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Date: November 22, 2024 Fri
Time: 11:46 am
Time: 11:46 am
Results for crime places
3 results foundAuthor: Clear, Todd R. Title: Predicting Crime through Incarceration: The Impact of Rates of Prison Cycling On Rates of Crime in Communities Summary: The purpose of this project has been to estimate the impact of "prison cycling" -the flow into and out of prison - on crime rates in communities, with special concern about areas that have high rates of prison cycling. In this work, we explicitly hypothesized that: (1) there would be a positive impact of neighborhood reentry rates on neighborhood crime rates, controlling for neighborhood characteristics; (2) there would be a positive effect of neighborhood removal rates (admissions) on neighborhood crime rates, controlling for neighborhood characteristics; (3) the effect of the rate of both removal and reentry on the neighborhood crime rate would depend upon the level of removal and reentry (tipping point); and (4) the effect of the rate of both removal and reentry on crime the neighborhood crime rate would depend upon the level of concentrated disadvantage in the neighborhood (interaction effect). To complete the proposed work, we compiled datasets on prison admissions and releases that would be comparable across places and geocoded and mapped those data onto crime rates across those same places. The data used were panel data. The data were quarterly or annual data, depending on the location, from a mix of urban (Boston, Newark and Trenton) and rural communities in New Jersey covering various years between 2000 and 2012. Census tract characteristics come from the 2000 Census Summary File 3. The crime, release, and admission data were individual level data that were then aggregated from the individual incident level to the census tract level by quarter (in Boston and Newark) or year (in Trenton). The analyses centered on the effects of rates of prison removals and returns on rates of crime in communities (defined as census tracts) in the cities of Boston, Massachusetts, Newark, New Jersey, and Trenton, New Jersey, and across rural municipalities in New Jersey. Our analytic strategy, was one of analytic triangulation. Through the data collection associated with this project, we amassed a uniquely comprehensive crime and incarceration dataset over time - arguably one of the most comprehensive assembled to date. This dataset allowed us to model the relationship between crime and incarceration using a range of techniques (fixed effects panel models, Arrellano-Bond estimations, and vector auto-regression) taking advantage of each and being partially freed of the limitations of any one. We gave considerable attention to the problem of modeling. As might be expected, different models often provide different results. The most parsimonious models provide small standard errors with significant results, but there are sometimes sign changes when new control variables are added, suggesting instability in the modeling strategy. By contrast, the most stable results are provided by fixed effects models that, while intuitively attractive, have the disadvantage of large standard errors. When we use this analytic approach, we achieve results that, we believe, are more reliable. Overall, our work finds strong support for the impact of prison cycling on crime. It seems that such cycling has different effects in different kinds of neighborhoods, consistent with the idea of a "tipping point" but more clearly expressed as an interaction between crime policy and type of neighborhood. The results in Tallahassee, Boston, and Trenton provide consistent support for this idea. In Newark, as a result of the city's limited variability in neighborhood disadvantage, we failed to find the same pattern. Further research will investigate whether this neighborhood interaction holds in other sites. It will also enable us to think about how neighborhood change over time affects the prison cycling-crime relationship. Do neighborhoods that improve start to benefit from incarceration policy? In contrast, does current incarceration policy become a factor that inhibits neighborhood improvement? Details: Final Report submitted to the U.S. National Institute of Justice, 2014. 141p. Source: Internet Resource: Accessed August 11, 2014 at: https://www.ncjrs.gov/pdffiles1/nij/grants/247318.pdf Year: 2014 Country: United States URL: https://www.ncjrs.gov/pdffiles1/nij/grants/247318.pdf Shelf Number: 132949 Keywords: Crime ModelingCrime PlacesHotspotsNeighborhoods and CrimeRecidivismSocioeconomic Conditions and Crime |
