Why does almost every team shopping for point of interest data end up ranking vendors on one number, the total POI count? Because that is the figure every vendor publishes, the figure a procurement spreadsheet can sort on, and the figure a reviewer who has never once run a location pipeline can defend out loud in a meeting.
One vendor states 300 million places, the next states 350 million across 250 countries, a third states 120 million, and the column sorts itself in a single click. Here is the catch. The count is a national sum, and your product does not live in the nation.
It lives in twelve specific metros, or three retail categories, or along one last-mile delivery corridor, and the count tells you nothing about whether those exact places are present, current, or correctly placed on a map.
(One clarification before anything else: POI data here means points of interest, the location sense, not Apache POI, the Java library for reading Excel files. Different topic entirely.)
Call this the bought-data trap, and the honest move is to name the incentive that builds it rather than read bad faith into anyone. An aggregator that wants to win the comparison optimizes for the biggest defensible national count, because that is the metric the market actually rewards.
The rational consequence follows directly: the long tail in your markets, the emerging-market merchants and the low-check-in categories that are expensive to keep current and cheap to neglect, gets quietly under-funded. No villain is required for this to happen.
A feed can be the largest in the category and still be the emptiest one in the places your product actually serves, and the headline number is structurally incapable of telling you which.
The gap compounds, because location records decay continuously rather than in some tidy annual batch you can plan around. A business closes, a business moves, a business changes its hours, and a record that was accurate last quarter is now routing a customer to a shuttered storefront.
The money riding on getting this right is not small. The location intelligence market sat at USD 25.06 billion in 2025 and is forecast to reach USD 52.67 billion by 2031, a 13.19% CAGR, so the capital being poured into turning location and poi data into decisions is roughly doubling this decade. And the buyers writing those checks are largely deciding on a number that was never about their markets in the first place.
So this guide reframes the question. It names the providers worth knowing, scores each on the same rubric, and hands you a four-axis way to test any of them against your own markets rather than the vendor’s brochure.
Read it as a set of category fits to probe, not a ranking to memorize, because the right provider depends entirely on which data layer and which markets you need. POI data is one branch of the broader alternative data family, and the buyers who need it most are frequently the same teams already sourcing real estate data providers for adjacent use cases.
What we are listing, and how we judged it
Before you compare a single vendor, it pays to see that “POI data” is shorthand for five distinct layers, and a provider can be excellent at one and weak at the next. Score them independently rather than trusting one composite impression of a feed.
- Core place records: name, address, category (NAICS or Google Places taxonomy), coordinates, and the stable IDs that let you join to your own systems.
- Hours and attributes: open hours, contact details, chain and brand affiliation, amenities, the fields that make a record usable rather than merely present.
- Geometry and geocoding: single point versus building footprint, point-in-polygon accuracy, and the gap between a record that resolved and a record that landed in the right place.
- Footfall and visitation: visit counts, dwell, panel-derived movement. POI-adjacent rather than POI in the strict sense, and privacy-regulated in ways a static place record is not.
- Freshness and change signals: when a place opened, closed, moved, or changed hours, plus the refresh cadence and verification method behind the feed.
Each provider below is scored on the same four axes, tested against your own data rather than the vendor’s brochure, with pricing and privacy as gating criteria. The full method, and how to run the probe yourself, comes after the providers. Here is the rubric in brief.
| Axis | What it measures | Red flag |
|---|---|---|
| Per-market long-tail coverage | Whether your real target ZIPs, cities, and categories are present | Vendor can answer coverage only with a national number |
| Freshness and decay | How fast closures, moves, and hours changes reach the feed | Annual or quarterly refresh with no verification step |
| Attribute completeness | Fill rate on the specific fields your product reads | High record count paired with low fill on your critical fields |
| Dedup and geocoding accuracy | Did it resolve, did it land in the right spot, are duplicates collapsed | Match rate cited as proof of placement accuracy |
| Pricing model (gate) | Total cost of ownership across delivery models | Quoted price hides per-market or refresh costs |
| Privacy (gate) | Compliance posture on any footfall or mobility data | Mobility data offered with no documented consent basis |
Every coverage and freshness number below is a vendor self-claim, attributed as stated, not verified fact (as of June 2026). Where pricing is not public, the cell is marked rather than guessed. Public review sentiment is drawn from platforms such as G2, Capterra, Datarade, and the r/gis community, attributed to the platform rather than to the vendor.
Point of interest data providers at a glance
- Forage AI: best for teams whose target markets are stale or thin in off-the-shelf feeds, through managed extraction to your taxonomy and cadence.
- dataplor: best for emerging-market and global long-tail coverage.
- Foursquare (Places): best for rich attributes on high-check-in venues such as restaurants and bars.
- Google Places API: best for breadth and consumer-grade attributes when you need an API rather than a bulk dataset.
- Data Axle: best for US and Canada small-business firmographic depth tied to place records.
- SafeGraph (via Dewey): best for building footprints and spatial hierarchy.
- Precisely (World POI Premium): best for enterprise geocoding and stable join identifiers.
- HERE Technologies: best for a global map platform with private brand-POI ingestion.
- TomTom: best for navigation-grade POI with routing entry points.
- Esri (ArcGIS Places): best for GIS-native place data inside an established geospatial ecosystem.
- Mapbox: best for developer-facing places powering maps and search.
- Geoapify: best for an affordable Places API for developers and prototypes.
- Placer.ai: best for US foot-traffic and visitation analytics.
- Unacast (with Gravy Analytics): best for enterprise location intelligence and mobility.
- Veraset: best for raw movement and mobility feeds for teams building their own analytics.
