Launching Location and Mobile Ad Targeting Tools

To enhance our Global Places offering, we are announcing three new APIs that will provide developers with powerful geointelligence tools and data designed to increase contextual relevance in today’s mobile world:

Geopulse API

Discover everything that Factual knows about a location: you pass us a latitude and longitude, and we return additional attributes, called ‘pulses’, about that location.  Pulses employ Factual’s network of signals, calculated metrics, and census data to give a unique purview of a given local point.  We’re launching with:

  • Factual Commercial Density: Relative density of businesses near a location
  • Factual Commercial Profile: Types of businesses near a location
  • Demographics: Age, gender, race, median household income for a given location (US only)

We’ll be adding more over the coming months.  Have a look at the demo and documentation to learn more.  Pricing will depend on use case and usage volume.

Reverse Geocoder API

Convert a longitude and latitude into an address for the US and 49 other countries.  More details.

World Geographies API

Learn the names and interrelationships between the world’s natural and administrative geographies — countries, cities, states, continents, regions, and time zones — enhancing the global 60 million businesses and landmarks we currently offer. You will be able to access ~6 million geographies and over 8 million place names in numerous languages.  More details.

With data accessible via these additional APIs, it will make it even easier for developers and enterprises to build smarter location-based applications and analytics / business intelligence models. These services are production-ready but we’re launching them in beta, which means we will likely be making changes in the short-term as we incorporate developer feedback.

These new APIs are focused on addressing the mobile ad targeting market.  ThinkNear, a hyperlocal mobile demand side platform, is using Factual services to build a more robust ad targeting platform, and in the next few weeks we’ll be announcing more partnerships who are providing advertisers with advanced targeting and analytics using our location-centric toolkit.

This is really just the beginning — more services are coming , but we hope this gives you a sense of where we are headed and the means to take them for a spin and provide feedback.

Factual Endpoints

Lastly, as an added bonus, we’ve made all data for a Factual entity accessible from a single endpoint — add the Factual ID to the end of and we will provide the core attributes with extended, rich data from our Crosswalk, Restaurants, and Healthcare Providers products.   A few examples:

We’re looking forward to seeing the innovative applications in which our developer community employs these new services.  Don’t hesitate to hit us up with feedback and feature requests.

Bill Michels
VP of Product Management and Partnerships
(currently at 34.06, -118.42)

Factual Releases Drivers that Matter: Python, Clojure, Haskell

As Factual adds more data and features to its APIs, it’s important that developers have it easy. So we’ve put our heart into releasing client side drivers that matter. Yes, we’ve got drivers like the Java driver and the PHP driver and stuff like that, if that’s what you need. But we’ve also fielded a few drivers that matter in their own special ways:


Python is the greatest scripting language ever created. It does “everything-is-an-object” and gets it right, has a general feel of solid design, and is an unmitigated joy to hack with. Grab Factual’s Python driver (it’s in PyPI), authenticate, and get going:

from factual import Factual
factual = Factual(YOUR_KEY, YOUR_SECRET)
factual.table("places").search("sushi santa monica")

That gives you businesses in Santa Monica that match a full text search for “sushi”. Select one and use Factual’s Crosswalk to get more info from other respected web authorities:


BAM! Tasty data, check it out:

  "url": "",
  "factual_id": "110ace9f-80a7-47d3-9170-e9317624ebd9",
  "namespace_id": "69110",
  "namespace": "allmenus"
  "url": "",
  "factual_id": "110ace9f-80a7-47d3-9170-e9317624ebd9",
  "namespace_id": "67e030af8d75b7d595bf5e86d6ad5126",
  "namespace": "bitehunter"
  "url": "",
  "factual_id": "110ace9f-80a7-47d3-9170-e9317624ebd9",
  "namespace_id": "152206654808652",
  "namespace": "facebook"

That Crosswalk stuff, BTW, is neat-o. See how Factual gives you links out to guys like Facebook and AllMenus? There’s also Crosswalk data for Yelp, Foursquare, and lots of others.


Clojure is a Lisp and Lisp is the most powerful programming language known to mankind. Virtually free reign with macros, closures, DSLs… it’s crazy.

If you want to use the world’s most powerful programming language to query the world’s most powerful open data platform, grab PrettyQL (available via Clojars) and make it happen:

(select restaurants-us
  (around {:lat 34.06021 :lon -118.4183 :miles 3})
  (search "cigar")
      (= :alcohol true)
      (= :meal_dinner true)
      (= :parking_free true)))

That pretty snippet of code searches for a cigar shop near me where I can have dinner and drinks and not pay for parking. Check out the spicy results, mate:

({:open_24hrs false,
  :status "1",
  :country "US",
  :longitude -118.408088,
  :factual_id "5e2c36e4-aeb8-49b8-8652-defcd8ad5808",
  :parking_free true,
  :payment_cashonly false,
  :name "Buena Vista Cigar Club",
  :postcode "90210",
  :locality "Beverly Hills",
  :alcohol true,
  :alcohol_bar true,
  :owner "Rigo Fernandez",
  :latitude 34.058085,
  :meal_dinner true,
  :meal_cater true,
  :parking true,
  :region "CA",
  :address "9715 Santa Monica Blvd",
  :website "",
  :tel "(310) 273-8100",
  :category "Shopping > Tobacco Shops",
  :$distance 969.9574})


Haskell is the king of purely-functional, lazy, statically-typed languages. Write some Haskell and know that “if your code compiles it runs correctly”. Enjoy the same level of confidence using Factual… just plug in the Factual Haskell driver (factual-api in Cabal), and away you go:

let token = generateToken yourKey yourSecret
let query = ResolveQuery [ ResolveStr "name" "McDonalds"
                         , ResolveStr "address" "10451 Santa Monica Blvd" ]

That runs Factual’s Resolve service on a few business attributes and gives you back a “resolved” Factual entity. Now you can have confidence in your code AND your data.

