Using Big Data to Identify and Prevent Crimes

March 27th, 2014 by - 23,420 views

Predictive analytics tools have helped major corporations gain consumer insights, using them to drive profit growth and marketing campaigns. On the other end of the spectrum, law enforcement agencies on the national and municipal levels are using Big Data to identify and predict criminal behavior. Surveillance capabilities aside, the new techniques may discourage so-called “bad behavior” throughout the United States.

A Neighborhood Watch sign in a community.

A Neighborhood Watch sign in a community.

An example of success 
The Wisconsin State Journal reported that Madison, Wisc., police authorities consulted with analysts in the surrounding areas in anticipating a December crime wave that would sweep the University of Wisconsin’s College Court area. Apparently, once students leave for winter break in December, law enforcement officials receive numerous burglary reports.

The news source noted that three crime analysts are employed by the Madison Police Department. Operating through a cloud server, the professionals are able to help officers prioritize their efforts. The unit has been with the organization for nearly 10 years, garnering headline-worthy attention when one analyst helped a detective identify patterns in a string of bank robberies that occurred earlier this year.

Caleb Kelbig, one of the data experts working with the authorities, told police in Madison and surrounding cities that the perpetrator could hit 1 of 11 possible targets on the afternoon of March 5 or 6. Amazingly, the robber appeared at one of the locations in Middleton, Wisc., at about 2:30 pm on March 5.

Prioritizing intentions, citing appropriate uses
Jignesh Patel, an expert in Big Data use and a professor at UW-Madison, noted that cloud computing has made predictive analytics tools easier to use. Developments in IT have also opened up new avenues through which digital information can be collected. For example, smartphone software has contributed significantly to the data-gathering trend.

The use of this technology has been received with a combination of awe and apprehension. Yaniv Mor, a contributor to Wired, noted that the techniques used by the U.S. National Security Agency evoked questions concerning breach of personal privacy and widespread fear that “Big Brother” had now become a reality. The writer pointed out that although the organization’s methods were unethical, they were ultimately after bad people planning on committing transgressions.

Patel acknowledged that the country has yet to find a solid balance between crime prevention and citizen privacy. How the U.S. reaches that accord is a matter of public policy and the activism of residents.

How it works
Tom Scholten, an analyst with the Madison Police Department, told The Wisconsin State Journal that his team uses software programs to access the organization’s cloud storage and break down the data within records and reports into applicable patterns. Sergeant Tim Radke stated that they’re looking for subtleties that could reveal specific information about perpetrators.

“Not just that someone kicked in a door, but how it was kicked in,” Radke told the news source.

Mor acknowledged the programs many national and municipal agencies have developed and implemented are offering law enforcement greater processing capabilities. One particular project heralded by the U.S. Department of Homeland Security created the Future Attribute Screen Technology initiative, which seeks to identify potential terrorists by using computers in mobile labs to monitor the vital signs, body language, and other physiological patterns of individuals.

Another system, RTM Dx, was developed by researchers at Rutgers University. Working through a cloud infrastructure, the application lets law enforcement diagnose the spatial correlation between where crimes have occurred in relation to different features of the environment, such as a schools or nightclubs.

Many constituents have recognized the benefits of crime fighting predictive analytics, but a large contingency remains concerned about how it will be used. If national agencies and municipal authorities make a public effort to reassure citizens that their personal data isn’t being misused, then widespread approval may follow.

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