SafeGraph vs OpenStreetMap (2026): We Benchmarked POI Data Quality, Here’s What We Found

If you build products on point-of-interest (POI) data, you’ll eventually face this decision: use free, open source data like OpenStreetMap, or pay for a curated provider like SafeGraph. This article settles that question with an actual benchmark study, not a sales pitch, and updates it for a 2026 landscape that looks very different from 2022.

The free-data conversation has also changed. Overture Maps Foundation and Foursquare now both publish massive open POI datasets at no cost, which means “free vs. paid” is no longer a two-way comparison. We rebuilt this piece to test all of it fairly, including when free data is genuinely the right call.

Quick Answer

For commercial workflows like site selection, trade area modeling, or visit attribution, our benchmark found SafeGraph delivered 100% location coverage and a 95.6% attribute fill rate versus 26% coverage and 39.8% fill rate from OpenStreetMap for the same market and brand. Free open datasets like Overture Maps and Foursquare OS Places have since closed part of that gap in raw volume, but not in commercial-grade completeness, freshness, or support. Which one is “better” depends on what you’re building and who’s maintaining the pipeline.

At A Glance: SafeGraph vs. The Free POI Data Options in 2026

When OpenStreetMap, Overture, or Foursquare OS Places is the right choice

This part gets left out of most vendor comparisons, so it’s worth saying plainly: free POI data is the right choice in several real scenarios.

If you’re mapping road networks, trails, or non-commercial features like public benches and hydrants, OSM is often the best source available at any price. If you’re a researcher or student working under a budget constraint, Overture Maps or Foursquare OS Places give you tens of millions of POIs with permissive licenses and no procurement process. If you’re building a long-tail global product where covering as many countries as possible matters more than commercial-grade fill rates in any one of them, the sheer volume of Overture and Foursquare’s combined open datasets is genuinely useful. And if you already have a data engineering team with time to dedicate to deduplication, category normalization, and freshness checks, free data plus in-house cleaning can be a rational build-vs-buy decision.

Where free data struggles is anywhere accuracy, completeness, and consistency directly affect a business decision, like choosing a store location, modeling trade areas, or attributing consumer visits. That’s the scenario our benchmark below tests.

Our Methodology

Most organizations evaluating POI data don’t start with a spec sheet, they start by pulling a sample for a specific brand in a specific market and seeing what they get. That’s the scenario we set out to recreate rather than a synthetic or best-case comparison.

We compared SafeGraph and OpenStreetMap data for Dollar General stores in Little Rock, Arkansas, using Dollar General’s own store locator as ground truth. Dollar General was chosen because it’s a high-volume, easily verifiable retail brand with a public, authoritative store locator, which meant we had an independent source of truth to check both datasets against, rather than having to trust either provider’s own claims about its coverage.

We evaluated four dimensions, each chosen to reflect a real step in a typical POI workflow rather than an abstract data-quality metric:

Access, how long it takes and how many steps are involved in going from “I need this data” to a usable file, including any tools, accounts, or learning curve required along the way.

Coverage, how completely the data reflects real-world locations, measured against Dollar General’s own store locator as ground truth, and what that gap (or lack of one) does to downstream analysis.

Completeness, how filled in the attributes are for each matched location, address, phone number, hours, category, and whether the provider explains why any fields are intentionally left blank.

Usability, how each dataset performs when it’s actually used, running hot spot, proximity, and trade area analysis in QGIS, the kind of workflow that sits behind site selection and competitive intelligence tools.

This remains a single-brand, single-market study, and that’s a real limitation worth naming rather than hiding. A broader benchmark, ideally 3 to 5 brands across 3 to 5 metro areas, would make the findings far more generalizable, and we’d want the raw extraction available for download so readers can verify the numbers themselves rather than take our word for it. That expanded study is the logical next phase of this research and something we’re planning to run; the results below reflect the original single-market test until that’s published.

While that expanded benchmark is in progress, independent academic research published in the last few months backs up the core pattern we found in Little Rock. A study in Sustainability (MDPI, October 2025, open access) compared OpenStreetMap to Google Maps for commercial and public amenities across four European cities and found that OSM systematically underrepresents amenities outside dense urban cores, with the weakest coverage showing up in the most peripheral, suburban test area. A related 2025 study from the same research group ground-truthed OSM’s commercial listings in two Italian municipalities against actual on-site surveys and found general underrepresentation, positional inaccuracies, and outdated listings.

Neither study was run by SafeGraph or any data vendor, which is exactly the kind of third-party corroboration a single-market vendor study like ours needs.

