Where is Where 2.0?

CNet’s Rafe Needleman reports in “Toronto or San Jose: where am I, anyway?” a nice example of badly inferred geo data at the… Where 2.0 conference.

I’m at the Where 2.0 conference in San Jose. Unfortunately, the Loki location-finding software on my laptop, which I raved about in a previous blog post, thinks I’m in Toronto. Probably the conference team picked up its Wi-Fi access points from an office or event in Toronto and shipped them down here. At any rate, it’s ironic, given the topic of the conference, but more importantly than that, for a few moments, Google thought I was in Canada and sent me to the Canadian version of the site (www.google.ca) when I tried to search. It was no big deal, but it shows you how location data applies to things you don’t always think of as location related. And the potential downsides to poor location data can be serious. Imagine if I had some emergency-response product that thought I was in Toronto instead of San Jose–or if I was on a VoIP phone that was registered to a different location, and then I dialed 911.

He then continues by mentioning the OpenStreetMap project as a potential answer to such issues:

the more people report where they are and where they’ve been, the more accurate maps and location-finding data will be available for everybody.

While the “wisdom of the crowd” can create improved geospatial data, it surely can also create errors and ambiguities.

Finally Suzanne Axtell reveals the source of the problem:

we’ve looked into this some more and found that the Fairmont Hotel IP block is registered in Toronto Canada. All our traffic uses the Fairmont IP.” There it is then.

Note Plazes often takes minutes before updating my location… that’s an example of location timeliness that can be the source of spatial uncertainty.

Relation to my thesis: Bad or irregular location quality is an issue that is not often takled by location-aware applications. These systems often rely on the user to notice the error and act upton it.