility Data

Mobility data captures how, when, and where people move, derived from anonymized, aggregated GPS signals from opted-in mobile devices. Rather than raw coordinate streams, mobility data is typically pre-processed into metrics like visit counts, dwell time, and trade-area origins. Reputable mobility datasets aggregate to the POI or Census Block Group level rather than exposing individual device paths, which is what makes this category broadly usable for business purposes while remaining privacy-compliant.

Common Uses

• Retail site and ad placement decisions
• Competitive benchmarking of visit trends over time
• Insurance liability pricing calibrated to actual visitor volume
• Urban and transportation planning around commuter flows
• Tourism and event impact measurement
  
  

What to Evaluate Before Buying

• Visit count baseline and median dwell time, to separate real visitors from passersby
• Visitor home and work geography, which defines a location’s true trade area
• Cross-visit patterns, revealing competitive or complementary relationships
• Visitor frequency (first-time vs. repeat), a proxy for loyalty
  
  

Where to Source It: Veraset + Unacast

Veraset and Unacast are frequently used together for comprehensive mobility coverage. Veraset offers a Movement dataset (anonymized GPS pings) and a companion Visits dataset (movement snapped to POIs with dwell-time and visit metrics), with a large device panel concentrated in North America plus additional coverage across Asia-Pacific and African markets. Unacast turns raw location data into mobility insights accessible via API or bulk delivery, with global coverage spanning more than 200 countries, well suited to broader international queries where Veraset’s depth is more US-centric.

4. Demographic Data

Demographic data describes the people who live in a given area: population counts, age, income, household composition, education, and employment, typically sourced from government censuses and surveys. Demographic data is most powerful in combination with other categories. Paired with mobility data, it shows not just who lives somewhere but who visits, and whether visitors match a target customer profile. Paired with POI data, it helps assess whether nearby businesses serve residents or pull in visitors from elsewhere.

Common Uses

• Trade area profiling around a candidate site
• Store and product strategy tailored to local demographics
• Advertising targeting by geography
• Identifying underserved populations for public health or financial service planning
  
  

What to Evaluate Before Buying

• Geographic resolution: Census Block Group level offers far more precision than ZIP or county
• Attribute breadth: income, education, housing, household size, not just population counts
• Vintage: which year’s data, and does the provider model forward to current estimates?
• Integration: is the data pre-formatted to join with your POI or mobility data?
  
  

Where to Source It: SafeGraph Open Census + Spatial.ai + Esri

Three providers serve distinct needs here. SafeGraph’s Open Census offers pre-processes US Census Bureau American Community Survey data into thousands of attributes organized by Census Block Group, free of charge and pre-formatted to join with SafeGraph Places. Spatial.ai goes further, layering psychographic and lifestyle intelligence on top of demographic counts, useful when you need to understand not just who lives somewhere but how they think and shop. Esri offers a comprehensive enriched demographic product through its Business Analyst platform, combining census data with proprietary current-year estimates and multi-year forward projections across more than 15,000 variables and 130+ countries.

5. Address Data

Address data is the connective tissue of the geospatial ecosystem, every other category ultimately references it, whether directly (a POI’s street address) or indirectly (a boundary containing thousands of addresses). A comprehensive address record includes the full structured address, corresponding coordinates, and ideally a validation flag confirming it corresponds to a real, deliverable location. Standardization is the core technical challenge here: the same address can be written dozens of legitimate ways, and systems that don’t normalize this create duplicate records and broken joins.

Common Uses

• Geocoding and reverse geocoding for any location-enabled application
• Address validation for e-commerce and fraud prevention
• Residential mapping, since address data includes private dwellings unlike POI data
• Last-mile delivery routing accuracy

What to Evaluate Before Buying

• Standardization quality: does the provider normalize abbreviations and international formats?
• Validation: does each record carry a deliverability flag?
• International depth: coverage and schema consistency outside the US
  
  

Where to Source It: SafeGraph Address + Geopostcodes

SafeGraph Address provides standardized, geocoded address data for global regions where public records are often incomplete or fragmented, useful for organizations expanding into markets with less mature addressing infrastructure. Geopostcodes complements this with postal-code-level validation and standardization across a very large number of countries, built from a wide network of authoritative sources and supporting multiple languages, making the pairing well suited to global e-commerce, logistics, and CRM hygiene use cases.

6. Boundary Data

Boundary data is, in effect, a larger-scale version of property data: instead of outlining a single building, it outlines a geographic area containing many addresses, properties, and POIs. Boundaries come in several forms, political (countries, states, counties), administrative (postal codes, census tracts), service area (school districts, hospital catchments), and custom (trade areas, sales territories). The analytical value of boundary data lies in giving structure to everything else, without it, location data is just a cloud of coordinates with no organizational context.

Common Uses

• Political and regulatory mapping
• Catchment area analysis for schools or hospitals
• Retail territory management and performance comparison
• Understanding which tax or zoning jurisdictions a property falls within
  
  

What to Evaluate Before Buying

• Boundary type coverage: political, administrative, postal, and custom, or only some?
• Topology: are adjacent polygons clean, with no gaps or overlaps?
• International consistency: does quality hold up outside core markets?
• Update frequency: boundaries shift as municipalities merge or postal codes redraw
  
  

Where to Source It: Mapbox + CARTO

Mapbox and CARTO serve different strengths. Mapbox Boundaries offers a large curated set of global boundaries spanning administrative, legislative, locality, postal, and statistical layers, edge-matched at all zoom levels and widely used as the underlying boundary layer for BI platforms. CARTO’s Spatial Data Catalog includes a large library of boundary datasets within its broader spatial catalog, geared more toward analytical spatial intelligence and enterprise data workflows than rendered mapping.