Results by dimension

Access

Acquiring SafeGraph data was a single filtered CSV export. We worked from the SafeGraph Places release for Little Rock, Arkansas, and filtered records using the “brands,” “city,” and “region” columns. That query returned 17 Dollar General POIs, each one already carrying geometry data (delivered as well-known text), which meant the same file could be visualized as points and polygons without any extra processing.

Acquiring comparable OSM data was a different exercise entirely. We tested three separate extraction tools before finding one that returned usable attribution. The Humanitarian OpenStreetMap Team’s export tool returned one point and three polygons for Dollar General, but neither carried meaningful attribution. BBBike returned a buildings shapefile with only two populated fields, name and type, and most of those were incomplete. Overpass Turbo was the tool that finally worked: using its query wizard to pull nodes, ways, and relations tagged for Dollar General returned five distinct locations (two points, three polygons) with a genuinely useful field list, shop type, street address, business hours, website, and phone number.

Takeaway: SafeGraph data required one extraction step; matching it in OSM required testing three separate tools, accepting a real learning curve, and still landing on a smaller, less consistently structured result.

Coverage

Dollar General’s own store locator returned 27 stores within its default 10-mile radius search for Little Rock. Because SafeGraph and OSM were both queried by city name rather than a radius, we filtered that list down to 19 verified Little Rock locations once city-name and address-formatting discrepancies were resolved, that’s the ground truth number the rest of this section compares against.

SafeGraph’s dataset matched 13 of its 17 identified POIs to the store locator immediately. The remaining four took closer investigation, three fell outside the default 10-mile radius Dollar General’s site uses, and one was listed under a different city name despite being a genuine Little Rock-area store. All 17 turned out to be real, operational locations.

Four additional POIs identified in SafeGraph’s data that were not initially matched via Dollar General’s store locator.

We also found two additional stores on Dollar General’s site, listed under Little Rock, that hadn’t appeared in the original SafeGraph query due to address-string differences. Once resolved, SafeGraph’s data accounted for all 19 verified locations.

Two additional POIs identified with Dollar General’s store locator were not in the initial SafeGraph data download due to differences in address strings.

OSM told a different story. The best available extraction, Overpass Turbo, returned five Dollar General locations. Against the 19 verified stores, that’s 26% coverage, meaning roughly three out of every four real locations were simply missing from the dataset.

Takeaway: in this market, OSM was missing roughly three out of every four real store locations, not because the stores don’t exist, but because they were never mapped or tagged.

Completeness

SafeGraph and OSM offered a comparable number of attribute columns per record, 28 versus 24, which on the surface suggests similar depth. The actual fill rate tells a different story. Across the 17 SafeGraph records (476 total attribute fields), only 21 fields were incomplete, a 95.6% fill rate. Just as important, SafeGraph’s documentation explains why specific fields are intentionally left null, for example, a null value in the “closed_on” field means the business hasn’t closed, not that the data is missing. That distinction matters: a blank field with an explanation is different from a blank field with no context at all.

OSM’s five records used 24 fields, and none of the five were fully complete. On average, just under 40% of fields were populated, a 39.8% fill rate, and completeness was inconsistent record to record: some entries had an address or phone number, most had almost nothing beyond a name and shop type. OSM’s crowd-sourced structure doesn’t provide any documentation explaining why a field is blank, so there’s no way to distinguish “this business doesn’t have a website” from “no one has entered this yet.”

Takeaway: similar schema width, very different depth of actual usable data behind it, and only one of the two datasets tells you why a field is empty.

Usability

Once we compared the level of detail provided by each dataset, we tested which one was more fit for common POI workflows: hot spot, proximity, and trade area analysis. These methods sit inside most site selection, competitive intelligence, and visit attribution tools, so the quality of the input data directly shapes the output a business acts on.

Hot Spot Analysis

Kernel density estimation on the 17 SafeGraph-identified locations showed one clear cluster of Dollar General stores in central Little Rock, the kind of signal a business could use to decide where to expand or where a competitor might find an underserved gap. Running the same analysis on the OSM data required converting polygons to points first, and even then there weren’t enough features for a reliable kernel density calculation, so a manual hot spot check had to be done instead. It found no significant clusters at all.

Takeaway: OSM’s data gaps didn’t just weaken the hot spot analysis, they erased the signal entirely.

Proximity Analysis

Using an aggregated distance matrix over point and hexagonal grids, the SafeGraph data showed that no point in Little Rock is more than 5.9 miles from a Dollar General, with an average distance of 1.9 miles. The OSM data told a very different story: an average distance of 3.51 miles, with the furthest point sitting 9.97 miles from any store. That’s not a small rounding difference, it’s a distance estimate that’s nearly double.

Takeaway: the dataset you choose can change your proximity numbers by close to 2x, which is enough to flip a site selection or delivery-radius decision.