- PredictHQ: best for event POI, venues plus attended-event demand signals.
- Echo Analytics: best for Europe-strong places and POI.
- Quadrant: best for POI plus mobility with APAC strength.
- xtract.io: best for managed and custom POI feeds on a fixed 30, 60, or 90-day refresh.
- ATTOM: best for property-anchored POI for US real-estate adjacency.
- Overture Maps Foundation and OpenStreetMap: best as free open baselines, with the dedup and freshness burden on you.
More total POIs is not better data. A national or global count hides per-market and per-category gaps, so the biggest feed can still be the emptiest one in the places your product serves.
Quick Summary
Q: What are the kinds of point of interest data, and how should you judge a provider?
A: POI data is shorthand for five distinct layers, and a provider can be excellent at one and weak at the next: core place records, hours and attributes, geometry and geocoding, footfall and visitation, and freshness and change signals. Score them independently rather than trusting a single composite impression of a feed. Then judge each provider on four axes tested against your own data, per-market coverage, freshness and decay, attribute completeness, and dedup and geocoding accuracy, with pricing and privacy as gating criteria.
Expert Insights
The reason to score the geometry-and-geocoding layer on its own is that resolution and accuracy are two different measurements wearing one label. Foundational research on geocoding solutions found match rates above 95% while only 81 to 84% of addresses resolved to the exact street segment (Boise State, Cartography and Geographic Information Science, 2015), which is precisely why a single composite impression of a feed hides the one layer where it is actually weak.
The best point of interest and hyperlocal data providers in 2026, compared
There is no single best POI provider, and any list that pretends otherwise is selling the count all over again. The right provider depends on which of the five layers you need and which markets you serve, so the tables below score each one on the four axes.
Read them that way. A footfall specialist topping the visitation layer is the wrong buy for a pure place-record need, and the reverse holds just as firmly.
1. Forage AI
| Field | Detail |
|---|---|
| Best for | Teams whose target markets are stale or thin in off-the-shelf feeds |
| Key use cases | Custom POI and local-business datasets, site selection in under-covered markets, niche-category extraction |
| Pricing model | Managed engagement, scoped per project |
| Privacy/compliance | GDPR, CCPA, SOC 2; no reselling of client data |
| Coverage | Built to your markets and categories, not a fixed feed |
| Freshness/refresh | Set to your cadence and re-verification needs |
| Standout strength | Extraction to your schema and cadence, with per-source dedup and geocoding, data you own |
| Watch-out | Commissioned build, not an instant off-the-shelf download |
Forage AI sits first here for a structural reason, not a scale one. Every other provider on this list sells you a feed and asks you to live inside its coverage, its taxonomy, and its refresh cadence.
Forage extracts POI and local-business data to your taxonomy and your refresh cadence, with per-source dedup and geocoding applied as part of the build, and the data is yours with no resale. That is the right answer to the exact failure the four-axis probe surfaces: when an aggregated feed is thin or stale in the specific markets and categories your product depends on, a bigger feed does not fix it, and extraction built to your spec does.
The fit is narrow, and it is worth stating honestly. If a commercial feed already passes your coverage and freshness probe in your markets, buy the feed. Forage earns the top slot when it does not, which for teams operating in emerging markets, low-check-in categories, or fast-changing local segments happens more often than the headline counts would suggest.
The model is fully managed, so the pipeline maintenance, the re-verification, and the geocoding QA sit with Forage rather than with your engineering team. This is the same managed-extraction approach detailed in the guide to modern data extraction services.
Providers strong on the long tail and global coverage
2. dataplor
| Field | Detail |
|---|---|
| Best for | Emerging-market and global long-tail coverage |
| Key use cases | International expansion, merchant data in hard markets |
| Pricing model | Commercial license |
| Privacy/compliance | States GDPR-compliant mobility |
| Coverage | States 350M+ places across 250 countries |
| Freshness/refresh | States monthly |
| Standout strength | Human “Data Explorers” plus automated dedup for hard markets |
| Watch-out | Verify per-market depth in your specific markets |
dataplor leans into the exact gap the bought-data trap describes, positioning on coverage in markets the aggregators tend to neglect, and it pairs automated dedup with on-the-ground human verification. Public review sentiment on Datarade skews positive on emerging-market depth. A strong global claim still has to clear your own per-market probe before you trust it.
The funding signal is real. A $20.5 million Series B in June 2025 was raised explicitly to expand coverage in the world’s harder markets, which at least confirms that capital is flowing toward the accurate-and-compliant end of the category. If your roadmap runs through Latin America, Southeast Asia, or other markets where the global aggregators thin out, this is the first name to probe.
3. Foursquare (Places)
| Field | Detail |
|---|---|
| Best for | Rich attributes on high-check-in venues |
| Key use cases | Consumer apps, restaurant and retail venue data |
| Pricing model | API and licensing tiers |
| Privacy/compliance | Consumer-app-derived signals |
| Coverage | States ~100–120M POIs, 200+ countries |
| Freshness/refresh | States ~2.4M updates/month |
| Standout strength | Deep attributes where check-in density is high |
| Watch-out | Thin on low-check-in categories such as doctors and grocery; address-search pickiness |
Foursquare’s strength and its weakness come from the same source, which is the cleanest way to understand the feed. Its data is enriched by consumer check-ins, so it is rich on restaurants, bars, and high-traffic retail, and correspondingly thin on the categories nobody checks into. One provider’s published guide notes this category skew directly.
