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…

For San Francisco in 1953, it was about 9 lbs. Recent acquisition to the UMI rare book collection includes this gem. Over 2200 pages of goodness that is chock full of advertising–on the spine, front cover, back cover, margins, every which way…makes today’s YP products pale in comparison.

Tonight on the local CBS affiliate’s crack I-Team, Yelp was profiled, or more accurately, skewered [unfortunately video was not posted], by Jeanette Pavini, KPIX’s own ConsumerWatch I-Team type. Granted 1) this news segment has a strong pro-consumer bias (and perhaps that’s a good thing), 2) small business owners were portrayed as victimized by the evil, capitalist-breeding Yelp empire, and 3) Yelp did have a (brief) response during the segment, the reporting didn’t clearly sketch out that Yelp, in fact, makes money by selling premium advertising to these same businesses.

From the text summary of the story:

So she paid for five months, a total of $1750. Seaton said some of her negative reviews were moved to the bottom. But, Seaton said, “All of a sudden some more negative reviews got posted, but there were no favorable or positive reviews.”

So she canceled [her Yelp sponsorship]. “I feel like they are extorting money and preying basically on business owners,” said Seaton.

It’s not clear what’s more sad: that Seaton (and other business owners featured in the segment) appear as helpless, naive and unknowing in the ways of the world or that if this is the state of consumer awareness (in San Francisco, no less), it will be helluva long time before Main Street catches on to this whole Internets thing.

Preemptive clarification: The “helpless, naive and unknowing in the ways of the world” quote stands in  contrast with the myriad forms of marketing and advertising that have been employed for many decades (yellow pages, newspapers, broadcast, direct mail). Further, the story did not speak to the benefits of advertising on Yelp (or other local search sites): increased awareness, leads, closed business, etc..

UMI’s own Ian White will be making an inaugural appearance at Search Engine Strategies on August 21. The session, Local 2.0: The Evolution of Local Search, programmed by The Kelsey Group, will be moderated by Mike Boland.

UMI’s been laying groundwork for tying online marketing with offline results and will be sharing the results of some research.

A few months ago Yahoo! announced it had incorporated neighborhood search into Local and other properties. We’re pleased to say this is brought to you by Umibot and the hard-working team at Urban Mapping, so Yahoo! can now enjoy the same neighborhood goodness as many of our other satisfied customers.

Here’s the official news.

Umibot’s been a proud servant to AT&T’s YellowPages.com for some time, but neighborhood search functionality is now front and center, so search by neighborhood to your heart’s delight!

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.

As in, literally…

Today we’re excited to announce a partnership with Intelligent Direct, the market leader of custom print and data solutions for business. Through publication of custom electronic and printed maps of all sizes, companies can better manage the impact of location, geography and demographics.

Urban Mapping’s neighborhood boundary database will be incorporated into custom solutions. IDI’s MarketMAPS division has served thousands of clients in its 25+ year history.

Umibot is most excited about the offline possibilities. Even though it’s not clear if a bot can exist in the real world, there’s no question direct marketing does. And it’s huge. And small/medium-sized businesses think about overall marketing impacts and budgets, not segmenting by channel. This announcement is the first of several to link interactive and direct marketing efforts.

-The official news

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