Foursquare continues to enhance QA processes to ensure the quality of the data remains high. Examples of our QA processes include:
- Detailed QA for deep data analysis: We sample chains and randomly selected POIs in major markets to validate their existence and accuracy.
- Ground truth comparisons for key metropolitan areas: We demonstrate our data accuracy against data on the ground, verified by real people.
- Spot reviews for each data release: Focusing on data corruption, any significant changes to the data set size, and randomized samples to check.
Foursquare data is intended for use in a local search context. All places must be identified to a real postal address, which will be publicly visible.
If you discover inaccurate information in a Foursquare record, please submit the corrections as new records, including all of the required attributes. Your accurate contribution will be added to the existing record and should outvote most inaccurate values. If you don't provide enough attributes, we may not be able to make the proper correction. Of course, please make sure that the correct contribution persists in your future submissions.
Updated 4 months ago