Forecasting Municipality Crime Counts in the Philadelphia [Pennsylvania] Metropolitan Area, 2000-2008 (ICPSR 35319)

Version Date: Jun 26, 2017 View help for published

Principal Investigator(s): View help for Principal Investigator(s)
Ralph Taylor, Temple University; Elizabeth Groff, Temple University; David Elesh, Temple University

https://doi.org/10.3886/ICPSR35319.v1

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These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed.

This study examines municipal crime levels and changes over a nine year time frame, from 2000-2008, in the fifth largest primary Metropolitan Statistical Area (MSA) in the United States, the Philadelphia metropolitan region. Crime levels and crime changes are linked to demographic features of jurisdictions, policing arrangements and coverage levels, and street and public transit network features.

Taylor, Ralph, Groff, Elizabeth, and Elesh, David. Forecasting Municipality Crime Counts in the Philadelphia [Pennsylvania] Metropolitan Area, 2000-2008. [distributor], 2017-06-26. https://doi.org/10.3886/ICPSR35319.v1

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United States Department of Justice. Office of Justice Programs. National Institute of Justice (2009-IJ-CX-0026)

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Add the standard NACJD restricted access wording: Access to these data is restricted. Users interested in obtaining these data must complete a Restricted Data Use Agreement, specify the reason for the request, and obtain IRB approval or notice of exemption for their research.

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2000 -- 2008
2010 -- 2013
  1. 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.

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The purpose of this study was to examine four types of questions: the size and direction of the impacts of community fabric and law enforcement coverage on crime; the spatial patterning of crime and crime links, the spatiotemporal patterning of crime and crime links; and the predictability of one or three-year-look-ahead crime rates using the available variables.

This study aggregates jurisdiction level crime data in order to compare it to numerous findings about socioeconomic status, residential stability, and race composition factors.

The data compiled for this study began with the most basic form of Unified Crime Reporting (UCR) data, obtained directly from the FBI: Return A. Pennsylvania State Police data were derived from the Pennsylvania UCR webpage. New Jersey data were taken from the Crime Reports and Statistics webpage of the New Jersey State Police. In addition, not all police agencies provide 12 months of crime data to the FBI. Counts were not adjusted for missing months. GIS shape files have been provided to show jurisdiction areas.

For the permeability data, a variety of publicly available indicators were collected which the researchers agreed represented the four permeability domains. Internally consistent indices were created tapping three of the domains of interest. In constructing each index, individual variables were z scored and then averaged. Scores on indices were mapped to verify that they provided geographically differentiated scores.

Demographic indices were contructed form individual variables. A population weighted percentile (PWP) form of an indicator or index captures the position of the municipal or civil division (MCD), relative to the entire population in the rest of the Metropolitan Statistical Area (MSA), in that year. Each PWP equals the percent of the population, at the MCD level, in the entire MSA, with scores equal to or less than the PWP of the target MCD.

The UCR data used are fixed length records with monthly counts, by crime category, of unfounded offenses, actual offenses, total offenses cleared by arrest, and juvenile arrests. The most common coverage style was that an MCD maintained its own police department, responsible for the population of the MCD. During the period 276 out of 355 MCDs had this arrangement (76.6 percent). Both Pennsylvania and New Jersey have state police agencies which provide policing coverage and prepare state-level reports. Because each is a distinct governmental entity, each has the ability to organize its data as it sees fit. This has significant implications. Pennsylvania State Police data were derived from the Pennsylvania Uniform Crime Reporting webpage. New Jersey data are available from the Crime Reports and Statistics webpage of the New Jersey State Police. Data issues arose from the varied nature of policing arrangements at the MCD level in the Philadelphia MSA.

Not applicable.

Longitudinal

All jurisdictions in Philadelphia [Pennsylvania] metro area between 2000 and 2008

Jurisdiction

The study includes four data sets and two geographic base shape files.

  1. The agency_crime_structure_final dataset (38 variables and 3,195 cases), consists of geographic variables such as county name, jurisdiction name, area key. Additionally, it contains numerous crime variables, for instance, spatially lagged property crime counts, reported property crimes, and reported violent crimes. The file also includes variables about police, such as the ratio of police employees per 1000 people, and natural log 1 plus the number of sworn in police officers.
  2. The demographics_final data (37 variables and 3,195 cases) consists of variables pertaining to characteristics of homes, such as total housing units, median household income, occupied housing units total, median gross rent, and percent multi-person households. It also contains demographics variables, for example, percent Asian population, percent black population, and percent population aged 50-54.
  3. The ID_all_final dataset (10 variables and 355 cases) contains location ID variables such as PA or NJ, MCD polygon number, and others.
  4. The permeability_final dataset (106 variables and 355 cases) contains transportation variables such as dead ends, bus stops, external barriers, highway miles, rail and subway stops, and collector road miles. The file also includes yearly crime variables, for example, spatially lagged violent crimes from 2000 to 2008, and spatially lagged property crime from 2000 to 2008.
  5. In addition, GIS shape files have been provided to show jurisdiction areas in the Philadelphia metro area. The MCD shape file contains the boundaries of all of the municipal civil division units (n = 355) within the Philadelphia region. The Phila_metro_counties shape file contains the boundaries of the nine counties in the Philadelphia region.

Not applicable.

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2017-06-26

2018-02-15 The citation of this study may have changed due to the new version control system that has been implemented. The previous citation was:
  • Taylor, Ralph, Elizabeth Groff, and David Elesh. Forecasting Municipality Crime Counts in the Philadelphia [Pennsylvania] Metropolitan Area, 2000-2008. ICPSR35319-v1. [distributor], 2017-06-26. http://doi.org/10.3886/ICPSR35319.v1
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Except for weighting applied to tables describing demographic factors, all MCDs were treated equally, regardless of their size. The focus is on jurisdiction dynamics within the metro area, not description of one relationship for the entire metro area.

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Notes

  • 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.

  • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

  • One or more files in this data collection have special restrictions. Restricted data files are not available for direct download from the website; click on the Restricted Data button to learn more.