Suchergebnisse

Introduction -- Law enforcement intelligence -- Organizational influences on police change and intelligence -- Research design exploring ILP adoption -- Empirical findings of ILP adoption -- Case study: Florida fusion center -- Case study: southern Nevada counter-terrorism center -- Recommendations for ILP adoption -- References -- Index

Scenario planning is one of the most prominent methods applied by organisations to assist long-term decision making. This paper uses a case study method to demonstrate how scenarios can be operationalised to inform future strategies and to challenge rigid silo-based decision making approaches. The WaterProof Northwest scenarios developed by the authors in collaboration with a range of stakeholders, and described within this paper, offer a platform for considering the future of the water environment. The scenarios were developed in the context of meeting the goals of the European Water Framework Directive. This Directive has the core aim of improving the chemical and ecological status of Europe's water bodies. The scenarios highlight that water bodies in the case study area (the region of Northwest England) are impacted directly by a wide array of driving forces which will affect the state of the water environment over the coming decades. This analysis demonstrates that organisations responsible for creating and implementing long-term plans and policies to manage water are often far removed from the forces that will influence the effectiveness of the exercises that they are engaged in. The WaterProof Northwest scenarios highlight that organisations need different decision making approaches in order to adapt to modern environmental challenges. They also raise questions over whether environmental legislation such as the Water Framework Directive should incorporate a futures perspective in recognition of the wide ranging forces influencing their implementation. © 2012 Elsevier Ltd.

Cell phones are one of the most challenging forms of contraband for correctional facilities. The size of this problem is difficult to quantify. Confiscation data are only able to tell a partial story. Through the use of a unique data collection effort, this research details the number of contraband cell phones within a facility and offers the first attempt to estimate the gap that exists between the number of contraband cell phones available and the number that are confiscated. In light of the findings, implications for policy and an agenda for research on contraband cell phone market dynamics are provided.

Abstract
Background
Crime, traffic accidents, terrorist attacks, and other space-time random events are unevenly distributed in space and time. In the case of crime, hotspot and other proactive policing programs aim to focus limited resources at the highest risk crime and social harm hotspots in a city. A crucial step in the implementation of these strategies is the construction of scoring models used to rank spatial hotspots. While these methods are evaluated by area normalized Recall@k (called the predictive accuracy index), models are typically trained via maximum likelihood or rules of thumb that may not prioritize model accuracy in the top k hotspots. Furthermore, current algorithms are defined on fixed grids that fail to capture risk patterns occurring in neighborhoods and on road networks with complex geometries.

Results
We introduce CrimeRank, a learning to rank boosting algorithm for determining a crime hotspot map that directly optimizes the percentage of crime captured by the top ranked hotspots. The method employs a floating grid combined with a greedy hotspot selection algorithm for accurately capturing spatial risk in complex geometries. We illustrate the performance using crime and traffic incident data provided by the Indianapolis Metropolitan Police Department, IED attacks in Iraq, and data from the 2017 NIJ Real-time crime forecasting challenge.

Conclusion
Our learning to rank strategy was the top performing solution (PAI metric) in the 2017 challenge. We show that CrimeRank achieves even greater gains when the competition rules are relaxed by removing the constraint that grid cells be a regular tessellation.

Background: 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.

AbstractGunshot detection technology (GDT) is expected to impact gun violence by accelerating the discovery and response to gunfire. GDT should further collect more accurate spatial data, as gunfire is assigned to coordinates measured by acoustic sensors rather than addresses reported via 9-1-1 calls for service (CFS). The current study explores the level to which GDT achieves these benefits over its first 5 years of operation in Kansas City, Missouri. Data systems are triangulated to determine the time and location gunfire was reported by GDT and CFS. The temporal and spatial distances between GDT and CFS are then calculated. Findings indicate GDT generates time savings and increases spatial precision as compared to CFS. This may facilitate police responses to gunfire events and provide more spatially accurate data to inform policing strategies. Results of generalized linear and multinomial logistic regression models indicate that GDT benefits are influenced by a number of situational factors.

Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain social distance when in public, school closures, limitations on gatherings and business operations, and instructions to remain at home. Social distancing may have an impact on the volume and distribution of crime. Crimes such as residential burglary may decrease as a byproduct of increased guardianship over personal space and property. Crimes such as domestic violence may increase because of extended periods of contact between potential offenders and victims. Understanding the impact of social distancing on crime is critical for ensuring the safety of police and government capacity to deal with the evolving crisis. Understanding how social distancing policies impact crime may also provide insights into whether people are complying with public health measures. Examination of the most recently available data from both Los Angeles, CA, and Indianapolis, IN, shows that social distancing has had a statistically significant impact on a few specific crime types. However, the overall effect is notably less than might be expected given the scale of the disruption to social and economic life.