Explaining Developmental Crime Trajectories at Places: A Study of "Crime Waves" and "Crime Drops" at Micro Units of Geography in Seattle, Washington, 1989-2004 (ICPSR 28161)
This study extends a prior National Institute (NIJ) funded study on mirco level places that examined the concentration of crime at places over time. The current study links longitudinal crime data to a series of other databases. The purpose of the study was to examine the possible correlates of variability in crime trends over time. The focus was on how crime distributes across very small units of geography. Specifically, this study investigated the geographic distribution of crime and the specific correlates of crime at the micro level of geography. The study reported on a large empirical study that investigated the "criminology of place." The study linked 16 years of official crime data on street segments (a street block between two intersections) in Seattle, Washington, to a series of datasets examining social and physical characteristics of micro places over time, and examined not only the geography of developmental patterns of crime at place but also the specific factors that are related to different trajectories of crime. The study used two key criminological perspectives, social disorganization theories and opportunity theories, to inform their identification of risk factors in the study and then contrast the impacts of these perspectives in the context of multivariate statistical models.
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Weisburd, David, Elizabeth Groff, and Sue-Ming Yang. Explaining Developmental Crime Trajectories at Places: A Study of "Crime Waves" and "Crime Drops" at Micro Units of Geography in Seattle, Washington, 1989-2004. ICPSR28161-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2013-08-05. http://doi.org/10.3886/ICPSR28161.v1
Persistent URL: http://doi.org/10.3886/ICPSR28161.v1
This study was funded by:
- United States Department of Justice. Office of Justice Programs. National Institute of Justice (2005-IJ-CX-0006 )
Scope of Study
Subject Terms: crime mapping, crime prediction, crime rates, crime statistics, employment, geographic information systems, land ownership, property values, public assistance programs, public housing, socioeconomic status, truancy, urbanization, voter registration
Part 2 and Part 3 are zip archive files that are for use with mapping software.
In this logitudinal study a total of 24,023 street segments in Seattle were analyzed. Computerized records of crime incident reports were used to represent crime. Incident reports were generated by police officers or detectives after an initial response to a request for police service. A total of 1,697,212 crime records were joined to their corresponding street segments so that crime frequencies for each of the 24,023 segments for each year could be calculated.
The data collected for the study includes both aspatial (Dataset 1, Seattle Street Segments Aspatial Data) and spatial data (Dataset 2, Seattle Street Centerline Spatial Data and Dataset 3, Seattle Street Midpoint Spatial Data). The spatial data are in ESRI shape file format. Two base files are included that correspond to the units of analysis used in the study. The UofA_lines file contains the vector representation of the street segments in Seattle, Washington, as defined by the study. Only residential and arterial streets were included in the study. Limited access highways were excluded because of their lack of interactive human activity. This left 24,023 units of analysis (i.e., street segments) in Seattle. For this study, the unit of analysis is defined as a street segment (i.e., as both sides of the street between two intersections). The original street centerline file obtained from the Seattle GIS department was edited to make sure each street met the study definition. Each street has a Street_id which is a unique identifier. The UofA_points file contains the mid-points of all the street segments. This shape file also contains a Street_id field which is a unique identifier. The aspatial data file can be joined to either the line file or the point file using the Street_id field.
Sample: The geographic unit of analysis for this study is the street segment (sometimes referred to as a street block or face block). The street segment was defined as both sides of the street between two intersections. Only residential and arterial streets were included in the study. Limited access highways were excluded because of their lack of interactive human activity, leaving the school with 24,023 units of analysis (i.e., street segments) in Seattle.
Seattle Public Schools
InfoUSA database of all businesses in Seattle
Labels and Lists Inc. (voter registration)
Fleets and Facilities Department, City of Seattle
Seattle Public Libraries
Seattle School District
Department of Transportation (Metro Transit Division)
Seattle Public Utilities
Seattle Planning Department and parcel boundaries King County GIS
Description of Variables: The study contains 103 variables including crime information, social disorganization information and measures representing opportunity theories. Crime count information included crime counts in Seattle for each year between 1989 and 2004 and moving averages for crime rates in the first (1989-1991) and last (2002-2004) three years. Social disorganization information included property value, housing assistance, race, truant student residents, voting behavior, unsupervised teens, physical disorder, and urbanization. Measures representing opportunity theories included high-risk juvenile residents, location of public facilities, number of public facilities, street lighting, public transportation, street networks, land use, and business sales.
Extent of Processing: 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:
- Standardized missing values.
- Checked for undocumented or out-of-range codes.
Original ICPSR Release: 2013-08-05
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