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Date: April 30, 2024 Tue
Time: 12:10 am
Time: 12:10 am
Results for crime data
4 results foundAuthor: Targonski, Joseph Robert Title: A Comparison of Imputation Methodologies in the Offenses-Known Uniform Crime Reports Summary: One of the most widely used and important sources of crime data for criminologists and criminal justice policy stakeholders is the Offenses-Known Uniform Crime Reports (UCR). However, it comes with many limitations, including missing data from non-compliant police agencies. The missing data are adjusted for by imputing data based on a cross-sectional methodology to maintain comparable trending analysis. The purpose of this study was to reexamine and recode missing data in the UCR for the years 1977-2000 for all police agencies in the United States. With the newly cleaned dataset, a clearer picture of the UCR error structure would emerge and patterns of missing data could more accurately be described. The study found that there are more missing data than identified by the FBI’s quality control. The next phase of the project was to create a dataset with only full reporting agencies for a 10 year period, which would be used to test the cross-sectional method against a longitudinal method. This was done by creating simulation data sets that “punched out” the real crime values, thus artificially creating missing data. Each imputation method could then be tested by comparing the imputed value to the actual value. The overall results showed that in most circumstances, the longitudinal method was more accurate at estimating the missing crime data points. Details: Chicago: University of Illinois at Chicago, 2011. 148p. Source: Internet Resource: Dissertation: Accessed July 27, 2011 at: https://www.ncjrs.gov/pdffiles1/nij/grants/235152.pdf Year: 2011 Country: United States URL: https://www.ncjrs.gov/pdffiles1/nij/grants/235152.pdf Shelf Number: 122174 Keywords: Crime DataCrime StatisticsUniform Crime Reports (U.S.) |
Author: LaFree, Gary Title: Hot Spots of Terrorism and Other Crimes in the United States, 1970 to 2008 Summary: While efforts are increasingly aimed at understanding and identifying “hot spots” of ordinary crime, little is known about the geographic concentration of terrorist attacks. What areas are most prone to terrorism? Does the geographic concentration of attacks change over time? Do specific ideologies motivate and concentrate terrorist attacks? Moreover, what factors increase the risk that an attack will occur in a particular area? Using recently released data from the Global Terrorism Database, we address these gaps in our knowledge by examining county-level trends in terrorist attacks in the United States from 1970 through 2008. This research was motivated by issues related to three research areas: geographic concentration of terrorist attacks, terrorism and ordinary crime, and predicting geographic concentrations of terrorist attacks. Like ordinary crime, terrorism hot spots are predominately located in large, metropolitan areas. While some locales remain targets of terrorist attacks, to a large extent hot spots of terrorist attacks demonstrate a significant amount of variability over time. Moreover, we find significant variability in the ideologies motivating terrorist attacks across decades. Terrorism and ordinary crime occur in many of the same areas. We find that while some traditional predictors of ordinary crime also predict terrorist attacks, many robust correlates of ordinary crime do not. These data were limited in some respects; much more work in this area is needed to fully understand the linkages between terrorism and ordinary crime. Details: College Park, MD: START, 2012. 36p. Source: Final Report to Human Factors/Behavioral Science Division, Science and Technology Directorate, U.S. Department of Homeland Security: Internet Resource: Accessed February 4, 2012 at http://start.umd.edu/start/publications/research_briefs/LaFree_Bersani_HotSpotsOfUSTerrorism.pdf Year: 2012 Country: United States URL: http://start.umd.edu/start/publications/research_briefs/LaFree_Bersani_HotSpotsOfUSTerrorism.pdf Shelf Number: 123973 Keywords: Crime DataCrime StatisticsGeographic Distribution of CrimeGeographic StudiesHot SpotsTerrorismUniform Crime Reports |
Author: Haas, Stephen M. Title: Assessing the Validity of Hate Crime Reporting: An Analysis of NIBRS Data Summary: The Uniform Crime Reporting (UCR) Program was developed over eighty years ago to meet the need for reliable crime statistics for the nation. Today, nearly 17,000 law enforcement agencies across the US participate in this voluntary program. UCR, and the modernized National Incident-Based Reporting System (NIBRS), are recognized as the primary source of information about crimes reported to the police. While the UCR Program is critical to understanding crime, there are known limitations to these data such as underreporting and misclassification. As with any large scale data collection system, errors are inevitable and occur for a variety of reasons. While it is unlikely that all error will be eliminated, it is important to understand and measure it. Classification error occurs when the facts of the crime are recorded by the police, but the crime type is identified incorrectly. These errors can occur for many reasons including inaccurate interpretation of UCR definitions, reliance on criminal rather than statistical definitions, record automation issues, and even purposive actions in an attempt to downgrade crime. Classification error is particularly important since it can ultimately impact the statistical accuracy of reported crime statistics. The purpose of the current study is to examine the misclassification of crimes as they relate to hate. That is, the degree to which classification error impacts the statistical accuracy of reported hate crimes. Such error can vary by crime type and result in both the undercounting and overcounting of crimes in official statistics. To focus this study on hate crimes is noteworthy because, by their very nature, a unique set of issues converge when seeking to properly classify these incidents. Inherently, the intention of people involved and/or their motivation for committing a crime must be taken into account by officers when determining whether a particular incident constitutes a hate crime. For this reason, and others to be discussed later in this report, it is often speculated that many hate crimes are not accurately recorded in official records. Through a systematic review of official records, this study seeks to examine the degree to which classification error impacts the statistical accuracy of hate crime, as reported in official law enforcement