YOUR ALGORITHM SUCCESSFULLY PREDICTED CRIME IN US CITIES A WEEK BEFORE THEY HAPPENED. HOW DID YOU BUILD THE ALGORITHM?
The city of Chicago and the seven other cities that we looked at have started putting out crime event logs in the public domain. In Chicago, these are actually updated daily with a week's delay.
These event logs contain information about what happened, what type of crime it was, where it happened, the latitude, longitude, and a timestamp. In Chicago, we also have information about if there were any arrests made when there were interactions with the police officers.
So we start with this event log and then digitise the city into small areas of about two blocks by two blocks about 1,000 feet [300 metres] across.
And in one of those tiles, we'll see this time series of these different events, like violent crimes, property crimes, homicides and so on. This results in tens of thousands of time series that are coevolving.
What our algorithm does is look at these coevolving time series, then figures out how they are dependent on one another and how they're constraining one another - so how they're shaping one another. That brings up a really complex model.
You can then make predictions on what's going to happen, say, a week in advance at a particular tile, plus or minus one day. In Chicago, for example, today is Wednesday. Using our algorithm, you can say that next Wednesday, on the intersection of 37th Street and Southwestern Avenue, there would be homicide.
HOW DO YOU ENVISAGE THE WAYS YOUR ALGORITHM COULD BE USED?
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