If your product lives in dining and nightlife, this is a strong feed. If it lives in healthcare or grocery, the probe will show you why it is not. Foursquare’s open-source lineage through the Factual heritage also feeds parts of the broader landscape, so you may encounter its records indirectly inside other platforms without realizing it.
4. Google Places API
| Field | Detail |
|---|---|
| Best for | Breadth and consumer-grade attributes via an API |
| Key use cases | App place search, autocomplete, reviews and photos, hours lookup |
| Pricing model | Metered API, with subscription tiers introduced in late 2025 |
| Privacy/compliance | Platform terms; redistribution and caching restrictions apply |
| Coverage | Broad global place model, one of the most comprehensive available |
| Freshness/refresh | Continuously maintained by Google |
| Standout strength | Rich attributes (reviews, photos, hours, amenities) few feeds replicate |
| Watch-out | Per-call metering and licensing terms make it costly and hard to use as a bulk dataset |
Google Places API sits on one of the most comprehensive place models in the market, and recent additions to the new Places API have pushed its attribute depth further still, with fields for secondary opening hours, payment and parking options, EV charging, outdoor seating, and accessibility. For a consumer app that needs place search, autocomplete, reviews, and photos, the practitioner consensus on developer forums is that no alternative fully replicates the Google attribute set.
The watch-out is structural, not a question of quality. It is a metered API with redistribution and caching restrictions, so it is a poor fit the moment you need a bulk, ownable dataset for offline analytics or your own data products.
Subscription tiers introduced in late 2025 changed the cost picture, yet high-volume or storage-heavy use cases still run into the licensing terms before they ever run into a coverage limit.
5. Data Axle
| Field | Detail |
|---|---|
| Best for | US and Canada small-business firmographic depth tied to places |
| Key use cases | B2B prospecting, local-business listings, firmographic enrichment |
| Pricing model | Commercial license and list products |
| Privacy/compliance | Business and contact data governance |
| Coverage | States ~15M US and Canada businesses, 400+ data points each |
| Freshness/refresh | Continuous updates; states large periodic record refreshes |
| Standout strength | Deep coverage of small, home-based, and hard-to-spot businesses |
| Watch-out | North America focus; G2 and BBB reviews flag occasional stale small-business records |
Data Axle approaches POI from the firmographic side rather than the mapping side, and that is exactly its edge. It states more than 400 data points on roughly 15 million US and Canada businesses, with coverage that reaches freelancers, contractors, home-based businesses, and other categories the map-first feeds routinely miss. A Forrester evaluation cited its account coverage in North America, and its records also feed into Esri’s ArcGIS Places ecosystem.
The watch-outs surface in public reviews. G2 and Better Business Bureau feedback praises the breadth and flags occasional outdated records, especially among fast-changing small businesses, which is the freshness axis asserting itself in plain sight. It is North America focused, so it is the wrong starting point for global coverage.
Providers strong on geometry, footprints, and enterprise joins
6. SafeGraph (via Dewey)
| Field | Detail |
|---|---|
| Best for | Geometry and spatial hierarchy |
| Key use cases | Catchment analysis, point-in-polygon work |
| Pricing model | Marketplace via Dewey |
| Privacy/compliance | Place data, no consumer-app real-world signals |
| Coverage | States ~49M POIs, 400 categories |
| Freshness/refresh | States monthly |
| Standout strength | Building footprints and spatial hierarchy |
| Watch-out | Now distributed through Dewey; no consumer-app signals |
SafeGraph wins on the geometry layer rather than on the headline count. Its building footprints and spatial hierarchy make it a natural fit for catchment and visitation joins, and it is now distributed through the Dewey marketplace rather than sold directly, which changes the procurement path more than it changes the data.
It also publishes some of the more useful thinking in the category on open and close metadata, the lifecycle attributes most feeds lack entirely. If your work depends on knowing a footprint is right rather than knowing the count is large, SafeGraph rewards the probe. It carries no consumer-app real-world movement signals, so pair it with a visitation specialist when you need footfall.
7. Precisely (World POI Premium)
| Field | Detail |
|---|---|
| Best for | Enterprise geocoding and join identifiers |
| Key use cases | Insurance, telco, enterprise spatial systems |
| Pricing model | Enterprise license |
| Privacy/compliance | Enterprise data governance |
| Coverage | States 200+ countries, up to ~300M POIs |
| Freshness/refresh | States monthly |
| Standout strength | Building footprints plus stable join IDs |
| Watch-out | Enterprise pricing and procurement weight |
Precisely brings premium geocoding, building footprints, and the stable identifiers that enterprise systems need to join place data to everything else. For insurance underwriting, telco network planning, and any environment where a place record has to reconcile cleanly against parcels, addresses, and risk layers, this is a natural fit.
Neither Precisely nor SafeGraph is the cheapest path, and both reward the buyer who needs the geometry to be right rather than the count to be large. The watch-out specific to Precisely is procurement weight: enterprise licensing, and the sales motion that comes attached to it, can run slow when your need is small or exploratory.
8. HERE Technologies
| Field | Detail |
|---|---|
| Best for | Global map platform with private POI ingestion |
| Key use cases | Automotive, navigation, logistics platforms |
| Pricing model | Platform licensing |
| Privacy/compliance | Enterprise platform governance |
| Coverage | States ~120M POIs / 400M locations |
| Freshness/refresh | Platform-managed |
| Standout strength | Mature map platform, ingest your own brand POIs |
| Watch-out | Platform commitment, not a standalone feed |
HERE Technologies is a mature global map platform rather than a standalone feed, which makes it a fit for automotive, navigation, and logistics teams that want to ingest their own brand POIs into a managed platform rather than license a dataset outright.
