We are very pleased to announce a number of significant enhancements to our US Places dataset soon to be followed by similar improvements to the rest of the world.
Enrichments to US Data
We’ve added a boatload of new entities to the US including 80K landmarks (parks, memorials, historic buildings, and other monuments), 25K transport hubs (airports, rail stations and a handful of ports), and 190K new ATM locations. We’ve also included over 50 million additional references and edits from our partners to improve both coverage and accuracy. This brings us up to just over 23 million entities in the US alone, and over 63 million places in 50 countries worldwide.
Category Enhancements Globally
Our categories have taken an increasingly central role in the distribution and management of our data, so we’ve made our categorization framework more friendly to humans and more efficient for machines. These improvements include:
- 50 new categories for better, more granular classification
- Numeric category IDs for more structured search and data management
- Category translations available in Italian, German, French, Spanish, Korean, Japanese, and Chinese
We’ve made the entire category hierarchy available as a Factual table so you can query it in all languages, and also made it available as a JSON file on Github to facilitate baking in client-side category logic. See more information on categories here.
Chains — stores representing both local and national brands — are often included in Places data sets but can rarely be managed as distinct entities. Factual now manages a table of chains which connects directly to our Places: developers can query by explicit chain ID to get the complete list of our first 150 authoritative chains from our partners Location3 and Universal Business Listings (many more coming) that connect to over 333K places. We also have an additional 775 auto-generated chains produced by machine clustering — these are experimental and won’t have the same coverage or precision, so experiment with care. We’re testing these features out in the US before expanding globally — see more on chains here.
Factual Place Rank
With over 23MM Places in the US, developers of Local applications often find that there are too many records to present to the user, and it is difficult to filter those most meaningful for your app. Factual Place Rank aims to provide a relative metric by which developers can sort places by their informatic and social footprint, to ensure the most prominent places rise to the top of the stack. We’re using Factual Place Rank as the default ranking for searches — the feature is in beta so we’re testing it in the US only. See more on Factual Place Rank and all Global Places Attributes here.
Taken together, these changes are not insignificant and could bork existing code. We’re therefore releasing this US dataset as a new, versioned resource. We’ll follow with new revs of our US Restaurants and US Hotels data. All other countries will follow shortly, and this will become the production Global Places dataset. We’ve posted a migration overview online that describes the changes in more detail and helps you minimize disruption.
We’ve been working on these features for some time and it’s great to be getting them out the door. We’ll have a second, follow-on announcement on further features in a few weeks, so stay tuned.
Our Data is Singular