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Crime Hot Spot Forecasting with Data from the Pittsburgh [Pennsylvania] Bureau of Police, 1990-1998 (ICPSR 3469)

Released/updated on: 2015-08-07

Geographic coverage: United States, Pennsylvania, Pittsburgh

Time period: 1990-01-01--1998-01-01

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).

Curated

Evaluation of the Community Supervision Mapping System for Released Prisoners in Rhode Island, 2008-2010 (ICPSR 32004)

Released/updated on: 2014-09-30

Geographic coverage: Rhode Island, United States

Time period: 2008-01-01--2010-01-01

This study evaluated the Community Supervision Mapping System (CSMS), an online geospatial tool that enables users to map the formerly incarcerated and others on probation, along with related data such as service provider locations and police districts. Probation officers in the state of Rhode Island were surveyed a few weeks before and 18 months after the implementation of CSMS. A total of 56 probation officers participated in the first wave of the study (pre-implementation survey), and 52 probation officers participated in the second wave (post-implementation survey), yielding an overall sample size of 108 probation officers. Dataset 1 contains the data for both waves of the study. The dataset is comprised of 140 variables. Both waves of the study examined the following categories of variables: the probation officer's professional background, contact with clients, amount of time spent on job duties specific to the profession, contact with other agencies, and computer usage. The second wave added 86 variables to explore officers' experiences with CSMS, which features they used, how it impacted their work, and their expected use of CSMS in the future.

Curated

Reentry Mapping Network Project in Milwaukee, Wisconsin, Washington, DC, and Winston-Salem, North Carolina, 2003-2004 (ICPSR 20560)

Released/updated on: 2010-07-30

Geographic coverage: North Carolina, Milwaukee, District of Columbia, United States, Winston-Salem, Wisconsin

Time period: 2003-01-01--2003-12-31, 2004-01-01--2004-12-31, 2003-01-01--2003-12-31

The Urban Institute established the Reentry Mapping Network (RMN), a group of jurisdictions applying a data-driven, spatial approach to prisoner reentry. The purpose of the study was to examine three National Institute of Justice-funded RMN sites: Milwaukee, Wisconsin, Washington, DC, and Winston-Salem, North Carolina. As members of the Reentry Mapping Network, the three sites collected local data related to incarceration, reentry, and community well-being. The Nonprofit Center of Milwaukee's Neighborhood Data Center was the lead Reentry Mapping Network partner in Milwaukee. Data on a total of 168 census tracts in Milwaukee (Part 1) during the calendar year 2003 were obtained from the Wisconsin Department of Corrections. NeighborhoodInfo DC was the lead reentry mapping network partner in Washington, DC. Data on a total of 7,286 ex-offenders in Washington, DC (Part 2) during the calendar year 2004 were obtained from the Court Services and Offender Supervision Agency (CSOSA) for the District of Columbia. The Winston-Salem Reentry Mapping Network project was managed by the Center for Community Safety (CCS), a public service and research center of Winston-Salem State University. Data on a total of 2,896 ex-offenders in Forsyth County (Part 3) during the calendar year 2003 were obtained from the North Carolina Department of Corrections (DOC), the Forsyth County Sheriff's Department (Forsyth County Detention Center [FCDC]), and the North Carolina Department of Juvenile Justice and Delinquency Prevention (DJJDP). The Milwaukee, Wisconsin Data (Part 1) contain a total of 95 variables including race, ethnicity, gender, marital status, education, job status, dependents, general risk assessment, alcohol risk, drug risk, need for alcohol treatment, and need for drug treatment. Also included are four geographic variables. The Washington, DC Data (Part 2) contain a total of 13 variables including supervision type, whether supervision began in calendar year 2004, date supervision period began, date supervision period ended, sex, marital status, ethnicity, age, education, unemployment status, state, and Census tract. The Winston-Salem, North Carolina Data (Part 3) contain a total of 14 variables including race, sex, primary offense, admittance date, date pardoned, street, city, state, status, jurisdiction, and age at admission.

Curated

Spatial Configuration of Places Related to Homicide Events in Washington, DC, 1990-2002 (ICPSR 4544)

Released/updated on: 2015-07-29

Geographic coverage: District of Columbia, United States

Time period: 1990-01-01--2002-12-01

The purpose of this research was to further understanding of why crime occurs where it does by exploring the spatial etiology of homicides that occurred in Washington, DC, during the 13-year period 1990-2002.

The researchers accessed records from the case management system of the Metropolitan Police, District of Columbia (MPDC) Homicide Division to collect data regarding offenders and victims associated with the homicide cases. Using geographic information systems (GIS) software, the researchers geocoded the addresses of the incident location, the victim's residence, and offender's residence for each homicide case. They then calculated both Euclidean distance and shortest path distance along the streets between each address per case. Upon applying the concept of triad as developed by Block et al. (2004) in order to create a unit of analysis for studying the convergence of victims and offenders in space, the researchers categorized the triads according to the geometry of locations associated with each case. (Dots represented homicides in which the victim and offender both lived in the residence where the homicide occurred; lines represented homicides that occurred in the home of either the victim or the offender; and triangles represented three non-coincident locations: the separate residences of the victim and offender, as well as the location of the homicide incident.) The researchers then classified each triad according to two separate mobility triangle classification schemes: Traditional Mobility, based on shared or disparate social areas, and Distance Mobility, based on relative distance categories between locations. Finally, the researchers classified each triad by the neighborhood associated with the location of the homicide incident, the location of the victim's residence, and the location of the offender's residence.

A total of 3 statistical datasets and 7 geographic information systems (GIS) shapefiles resulted from this study. Note: All datasets exclude open homicide cases. The statistical datasets consist of Offender Characteristics (Dataset 1) with 2,966 cases; Victim Characteristics (Dataset 2) with 2,311 cases; and Triads Data (Dataset 3) with 2,510 cases. The GIS shapefiles have been grouped into a zip file (Dataset 4). Included are point data for homicide locations, offender residences, triads, and victim residences; line data for streets in the District of Columbia, Maryland, and Virginia; and polygon data for neighborhood clusters in the District of Columbia.