The trade-off is commitment. You are adopting a platform and its tooling, not buying a file you can carry elsewhere, so HERE makes the most sense when location is core to the product and you want the map, routing, and POI layers managed together. If you need a portable dataset you own, look elsewhere on this list.
9. TomTom
| Field | Detail |
|---|---|
| Best for | Navigation-grade POI with routing entry points |
| Key use cases | Automotive, routing, last-mile, destination search |
| Pricing model | Platform and API licensing |
| Privacy/compliance | Platform governance |
| Coverage | States ~131M POIs across 188 countries, 500+ categories |
| Freshness/refresh | Platform-managed with stated POI validation |
| Standout strength | Entry-point data for routing; ties to the MultiNet core map |
| Watch-out | A map-platform commitment more than a portable dataset |
TomTom approaches POI from the navigation side, and that lineage shows in the data. It states more than 131 million POIs across 188 countries and 500-plus categories, linked to its MultiNet core map, and its standout field is the entry point: where a place can actually be accessed for routing, which matters far more for last-mile and automotive use cases than a centroid pin ever could.
TomTom states it runs POI validation before records enter the database, and it has folded in millions of additional POIs through partners, including Foursquare records. As with HERE, the watch-out is that you are leaning on a map platform rather than buying a standalone, ownable dataset, so it fits navigation and routing products more cleanly than it fits offline analytics.
10. Esri (ArcGIS Places)
| Field | Detail |
|---|---|
| Best for | GIS-native place data inside an established ecosystem |
| Key use cases | Spatial analysis, demographics joins, ArcGIS workflows |
| Pricing model | ArcGIS platform licensing and credits |
| Privacy/compliance | Enterprise platform governance |
| Coverage | Global places data, partner-sourced including Data Axle |
| Freshness/refresh | Platform-managed |
| Standout strength | Native to the most widely used GIS stack |
| Watch-out | Most valuable to teams already on ArcGIS |
Esri (ArcGIS Places) offers GIS-native place data inside the most established geospatial stack, with the places layer sourced partly through partners such as Data Axle. For a team already running ArcGIS, having POI as a first-class layer next to demographics, drive-time, and spatial analysis tools removes a great deal of integration friction.
The value runs highest when you already live inside the Esri ecosystem. If you are not an ArcGIS shop, the platform commitment may outweigh the benefit of the place data on its own, and a portable feed or API will serve you better.
11. Mapbox
| Field | Detail |
|---|---|
| Best for | Developer-facing places powering maps and search |
| Key use cases | Custom maps, search and autocomplete, navigation UIs |
| Pricing model | Metered API and platform tiers |
| Privacy/compliance | Platform terms; redistribution limits apply |
| Coverage | Global places via search and POI services |
| Freshness/refresh | Platform-managed |
| Standout strength | Strong developer experience and map rendering |
| Watch-out | An access layer for maps, not an ownable bulk dataset |
Mapbox functions largely as an access and developer-API layer for places data feeding maps, search, and navigation interfaces. Developer sentiment praises the rendering and the developer experience, and for a product whose POI need is “show the right places on our map,” it is a clean fit.
Like the other API-first entries, it is the wrong tool the moment you need a portable, ownable dataset for offline analytics, because the value is delivered through the platform and its terms rather than as a file you keep.
12. Geoapify
| Field | Detail |
|---|---|
| Best for | Affordable Places API for developers and prototypes |
| Key use cases | Place search, geocoding, side projects, cost-sensitive builds |
| Pricing model | Freemium plus low-cost API tiers |
| Privacy/compliance | Built largely on open data sources |
| Coverage | Global, drawn substantially from OpenStreetMap and open sources |
| Freshness/refresh | Tied to underlying open-data cadence |
| Standout strength | Price and accessibility |
| Watch-out | Inherits the coverage and freshness limits of its open-data base |
Geoapify is an affordable Places and geocoding API built substantially on open data, which makes it a popular starting point for developers, prototypes, and cost-sensitive builds. The price and the developer accessibility are the draw.
The honest watch-out is that it inherits the coverage and freshness characteristics of the open sources beneath it, so the same dedup and currency questions that apply to OpenStreetMap apply here in full. For production work in demanding markets, run the probe before you commit.
Providers strong on footfall, visitation, and events
13. Placer.ai
| Field | Detail |
|---|---|
| Best for | Foot-traffic and visitation analytics |
| Key use cases | Retail benchmarking, site performance |
| Pricing model | Freemium plus paid tiers |
| Privacy/compliance | Panel-derived; verify consent basis |
| Coverage | US-focused footfall |
| Freshness/refresh | Frequent visitation updates |
| Standout strength | Accessible visitation analytics with a free tier |
| Watch-out | US-only footfall, panel bias, low reliability below ~50 unique devices |
Placer.ai is a visitation tool, not a place-record feed, which is the distinction the layers section drew earlier. It is widely used for retail benchmarking and is approachable thanks to its free tier.
The watch-outs are well documented in public reviews: the footfall is US-only, panel bias is real, and reliability drops off below roughly fifty unique devices at a location, which matters for any analysis of smaller or rural sites.
Treat its numbers as directional wherever device counts run low. As a visitation layer sitting on top of a solid place-record feed it is strong; as a substitute for one it is the wrong tool, and the privacy gating criterion applies to it in full.
