Places data brings together firmographic venue details from authoritative third-party sources and billions of user-generated photos, tips, and reviews from our decade of experience crowd-sourcing consumer feedback.
Foursquare’s Places Flat File is available for delivery as a TSV file with GZIP compression.
Get access to:
- 100M+ commercial POI including restaurants, shops & services, and more
- 200+ countries and territories
- 1100+ venue categories
Foursquare’s Places Flat File comes with 25 core attributes and a multitude of rich attributes. Core Attributes are included in all of our packages, while rich attributes can be added at an additional cost.
Determine which Places flat file package is right for you by following a simple three-step process:
- Select the countries or regions you’re interested in
- Decide if you’d like to add on any rich attribute packages
- Decide if you need data on specific categories based on your business needs
Can be delivered daily, weekly, monthly or quarterly.
- Delivered as a TSV file with GZIP compression via Amazon S3. For assistance on how to obtain your user ARN or how to use S3, read this guide or contact your technical account manager at Foursquare.
- Provided in compressed, tab-delimited files, one file per country.
- The format and the standard filename convention is as follows:
Where orgId is a 25-character string unique to your organization.
- Compression is gzip format -- it can be opened with most Windows, Linux, and Mac compression utilities.
Foursquare has also partnered with leading GIS mapping software and cloud providers to help you quickly access, analyze and utilize our POI dataset in your preferred, supported environment. With these partnerships, your teams can save technical onboarding time, as well as data ingestion and storage costs. We have a variety of samples and datasets available, as well as self-service capabilities for our customers.. Below is our list of current partners:
Foursquare’s standard offering of its Places dataset utilizes filters of the following calculated scores to offer a “quality” cut of the data that is optimized for comprehensiveness and quality. Deliveries of the data which include all available POIs are also available for customers.
What it is: This attribute represents the probability that a given POI is no longer in business. Foursquare uses a machine-learning model to assess the current operational status of each POI. This closed-score model is trained on thousands of human annotations of Foursquare’s POI and uses features that reference how recent internet sources for the POI have been updated, when the last time the POI had a check-in/tip/photo, etc.
Using the Closed score that comes out of the model, we assign each POI to a closed_bucket of:
- VeryLikelyClosed: places with probabilities ≥ 85% being closed
- LikelyClosed: places with probabilities 65-85% being closed
- Unconfirmed: places with probabilities less than 65% closed or open
- LikelyOpen: places with probabilities 65-85% being open
- VeryLikelyOpen: places with probabilities ≥ 85% being open
Note that legacy Factual customers that have relied on the existence attribute should migrate to using closed_bucket as existence is no longer supported.
What it is: This attribute represents how “real” Foursquare believes a POI to be. As POIs can be submitted directly by users, Foursquare uses a machine-learning model to assess real POIs (public places like a popular restaurant, store, concert venue, etc) versus a private or nonexistent POI. Foursquare’s VRS model uses a combination of explicit and implicit signals (examples include number of searches on Foursquare, number of photos/tips submitted, number of references across the open internet, etc) to score each POI. Using the Venue Reality score, we assign each POI to a Venue Reality bucket of Low, Medium, High, or Very High.
What it is: The date when FSQ last saw any single reference refreshed from crawl, Listing Syndicators, users or human validation for a given POI.
Notes on attribute level filtering: We currently don’t have a default recommendation for filtering on date_refreshed, but some customers with a higher precision use case may want to use this attribute to filter out potentially stale POIs with a date_refreshed value that is older than 2 years.
To ensure both precision and recall in the POIs represented in our dataset, we use the following pre-filters as part of our standard delivery:
- filter out POIs where venue_reality_bucket = “Low”
- filter out POIs where closed_bucket = “VeryLikelyClosed” OR “LikelyClosed”
For customers with a use case that requires higher precision, we recommend using these additional filters:
- filter out POIs where closed_bucket = “Unconfirmed"
- filter out POIs where with a date_refreshed value that is older than 2 years
Updated 5 months ago