April 1, 2010
Opening the Placecast Match API

Placecast has spent nearly five years building a platform that resolves the problems of delivering location-based programs at scale, and introducing ways to monetize them. Yesterday we opened up some of that functionality for free to the LBS ecosystem - our MatchAPI - in the hopes that everyone working on the challenges of location-based offerings can focus on generating revenue from location-based services as we will all benefit from attracting marketing spend to the space.

The explosive adoption of smartphones over the past year (Thank you, Steve Jobs) has kicked-off a wave of innovation in the area of location-based offerings, specifically in mobile. We are so excited at the prospects for everyone in the ecosystem to bring new products and services to consumers and brands – 2010 really is “The year of mobile…” in that we are finally seeing mainstream adoption by marketers, even if it is still in its infancy.

One of the first building blocks – part of the plumbing – of generating revenue from location-based offerings is the ability for everyone in the ecosystem to seamlessly identify places on the planet. While on the surface this appears to be a pretty straightforward exercise, when you dig deeper, you discover (as many of the LBS start-ups are learning) that it can actually be a frustrating, time-consuming problem because there are so many different ways to refer to a place. 

One approach to solving this problem is to try and establish a standard referencing scheme. At Placecast, we took a different approach, realizing that standards are hard to achieve in a newly emerging industry. The reality is that different companies will want to keep their different ways of referencing locations – and that should be OK. 

The Placecast Match API is a free tool that enables location content providers and location-based application developers to refer to a location in any number of ways, and validate that those references resolve to one true location on the planet. It resolves two basic problems of working with large location-based data sets:

  • First, it disambiguates addresses - identifying all of the different ways to express the address of a location, and verifying that those different expressions refer to the same place on the planet. So a social check-in app, for example, can de-duplicate the many different ways their users might refer to the places where they are spending time.
  • Second, it maps all the relevant IDs from different content providers to that same place on the planet, so that it is always referred to correctly by any other system. Here, a content provider aggregating from many different sources can reconcile the different references to the same places in their system.

Best of all, LBS companies do not need to adopt a new location referencing system – they can keep whatever they have. We’ve been using this system for over a year now and pressure-tested it with millions of records worldwide. We hope anyone who uses it will find this a valuable contribution towards the goal of all of us monetizing our location-based offerings.

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