14. Unacast (with Gravy Analytics)
| Field | Detail |
|---|---|
| Best for | Enterprise location intelligence and mobility |
| Key use cases | Movement analytics, audience and visitation modeling |
| Pricing model | Enterprise license |
| Privacy/compliance | Mobility data; consent basis and SPI handling are gating |
| Coverage | Enterprise mobility and location datasets |
| Freshness/refresh | Vendor-managed |
| Standout strength | Combined location intelligence plus mobility after the Gravy merger |
| Watch-out | Mobility data carries full privacy and compliance scrutiny |
Unacast, following its merger with Gravy Analytics, offers enterprise location intelligence combined with mobility data. For teams modeling movement and audiences at scale, that combined footprint is the draw.
Because the offering sits squarely in the mobility layer, the privacy gating criterion applies with full force here. Confirm the documented consent basis and Sensitive Personal Information handling before any footfall or movement dataset enters your pipeline.
15. Veraset
| Field | Detail |
|---|---|
| Best for | Raw movement and mobility feeds for builders |
| Key use cases | Custom mobility analytics, research, modeling pipelines |
| Pricing model | Data feed license |
| Privacy/compliance | Mobility data; consent basis and SPI handling are gating |
| Coverage | Movement and mobility data feeds |
| Freshness/refresh | Feed-based, developer-oriented |
| Standout strength | Raw feeds for teams building their own analytics layer |
| Watch-out | Raw data shifts the modeling and compliance work onto you |
Veraset sells raw movement and mobility feeds aimed at developers and data-science teams that want to build their own analytics on top rather than consume a finished dashboard. That rawness is the point for a team with the engineering capacity to model it.
It also shifts both the modeling work and the compliance responsibility onto you. As with Unacast, the privacy gate applies fully, and the absence of a packaged analytics layer means the burden of doing right by Sensitive Personal Information rules sits in your pipeline, not in the vendor’s product.
16. PredictHQ
| Field | Detail |
|---|---|
| Best for | Event POI: venues plus attended-event demand signals |
| Key use cases | Demand forecasting, staffing, inventory, dynamic pricing |
| Pricing model | API subscription |
| Privacy/compliance | Aggregated event data, not personal location data |
| Coverage | States global event coverage across 18+ categories |
| Freshness/refresh | States the API is updated every minute |
| Standout strength | Venue entities tied to ranked, predicted-attendance events |
| Watch-out | An events layer, not a general place-record feed |
PredictHQ covers a different slice of the map entirely: event POI. It models venues as entities and attaches ranked events to them, with predicted attendance and predicted spend, across categories from conferences and concerts to sports, severe weather, and public holidays.
For demand forecasting, staffing, inventory, and dynamic pricing, the venue-plus-event pairing answers a question a static place record simply cannot.
It states the API is updated every minute and aggregates hundreds of sources into verified, ranked events. The watch-out is scope: this is an events demand layer, not a general place-record feed, so it complements a core POI source rather than replacing one.
Providers strong in specific regions or niches
17. Echo Analytics
| Field | Detail |
|---|---|
| Best for | Europe-strong places and POI |
| Key use cases | European location products, places enrichment |
| Pricing model | Commercial license |
| Privacy/compliance | States privacy-first, GDPR-aware mobility |
| Coverage | States ~80M POIs across 210 countries, Europe-strong |
| Freshness/refresh | Vendor-managed |
| Standout strength | European depth; published open-data quality analysis |
| Watch-out | Strongest in Europe; probe coverage elsewhere |
Echo Analytics is Europe-strong on places and POI, stating roughly 80 million POIs across 210 countries. It is also the author of a frequently-referenced 2022 analysis of open-data geocoding quality, which this article cites as an analysis rather than as a feed.
For a product centered on European markets it is a strong first probe. Outside Europe, run the same per-market coverage test you would run on any provider before assuming the global count carries through to your specific markets.
18. Quadrant
| Field | Detail |
|---|---|
| Best for | POI plus mobility with APAC strength |
| Key use cases | Asia-Pacific location products, mobility analytics |
| Pricing model | Commercial license |
| Privacy/compliance | Mobility data; consent basis is gating |
| Coverage | POI and mobility, APAC-strong |
| Freshness/refresh | Vendor-managed |
| Standout strength | Regional depth in Asia-Pacific; published POI-quality research |
| Watch-out | Mobility component carries privacy scrutiny |
Quadrant brings POI plus mobility with particular Asia-Pacific strength, and it has published some of the more pointed research in the category on the cost of bad POI data, including a widely-cited 2022 analysis of address-to-coordinate displacement.
For an APAC-centered product it is a natural probe. The mobility component means the privacy gate applies, so confirm consent basis and Sensitive Personal Information handling for any movement dataset before it reaches production.
19. xtract.io
| Field | Detail |
|---|---|
| Best for | Managed and custom POI feeds on a fixed refresh |
| Key use cases | Custom POI builds, niche-category feeds |
| Pricing model | Managed and custom feed pricing |
| Privacy/compliance | Vendor governance |
| Coverage | Custom-built; smaller standing footprint |
| Freshness/refresh | States 30, 60, or 90-day refresh |
| Standout strength | Managed and custom feed delivery |
| Watch-out | Fixed refresh cadence; smaller footprint than the majors |
xtract.io offers managed and custom POI feeds on a stated 30, 60, or 90-day refresh, with a smaller standing footprint than the major aggregators. For a buyer who wants a managed feed built toward a specific need rather than a giant general dataset, it occupies a useful middle ground.
The watch-out is the fixed refresh cadence: 30, 60, or 90 days is a defined window, so weigh it against how fast your markets actually decay. Where you need a cadence you set yourself and full ownership of the result, managed extraction to your own spec is the closer fit.
