The Chicago Police Department - Predictive Analytics Group

The face and function of the traditional municipal public works department will change.  The new organization will have the designation of "Department of Asset Management" run by a certified asset manager.  One engineer will be in charge of predictive analytics - the ability to pinpoint a potential source of problems and predict outcomes by analyzing  big-data will be a true game changer for the old public works department.

The police department of the work are getting these new powers and opportunities.  From a interview with the founder of the Chicago Police Department Predictive Analytics Group, Brett Goldstein (link):

"I think predictive analytics is a game changer. Prediction is how you take data to the next level. First, using the data that’s available, you take a picture of the current status and try to identify what we know now. Then, you couple this with some of the more traditional research, the classical journal research that looks at a social science problem over several years to try to understand what drives outcomes. Prediction is when you bring together these pieces together.

What does prediction mean for the city? I would argue that Chicago, like any other city, is an ecosystem. Within Chicago, we have many ecosystems within the broader ecosystem, such as neighborhoods. As we take data and all of these different sensors within urban science, including data from 311, 911, bus movements, and crime, you start to understand how things are related to one another. As you understand how things become leading indicators for other things, that’s when you can start to tweak what happens. Imagine instead of us dealing with problems reactively, we’re able to think about dealing with issues earlier to prevent a certain outcome.

A couple of examples might help illustrate this. One, there’s a small area of Chicago where when the alley lights go out, the garbage cans disappear. Every garbage can costs us money to replace. This seems like a great opportunity to use our understanding of the system to prevent that outcome. Let’s think of other cases. Say we’re about to get a big rainstorm. Where are the areas that have the highest probability of flooding? This can help us stage our resources appropriately. This all comes back to an idea that it is not okay with the mayor and not okay with me, which is “good enough for government work.” By leveraging this data, identifying the patterns, and identifying the leading indicators, we’re doing what medicine started to do a number of years ago. We start preventing problems instead of reacting to them, and that’s the core of prediction."