Our neighborhood database now includes over 2,400 cities across 50 states (and a few territories). We continue to increase breadth and depth, supporting enterprise customers and startups leveraging our Neighborhood API. International coverage continues to grow, and more news shortly!

The official word

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.

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

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