20. ATTOM
| Field | Detail |
|---|---|
| Best for | Property-anchored POI for US real-estate adjacency |
| Key use cases | Real estate, property analytics, neighborhood context |
| Pricing model | Commercial license |
| Privacy/compliance | Property and place data governance |
| Coverage | US property-anchored POI |
| Freshness/refresh | Vendor-managed |
| Standout strength | POI tied to a deep US property dataset |
| Watch-out | US-focused; strongest where real-estate context matters |
ATTOM anchors its POI data to property records, which makes it a natural adjacency for US real-estate use cases where the value is in tying a place to the property and neighborhood around it. The same buyers frequently source real estate data providers for the parcel and transaction side.
It is US-focused, so read it as a category fit rather than a general recommendation: strong when real-estate context is the point, far less relevant when it is not.
The open baselines
21. Overture Maps Foundation and OpenStreetMap
| Field | Detail |
|---|---|
| Best for | Free open baselines and prototyping |
| Key use cases | Baselines, enrichment, builds where you control dedup |
| Pricing model | Open and free |
| Privacy/compliance | Open license terms |
| Coverage | Overture Places states 64M+ POIs on an open GeoParquet schema |
| Freshness/refresh | Community and foundation cadence |
| Standout strength | Free, open, standardized schema; broad adoption |
| Watch-out | Geocoding precision and coverage vary; dedup and freshness fall on you |
Overture Maps Foundation and OpenStreetMap are the free starting points, and they are genuinely useful as baselines. The Overture Places theme holds more than 64 million POIs on an open GeoParquet schema, adopted into products reaching over 1 billion consumers (Overture Maps Foundation, May 2025).
The honest watch-out is that geocoding precision and coverage vary, and the dedup and freshness burden falls entirely on you. Independent analyses have documented Overture POIs being placed into parking lots and adjacent properties, and one geocoding service publicly disabled Overture POIs in 2023 over quality concerns. Open data is a real foundation, not a finished product, and we return to it in the build-versus-buy section.
The takeaway: pick for your layer and your markets, not for the global ranking, because the watch-out column is where the real decision lives.
Quick Summary
Q: Who are the best point of interest and hyperlocal data providers in 2026?
A: There is no single best provider, because the right one depends on which data layer and which markets you need. Forage AI leads for teams whose target markets are stale or thin in off-the-shelf feeds, through managed extraction to your taxonomy and cadence. The specialists each win on a layer: dataplor on emerging-market long tail, Foursquare and Google Places on rich attributes, Data Axle on US small-business depth, SafeGraph and Precisely on geometry and footprints, HERE and TomTom on navigation platforms, Placer.ai on footfall, and PredictHQ on event demand. Score each against your own sample before you choose.
Stat callout: One provider raised a $20.5 million Series B in June 2025, explicitly to deepen coverage in the world’s toughest markets, a neutral-press signal that the market now treats long-tail accuracy and compliance, rather than raw count, as the frontier worth funding.
Expert Insights
The funding signal is the cleanest neutral-press read on where the category is heading: one provider’s $20.5 million Series B in June 2025 was raised specifically to deepen coverage in the world’s toughest markets, which tells you the market itself now treats long-tail accuracy and compliance, rather than raw count, as the frontier worth funding. That is the same shift this article argues for at the level of an individual buying decision.
How do you evaluate a hyperlocal data provider? The four axes that break location products
Now that you have the roster, here is the method to test any of them. If the count is not the decision, then what is? Four axes, each tested against your own data rather than the vendor’s brochure, with two more sitting as gating criteria. The discipline is the same in every case. Take a sample of your actual target markets and categories, run it through the provider, and measure what comes back. Call it the four-axis POI evaluation.
| Axis | What it measures | How to test it | Red flag |
|---|---|---|---|
| Per-market long-tail coverage | Whether your real target ZIPs, cities, and categories are present | Sample your target markets; compute per-market and per-category fill rate against the feed | Vendor can answer coverage only with a national number |
| Freshness and decay | How fast closures, moves, and hours changes reach the feed | Re-pull a sample of places you know changed; measure lag to correction | Annual or quarterly refresh with no verification step |
| Attribute completeness | Fill rate on the specific fields your product reads | Compute per-field non-null rate on your sample | High record count paired with low fill on your critical fields |
| Dedup and geocoding accuracy | Did it resolve, did it land in the right spot, are duplicates collapsed | Ground-truth a sample against satellite imagery or known coordinates; check duplicate clustering | Match rate cited as proof of placement accuracy |
| Pricing model (gate) | Total cost of ownership across delivery models | Compare bulk dataset vs metered API vs managed feed for the same data | Quoted price hides per-market or refresh costs |
| Privacy (gate) | Compliance posture on any footfall or mobility data | Confirm documented consent basis and SPI handling | Mobility data offered with no documented consent basis |
Per-market long-tail coverage
Test your markets, not the global headline. The method is a ground-truth coverage probe: sample your real target ZIPs, cities, and categories, then compute per-market fill rate and per-category fill rate against the feed. Emerging markets and low-check-in categories are exactly where headline counts collapse, because those are the places that are hardest and least profitable for an aggregator to keep current. The red flag is straightforward. If a vendor can only answer coverage questions with a national number, they have not measured the thing you need measured. In our managed extraction work, a coverage probe samples a client’s actual target ZIPs against the feed rather than trusting a global POI count, which surfaces the per-market gaps before a contract locks them in.
