Key Takeaways
- Choosing a data partner matters as much as choosing the data itself, especially in location intelligence.
- Data quality, accuracy, and joinability matter more than raw data volume when you're evaluating POI data quality.
- SafeGraph Places now maps more than 80 million verified points of interest (POIs) worldwide, refreshed every month.
- Accurate brand identification and high-precision POI polygons directly improve model and analysis accuracy.
- Placekey, SafeGraph's standard join key, makes it fast to combine Places, Geometry, Address, and Spend data, or join to your own datasets.
- Frequent updates, transparent documentation, and published accuracy methodology help data science teams trust and operationalize the data faster.
Not all data sources are created equal, and neither are the companies that produce them. If you’re evaluating a location data partner, you need clarity on what you’re trying to achieve, because more data isn’t always better data. If the quality, accuracy, and joinability aren’t there, no amount of volume will make up for it.
Bad location data costs data science teams real time: hours spent cleaning records, failed joins against internal tables, and models trained on stale or mislabeled places. In the worst cases, it leads to decisions that are both costly and hard to defend when someone asks how you got there. Choosing the right partner for location data for machine learning and analytics isn’t something to do haphazardly, especially at the scale most data teams operate at.
What Data Scientists Need From a Location Data Partner
Before picking a vendor, it helps to know what you’re actually evaluating. SafeGraph’s own data evaluation checklist breaks this into four questions: is the source credible, what can (and can’t) the data tell you, how usable is it out of the box, and how well does it join to what you already have. Data scientists who evaluate POI data quality against those questions consistently land on the same shortlist of priorities: quality over volume, clean joins, transparent documentation, and freshness they can verify. Here’s how SafeGraph holds up against each.
6 Reasons Data Scientists Choose SafeGraph Data
1. Accurate brand identification
The SafeGraph Places dataset captures core location information, spatial hierarchy, and brand-level detail across a growing base of brands worldwide. Coverage isn’t static: in the July 2026 release alone, SafeGraph added 74 new brands across 29 countries. You can check the current brand count and freshness for any brand or country in the Places Summary Statistics, which update with every monthly release.
SafeGraph also publishes its accuracy methodology rather than leaving you to take a headline number on faith. The Places Accuracy docs break performance into recall (how much of the real world is captured) and precision (how clean each entry is), measured against truth sets and documented in the accuracy metric methodologies. That’s the kind of transparency data scientists can actually defend in a model review.
2. High-precision POI polygons and spatial hierarchy
Every POI in the Places dataset is matched to its best-fitting polygon, which in most cases aligns to the building footprint. The harder cases, like strip malls or multi-tenant buildings, get extra scrutiny so the geometry still reflects reality. High-quality polygons are difficult to engineer at scale, which is why a meaningful share of SafeGraph’s engineering team is dedicated to exactly this problem.
The dataset also captures spatial hierarchy: whether a place contains other places (a mall containing individual stores), whether a place is fully enclosed inside another (a Starbucks inside an airport), and whether a polygon is uniquely owned or shared. Those relationships, tracked through parent_placekey, are what let you accurately separate a mall from its tenants instead of double-counting foot traffic or misattributing visits.
3. Monthly refresh cadence, built around real-world churn
SafeGraph releases an updated Places dataset every month, faster than most POI providers, who typically refresh on a quarterly or annual cycle. That cadence matters because businesses open and close constantly. In the July 2026 release, SafeGraph flagged 647 brands with at least one store closure and 2,088 brands with at least one store opening in June 2026 alone. You can see the detail in the July 2026 Release Notes, including targeted accuracy work like Spain’s expanded pharmacy coverage (roughly 22,000 new POIs) and cleanup across gas station and convenience store categories.
The takeaway: with this much change happening every month, a dataset that’s stale by even a quarter can quietly skew your analysis.