But wait there’s more!

This was a whirlwind tour of a few selected client side drivers for Factual. But this hardly touches the full set of Factual’s live data and APIs. And there’s more data on the way, and more features on top of that.

We’ve also published drivers for Java, Ruby, PHP and iOS, with .NET and Android on the way. Just in case you’re… you know… into that sort of thing.

Best wishes,
Aaron Crow
Software Engineer

Heads-Up: New Categories for Global Places On-Deck

This is a quick heads-up to Factual developers and data partners to note that we will shortly be making significant changes to how our Global Places are categorized.

The Reasoning:

Our current categorization is based entirely on strings and does not use IDs; this makes queries cumbersome, locks us into a single language, and ossifies the classification system. All require fixing. We also received a large number of comments from our developers and partners in the last year on where the classification system can be improved, and we want to implement these. Lastly, we’re laying the groundwork for our move towards multi-categorization and a polyhierarchical taxonomy.

The Changes:

  • Each category will have a numeric ID; labels for each category will be provided in multiple languages
  • The shape of the hierarchy has been modified, and new nodes added (a few have been removed; we’re keen to keep it lean). Some categories have been split for better granularity
  • All Global Places are being re-classified for accuracy, as will our associated US Restaurants and US Healthcare Provides tables
  • The hierarchy is available as a structured JSON file to permit greater intelligence & query pre-processing on the client side; we’ll also provide a mapping file between old-and-new categories
  • Category search in the API will include all subcategories by default
  • Category strings in English will remain available for all Global Places
  • We’ve added Landmarks & Transportation nodes, and our Social and Retail nodes have made significant improvement in accuracy and coverage

The Timing:

You’ll see this happen in May 2012. We’ll keep our old categories attached to a static copy of the data through October, so no code changes on your side will be required immediately. However, you’ll want to move over as soon as convenient to take advantage of the new categories and new Places. Above all else, our goal is to increase the power of classification while minimizing developer disturbance. We’ll provide more details here and on the developer list as we have them. In the interim, have a look at the new Factual taxonomy on Github.

-Tyler Bell
Categorically Factual

Announcing China Places

你 好! Welcome to China, the world’s fastest-growing major economy, with annual growth rates averaging 10% over the past 30 years. This incredible growth has led China to become the world’s largest exporter and second largest importer of goods. If you have ever thought about tapping into this behemoth of a country, you may be interested with the latest country to leave beta.

Factual’s Shanghai team is excited to announce the formal release of China Places, covering the mainland region of People’s Republic of China. It is currently the second largest country dataset in Factual’s Global Places with over 9.5 million records, including 800K in Shanghai, 600K in Beijing and 250K entities in Guangzhou. The category makeup is around 43% Business & Professional Services, 11% Shopping and 9% Food & Beverage.

If you are developing apps that require China places, points of interest, and landmarks, data is now available through our Global API.

Learn More:

Allen Zheng
Factual Data Engineer, Shanghai



Hi! 欢迎来到中国。随着30年以来近10%的年经济增长,中国已经成为世界上发展最快的经济体。惊人的发展速度使得中国成为世界上最大的出口国和第二大的货物贸易进口国。如果你曾经想了解中国这个“庞然大物”,那么现在你也许会对我们新发布的中国地点数据感兴趣。

Factual上海团队很高兴地向大家宣布今天我们发布了中国地点数据的正式版。新版本的中国地点数据总共拥有大约950万条记录,覆盖了整个中国大陆地区,是目前Factual全球地点数据库中第二大的国家数据集。其中上海地区拥有约80万条数据记录,北京地区约有60万条记录,广州约有20万条记录。这些数据涵盖了商务公司(43%), 购物消费(11%),餐饮住宿(9%)等主要分类。

如果你正在开发与中国兴趣地点相关的应用,你现在可以通过我们的Global API来使用这些数据。


Allen Zheng

Factual at Where Conference and Data 2.0 Summit

Next week is a big week for conferences with both Where Conference (April 2 – 4)  and Data 2.0 Summit (April 3) taking place in San Francisco.

Tyler Bell, Factual’s Director of Product, will be at Where Conference giving a talk called “Automated Engagement: Electronic Receipts & the Future of Geo.”  This will be at 1:40 pm on Tuesday April 3 in Yerba Buena Salon 1 – 3.

The future of mobile payments contains real excitement: not around simple convenience, but rather the return of purchase history back to the hands of the user. While the commercial implications are of course enormous, the geotechnical potential – employing an artifact of the payments process – is huge. This brief talk aims to provide an overview to some of that hugeness, and discuss what how the game is taking shape on the table.

Gil Elbaz, Factual’s CEO and Founder, will be at Data 2.0 speaking on a panel entitled “How Open is the Open Web.”  This panel will be in the Robertson Auditorium from 1:30 pm to 2:00 pm.

“Web 2.0” is a misnomer because the protocols behind “Web 2.0” and the world wide web are not social or object-oriented at all. “Web 2.0” is based on the same principles as web 1.0 where the protocols for searching and organizing information are based on static pages linking to static pages. What makes the internet social, object-oriented, and ‘deep’ is how websites silo their own closed proprietary data. Though the rise of the API has allowed my data to talk to your data, we live in the age of a “closed web” where data is owned by applications. There are benefits of a “Closed Web”, where each application can design its data architecture in a secure, scalable, organized manner. Yet the concept of an “Open Web” is gaining popularity, where data is increasingly open, self-owning, or ownerless across applications. Is a true “open web” of data even possible? Which types of data will always live in closed applications? What types of new startups, business models, and advertising emerge in an age of open social data / open place data / open object data?