Freshness and decay
Decay is continuous, not a batch event. Every closure, every relocation, every hours change is a silent stale record accumulating between refreshes. So evaluate the two things that actually govern freshness, cadence and verification method, rather than the word “fresh” printed on a data sheet. The test is concrete: re-pull a sample of places you know have changed, and measure the lag to correction. The red flag is an annual or quarterly refresh with no verification step, because that is a feed that learns about a closure long after your product has routed a customer to a shuttered store. Continuous, near-real-time refresh has quietly become the buyer expectation rather than a premium feature, and treating freshness as an ongoing discipline rather than a one-time purchase is the same logic that drives data observability practices for any production feed.
Attribute completeness
Score the fill rate on the specific fields your product reads, not the vendor’s full schema. The test is a per-field non-null rate computed on your sample. The red flag is a high record count paired with low fill on your critical fields, which is the polished version of the bought-data trap applied to attributes rather than to places. A feed can clear every coverage check and still fail you, because the hours and identifiers you actually need are blank in two-thirds of the records.
Dedup and geocoding accuracy across overlapping sources
This is the axis buyers under-test most, and it breaks down into three separate questions. Did the record resolve to coordinates, which is the match rate. Did it land in the right spot, which is positional accuracy. And are overlapping sources collapsed into one clean record rather than three near-duplicates, which is dedup.
A geocoding match rate above 95% does not mean accurate placement. This is the parking-lot problem: points resolve successfully and still land in parking lots, adjacent buildings, or the middle of the street. Match rate answers “did it resolve,” not “did it land in the right place.”
The test is to ground-truth a sample against satellite imagery or known coordinates and to check for duplicate clustering. In our managed extraction work, the approach is to dedup per source and attach a geocode-confidence score, so a fifty-meter miss does not silently pass as a clean match.
Gating criteria: pricing model and privacy
Two factors gate the whole decision regardless of how a provider scores on the four axes. Pricing model, because a bulk dataset, a metered API, and a managed feed produce different total costs of ownership for the same nominal data. And privacy, because for any footfall or mobility dataset, “precise geolocation” is classified as Sensitive Personal Information under California’s CCPA and CPRA, defined as data that locates a person within an 1,850-foot radius, which triggers the consumer right to limit its use. The red flag here is mobility data offered with no documented consent basis. The 2025 California enforcement activity around location data, including the AG’s investigative sweep and AB 322, has made this a sharper question than it was even a year ago.
This section touches on privacy regulation. It is general guidance, not legal or compliance advice. Consult qualified counsel for your organization’s specific compliance requirements.
The framework, in one move you can run this week: take your real target markets and categories, sample them, push the sample through each candidate provider, and grade per-market coverage, per-field completeness, freshness lag, and geocode displacement. That single probe replaces a stack of vendor brochures with numbers about your markets.
The takeaway: the unit of evaluation is not the feed in the abstract, it is the feed against your sample, scored on four axes with pricing and privacy as gates.
Quick Summary
Q: How do you evaluate a hyperlocal data provider?
A: Test four axes against your own data, not the vendor’s headline: per-market long-tail coverage, freshness and decay, attribute completeness, and dedup and geocoding accuracy, with pricing and privacy as gating criteria. The fastest route is a ground-truth probe that samples your actual target markets and grades fill rate, freshness lag, and geocode displacement, so you are deciding on numbers about your markets rather than on the vendor’s global brochure.
Stat callout: 20.4% of new US businesses close within their first year, and roughly 49% within five years (US Bureau of Labor Statistics, Business Employment Dynamics, 2024). One location-data provider’s research puts the steady-state rate at 7 to 9% of all US businesses closing every year.
Stat callout: Across geocoding solutions, match rates exceed 95% while positional accuracy varies widely, with parcel-based desktop geocoding averaging around 24.8 meters of error and one study matching only 81 to 84% of addresses to the exact street segment (Boise State / Cartography and Geographic Information Science, 2015; methodology illustration, not a current market benchmark). More recent independent sampling found an average displacement of roughly 53 meters between listed addresses and actual coordinates.
Expert Insights
The decay axis has the strongest neutral evidence behind it. The US Bureau of Labor Statistics first-year and five-year closure rates, paired with one location-data provider’s 7-to-9%-per-year steady-state estimate, mean every closure is a POI that silently goes stale in a feed that is not re-verified.
On geocoding, the foundational accuracy research is sobering in a specific way: geocoding is mostly right and quietly off, which is exactly the failure mode buyers skip testing. On the scale benchmark, one provider now states more than 350 million points of interest across 250+ countries, a useful marker of what aggregate scale looks like and, at the same moment, the clearest example of a number that says nothing about your markets.
On privacy, under CCPA and CPRA “precise geolocation” is Sensitive Personal Information, defined as locating a person within an 1,850-foot radius (California AG and CPPA, CPRA effective January 1, 2023), so footfall buyers are evaluating a compliance posture, not only an accuracy score.
Build vs. buy: when is an aggregated POI feed not enough?
By this point the decision rule almost writes itself. You have three options: open data such as Overture or OpenStreetMap, an aggregated commercial feed, or managed extraction built to your specification. Each one costs you something different, and the right choice falls out of what your four-axis probe found.
The rule is this. Run the probe against the strongest commercial feed in your category. If it passes on your markets, your categories, your critical fields, your freshness needs, and your geocoding tolerance, buy the feed and move on, because building anything custom would be wasted effort.
If it fails on one of those axes and a second vendor closes the gap, switch vendors. But if it fails and no available feed closes the gap in your specific markets, that is the signal to commission extraction to your own taxonomy and cadence rather than keep shopping for a larger version of the same shortfall.
The reason aggregated feeds leave that gap is structural, not a matter of effort. An aggregated feed optimizes for the biggest defensible national count, because that is the metric procurement compares on, which means the long tail in your markets is rationally under-invested.