4. Transparent documentation and schema
SafeGraph documents its schema, summary statistics, and data science resources on the SafeGraph Places Documentation site, including base attributes, rich attributes, and geometry fields. When errors surface, SafeGraph proactively communicates them and tracks the fix in the changelog rather than quietly patching the data. For a deeper technical walkthrough, the Places Data Technical Guide covers schema and use cases in more depth.
5. Easy joins via Placekey and standard identifiers
Every SafeGraph dataset uses placekey as its primary key. That single identifier is what lets you join Places, Geometry, Address, and Spend data together, or join SafeGraph data to your own internal tables, without wrestling with fuzzy name-and-address matching. For a data scientist, this is often the difference between a join that takes an afternoon and one that takes a sprint. Store-level identifiers and brand IDs add another layer of precision for chain-level analysis; see the places base attributes docs for the full field list.
6. Global coverage with flexible delivery
SafeGraph Places now covers more than 80 million verified points of interest (POIs) worldwide, organized into four coverage tiers so you know exactly how deep the data goes in any given country, from heavily verified Tier 1 markets to emerging Tier 4 coverage. You can explore coverage by country in the Places Statistics visualization. SafeGraph’s separate Address dataset extends this further with geocoded address data across 35+ countries, including hard-to-source markets in Eastern Europe, the Balkans, and MENA.
On delivery, SafeGraph data is available through S3, Snowflake, Databricks, CARTO, Esri, and the AWS Marketplace, so it fits into whatever stack your team already runs on.
What Customers Say
“After testing a sample of the SafeGraph Places dataset, we found it to be far more accurate than other data sources we’ve used in the past. Not to mention, the way the data was packaged was user friendly; its taxonomy simply made sense,” said Andy Stevens, Chief Data Officer for Clear Channel Europe, in SafeGraph’s Clear Channel case study. Clear Channel Europe now uses SafeGraph Places to power proximity analysis across its RADAR ad-planning platform, covering 280,000 advertising sites across 17 European markets.
For more examples of teams building on SafeGraph data, see how Avison Young uses SafeGraph for commercial real estate site selection and how Dosh built its card-linked offers platform on Places data, or browse the full customer library.
Try the Data Yourself
Data scientists generally want to test before they talk to sales, so start wherever makes sense for you:
- Get sample data. Download the global POI sample, the geometry sample, or the Starbucks vs. Dunkin’ US dataset to see real records before committing to anything.
- Read the docs. Review the schema, accuracy metrics, and FAQs at your own pace.
- Schedule a demo. When you’re ready to talk specifics, schedule a demo with the team.
Frequently Asked Questions
1. Why do data scientists prioritize data quality over data volume?
A large dataset offers little value if it’s inaccurate, outdated, or hard to join to existing data. Poor-quality data increases cleaning time and risks flawed analysis, which is why evaluating POI data quality matters more than counting rows.
2.What makes SafeGraph's Places data reliable for data science work?
SafeGraph combines accurate brand identification, precise POI polygons with documented spatial hierarchy, monthly updates, and a published accuracy methodology (recall and precision measured against truth sets) rather than relying on a single unverified number.
3. How often is SafeGraph Places data updated?
The dataset is updated monthly. Each release documents what changed, including new brands, coverage expansions, and openings and closings, in the public changelog.
4. What is Placekey, and how does it help data scientists join datasets?
Placekey is the standard identifier used as the primary key across all SafeGraph datasets. It lets you join Places, Geometry, Address, and Spend data together, or join SafeGraph data to your own internal tables, without relying on fuzzy name-and-address matching.
5. How does SafeGraph verify the accuracy of its POI data?
SafeGraph measures data quality along two axes, recall (completeness) and precision (correctness), benchmarked against truth sets that include government sources, industry-standard datasets, and manually verified samples. The full methodology is documented publicly.
6. How does SafeGraph compare to other POI data providers?
SafeGraph combines machine learning sourcing with human verification and updates monthly, compared to the quarterly or annual cadence common among other providers. For a closer look at alternatives, see SafeGraph’s comparisons of free and open data sources and Google Places API alternatives.
