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Date: April 30, 2024 Tue
Time: 2:40 am
Time: 2:40 am
Results for computer technology
2 results foundAuthor: Jackson, Brian A. Title: Fostering Innovation in Community and Institutional Corrections: Identifying High-Priority Technology and Other Needs for the U.S. Correctional Sector Summary: The agencies of the U.S. corrections enterprise manage offenders confined in prisons and jails and those who have been released into the community on probation and parole. The enterprise is one of the three central pillars of the criminal justice system, along with police and the courts. Corrections agencies face major challenges from declining budgets, increasing populations under supervision, problems of equity and fairness in administrating justice, and other concerns. To better achieve its objectives and play its role within the criminal justice enterprise, the sector needs innovation in corrections technology, policy, and practice. This report draws on published literature and new structured deliberations of a practitioner Corrections Advisory Panel to frame an innovation agenda. It identifies and prioritizes potential improvements in technology, policy, and practice in both community and institutional corrections. Some of the top-tier needs identified by the panel and researchers include adapting transcription and translation tools for the corrections environment, developing training for officers on best practices for managing offenders with mental health needs, and changing visitation policies (for example, using video visitation) to reduce opportunities for visitors to bring contraband into jails and prisons. Such high-priority needs provide a menu of innovation options for addressing key problems or capitalizing on emerging opportunities in the corrections sector. This report is part of a larger effort to assess and prioritize technology and related needs across the criminal justice community for the National Institute of Justice's National Law Enforcement and Corrections Technology Center system. Details: Santa Monica, CA: RAND, 2015. 133p. Source: Internet Resource: Accessed February 3, 2015 at: http://www.rand.org/pubs/research_reports/RR820.html Year: 2015 Country: United States URL: http://www.rand.org/pubs/research_reports/RR820.html Shelf Number: 134524 Keywords: Computer TechnologyCorrectional Administration (U.S.)JailsPrisoner ReentryPrisons |
Author: Ratcliffe, Jerry H. Title: Predictive Modeling Combining Short and Long-Term Crime Risk Potential: Final Report Summary: This research team (Temple University and industry partner Azavea) developed a technology capable of predicting future crime risk potential based on a number of grounded theoretical approaches to understanding localized spatial crime patterns. With regard to long-term crime risk changes, a stable crime niche model assumes that communities occupy crime niches in a broader jurisdiction, niches that are largely stable from year to year and have self-maintaining properties. Thus crime in one year may be predicted best by crime from the previous year. Alternatively, a structural model assumes that key current demographic conditions, such as socioeconomic status and racial composition, generally shape crime levels. Finally, a dynamic ecological and structural model assumes, net of the connections between current crime and demographic structure, that current structural conditions influence future long term changes in crime for a year in the future. The focus here is on ecological crime discontinuities, with priority assigned to demographic factors shaping such crime shifts over time. At the same time, ecological crime continuities also are present to a degree, linking current and future crime levels. These models were compared in the research study. The research team also examined what role near-repeat crime events, indicative of a short-term change in relative risk, have in modifying this relationship. Near repeats occur when a crime influences the likelihood of another crime within a narrow space and time window after the originator event. In particular, the 'boost' hypothesis (also known as 'event dependency') suggests that subsequent events are conditional on the originator event because (for example) the same offender returns to the area, or there is a retaliatory event. Using 2009 and 2010 reported crime for the City of Philadelphia, PA (USA) we identified that the demographics-plus-crime was the most parsimonious and accurate for robbery, burglary, aggravated assault, and vehicle theft when predicted from year-to-year in small geographic areas of 500 feet by 500 feet grid cells. Lower volume crime types (homicide and rape) were predicted as well as, or better, by the demographics-only model. We then added an event-dependency risk surface to the long-term crime risk predictions and estimated what impact this near repeat surface played in changing the accuracy and parsimony of the crime prediction. The best combination of accuracy and model parsimony was estimated by comparing differences in Bayesian Information Criterion (BIC) values. Near repeat patterns were estimated for two week periods across spatial bands of 250 feet width. These near repeat patterns were translated to a mapped risk surface and added to the long-term risk prediction surfaces. In this part of the study, 2012 crime was used to predict 2013 crime in the City of Philadelphia for two of the most frequent types of part 1 crime: robbery and burglary. With repeated examination of two-week predictions across 500 foot square grid cells, the strongest BIC value was identified with a model that combines crime from the previous year, change in demographic structure, and an adjustment for the near repeat phenomenon. Mixed effects logit models suggest that long-term (year-on-year) crime and demographic changes are more influential in this model than near repeats. Theoretically, this means that long term ecological crime continuities, long term crime discontinuities arising from stratification patterns in class and race, and near-term crime continuities in time and space all shape the two week, micro-scale predictions. In summary, a model combining community structural characteristics, crime counts from the previous year, and an estimate of near repeat activity generated the best results overall. This tells us that small scale, short term crime occurrences reflect a complex mix of near-term crime continuities, ecological crime continuities, and ecological structure which generates ecological crime discontinuities forward in time. The industry partner, Azavea, has created a free software program (PROVE) to perform these calculations for state and municipal police departments. Details: Philadelphia: Temple University, Center for Security and Crime Science, 2016. 131p. Source: Internet Resource: Accessed July 26, 2016 at: https://www.ncjrs.gov/pdffiles1/nij/grants/249934.pdf Year: 2016 Country: United States URL: https://www.ncjrs.gov/pdffiles1/nij/grants/249934.pdf Shelf Number: 139857 Keywords: Computer TechnologyCrime AnalysisCrime Prediction |