Linking Theory to Practice: Examining Geospatial Predictive Policing, Denver, Colorado, 2013-2015 (ICPSR 37299)

Version Date: Feb 26, 2020 View help for published

Principal Investigator(s): View help for Principal Investigator(s)
Craig D. Uchida, Justice & Security Strategies, Inc.

https://doi.org/10.3886/ICPSR37299.v1

Version V1

This research sought to examine and evaluate geospatial predictive policing models across the United States. The purpose of this applied research is three-fold: (1) to link theory and appropriate data/measures to the practice of predictive policing; (2) to determine the accuracy of various predictive policing algorithms to include traditional hotspot analyses, regression-based analyses, and data-mining algorithms; and (3) to determine how algorithms perform in a predictive policing process.

Specifically, the research project sought to answer questions such as:

  • What are the underlying criminological theories that guide the development of the algorithms and subsequent strategies?
  • What data are needed in what capacity and when?
  • What types of software and hardware are useful and necessary?
  • How does predictive policing "work" in the field? What is the practical utility of it?
  • How do we measure the impacts of predictive policing?

The project's primary phases included: (1) employing report card strategies to analyze, review and evaluate available data sources, software and analytic methods; (2) reviewing the literature on predictive tools and predictive strategies; and (3) evaluating how police agencies and researchers tested predictive algorithms and predictive policing processes.

Uchida, Craig D. Linking Theory to Practice: Examining Geospatial Predictive Policing, Denver, Colorado, 2013-2015. Inter-university Consortium for Political and Social Research [distributor], 2020-02-26. https://doi.org/10.3886/ICPSR37299.v1

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United States Department of Justice. Office of Justice Programs. National Institute of Justice (2013-IJ-CX-0054)

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Inter-university Consortium for Political and Social Research
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2013-01-01 -- 2015-12-31
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The purpose of this applied research is three-fold: (1) to link theory and appropriate data/measures to the practice of predictive policing; (2) to determine the accuracy of various predictive policing algorithms to include traditional hotspot analyses, regression-based analyses, and data-mining algorithms; and (3) to determine how algorithms perform in a predictive policing process.

The project's primary phases included: (1) employing report card strategies to analyze, review and evaluate available data sources, software, and analytic methods; (2) reviewing the literature on predictive tools and predictive strategies; and (3) evaluating how police agencies and researchers tested predictive algorithms and predictive policing processes.

Primarily, this study utilized a secondary analysis of time-series crime data obtained from DPD (2013-2015). These data were used to create benchmarks for evaluating the effectiveness of various crime forecasting algorithms.

As part of the data assessment process, researchers created data report cards to evaluate publicly available crime datasets on data quality and usability. For practitioners seeking implementation, vendor assessment tools to evaluate predictive software were created. Researchers also compiled a matrix of available software and models for predictive analysis, highlighting type of data used, mode of analysis, and crimes predicted.

All crimes reported to DPD between January 1, 2013, and December 31, 2015 were included in the datasets for analysis.

Time Series

All records of crimes reported to Denver Police Department (DPD) within the study time frame.

Event/Process

Crime dataset from Denver Open Data Catalog

Crime dataset variables include date and time of crime event and reporting, offense description, geographic grid, and NIBRS and UCR codes.

In the grid cells datasets, crimes are categorized into 5 groups: assault (excluding homicide and sexual assault), auto crimes, burglary, robbery, and gun-involved crimes. Variables include aggregated monthly counts for each type, forecasts for various baseline algorithms, and evaluations (hit, miss, false negative, false positive).

Not applicable.

None

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2020-02-26

2020-02-26 ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection:

  • Checked for undocumented or out-of-range codes.
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