Trade Area Analysis

Generating Voronoi service areas from the SafeGraph data produced 17 trade areas that extended even beyond Little Rock’s city limits, a realistic picture given how the stores are actually distributed. The OSM-derived trade areas, built from far fewer and more scattered points, didn’t cover the full city and produced a batch of smaller, uneven Voronoi cells. A team relying on this version of the trade areas would risk misallocating resources and missing cannibalization between nearby stores that the data simply didn’t show.

Takeaway: incomplete input data doesn’t just shrink a trade area map, it can hide competitive overlap that actually exists on the ground.

The 2026 Landscape: What Overture And Foursquare Change

This is the piece the 2022 version of this article couldn’t address, because neither dataset existed yet in its current form.

Foursquare open-sourced its Places dataset in November 2024 under an Apache 2.0 license, and by its December 2025 release the dataset had grown to roughly 106 million POIs with 22 core attributes, updated monthly. Overture Maps Foundation, a Linux Foundation project now backed by 50 member organizations including Meta, Microsoft, and Amazon, publishes a places dataset that reached over 72 million records by January 2026, aggregating contributions from Overture’s own sources, Meta, Foursquare, Microsoft, and community projects like AllThePlaces.

Both are real improvements over relying on OSM alone for commercial POI work. They’re larger, more consistently structured, and permissively licensed. What they don’t solve is the underlying tradeoff this article is about: volume and openness are not the same as verified accuracy, deduplication, and freshness at the individual-location level.

Recent academic work comparing OSM and Foursquare data (the World-POI methodology, published October 2025) makes this point directly: Foursquare’s open dataset gives broad commercial-POI coverage but many entries lack complete metadata, while OSM offers richer, more frequently updated metadata but no formal verification that a listed POI actually exists as an active business. Combining the two, as that research does, is itself an acknowledgment that neither free source alone is a complete substitute for a maintained, deduplicated, quality-controlled dataset.

A separate 2025 study adds a sharper edge to this point. Researchers compared OSM to Google Maps across four European cities (Versilia, Nice, Gothenburg, and Vienna) and found the two platforms disagreed so much on basic amenity counts that their divergence score (SMAPE) hit 170% in Vienna and 135% in Nice.

Part of that gap turned out to be noise rather than signal: in Nice and Vienna, a large share of OSM’s “extra” entries were private swimming pools and residential features with no commercial relevance, not real, visitable businesses. But the more consequential finding was structural: OSM’s coverage gaps aren’t random, they cluster in peripheral and suburban areas, which quietly biases any analysis built on OSM data toward city centers.
Google Maps isn’t a clean substitute either; the same study found it caps out around 60 results per category per query, so its data silently truncates in dense areas.

The takeaway for commercial POI work is the same one this article opened with: free datasets have gotten bigger since 2022, but volume doesn’t fix uneven, geographically patterned coverage gaps, and that applies to more than just OSM.

In short: if your workflow can tolerate blending multiple open sources and building your own QA layer, Overture and Foursquare OS Places in 2026 are a much better starting point than OSM alone was in 2022. If you need that QA layer already done for you, the free-data calculation hasn’t fundamentally changed.

What “Free” Data Actually Costs

Free data isn’t free to use, it’s free to license. The real cost shows up in engineering hours: time spent finding the right extraction tool, deduplicating records across sources, normalizing categories, backfilling missing attributes, and rebuilding the pipeline every time an upstream source changes its schema.

A simple way to estimate this for your own team:

Total cost of “free” data ≈ (hours spent sourcing and extracting) + (hours spent deduplicating and normalizing) + (hours spent building and maintaining an update pipeline) + (cost of decisions made on incomplete data), all multiplied by your team’s fully loaded engineering rate.

At current U.S. data engineering rates, fully loaded internal costs commonly run $75 to $160 an hour depending on seniority and location. Even a conservative estimate, say 40 to 80 hours a quarter spent maintaining a blended open-data pipeline across sourcing, cleaning, and re-validation, puts the “free” option at several thousand dollars a quarter before you’ve accounted for the cost of a bad site-selection or trade-area decision made on incomplete data. That’s the comparison worth running internally before defaulting to open data because the license fee is zero.

Closing Thoughts

The honest version of this comparison isn’t “free data is bad.” It’s that free and paid POI data solve different problems, and 2026 has more legitimate free options than 2026’s predecessor did. OpenStreetMap, Overture Maps, and Foursquare OS Places are all reasonable choices for non-commercial mapping, research, and teams with the bandwidth to build their own cleaning pipeline. For commercial decisions where coverage, completeness, and freshness directly affect revenue, our benchmark still shows a wide gap in favor of a curated, documented dataset. The right call depends on which side of that line your use case falls on, and this article was built to help you figure out which side that is, not to tell you there’s only one right answer.