The Wall Street Journal writes last month about geotargeting. While not new news, percolating into the business press no doubt took a fair mount of work from the Quova PR team. The article highlights the strengths: regional content targeting (to watch or not to watch the Red Sox depending on where you are), anti fraud and other measures. The money sentence:

Still, geolocation technology won’t pinpoint Web visitors’ locations beyond the city level, which won’t satisfy advertisers seeking to target potential customers by neighborhood or street.

Correct, indeed. Umibot has often-written about Urban Mapping’s geographically-modified keyword research tool (many enhancements coming soon) that can help overcome this technical limitation of IP-based geotargeting. However, simply being able to target at a local leavel, without considering inventory and advertisers, is folly. As Ian White discussed in a recent location-based services conference in Berlin, the debunking of the “local mobile Starbucks advertising” (nb, extremely tired example), whereby a person walks past a Starbucks and a coupon for a latte suddenly appears, is patently false–now and forever in the near future. But more on that soon…

UMI’s own Ian White will share the podium with several yet-to-be-determined panelists at the SMX Local & Mobile conference in San Francisco July 24-25 at the Marriott Hotel. The panel, Monetizing Local & Mobile: Who’s Making Money, is bound to be provocative if past panels are any indication.

This just in…Umibot is pleased to announce a few enhancements to our Neighborhood REST API…

In addition to our significantly-increased neighborhood coverage
we’ve responded to developer requests and enhanced the REST API’s getNeighborhoodByLatLng to offer the option to return zero-to-one results, as opposed to the default zero-to-n results.

Why does this matter? Particularly in urban areas, neighborhood boundaries are organic, complex and because they are culturally defined phenomena. They are often with overlapping and/or hierarchical, and sometimes vague spatial relationships.

If you are enabling local search for your records, associating them with multiple neighborhoods will provide your users with more search options. However, some application developers want to know the neighborhood for a particular location. For this case, users can rely on our algorithms to take into account the underlying spatial relationships and geometries of all the neighborhoods which include the point to provide the best answer in response.

A final minor enhancement:, we have added ‘distance’ field to the result of ‘getNearestNeighborhood’ representing the distance to the centroid.

[Background Music: Begin Dirge]

Please note that we are deprecating the SOAP API. We have observed that the complexity of SOAP clients causes far more headaches for our end users, and our development overhead is not insignificant. As a small team, we have decided to focus our energy on expanding our coverage, and enhancing the REST API in response to user feedback. If you haven’t already, we encourage you to move over to REST.

Phew! After more than a year in development and two years deep in Umibot’s RAM, today we unveil a grand plan: normalized mass transit data for (today) 53 public transportation systems in the US, Canada and UK. To get here we had to develop other pieces–a data intake platform and a schema. Some more info on all of these:

Web-based Mass Transit Data Intake Platform (no acronym yet) Umibot believes the greatest cost in data collection is identifying and purging the system of dirty data. By auto-validating data at point of input, we’re able to significantly reduce this cost. UMI’s proprietary web-based platform is flexible and captures the vast collection of spatial and attribute data we manage. This includes things like routes, station footprints, exits (you can’t generally exit at a ‘station’), hours of operation, handicap accessibility, elevator location, amenities (retail, bathroom, telephone, etc…) and a great deal more. We then associate this attribute data with the ‘spine’ of spatial data and then compute a graph network, making the data ‘routing ready’ across a variety of platforms.

Transit agencies can take advantage of this platform by using UMI’s infrastructure as a platform to inventory their own data. It’s a well-known fact that transit agencies face bureaucratic, technical and legal challenges to releasing data, and this platform is one more reason for transit agencies to partner with industry to increase data distribution and support increased ridership by driving awareness.

