skip navigation

Add your conference to our Justice Events calendar


Register for Latest Research

Stay Informed
Register with NCJRS to receive NCJRS's biweekly e-newsletter JUSTINFO and additional periodic emails from NCJRS and the NCJRS federal sponsors that highlight the latest research published or sponsored by the Office of Justice Programs.

NCJRS Abstract

The document referenced below is part of the NCJRS Library collection. To conduct further searches of the collection, visit the NCJRS Abstracts Database. See the Obtain Documents page for direction on how to access resources online, via mail, through interlibrary loans, or in a local library.

  NCJ Number: NCJ 211973   Add to Shopping cart   Find in a Library
  Title: Development of Crime Forecasting and Mapping Systems for Use by Police
  Document URL: PDF 
  Dataset URL: DATASET 1
  Author(s): Jacqueline Cohen ; Wilpen L. Gorr
  Corporate Author: Carnegie Mellon University
H. John Heinz III School of Public Policy and Management
United States of America
  Date Published: 02/2005
  Page Count: 155
  Annotation: This report presents the results of research on the new field of crime forecasting and represents work on the second of two National Institute of Justice (NIJ) grants awarded for crime forecasting research.
  Abstract: The research involved with the first NIJ grant established that crime forecasting was feasible using simple time series methods. The current research extends work from the first grant using new data and tests three advanced time series methods in order to improve the accuracy of crime forecasting. The three methods tested are the so-called naïve methods, univariate time series methods, and leading indicators models. The final goal of the research has been to develop crime forecasting capacity for police in support of the tactical deployment of resources. Data included 6 million offense reports and CAD calls for 24 crime types from Pittsburgh, PA, and Rochester, NY, which were calculated into monthly time series data for the period 1990 through 2001 across 5 geographies. Section 4 provides the details of the data collection and processing work, which were extensive and involved the use of geography to aggregate and forecast crime levels. The authors developed an experimental design involving a state-of-the-art rolling horizon forecast experiment. Two approaches for assessing forecast accuracy, the traditional average forecast error criterion and an innovative decision rule criterion, are described. The results demonstrate the potential of crime forecasting to support short-term decisionmaking by police. The best method for producing crime forecasts was determined to be exponential smoothing with seasonality estimated with pooled citywide data. Worst methods for crime forecasting were the commonly used naïve approach and the leading indicator models. The leading indicator models, however, were best for forecasting large crime changes. The accuracy attained was sufficient to support car beat-level crime forecasting as well as high-volume individual crime types at the precinct level. Recommendations are offered and include the advice of conceptualizing police decisionmaking and crime analysis into macro, meso, and micro levels. Figures, tables, references, appendixes
  Main Term(s): Crime prediction
  Index Term(s): Estimating methods ; Research methods ; Research uses in policymaking ; Criminal justice system policy ; NIJ grant-related documents
  Sponsoring Agency: National Institute of Justice (NIJ)
US Department of Justice
Office of Justice Programs
United States of America
  Grant Number: 2001-IJ-CX-0018
  Sale Source: Carnegie Mellon University
H. John Heinz III School of Public Policy and Management
Pittsburgh, PA 15213
United States of America
  Type: Report (Study/Research)
  Country: United States of America
  Language: English
  To cite this abstract, use the following link:

*A link to the full-text document is provided whenever possible. For documents not available online, a link to the publisher's website is provided. Tell us how you use the NCJRS Library and Abstracts Database - send us your feedback.