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NCJRS Abstract

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NCJ Number: 251464 Find in a Library
Title: Real Time Crime Forecasting Challenge: Post-Mortem Analysis Challenge Performance
Date Published: January 2018
Page Count: 12
Sponsoring Agency: Conduent Business Services, LLC.

National Institute of Justice (NIJ)
Washington, DC 20531
Team Conduent Public Safety Solutions

US Dept of Justice NIJ Pub
Washington, DC 20531
Grant Number: 2016-NIJ-Challenge-0031
Sale Source: US Dept of Justice NIJ Pub
810 Seventh Street, NW
Washington, DC 20531
United States of America
Document: PDF
Type: Program/Project Description; Report (Grant Sponsored); Report (Study/Research); Research (Applied/Empirical)
Format: Document; Document (Online)
Language: English
Country: United States of America
Annotation: In responding to the U.S. Justice Department’s National Institute of Justice’s (NIJ’s) challenge to develop a means for real-time crime forecasting, this report describes the methodology and findings presented by the Conduent Team (the Team), which deployed functionality from the Operational Analytics module within the Conduent Business Intelligence platform (CBI).
Abstract: CBI is a configurable, flexible platform that provides a user-friendly interface for running machine-learning-based analytics. The project focused on crime data exported from a Record Management System (RMS). The Team further refined its predictive models with census tract data, which provides population, education, and OpenStreetMap data. These provide points of interest. The objective was to highlight fact-based, yet often unintuitive, actionable insights. The following analytical features were used: 1) clustering of law enforcement agencies with similar crime patterns, effectively peer-ranking predictive models; 2) benchmarking agencies based on relevant key performance indicators; 3) identifying the most important domain-relevant attributes, such as demographics and city characteristics, that influence crime patterns of the regions; and 4) hot-spot prediction to forecast the time and type of the next likely crime event. This report focuses on the technical details of this last feature in providing context to hot-spot prediction. The report concludes that the Team’s efforts provide important insights into the practical aspects of detecting hot spots in real-time crime analytics, thus improving hot spot prediction. The team looks forward to workshops, demonstrations, and collaborations across companies and with NIJ subject-matter experts in deploying technology in new and creative ways to reduce the impact of crime. 3 figures
Main Term(s): Automated crime analysis
Index Term(s): Crime analysis; Crime prediction; Geographic distribution of crime; National Institute of Justice (NIJ); NIJ final report; Police resource allocation
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