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                            Funding & Awards
 Simulation Tool to Assess Dynamic Strategies for Deployment of Police Resources
Note:
              This awardee has received supplemental funding. This award detail page includes information about both the original award and supplemental awards.
Award Information
Awardee
              The University of Texas at Arlington
Award #
              2015-R2-CX-0006
Funding Category
              Continuation
Location
                    Arlington, TX
Awardee County
              Tarrant
Congressional District
              6
Status
              Closed
Funding First Awarded
              2015
Total funding (to date)
              $133,163
Original Solicitation
              NIJ FY 15 Graduate Research Fellowship Program in Science, Technology, Engineering, and Mathematics
Description of original award (Fiscal Year 2015, $44,094)
This award was competitively made in response to a proposal submitted by University of Texas at Arlington to a National Institute of Justice FY 2015 solicitation:  Graduate Research Fellowship in Science, Technology, Engineering & Mathematics.  With this solicitation, NIJ sought applications for funding innovative doctoral dissertation research in science, technology, engineering, or mathematics that is relevant to providing solutions to better ensure public safety, prevent and control crime, and ensure the fair and impartial administration of criminal justice in the United States.  The ultimate goal of this solicitation is to increase the pool of researchers in science, technology, engineering, and mathematics fields who are involved in research relevant to criminal justice applications. In its application, University of Texas at Arlington proposes to build a framework to bridge the gap between predictive policing and dynamic deployment strategies.  University of Texas at Arlington proposes to do this by creating a discrete-event simulation tool that will enable the framework to facilitate assessment of dynamic strategies to reduce crime, in particular, staff allocation and deployment strategies. The advantage of a simulation tool is the ability to inexpensively explore what-if scenarios. The proposed framework would enable exploration of a variety of dynamic strategies, including those that are current employed by police departments, such as task forces and disruption units.  This project is being funded incrementally in 3 phases, with the effort funded in FY15 representing the first phase.
This project contains a research and/or development component, as defined in applicable law.
ca/ncf
Supplemental Award 1 (Fiscal Year 2016, $44,094)
As submitted by the proposer:  Significant research has been conducted in predictive policing, with the intention of reducing crime.
However, too many studies focus on the accuracy of prediction, without recognizing that the utility of a predictive model depends on the factors upon which the model is built. If the factors are not controllable, then the model cannot be directly used to take action. Further, the policing system is highly dynamic over space and time. If a police department employs an annual staffing allocation model, then the dynamic deployment strategy required to continually react to crime predictions may not be feasible.
The goal of the proposed research is to build a framework to bridge this gap between predictive policing and dynamic strategies. The purpose is to create a discrete-event simulation tool to facilitate assessment of dynamic strategies. Our research questions are:  (1) What are the key structural components of dynamic strategies? (2) What constraints limit them? (3) How should predictive policing be incorporated in our simulation tool? (4) What additional data are needed to inform dynamic strategies? We have been collaborating with the Arlington, Texas Police Department, which has provided us with motor vehicle burglary case data and staffing data for the proposed framework. Additional data may be obtained, if available.  ca/ncf
    Supplemental Award 2 (Fiscal Year 2017, $44,975)
Significant research has been conducted in predictive policing, with the intention of reducing crime. However, many studies focus on the accuracy of prediction, without recognizing that the utility of a predictive model depends on the factors upon which the model is built.
If the factors are not controllable, then the model cannot be directly used to take action. Further, the policing system is highly dynamic over space and time. If a police department employs an annual staffing allocation model, then the dynamic deployment strategy required to continually react to crime predictions may not be feasible.
The goal of the proposed research is to build a framework to bridge this gap between predictive policing and dynamic strategies. The purpose is to create a discrete-event simulation tool to facilitate assessment of dynamic strategies. The research questions are: (1) What are the key structural components of dynamic strategies? (2) What constraints limit them? (3) How should predictive policing be incorporated in our simulation tool? (4) What additional data are needed to inform dynamic strategies?
The researcher has been collaborating with the Arlington, Texas Police Department, which has provided motor vehicle burglary case data and staffing data for the proposed framework. Additional data may be obtained, if available.
This project contains a research and/or development component, as defined in the applicable law.
nca/ncf


  
Grant-Funded Publications
        
                      A discrete-event simulation model for adaptive allocation of police patrol

  
Date Created: September 15, 2015
    
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