7. Environmental Data

Environmental data covers natural geographic phenomena: weather, climate, terrain, seismic activity, flood zones, and air quality. It’s almost always raster-based, a continuous grid of values rather than discrete points or polygons. This category has moved well beyond its traditional scientific and conservation use base. As climate risk becomes a material business concern, a property’s flood exposure, wildfire history, or projected heat frequency over coming decades are now directly relevant to lending, insurance pricing, and real estate valuation.

Common Uses

• Insurance risk assessment by overlaying properties against flood, fire, and wind models
• Climate risk scoring for real estate due diligence
• Business continuity planning around supply chain nodes
• Agricultural monitoring of soil and crop conditions
  
  

What to Evaluate Before Buying

• Model source quality: peer-reviewed climate models, government agencies, or proprietary?
• Property-level resolution: individual properties, or only broad areas?
• Forecast horizon: historical baselines only, or future projections too?
• Peril coverage: which hazard types are included, and do they match your risk exposure?
  
  

Where to Source It: Ambee

Ambee delivers high-resolution real-time, historical, and forecast environmental intelligence, unifying satellite feeds, ground sensors, and climate models into a single harmonized dataset covering air quality, pollen, weather, and natural disaster risk. A free tier is available for evaluation, with paid tiers scaling by usage for enterprise needs.

8. Streets Data

Streets data describes road transportation networks: each segment as a geometric line with attributes like road class, speed limit, and turn restrictions. More advanced streets datasets add a dynamic layer, real-time and historical traffic speeds, congestion levels, and incident reports, turning static road geometry into a live operational tool. Streets data underpins both geocoding (translating addresses into coordinates requires knowing where streets are) and routing algorithms that calculate optimal paths accounting for distance, speed, and current conditions.

Common Uses

• Consumer navigation and turn-by-turn routing
• Last-mile logistics optimization across delivery fleets
• Retail drive-time catchment analysis
• Incident-based rerouting around closures or construction
  
  

What to Evaluate Before Buying

• Update frequency: road networks change constantly; real-time or daily updates matter
• Historical depth: how much traffic history is available for demand modeling?
• Global consistency: does quality hold outside the US and EU?
• Platform compatibility: does it integrate with your existing GIS or routing stack?
  
  

Where to Source It: StreetLight Data + OpenStreetMap

StreetLight Data is a widely adopted transportation analytics platform, processing data into on-demand traffic metrics across vehicles, bicycles, pedestrians, and transit for nearly every road and Census Block Group, validated against a large network of permanent traffic counters. OpenStreetMap complements this as the world’s largest free, open-source road network, maintained by a global volunteer community and used as the base road geometry layer by several major mapping platforms, the standard starting point for teams that need base road geometry at no cost.

9. Imagery Data

Imagery data provides photographic representations of the physical world, from continental satellite views to sub-meter aerial photography. It’s always raster-based. Forms include optical satellite imagery, multispectral imagery (capturing wavelengths beyond visible light), radar imagery (which can see through clouds), and street-level photography. Resolution varies enormously depending on use case: monitoring global deforestation needs far less resolution than assessing storm damage to a single building.

Common Uses

• Basemap rendering behind other geospatial layers
• Environmental change monitoring over time
• Construction and development tracking
• Disaster damage assessment comparing pre- and post-event imagery
• Agricultural crop health monitoring
  
  

What to Evaluate Before Buying

• Spatial resolution: sub-1-meter for building-level analysis; 10-30m for land cover
• Temporal resolution: how frequently is the same area re-imaged?
• Cloud cover handling: cloud-free composites, radar alternatives, or gap-filling?
• Archive depth: how far back does historical imagery go?
  
  

Where to Source It: Planet Labs + Albedo

Planet Labs and Albedo represent two complementary tiers of the satellite imagery market. Planet operates a large constellation of satellites capturing near-daily global imagery at a few meters of resolution, with higher-resolution tasking available on demand, widely used across agriculture, forestry, and energy monitoring. Albedo operates at a much lower orbital altitude than traditional satellites, enabling some of the highest resolution commercially available, including thermal imaging, suited to detailed site-level analysis where standard satellite resolution falls short.

BUYER’S DECISION MATRIX

Data Quality Considerations

Before purchasing from any provider, evaluate across three dimensions.

Accuracy

How close is the data to ground truth? Ask providers what methods or sources they use, and request any available accuracy benchmarks.

Freshness

How recently was the data collected, and how often is it refreshed? For time-sensitive use cases, this matters as much as accuracy itself.

Coverage

What share of the relevant population, geography, or asset base does the dataset represent? A sample that’s too narrow won’t generalize to your actual use case.

Always request a sample before committing, and cross-check it against something you already know to be true.

Closing Thoughts

These are some of the most reliable sources for the geospatial data your organization needs, whether you’re building out a single dataset or assembling a full location intelligence stack. Geospatial data buying isn’t a single decision, it’s a series of decisions, and the right source for a retail site selection project looks very different from the right source for a climate risk model. Define your use case precisely, request a sample, and validate it against something you already trust before committing.