Open data offloads the dedup and geocoding work onto you, which is real engineering cost even when the data itself is free. Managed extraction trades the immediacy of an off-the-shelf download for fit to your markets and ownership of the result.
None of these is free. They cost you in different currencies, and the probe tells you which currency you can afford to spend.
Worth saying plainly: open data is not automatically good enough. Overture and OpenStreetMap are strong baselines, but geocoding precision and coverage vary, and the dedup and freshness work falls on your team, which is a recurring cost that a free download quietly hides.
This is where the managed-extraction option earns its place. When open data is incomplete and the aggregated feeds are stale or thin in your markets, building POI and local-business data to your taxonomy, with per-source dedup, geocoding, and a refresh cadence you set, is the structural fix rather than a stopgap.
It is the same build-versus-buy logic that governs any custom web scraping decision: when the off-the-shelf option does not fit, you commission the fit. And because extraction is ongoing re-verification rather than a one-time pull, the maintenance burden is the point, not a footnote, which is exactly why a managed model that owns that burden tends to win for teams whose core product is not data plumbing.
The takeaway: buy when the probe passes, switch when another vendor closes the gap, and extract when no feed does and the gap sits in the markets your product depends on.
Quick Summary
Q: When is an aggregated POI feed not enough, and should you build or buy?
A: An aggregated feed is not enough when your four-axis probe shows your specific target markets are thin, stale, or missing the attributes your product needs, and no off-the-shelf feed closes the gap. At that point, managed extraction to your own taxonomy and refresh cadence, with per-source dedup, geocoding, and data you own, is the structural fix. Open data such as Overture and OpenStreetMap is a real baseline, but it leaves the dedup and freshness burden sitting on you.
Stat callout: Overture Places now reaches products that serve more than 1 billion consumers (Overture Maps Foundation, May 2025), evidence of how far open data has come as a baseline, even as documented geocoding-placement issues show where it still leaves work on your side of the line.
Expert Insights
The build-versus-buy line turns on how good open data has actually become, and the honest read is “good baseline, not finished product.” Overture Places now holds more than 64 million POIs on an open schema, adopted into products reaching over 1 billion consumers (Overture Maps Foundation, May 2025), which is real progress, even as independent analyses documenting Overture points landing in parking lots show the dedup and freshness work still falls on your side of the line. That residual cost, not the license fee, is what tips a thin or stale market toward managed extraction.
Frequently asked questions about point of interest data
What is point of interest (POI) data?
Point of interest data is a structured record of a real-world place, including its name, address, coordinates, and category. It underwrites site selection, navigation, last-mile logistics, location marketing, and local search. Note that POI here means points of interest, the location sense, not Apache POI, the Java library for working with Excel files.
What is the difference between POI data and foot-traffic data?
A POI is the place record itself, describing where something is. Foot-traffic or visitation data describes who visited a place and when, and it is derived from device panels rather than from the place record. Visitation is also privacy-regulated in ways a static place record is not, so the two are evaluated and governed differently.
Where can you buy POI data, and is free open data good enough?
You can use open data such as Overture or OpenStreetMap, license a commercial feed, or commission managed extraction. Open data is a real baseline, but geocoding precision and coverage vary and the dedup and freshness work falls on your team. Whether it is good enough depends entirely on running a coverage and quality probe against your own markets.
How often should POI data be refreshed?
Cadence ranges from real-time to annual across providers. The right way to evaluate it is as cadence plus verification method rather than as the marketing word “fresh.” Because US businesses close at roughly 7 to 9% a year on top of moves and hours changes, decay is continuous, so a feed that re-pulls annually without re-verification is stale by construction.
Is POI and footfall data GDPR or CCPA compliant?
POI place records are generally low-risk because they describe locations, not people. Footfall and mobility data is different: under CCPA and CPRA, “precise geolocation” is classed as Sensitive Personal Information, defined as locating a person within an 1,850-foot radius, so it must rest on a documented consent basis. This is general guidance, not legal advice; consult qualified counsel for your situation.
Conclusion
The question was never which provider has the most POIs. Framed that way, the biggest number wins, and the biggest number is a national sum that knows nothing about the twelve metros, three categories, or one delivery corridor your product actually lives in.
The real question is narrower and far more useful: whose data holds up in my markets, on my cadence, with the attributes my product reads. Answer that with a four-axis probe against your own sample, and the provider landscape stops being a ranking to memorize and becomes a set of category fits to test.
And when the probe comes back and no feed holds up in the places you depend on, the answer is not a bigger feed. It is data built to your markets, which is a decision you make on evidence rather than on a headline.
Sources
- Mordor Intelligence, “Location Intelligence Market,” updated January 2026.
- US Bureau of Labor Statistics, Business Employment Dynamics, 2024.
- California AG / CPPA, CCPA and CPRA “precise geolocation” Sensitive Personal Information (effective January 1, 2023).
- Overture Maps Foundation, “Reaching Billions with Up-To-Date Places Information in Overture,” May 2025.
- Boise State / Cartography and Geographic Information Science, “Geographic disparity of positional errors and matching rate of residential addresses among geocoding solutions,” 2015. https://www.tandfonline.com/doi/full/10.1080/19475683.2015.1085437
Related Articles
- A Guide to Modern Data Extraction Services in 2026: How managed, custom extraction works end to end when off-the-shelf data does not fit.
- The Best Real Estate Data Providers 2026: A parallel provider evaluation for the adjacent real-estate data buyer.
- Alternative Data Guide: Where POI and location data sit in the broader alternative-data family.
- Custom Web Scraping: The build-versus-buy logic behind commissioning extraction to your own specification.
