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Predictive Policing: Preventing Crime with Data and Analytics

Author: Jennifer Bacher
Organisation: Johns Hopkins University
Publish Date: 2013
Country: United States
Sector: Technology
Method: Creative thinking
Theme: Crime
Type: Report
Language: English
Tags: Crime, Crime prediction, Crime analysis, Predictive analytics, Crime data, Data mining, Policing

This report examines the theory, methods, and practice of predictive policing It begins with a brief overview of the origins of crime analysis and then places predictive policing within the broader framework of crime prevention. Although quantitative crime analysis has existed for centuries, the use of data and analytics to predict crime has only recently emerged as a distinct discipline and widely used practice. Today, predictive policing is viewed as one pillar of intelligence-led policing, a philosophy in which data drive operations. The report also describes the key components of predictive policing, including its theoretical foundations and the types of data and methods used to generate predictions. Spatial methods, such as clustering algorithms and density mapping, are used to detect high-risk areas. Methods that exploit variation in both time and space can be used to forecast the next crime in a sequence. Social network analysis, by identifying individuals who provide resources or control information flows, can inform interdiction tactics.
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