Spectacular footage of the B-2 Stealth bomber taking a dive on takeoff and crashing into billions of pieces (and greenbacks). Umibot is most interested in the underlying reasons for this catastrophe–the accident was preventable and not due to human error. A faulty data sensor was feeding exaggerated information about moisture content. This was complicated by incorrect airspeed readings. The crew safely ejected but had no chance to remediate the situation. The narration in this video is didactic:

Once could attribute this to simple bad luck, but in the case of large-scale systems, there’s probably (or should be) something else at work. The esteemed Don Norman (an adviser to UMI) has written extensively about this phenomenon. His editorial, “Human Error and the Design of Computer Systems,” from the ACM in 1990, states it clearly.

While UMI’s work does not have life- or national security-threatening undertones, the cost of bad data is still relevant. We wrote about this in a post last year (”How is Storm Tracking Like Local Search?”)–just as an automobile’s performance will be constrained by the inputs, an application’s value will be directly tied to the data flowing through it. Ian also spoke on The Design of Data in a 2006 conference.

-Thanks Treough

News from the National Weather Service that is sure to get (geo)data-wonks excited…

From the National Oceanographic & Atmospheric Adminstration (parent agency of NWS), the current means of tracking severe weather events is done in the following manner:

…The NWS currently issues and disseminates warnings for tornado, severe thunderstorm, flood and marine hazards using geopolitical boundaries.

As of 1 October 2007, this system will change to something new:

Storm-Based Warnings (threat-based polygon warnings), are essential to effectively warn for severe weather. Storm-Based Warnings show the specific meteorological or hydrological threat area and are not restricted to geopolitical boundaries. By focusing on the true threat area, warning polygons will improve NWS warning accuracy and quality…

You may want to ask Umibot “what’s the big deal?” Some graphics from the Storm-Based Warnings (NB: press release to follow on 10/01/07) illustrate this:

On the left, the county is used as the unit of measure–this means if a predicted storm path touches a county boundary, the entire county will receive an alert. This is especially cumbersome in some Western states, where counties can be extremely large. Deploying emergency resources (first responders, food, supplies, etc…) and alarming the public when not necessary could prove and expensive proposition.

The image on the right highlights the new approach: “threat-based polygons” might sound menacing, they are no different from what the NWS currently uses with a key exception: the granularity has changed such that the unit of measure is now the municipal boundary.

From UMI’s perspective, what is interesting to note is that NOAA prediction accuracy did not drive the Storm-Based Warnings program–there are meteorological (and related) advances that help officials understand patterns of severe weather, and that is independent from presenting those data. Because prediction science has become more accurate, a smaller unit of measure (ie, municpal area) can be used. From this perspective one could say predictions were ‘hiding’ behind the larger unit of measure (ie, county).

Umibot likes these kinds of stories because they play directly into his (or her?) sweet spot–the design of data. And this was the focus of a talk Ian gave last year on the very subject.

The analogy for local search is clear–data should drive the use case of an application. If one is going to offer an application that allows for (say) mobile search, will a user have the granularity that is needed to have a meaningful experience? An example here is “restaurants in San Francisco”–mobile means you are, well, mobile, on the go, and a city is (probably) not a meaningful geo-constraint. Something more granular, like a 2 mile radius (if the device is location-aware), cross street, or neighborhood will likely be more satisfying.

Ian White will present at the IDEA 2006 conference in Seattle, October 23-24. His presentation, The Design of Data, will address how spatial data has become relevant to everyone.

Update:
Link to the audio is here and PDF of slides here