statistics. Utilizing a methodology previously developed by the authors (Nolan, Haas, and Napier, 2011; Nolan, Haas, Lester, Kirby, and Jira, 2006) this study assesses the amount of classification error in hate crime reporting in WV. The researchers randomly selected cases, which were included in the state’s statistical data files, from designated offense categories for a detailed review of the officer’s written narrative of the incident. Though this approach has been applied to examine error across general crime types, no study to date has systematically focused on a crime category as widely believed to be underreported as hate crimes. While the previous study examined classification error across general crime types, the current study focuses specifically on identifying sources of error (i.e., over- and undercounts) contained in hate crime statistics. Additionally, this study further builds on the quantitative method described above by further capturing the perspectives of frontline officers. Qualitative information from a focus group is used to gain insight into the thought processes officers adhere to when deciding whether a specific incident constitutes a hate crime. Equipped with narratives of cases believed to contain errors, the researchers use a focus group approach to explore the various definitional and interpretation issues that are believed to result in classification error in these cases. Thus, it is anticipated that this study will not only yield an estimate of the error contained in officially reported hate crime statistics, but shed light on the inherent difficulties officers face in interpreting these incidents. In the end, it is the hope of the authors that this study will yield useful information for training officers on the reporting of hate crimes, get us closer to understanding the true magnitude of these crimes, and serve as a precursor for adjusting crime statistics to better estimate the actual number of hate crimes in the population. Details: West Virginia: Criminal Justice Statistical Analysis Center, Department of Military Affairs & Public Safety, 2011. 23p. Source: Internet Resource: Accessed March 10, 2012 at http://www.djcs.wv.gov/SAC/Documents/ORSP_WV_Hate_Crime_Report.pdf Year: 2011 Country: United States URL: http://www.djcs.wv.gov/SAC/Documents/ORSP_WV_Hate_Crime_Report.pdf Shelf Number: 124440 Keywords: Crime DataCrime StatisticsHate Crime (West Virginia)Uniform Crime Reports |
Author: Friedmann, Robert R. Title: Improving Crime Data Project Summary: The criminal justice system lags behind other social service providers and the private sector in the development, timely dissemination, and use of reliable statistical indicators to monitor, predict, and prevent crime. Deficiencies in the nation’s crime data infrastructure deny policymakers the ability to make decisions based on sound and timely information. In contrast, health, education, business, and economics possess readily available data for forecasting and planning. Too often in criminal justice data are not compiled in standardized formats suitable for policy development or program evaluation without time-consuming, repetitive, and costly compilation and analysis efforts. In smaller agencies large amounts of data are still collected by hand and few agencies are able to routinely share comparable data across jurisdictions. Moreover, law enforcement data are generally devoid of other relevant attributes (such as census information) and are reported in tabular or aggregate formats that are not suitable for policy-relevant research. These problems indicate a demonstrable need for improvements in criminal justice data collection, analysis, and dissemination methods to facilitate better strategic choices and policy decisions. Georgia State University and the University of Missouri-St. Louis, in conjunction with the Great Cities’ Universities (GCU) coalition of urban public universities, proposed to develop a model for improved data compilation, analysis, and dissemination across criminal justice jurisdictions in the United States. The objective was to enable agencies to “talk” to each other so that, for example, what is defined as a gang-related assault or drug-related robbery in one jurisdiction is comparable to the same behavior in another jurisdiction. The project focused on improving crime data produced and used primarily by law enforcement agencies. But the proposed model also can be applied across levels within the justice system so that data can be more smoothly transferred between the various system components (law enforcement agencies, prosecutors, courts, corrections). This project - Improving Crime Data (ICD) - was earmarked in legislation and housed in the National Institute of Justice. It is not a standard research project, but rather a demonstration project intended to devise and implement a model of crime data compilation and analysis that can be used by multiple agencies. The project team also provided technical assistance to participating agencies to facilitate implementation and use of the model. As important background, the project team conducted an assessment of the current state of major crime indicators (UCR, NIBRS). The major conclusion of that assessment is that the current indicators do not provide timely or useful information for policy development or evaluation or for strategic initiatives by police managers. The UCR data lack sufficient detail and are badly out of date when disseminated. NIBRS is more promising, but nationwide implementation is a long way off. In response, we sought to assemble a coalition of law enforcement agencies and with their continuous input develop model crime indicators and information technology to access and analyze them that can be applied directly and immediately to crime issues in the areas in which the agencies are located. The project succeeded in establishing basic “proof of concept” for the model crime indicators, a common data platform, and common analysis capabilities. But we had to address several challenges along the way, both technical and organizational, that highlight the fragility of interagency coalitions, the arduous task of sharing even basic data elements across agencies, and the ambivalence of many police managers regarding the utility of cross-jurisdictional data sharing for meeting pressing organizational objectives. Details: Atlanta, GA: Statistical Analysis Bureau, Georgia State University, 2010. 74p. Source: Internet Resource: Accessed April 24, 2012 at https://www.ncjrs.gov/pdffiles1/nij/grants/237988.pdf Year: 2010 Country: United States URL: https://www.ncjrs.gov/pdffiles1/nij/grants/237988.pdf Shelf Number: 125056 Keywords: Crime DataCrime StatisticsData AnalysisData Sharing |