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Browsing by Author "Piza, Eric L."
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Item Integrating Human Factors Engineering and Information Processing Approaches to Facilitate Evaluations in Criminal Justice Technology Research(Sage, 2015-05) Salvemini, Anthony V.; Piza, Eric L.; Carter, Jeremy G.; Grommon, Eric L.; Merritt, Nancy; School of Public and Environmental AffairsBackground: Evaluations are routinely conducted by government agencies and research organizations to assess the effectiveness of technology in criminal justice. Interdisciplinary research methods are salient to this effort. Technology evaluations are faced with a number of challenges including (1) the need to facilitate effective communication between social science researchers, technology specialists, and practitioners, (2) the need to better understand procedural and contextual aspects of a given technology, and (3) the need to generate findings that can be readily used for decision making and policy recommendations. Objectives: Process and outcome evaluations of technology can be enhanced by integrating concepts from human factors engineering and information processing. This systemic approach, which focuses on the interaction between humans, technology, and information, enables researchers to better assess how a given technology is used in practice. Subjects: Examples are drawn from complex technologies currently deployed within the criminal justice system where traditional evaluations have primarily focused on outcome metrics. Although this evidence-based approach has significant value, it is vulnerable to fully account for human and structural complexities that compose technology operations. Conclusions: Guiding principles for technology evaluations are described for identifying and defining key study metrics, facilitating communication within an interdisciplinary research team, and for understanding the interaction between users, technology, and information. The approach posited here can also enable researchers to better assess factors that may facilitate or degrade the operational impact of the technology and answer fundamental questions concerning whether the technology works as intended, at what level, and cost.Item Leveraging Wireless Broadband to Improve Police Land Mobile Radio Programming: Estimating the Resource Impact(Taylor & Francis, 2019) Carter, Jeremy G.; Piza, Eric L.; Grommon, Eric; School of Public and Environmental AffairsDespite rapid growth in criminological studies of police technology, examinations of police land mobile radios are absent in the literature. This is troubling given the central role mobile radios serve in police operations and their significant management costs. The present study seeks to fill this gap by introducing the functionality of wireless broadband radio programming. Current practice requires a police officer to physically drive to a radio programming location to manage their mobile radio. Wireless programming remedies this burdensome reality, thereby saving officer time and cost. Geospatial analyses are used to estimate distance saved associated with wireless programming. We then conduct a number of calculations to determine time and cost savings related to the observed differences between existing and wireless radio programming within the context of the North Carolina State Highway Patrol. Results suggest wireless radio programming can save significant personnel and financial resources. Implications are discussed.Item Predicting Initiator and Near Repeat Events in Spatiotemporal Crime Patterns: An Analysis of Residential Burglary and Motor Vehicle Theft(Taylor & Francis, 2018) Piza, Eric L.; Carter, Jeremy G.; School of Public and Environmental AffairsNear repeat analysis has been increasingly used to measure the spatiotemporal clustering of crime in contemporary criminology. Despite its predictive capacity, the typically short time frame of near repeat crime patterns can negatively affect the crime prevention utility of near repeat analysis. Thus, recent research has argued for a greater understanding of the types of places that are most likely to generate near repeat crime patterns. The current study contributes to the literature through a spatiotemporal analysis of residential burglary and motor vehicle theft in Indianapolis, IN. Near Repeat analyses were followed by multinomial logistic regression models to identify covariates related to the occurrence of initiator (the first event in a near repeat chain) and near repeat (the subsequent event in a near repeat chain) events. The overall findings provide additional support for the argument that neighborhood context can influence the formation and context of spatiotemporal crime patterns.Item Spatiotemporal Convergence of Crime and Vehicle Crash Hotspots: Additional Consideration for Policing Places(Sage, 2017) Carter, Jeremy G.; Piza, Eric L.; School of Public and Environmental AffairsPolicing strategies that seek to simultaneously combat crime and vehicle crashes operate under the assumption that these two problems have a corollary relationship—an assumption that has received scant empirical attention and is the focus of the present study. Geocoded vehicle crash, violent crime, and property crime totals across were aggregated to Indianapolis census blocks over a 36-month period (2011-2013). Time series negative binomial regression and local indicators of spatial autocorrelation analyses were conducted. Results indicate that both violent and property crime are significantly related to vehicle crash counts, both overall and during the temporal confines of patrol tours. Relationship strength was modest. Spatiotemporal analysis of crime and crash data can identify places for police intervention and improved scholarly evaluation.Item The Sensitivity of Repeat and Near Repeat Analysis to Geocoding Algorithms(Elsevier, 2020) Haberman, Cory P.; Hatten, David; Carter, Jeremy G.; Piza, Eric L.; School of Public and Environmental AffairsPurpose: To determine if repeat and near repeat analysis is sensitive to the geocoding algorithm used for the underlying crime incident data. Methods: The Indianapolis Metropolitan Police Department provided 2016 crime incident data for five crime types: (1) shootings, (2) robberies, (3) residential burglaries, (4) theft of automobiles, and (5) theft from automobiles. The incident data were geocoded using a dual ranges algorithm and a composite algorithm. First, descriptive analysis of the distances between the two point patterns were conducted. Second, repeat and near repeat analysis was performed. Third, the resulting repeat and near repeat patterns were compared across geocoding algorithms. Results: The underlying point patterns and repeat and near repeat analyses were similar across geocoding algorithms. Conclusions: While detailing geocoding processes increases transparency and future researchers can conduct sensitivity results to ensure their findings are robust, dual ranges geocoding algorithms are likely adequate for repeat and near repeat analysis.