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The Real-Time Crime Forecasting Challenge seeks to harness the advances in data science to address the challenges of crime and justice. It encourages data scientists across all scientific disciplines to foster innovation in forecasting methods. The goal is to develop algorithms that advance place-based crime forecasting through the use of data from one police jurisdiction.
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Based on the research findings, law enforcement officers appear to commit crimes at a much lower rate than the general public. However, in some cases, at times due to the stressors of the job and frequent exposure to trauma and violence, officers engage in misconduct or criminal behavior.
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This Research for the Real World seminar explores common police practices for responding to gun violence and the extent to which they are contributing to reductions in violent incidents. The panel will also explore the role of multi-disciplinary partners such as the public health sector in reducing gun violence, and discuss promising practices for law enforcement partnerships to leverage complimentary violence reduction efforts.
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