Open311 Data Prediction Challenge

As the federal government shuts down, there is no shortage of predictions about how it will shake out, when it will end, and who will take the blame.

Speaking of predictions (how’s that for a segue?), David Eaves (who writes a great blog for those who like the intersection of cities, governments and policy) just announced that the Open311 Prediction Data Competition is now live.  David and and the good folks at SeeClickFix are sponsoring this, as a follow on to a recent hackathon on the same topic.

For those not familiar: 311 is a semi-generic name for issue-reporting and question-asking/answering systems in cities (in Boston it’s called Citizens Connect).  In many cities, 311 is the city’s customer service desk, and was originally designed to simply make navigating the city bureaucracy easier. It was actually Bloomberg’s first major policy initiative in 2002.  

But, like a lot of data rich services, 311 has evolved to support secondary uses.  First, it’s essentially a citizen-sensor network, which, among other things, was credited with solving NYC’s Maple Syrup Smell mystery.

And second, it has potential as a predictive system.  There are all kinds of leading indicators hidden inside 311 data.  For example (and this one is logical and obvious, which still doesn’t mean it’s easy to see without data), Chicago used 311 data to predict when and where rodent problems would spike, based on 311 complaints about trash as the leading indicator.  This is just a tiny example of how big data sets like 311 can be used to make predictions that can help cities deploy their scarce resources more effectively.

So, if you’re into cities, data hacking and predictive analytics, check out the competition on Kaggle.

Related: Nate Silver is hiring stats- and prediction-savvy writers to focus on politics, sports and economics for   What a cool bunch of jobs those will be for the right people.

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