How Data Analysis is Helping Prevent Crime

Written by KOVA Corp

Crime prevention, while a the primary concern for law enforcement agencies, is a difficult thing to achieve. There’s no magic formula when it comes to preventing crime before it happens, but many agencies are trying to hone in on better ways to do so using data analysis.

Through analysis, agencies are able to find correlations between one seemingly unrelated factor and another, such as a connection between truancy from school and an increase in local robberies. The true future of policing is in examining complex patterns of data and developing a theory or method based on what that data tells them.

The Smart Policing Initiative and other programs

One of the most promising programs that seeks to use data analysis in crime prevention is called the Smart Policing Initiative, or SPI. The SPI is a program of the Bureau of Justice Assistance that provides funding and training for data-centered crime prevention. The SPI is currently in use in 38 local police departments around the United States.

What’s perhaps most interesting about the trend towards smart policing is that it’s not an especially new concept. The idea of deeply analyzing data to look for potential crime patterns has been in existence since the mid-1990s, when a program called CompStat was created. CompStat was a model that tracked the location of crimes and the status of various crimes and allowed for a primitive level of analysis.

Since then, there have been much more in-depth analytical models created, programs like GIS mapping, that allow police departments to look for less obvious, more deeply ingrained trends in crime and find reasons why those trends are occurring.

How does analysis help improve policing?

Analysis can also lead to better methods, like a concept called “hotspot policing,” which is the idea of focusing policing strategies on a small geographic area or places where crime seems to be concentrated. The decision on where to focus these strategies is made through intense data analysis, and it has delivered concrete, encouraging results.

When the Philadelphia police department tried it in 2009, for example, by increasing foot patrols in crime hot spots, they were rewarded with a 23% decrease in violent crime over a three-month period. Once that initial step provided encouraging results, the police department submitted a proposal to SPI for a larger experiment based on crime hot spots, and the training to take that concept to a larger scale.

By 2014, there were 26 officers training in data analysis in an area with 21 districts across the city, meaning that each district had at least one officer analyzing data at any given time.

Since that expansion, home burglaries decreased by 39%, and auto theft is down by a stunning 64%. And Philadelphia isn’t the only city that’s had success with data analysis and smart policing.

Indio, CA police analyzed data about truancy rates and used it to predict the potential areas where robberies were likely to occur. Within months of that analysis and the implementation of data-driven policing, there was a 2% decrease in robberies.

While it might be a daunting idea for some law enforcement agencies to consider retraining or reassigning their staff, it seems like the data-driven model is producing such positive results that it might be unavoidable. There’s so much potential for reducing crime or even preventing it from happening in the first place, that smart policing seems firmly entrenched as the wave of the future in the arena of public safety.

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