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The Future of Policing Might Be in the Cloud 

Microsoft and a technology partner worked with Chicago to create an analytics tool to help the Chicago Police Department.

What if police departments in cities and towns across the United States could use the cloud to get a real-time view of crimes that were occurring? And what if they could leverage data to better predict when and where certain kinds of crimes might happen?

That’s the vision Microsoft thinks could be just around the corner. The software giant, which has been ramping up its Azure cloud services business as more applications flow into the cloud, is hoping that cloud tools can help transform the way city governments tackle public safety. The City of Chicago is already experimenting with the technology.

Embracing Predictive Analytics

Parul Bhandari, government industry solutions lead on Microsoft's Worldwide Public Sector organization, says in a blog post that “thanks to everything from automatic license plate readers to gun sensors, the real-time data available to law enforcement agencies continues to skyrocket.”

Microsoft thinks that as police departments and their IT teams get flooded with more data, it can provide ways for the departments to make sense of all of that information and improve policing.

Bhandari says, thanks to analytics and cloud tools like its Power BI software, Azure Stream Analytics and Azure Machine Learning, “police departments now have the capability to predict when and where crimes will happen in the future.”

Third Eye Consulting Services & Solutions, a Microsoft partner, created a Power BI dashboard that leverages Chicago’s open data portal. The tool combines data from several different sources, including Chicago crime data, data from the U.S. Census Bureau and real-time weather and traffic feeds.

“The solution enables law enforcement agencies to capture an overall picture of criminal activity across the city — both what’s happened historically and what’s likely to occur in the future,” Bhandari says.

Police officers can access the dashboard to see how likely different kinds of crimes are to occur in various parts of Chicago, “based on different factors including the month, day, time of day, and traffic and weather patterns,” says Bhandari.

She adds, the dashboard uses a map that lets officers pinpoint “what crimes are most likely to happen by neighborhood at different times of the day and night. They can also see the predicted frequency of specific crimes, clicking on each type of crime to view where it’s likely to occur.”

Further, police officers can use the dashboard to view historical crime trends and compare these trends to what’s predicted for the future.

Multiple Benefits of Predictive Power

What possibilities could all of this cloud-based analytics open up for police departments?

Bhandari suggests departments could allocate resources more efficiently based upon predictions of when and where crimes might occur. Doing so could save departments money on manpower and equipment, with savings of potentially millions of dollars.

“Moreover, they can arrive at the scene more prepared, better protecting themselves and their communities,” Bhandari adds. “They also have greater insight into why crimes occur in the first place, giving them the tools required to prevent crimes before they occur.”

Using machine-learning technology, IT staff could test hypothetical scenarios to create models of what might happen in the field based upon specific variables.

“For fighting crime and keeping every citizen safe, Microsoft has the most sophisticated cloud-based big data technologies stack, which can help police departments not only understand why and how crime occurs but also predict when and where it can happen,” Dj Das, founder and CEO of Third Eye, says in the Microsoft blog post. “Powerful data analytics tools like Azure Stream Analytics and Azure ML, coupled with the dazzling Power BI visualization package, make it all possible at a reasonable cost and with the ability to scale up and down as needed.”

West Midlands Police/Flickr
Mar 24 2016

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