Normalized schema
Before we began data collection, a uniform schema that recognizes transit nuances and complexities needed to be developed. For example, scheduling for the London Tube operates on a headway, meaning trains depart every Xish minutes. New York’s MTA operates on a tabular schedule, with scheduled departure times. Sounds like a detail, and it that’s exactly what it is–multiply this nuance 100 times and there’s a great deal of data definition that matters. What we’ve developed is internal to UMI and offers tremendous flexibility to add new mode types (ferry, funicular, etc). It has nothing to do with the output customers receive, and we’ll have more news about that soon.

Coverage The map below reflects current US coverage. Across the 53 transit systems, UMI has defined over 14,000 individual stations and over 100,000 data attributes. Stay tuned for increased coverage, attributes, service delivery and partnerships!

And some fun transit statistics for current coverage:

• 22% of transit stations have bathrooms (they may not be operable/accessible, but they exist)

• 35% of transit stations have dedicated parking

FYI: Wire release

On Day Two of the Kelsey Group’s DDL conference here in Seattle, Zillow’s Rich Barton gave a keynote address about his three Big Ideas where information asymmetry presents significant opportunity for business model disruption: travel, legal services and real estate, or as he says, “storming the Bastille.” Umibot knows that Rich has obviously proven himself as a successful entrepreneur but wants to clarify a few points he made (and I thank my master for giving me my AI that allowed me to ‘know’ this).

Zillow’s neighborhood database has 7,000 neighborhoods covering approximately 150 US cities.

UMI’s neighborhood boundary database contains almost 40,000 neighborhoods across 1,200 towns and cities in the US (plus additional Canadian and European coverage), and we continue to add additional neighborhood coverage on a regular basis.

Rich said Zillow’s neighborhood boundary data is available via an API. I believe he misspoke. Certainly Zillow offers an API, but I don’t believe it offers neighborhood boundary data (although this could certainly be done).

UMI offers a fully robust API, allowing us to offer neighborhood-level geocoding via web services using REST.

Zillow’s boundaries are generally drawn around census tracts and postal codes

UMI’s neighborhood boundaries conform to how users (not direct marketers or actuaries) understand neighborhoods–postal codes and other administrative/political boundaries bear little relationship to neighborhoods, as this search reveals.

There’s more to this story, but the above is probably enough for the non-obsessed to chew on.

Urban Mapping’s own (guess who) Ian White will participate at MediaPost’s Search Insider Summit May 18-21 on Captiva Island, FL. Ian will participate on several panel discussions and breakout sessions. Umibot is thrilled that UMI will be at the event as it will provide a good opportunity to take the pulse of search engine marketing and local search.

Tis the season…

UMI’s own Ian White will participate on a panel Getting Found in Local Search & Maps at Search Engine Strategies 2008 Toronto show, June 16-18.

We’ve been working on it for quite some time, and today are thrilled to announce the first public release of our GeoMods geotargeting platform. In the past my master has written and spoken about the perils of IP-based geotargeting for local search.

Here’s the fact: for local targeting, where granularity matters (and some will argue it doesn’t yet matter), resolving an IP address to a location doesn’t work. The UMI solution doesn’t claim to be a technical solution, but rather aggregates large sets of geographic keywords around existing search behavior. It’s a mouthful to explain, but stand by for more on that front.

After some hard work/late nights and prodding from the geo-techno elite, we’ve humbly completed and are pleased to offer RESTful access to our neighborhood API.

What does this mean for developers? Probably less time spent developing and more time doing. Since we offered the SOAP-based free neighborhood API last month, dozens of individuals and companies have signed up, and we’re confident this announcement will spur another wave.

We’re also thrilled to be using Mashery to manage our multiple APIs. Ciaran and team twisted themselves pretzel-wise to get us up and running in no time at all. Calling Mashery ‘on demand’ is exactly what what they offer, and it’s what they delivered.

The news release with a quote from Brady Forrest and other goodness.

Urban Mapping’s Ian White will moderate a panel, Why Local Is Different, at the annual SES New York Conference & Expo at the Chicago Hilton, March 17-20.