Students take on crime with big data

Budget cuts have substantially hindered the effectiveness of law enforcement departments across the country in recent years. According to a survey conducted by the Police Executive Research Forum, 51 percent of participating agencies said they had seen their available resources reduced in the current fiscal year. In addition, the average respondent expected to receive a 5.3 percent budget cut by the end of 2013. To address these financial issues, many departments have been forced to refrain from bringing on additional police officers, while others have made sweeping layoffs. Forty-five percent of the survey participants reported implementing a hiring freeze, and 23 percent said they had recently reduced their manpower to save on operational costs.

With fewer available police officers to patrol communities, services in many areas have been scaled back, with many departments opting to not respond to crimes where the victims are not in any immediate danger, such as burglaries. According to the Christian Science Monitor, some citizens in Oakland, Calif., have even gone so far as to hire private security teams to patrol their neighborhoods in lieu of municipal protection

Identifying crime trends with big data and Hadoop
Considering the lack of available resources and manpower, police forces across the country need to allocate their officers as efficiently as possible. One way departments can do this is by leveraging big data and Hadoop software tools to identify specific locations that are at risk for violent crime. Forbes reported that students from a San Francisco-area high school recently applied data analytics tools to determine high risk areas within the city down to individual street corners. Using various sources of data including approximately 1.5 million crime records made publicly available by the city of San Francisco, students were able to identify several crime trends that could help law enforcement officials deploy their resources more effectively.

For instance, one block was found to have a significantly higher crime rate than any other in the city. After compiling the data and crunching the numbers, students found that, on average, a crime was reported there once every three hours. They also identified 10 liquor stores within a two mile radius of each other that experienced high levels crime in their immediate vicinity. Another factor that local police could consider is the effect that professional sporting events have on crime rates, as the students' analytics findings showed that the city experiences a 20 percent surge in crime during a home sports game.

Again, these high school students were able to glean this insight from publicly available information, and with the wealth of additional data stored on police servers, law enforcement officials could drill further down for actionable results. With an analytics application built on a Hadoop architecture, authorities would have the freedom to scale their projects to their needs, allowing them to pursue a cost-effective solution to their resource allocation dilemmas.

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