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Description & Citation--Study No. 6486 | |
Bibliographic Description | |
| ICPSR Study No.: | 6486 |
|---|---|
| Title: | Anticipating and Combating Community Decay and Crime in Washington, DC, and Cleveland, Ohio, 1980-1990 |
| Principal Investigator(s): | Adele Harrell, The Urban Institute |
| Caterina Gouvis, The Urban Institute | |
| Funding Agency: | United States Department of Justice. National Institute of Justice. |
| Grant Number: | 91-IJ-CX-K016 |
| Bibliographic Citation: | Harrell, Adele, and Caterina Gouvis. ANTICIPATING AND COMBATING COMMUNITY DECAY AND CRIME IN WASHINGTON, DC, AND CLEVELAND, OHIO, 1980-1990 [Computer file]. ICPSR06486-v1. Washington, DC: The Urban Institute [producer], 1994. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 1995. |
Scope of Study | |
| Summary: | The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a ''very high crime'' tract was based on its crime rate compared to crime rates for other tracts in the same city. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988. |
| Subject Term(s): | communities, crime prediction, crime prevention, crime rates, intervention strategies, neighborhood conditions, urban decline |
| Geographic Coverage: | Cleveland, District of Columbia, Ohio, United States |
| Time Period: | 1980 - 1990 |
| Date(s) of Collection: | 1992 |
| Unit of Observation: | census tract |
| Data Type: | aggregate data, and census/enumeration data |
Methodology | |
| Purpose of the Study: | The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. The assessment consisted of three parts: (1) a survey of innovative local programs designed to combat neighborhood decay and crime conducted with the Police Executive Research Forum, (2) a review of the literature to identify research findings on preventing decay and crime and promising areas of future research, and (3) the analysis of the predictive validity of alternative indicators of community decay. Out of the third segment came the data prepared for this data collection. Existing theories reflect considerable disagreement over the temporal sequence between decay and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. |
| Study Design: | Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a ''very high crime'' tract was based on its crime rate compared to crime rates in other tracts in the same city. The models use crime as the dependent variable, i.e., the outcome of social and economic distress at prior time-points and breakdowns in public order and violations that undermine the physical maintenance and quality of life in the neighborhood, controlling for earlier crime rates. The eight predictors of high crime risk used in this study comprise four general groups. The first group is the prior crime rate for the offense. The second group includes indicators of breakdown in public order and the presence of illegal activity harmful to neighborhood environment, including the drug arrest rate, the delinquency rate, and the rate of confirmed or suspected arson incidents. The third group reflects factors related to the maintenance of social control by a stable population with sufficient resources and a common interest in protecting the area. This includes an index of family poverty, the presence of public housing, and home ownership. The fourth group is comprised of the percentage of lots zoned for commercial use, representing access to situations that increase opportunities for certain crimes. Dichotomous variables were created to compare the highest-rate tracts to all others and used as dependent variables in the prediction equations. Because the accuracy of prediction would vary depending on the number of tracts in the ''very high crime'' group, alternative definitions grouped the worst 10 to 30 tracts in multiple tests of each model. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. In Washington, DC, tract population for interim years was estimated by using the change in city population from 1980 to 1990 to revise the 1980 tract population (average per year proportion change in population times the number of years since 1980). Also, the Washington, DC, data were aggregated by 1970 tract boundaries because some agencies did not shift their geographic coding to 1980 Census tract boundaries for several years and so did not reflect instances where a single tract was divided into two tracts as the population expanded. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. In Washington, DC, 12 tracts were deleted and in Cleveland, 10 tracts were deleted. |
| Sample: | The two cities, Washington, DC, and Cleveland, Ohio, were selected because data could be provided on multiple indicators for multiple years between 1980 and 1990. |
| Data Source: | District of Columbia Office of Planning, Police Department, Office of Criminal Justice Plans and Analysis, and Division of Research and Statistics of the Commission of Public Health, and the Center for Urban Poverty and Social Change, Mandel School of Applied Social Sciences, Case Western Reserve University |
| Mode of Data Collection: | Data for Washington, DC, were provided by the District of Columbia Office of Planning, Police Department, Office of Criminal Justice Plans and Analysis, and the Division of Research and Statistics of the District of Columbia Commission of Public Health. Data for Cleveland were provided by the Center for Urban Poverty and Social Change, Mandel School of Applied Social Sciences, Case Western Reserve University. |
| Description of Variables: | Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988. |
| Response Rates: | Not applicable. |
| Presence of Common Scales: | None |
| Extent of Processing: | Checks for undocumented codes were performed by ICPSR. Missing data codes were standardized and the data were reformatted by ICPSR. ICPSR also produced a codebook and generated SAS and SPSS setup files for this collection. |
Access and Availability | |
| Note: | A list of the data formats available for this study can be found in the summary of holdings. Detailed file-level information (such as LRECL, case count, and variable count) is listed in the file manifest. |
| Original ICPSR Release: | 1995-08-16 |
| Version History: | The last update of this study occurred on 2005-11-04. |
| 2006-01-12 - All files were removed from dataset 3 and flagged as study-level files, so that they will accompany all downloads. | |
| 2005-11-04 - On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. | |
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