The world changes fast — stay ahead of the curve with the most precise, reliable global POI data available.
Never worry about stale or inadequate data. Build the most informative tools and stay ahead of the competition with up-to-date and accurate places data.
We're solely focused on sourcing and cleaning POI data so you can spend time on what matters - developing the best product for your users. Build with confidence knowing your places data is a true representation of the physical world.
Quality is your priority, so it's ours too. We rigorously verify our data to power your products with the highest quality ingredients.
Point of interest (POI) data is information about geographical places that exist in the real world, such as the location of the physical place, ownership, open and close history, and more. POI data is extremely useful for business and competitive intelligence analysis and is used by investors, retail businesses, and much more.
With SafeGraph Places datasets, you can information on the physical location of the place, such as the latitude and longitude, street address, city, region, and postal code. On top of this, our Places datasets have information about brand affiliation, hours of operation, historical data on when businesses open and close at this location, and building footprint information, all of which further contextualize the location.
Like all of our data, the cost of Places depends on the amount of rows, columns, and frequency of delivery you request. Contact our sales team to learn about enterprise pricing.
SafeGraph issues updates to Places once per month, which is much more frequently than other POI vendors, who may update once every 3-6 months. We can do this because we work with more sources of data and are much more efficient at combining those sources. During each month, some subset of our sources will send us their updates, and we ensure that we onboard and integrate those changes quickly and easily.
This enables us to quickly reflect store openings and closings in our Places database. The time between a store opening/closing and being reflected in our Places database is approximately equal to the time that the store update is seen by one of our sources and the time it takes SafeGraph to reflect this in our data. The latter of these two is typically within the month, which is very fast compared to other providers, which might be within 3 months. The former of these two is hard to predict - but we do work with sources that generally receive updates very quickly.
Opened and closed dates are determined from metadata at the source level. If a new POI from an existing source repeatedly appears in our build pipeline, it is flagged as “opened_on” during the month in which it first appears. Similarly, if a POI from an existing source repeatedly disappears in our build pipeline, it is flagged as “closed_on” during the month in which it first disappears. These flags are added to the Places product permitting final QA checks and overall data hygiene.
Temporary closures are not captured in open/close tracking, and it became difficult to distinguish permanent closures from temporary closures at the onset of COVID-19. This resulted in a relatively low count of POIs with “closed_on” values between 2020-03 and 2020-06 as we erred towards the side of caution to not mistakenly mark temporarily closed businesses as permanently closed.
If a POI has not yet been sourced consistently enough to provide the metadata needed to determine “closed_on” dates, then it will have a null value in the tracking_closed_since column. In general, the SafeGraph Places product tracks opened and closed dates from as early as 2019-07 onward.
SafeGraph Places uses the North American Industry Classification System (NAICS) developed by the US Census Bureau, which consists of a numeric NAICS code up to 6 digits in length. Although this taxonomy was developed in the US, we have found it just as useful for categorizing POIs in other countries as well and will continue to use it until a better alternative presents itself.
The code itself is hierarchical; in other words, the first 2 digits describe a very general category, and additional digits describe more and more specific categories.