Author: Irvin-Erickson, Yasemin Title: Identifying Risky Places for Crime: An Analysis of the Criminogenic Spatiotemporal Influences of Landscape Features on Street Robberies Summary: In environmental criminology, it is widely accepted that crime risk is affected by the legitimate and illegitimate activities hosted at places. Most studies exploring this influence use the concepts of environmental criminology to explain how landscape features (such as cash businesses, illegal markets) can promote criminal behavior. However, studies based on place-based indicators provide an incomplete picture of crime emergence. First, most studies assume a temporally uniform crime-generating influence of landscape features, ignoring the social relevancy of these features at different times. Second, in most crime and place studies, the spatial influence - the ways in which features of a landscape affect places throughout the landscape (Caplan, 2011, p. 57) - is operationalized arbitrarily (Ratcliffe, 2012). Moreover, few studies examine the interactivity of the criminogenic spatial influences of different landscape features on crime risk (Caplan et al., 2011). To address these limitations, this dissertation examined the individual and combined criminogenic spatiotemporal influences of landscape features on 2010 street robbery risk in the City of Newark, NJ, using the principles of Risk Terrain Modeling. Street robberies were classified into six daily and hourly temporal groups. According to the results of this dissertation, criminogenic features are different for different time models, and the extent and weight of their criminogenic influences vary between and within time nested models. At-risk housing, schools, churches, grocery stores, hair and nail salons, pawn shops, sit-down restaurants, and take-out restaurants are the only features that have round-the clock criminogenic influences on street robberies in all time models. Drug charges, pawn shops, grocery stores, take-out restaurants, and hair and nail salons exert the strongest criminogenic spatial influences in different time models. At-risk housing's, schools', and churches' criminogenic influences are statistically significant, albeit weak. High-risk micro places identified by the combined criminogenic spatiotemporal influences of landscape features are high likely places for street robberies in Newark, NJ. Details: Newark, NJ: Rutgers University, School of Criminal Justice, 2014. 161p. Source: Internet Resource: Dissertation: Accessed April 1, 2015 at: https://www.ncjrs.gov/pdffiles1/nij/grants/248636.pdf Year: 2014 Country: United States URL: https://www.ncjrs.gov/pdffiles1/nij/grants/248636.pdf Shelf Number: 135114 Keywords: Crime AnalysisCrime PlacesEnvironmental CriminologyRisk ManagementSpatial AnalysisStreet Robbery |
Author: Olaghere, Ajima Title: The Everyday Activities that Bind for Crime: investigating the Process of Routine Activities Theory at Specific Places Summary: This dissertation explores why and how crime events routinely occur at specific places in high crime areas, such as street blocks, addresses, street corners, and intersections. Specifically, this dissertation considers what human activities, behaviors, routines, and situations contribute to crime occurring at these places. Routine activities theory and environmental criminology suggest that crime is a process resulting from the convergence of the daily human routines of offenders, targets, and guardians (or lack thereof). Furthermore, these opportunities for crime are sustained, enhanced, or limited based on surrounding physical and environmental features of where crimes occur. Many scholars have attempted to test the salience o f these theories using spatial data analysis, quantitative data analysis, and comp uter simulation modeling (Bosse, Elffers, Gerritsen, 2010; Cahill, 2004; Groff, 2007 ; Groff, 2008; Lum, 2003). However, these methods often fall short because the process of the routines and their link to crime occurrence are not actually observed, but instead e stimated from administrative data and he use of statistical modeling. This dissertation attempts to improve our understanding about the link between routine activities, the envi ronment, and crime using systematic social observation (SSO) of archived closed circuit television (CCTV) footage of crime events in Baltimore City. This approach serves as t he best possible and safest approach to explore the salience of routine activities theory a nd environmental criminology, short of observing routines in real time that unfold into cr imes. Given time and resource constraints, I examined 100 crime events from a col lection of the Baltimore Police Department's (BPD) archived footage. Systematic obs ervations of each archived crime event were completed using a theoretically informed instrumentation on site at a CCTV monitoring station for six and half months, culmina ting in over 2,340 hours of data collection of 397 hours of actual footage. Qualitative and exploratory data analysis produced findings largely about the routines leading up to drug crime events, with some comparison to violent and property crime. Systematic patterns of behavior leading up t o crime were observed, and could be categorized into a number of common features. With respect to drug crimes, eight common features emerged that help explain the proce ss of drug crimes unfolding in high crime places. The features varied to the degree in which they emerged, some features having a higher likelihood of occurrence than other s. These findings, while exploratory, have implications for routine activities theory and crime pattern theory, and future research Details: Fairfax, VA: George Mason University, 2015. 223p. Source: Internet Resource: Dissertation: Accessed August 3, 2016 at: http://digilib.gmu.edu/xmlui/bitstream/handle/1920/9658/Olaghere_gmu_0883E_10882.pdf?sequence=1&isAllowed=y Year: 2015 Country: United States URL: Shelf Number: 139972 Keywords: Crime PatternsCrime PlacesEnvironmental CriminologyHigh Crime AreasRoutine Activities |