Crime Hot Spot Forecasting with Data from the Pittsburgh [Pennsylvania] Bureau of Police, 1990-1998 (ICPSR 3469)
This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models.
The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months.
A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study.
The statistical datasets consist of
- Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases
- Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases
- Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases
- Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases
- Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases
- Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases
- Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases
- Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases
- Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases
- Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases
- Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases
- Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases
- Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases
- Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases
- Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases.
- The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).
Determinants of Case Growth in Federal District Courts in the United States, 1904-2002 (ICPSR 3987)
ECIN Replication Package for "Macroeconomic Forecasting During Recessions and Expansions in the US and the Euro Area" (ICPSR 238922)
Firm Volatility and Credit: A Macroeconomic Analysis (ICPSR 25062)
Linking Theory to Practice: Examining Geospatial Predictive Policing, Denver, Colorado, 2013-2015 (ICPSR 37299)
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.
Policing by Place: A Proposed Multi-level Analysis of the Effectiveness of Risk Terrain Modeling for Allocating Police Resources, 2014-2015 [New York City] (ICPSR 36899)
These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.
This study contains data from a project by the New York City Police Department (NYPD) involving GIS data on environmental risk factors that correlate with criminal behavior. The general goal of this project was to test whether risk terrain modeling (RTM) could accurately and effectively predict different crime types occurring across New York City. The ultimate aim was to build an enforcement prediction model to test strategies for effectiveness before deploying resources. Three separate phases were completed to assess the effectiveness and applicability of RTM to New York City and the NYPD. A total of four boroughs (Manhattan, Brooklyn, the Bronx, Queens), four patrol boroughs (Brooklyn North, Brooklyn South, Queens North, Queens South), and four precincts (24th, 44th, 73rd, 110th) were examined in 6-month time periods between 2014 and 2015. Across each time period, a total of three different crime types were analyzed: street robberies, felony assaults, and shootings.
The study includes three shapefiles relating to New York City Boundaries, four shapefiles relating to criminal offenses, and 40 shapefiles relating to risk factors.