(Looking) Back to the Future: using space-time patterns to better predict the location of street crime

Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread use of this analytical technique, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organisational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimises prevention and detection. This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimisation

Hot Routes: Developing a New Technique for the Spatial Analysis of Crime

The use of hotspot mapping techniques such as KDE to represent the geographical spread of linear events can be problematic. Network-constrained data (for example transport-related crime) require a different approach to visualize concentration. We propose a methodology called Hot Routes, which measures the risk distribution of crime along a linear network by calculating the rate of crimes per section of road. This method has been designed for everyday crime analysts, and requires only a Geographical Information System (GIS), and suitable data to calculate. A demonstration is provided using crime data collected from London bus routes

Profiling Illegal Waste Activity: Using Crime Scripts as a Data Collection and Analytical Strategy

The illegal treatment and trade of waste is an international problem which is widely assumed to be both evolving and growing. Emergent forms of criminality such as this often have the problem of data being in scarce supply, and as a result are difficult to study, and subsequently understand. In this paper we introduce the methodological concept of script analysis to assist a more objective assessment and understanding of illegal waste activity. This includes using crime scripts in two ways; to help identify data requirements, and as a tool to analyse illegal waste processes. We illustrate the utility of this methodology using waste electrical and electronic equipment. In doing so, we argue that this approach elicits a specific, focused account of what illegal activity has occurred, and nests it within the wider context of the waste management system. We anticipate that using this methodology will provide academics and practitioners a means of enhancing the investigation, detection and prevention of illegal waste activity

UK open source crime data: accuracy and possibilities for research

In the United Kingdom, since 2011 data regarding individual police recorded crimes have been made openly available to the public via the police.uk website. To protect the location privacy of victims these data are obfuscated using geomasking techniques to reduce their spatial accuracy. This paper examines the spatial accuracy of the police.uk data to determine at what level(s) of spatial resolution – if any – it is suitable for analysis in the context of theory testing and falsification, evaluation research, or crime analysis. Police.uk data are compared to police recorded data for one large metropolitan Police Force and spatial accuracy is quantified for four different levels of geography across five crime types. Hypotheses regarding systematic errors are tested using appropriate statistical approaches, including methods of maximum likelihood. Finally, a “best-fit” statistical model is presented to explain the error as well as to develop a model that can correct it. The implications of the findings for researchers using the police.uk data for spatial analysis are discussed

Information retrieval in systematic reviews: a case study of the crime prevention literature

OBJECTIVES: A defining feature of a systematic review is the data collection; the assembling of a meticulous, unbiased, and reproducible set of primary studies. This requires specialist skills to execute. The aim of this paper is to marshal tacit knowledge, gained through a systematic search of the crime prevention literature, to develop a ‘how-to guide’ for future evidence synthesists in allied fields. METHODS: Empirical results from a recent systematic search for evidence in crime prevention are supplied to illustrate key principles of information retrieval. RESULTS: Difficulties in operationalizing a systematic search are expounded and possible solutions discussed. Empirical results from optimizing the balance between sensitivity and precision with the criminological literature are presented. An estimation of database overlap for crime prevention studies is provided to guide other evidence synthesists in streamlining the search process. CONCLUSIONS: A high-quality search will involve a substantial time investment in honing the research question, specifying the precise scope of the work, and trialing and testing of search tactics. Electronic databases are a lucrative source of eligible studies, but they have important limitations. The diversity of expression across the criminological literature needs to be captured by the use of many search terms—both natural language and controlled vocabulary—in database searches. Complementary search tactics should be employed to locate eligible studies without common vocabulary. Grey literature should be ardently pursued, for it has a central role in the crime prevention evidence base

Family-focused treatment for childhood depression: model and case illustrations

Although the evidence base for treatment of depressive disorders in adolescents has strengthened in recent years, less is known about the treatment of depression in middle to late childhood. A family-based treatment may be optimal in addressing the interpersonal problems and symptoms frequently evident among depressed children during this developmental phase, particularly given data indicating that attributes of the family environment predict recovery versus continuing depression among depressed children. Family-Focused Treatment for Childhood Depression (FFT-CD) is designed as a 15-session family treatment with both the youth and parents targeting two putative mechanisms involved in recovery: (a) enhancing family support, specifically decreasing criticism and increasing supportive interactions; and (b) strengthening specific cognitive-behavioral skills within a family context that have been central to CBT for depression, specifically behavioral activation, communication, and problem solving. This article describes in detail the FFT-CD protocol and illustrates its implementation with three depressed children and their families. Common themes/challenges in treatment included family stressors, comorbidity, parental mental health challenges, and inclusion/integration of siblings into sessions. These three children experienced positive changes from pre- to posttreatment on assessor-rated depressive symptoms, parent- and child-rated depressive symptoms, and parent-rated internalizing and externalizing symptoms. These changes were maintained at follow-up evaluations 4 and 9 months following treatment completion.K23 MH101238 - NIMH NIH HHS; R01 MH082856 - NIMH NIH HHS; R01 MH082861 - NIMH NIH HH

A victim-centred cost–benefit analysis of a stalking prevention programme

Research suggests that stalking inflicts great psychological and financial costs on victims. Yet costs of victimisation are notoriously difficult to estimate and include as intangible costs in cost–benefit analysis. This study reports an innovative cost–benefit analysis that used focus groups with multi-agency teams to collect detailed data on operational resources used to manage stalking cases. This method is illustrated through the presentation of one case study. Best- and worst-case counterfactual scenarios were generated using the risk assessment scores and practitioner expertise. The findings suggest that intervening in high-risk stalking cases was cost-beneficial to the state in all the case studies we analysed (even if it incurs some institutional costs borne by the criminal justice system or health) and was often cost-beneficial to the victims too. We believe that this method might be useful in other fields where a victim- or client-centred approach is fundamental