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City of Chicago develops big data platform to improve the lives of citizens

Tom Macaulay | June 27, 2017
The app built on MongoDB adds a vast range of data sources such as 911 calls and building licenses to an interactive map.

Data under magnifying glass

Government data stockpiles continue to proliferate but their power often remains trapped in unexploited silos. The City of Chicago has unleashed the potential of its numerous data sources, from emergency calls to inspection records, in an in-house application that adds deep data analysis to a simple map interface build on MongoDB.

WindyGrit is a real-time situational awareness application used to see where and when events are happening in the city so they can respond faster and improve the quality of life of residents.

screenshot of integrated data sources
Image: MongoDB. Click on image to enlarge

A selection of 36 data sources is integrated into the MongoDB database to understand all manner of issues in the city as they develop over time. They include 911 calls, the non-emergency 311 line, business licenses, building violations, Tweets, city traffic, weather, emergency vehicles, and environmental complaints.

"It covers almost every single dimension of the city," says City of Chicago chief data officer Tom Schenk. "When you work with a department and they want to add their data, we say: 'That's no problem. What do you need to do? How do we use this to shape your operations?' We're very mindful of modifying the application so it's actually servicing and approving our operations."

The data is used to identify issues around everything from marathon routes, traffic accidents and criminal activity to managing disease outbreaks and opioid overdoses and plan how they should be managed by the government.

The city also used the application to improve food safety inspection. There are 16,000 restaurants in Chicago, but only 36 inspectors to check them. The predictive analytics in WindyGrit were used to plan which establishments to check.

The city's restaurants are all displayed as dots on a map, with information on the type of violation available by hovering over each. Rules can be added to reveal which failed their last inspection, and then display them on a heatmap to understand the areas where failures are most common.

Additional data sources such as weather, burglaries, sanitation issues, complaints, and alcohol or tobacco licenses in the vicinity can all be incorporated to understand how different factors influence inspection results.

Rudimentary machine learning then helps then categories which restaurants are most likely to fail. The sanitation manager can use this information to prioritise resources and send their inspectors to only places most at risk.

To measure the impact that the predictions had on finding critical violations a double-blind study was conducted to measure the impact that the predictions had on the speed at which the team could discover critical violations.

 

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