Testing time-sensitive influences of weather on street robbery

Although the relationship between weather and crime has been extensively investigated over the past century, little consensus has emerged on the directions of the relationships observed and the mechanisms through which weather might exert its influence. This paper advances an argument that the interpretation of weather, and subsequent activities based on that interpretation, leads to spatio-temporal variations in criminal opportunities, and hence crime. Two hypotheses relating to unseasonal weather and effects of weather on discretionary activities are proposed. Negative binomial regression models are used to test these at the 6-hour shift unit of analysis on street robberies in the Strathclyde region of Scotland. In line with predictions, in this temperate microclimate, more favourable weather in winter (higher temperatures and low wind speeds) was associated with increases in robbery. Partial support was also found for the hypothesis regarding time delineated for discretionary activities. Here, temperature, wind speed and humidity were seen to be significant predictors of robbery during the night shift and weekends. Notably rain was shown to have a negative relationship with robbery at the weekends. This affirms that people are less likely to venture outdoors when it is raining when travel behaviour is optional. Counter to our hypothesised effects, fog was the only variable to significantly interact with public holidays. We conclude by discussing how these analyses might be extended and briefly discuss implications for crime prevention

Concentrations of railway metal theft and the locations of scrap-metal dealers

Metal theft has become a substantial crime problem in many areas. In response, several countries have introduced legislation to regulate scrap-metal recycling yards. However, at present there is little evidence to support this use of the market reduction approach (MRA) in preventing metal theft. The present study sought to test the underlying assumption of the MRA that the presence of a market for stolen property (in this case provided by scrap yards) drives thefts in a local area. This study tested for a spatial association between the locations of scrap yards and those of metal thefts. The density of industry, local burglary rate and road-accessibility of an area were controlled for. Metal thefts from railway lines in England were shown to be significantly more common in areas with more scrap-metal yards, high road accessibility and high population density. The results support the use of the MRA in relation to metal theft

Mapping the crime reduction evidence base: a descriptive analysis of the WP1 Systematic Review Database.

This document gives some summary statistics for the sample of systematic reviews that met the WP1 inclusion criteria. These criteria are documented in the systematic review protocol for this work package. In summary, the final list of studies constituted 337 separate systematic reviews

The when and where of an emerging crime type: the example of metal theft from the railway network of Great Britain

Metal theft has become an increasingly common crime in recent years, but lack of data has limited research into it. The present study used police-recorded crime data to study the spatial and temporal concentration of metal theft from the railway network of Great Britain. Metal theft was found to exhibit only weak seasonality, to be concentrated at night and to cluster in a few locations close to – but not in – major cities. Repeat-victimisation risk continued for longer than has been found for other crime types. These and other features appear to point to metal theft being a planned, rather than opportunistic, offence and to the role of scrap-metal dealers as facilitators

Household occupancy and burglary: A case study using COVID-19 restrictions

INTRODUCTION: In response to COVID-19, governments imposed various restrictions on movement and activities. According to the routine activity perspective, these should alter where crime occurs. For burglary, greater household occupancy should increase guardianship against residential burglaries, particularly during the day considering factors such as working from home. Conversely, there should be less eyes on the street to protect against non-residential burglaries. METHODS: In this paper, we test these expectations using a spatio-temporal model with crime and Google Community Mobility data. RESULTS: As expected, burglary declined during the pandemic and restrictions. Different types of burglary were, however, affected differently but largely consistent with theoretical expectation. Residential and attempted residential burglaries both decreased significantly. This was particularly the case during the day for completed residential burglaries. Moreover, while changes were coincident with the timing and relaxation of restrictions, they were better explained by fluctuations in household occupancy. However, while there were significant decreases in non-residential and attempted non-residential burglary, these did not appear to be related to changes to activity patterns, but rather the lockdown phase. CONCLUSIONS: From a theoretical perspective, the results generally provide further support for routine activity perspective. From a practical perspective, they suggest considerations for anticipating future burglary trends

Predictive Crime Mapping: Arbitrary Grids or Street Networks?

OBJECTIVES: Decades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent; a finding that Weisburd (Criminology 53:133–157, 2015) has recently termed the ‘law of crime concentration at places’. Such findings suggest that the future locations of crime should—to some extent at least—be predictable. To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. No studies of which we are aware have developed and tested the accuracy of methods for predicting the future risk of crime at the street segment level. This is surprising given that it is at this level of place that many crimes are committed and policing resources are deployed. METHODS: Using data for property crimes for a large UK metropolitan police force area, we introduce and calibrate a network-based version of prospective crime mapping [e.g. Bowers et al. (Br J Criminol 44:641–658, 2004)], and compare its performance against grid-based alternatives. We also examine how measures of predictive accuracy can be translated to the network context, and show how differences in performance between the two cases can be quantified and tested. RESULTS: Findings demonstrate that the calibrated network-based model substantially outperforms a grid-based alternative in terms of predictive accuracy, with, for example, approximately 20 % more crime identified at a coverage level of 5 %. The improvement in accuracy is highly statistically significant at all coverage levels tested (from 1 to 10 %). CONCLUSIONS: This study suggests that, for property crime at least, network-based methods of crime forecasting are likely to outperform grid-based alternatives, and hence should be used in operational policing. More sophisticated variations of the model tested are possible and should be developed and tested in future research

A Stab in the Dark? Analysing Temporal Trends of Street Robbery

Objectives: Test the influence of darkness in the street robbery crime event alongside temperature. Methods: Negative binomial regression models tested darkness and temperature as predictors of street robbery. Units of analysis were four 6-hr time intervals in two U.K. study areas that have different levels of darkness and variations of temperature throughout the year. Results: Darkness is a key factor related to robbery events in both study areas. Traversing from full daylight to full darkness increased the predicted volume of robbery by a multiple of 2.6 in London and 1.2 in Glasgow. Temperature was significant only in the London study area. Interaction terms did not enhance the predictive power of the models. Conclusion: Darkness is an important driving factor in seasonal variation of street robbery. A further implication of the research is that time of the day patterns are crucial to understanding seasonal trends in crime data

Spatial and temporal analysis of crude oil theft in the Niger delta

This research sought to investigate patterns and correlates of the under-researched crime of crude oil theft (COT) in the context of the Niger Delta. The aim was to examine the feasibility of opportunity-, deprivation- and market-value-based explanations for COT patterns. A total of 1039 incidents of COT recorded by the Nigerian Oil Producers’ Trade Section during 2012–2014 were analysed. The results indicate that even when controlling for clustering of the oil pipeline infrastructure, spatial clustering of COT was statistically significant indicating manipulation of vulnerable situational contexts. No significant correlation was found between COT and the local unemployment or poverty rate. Finally, there was a moderate, significant positive temporal association between the volume of crude oil stolen and the international market price. The findings provide evidence that COTs are likely perpetrated by rationally motivated offenders and suggest that situational crime-prevention and market-reduction approaches show promise in proactively curtailing criminal opportunities

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

GROMACS is a widely used package for biomolecular simulation, and over the last two decades it has evolved from small-scale efficiency to advanced heterogeneous acceleration and multi-level parallelism targeting some of the largest supercomputers in the world. Here, we describe some of the ways we have been able to realize this through the use of parallelization on all levels, combined with a constant focus on absolute performance. Release 4.6 of GROMACS uses SIMD acceleration on a wide range of architectures, GPU offloading acceleration, and both OpenMP and MPI parallelism within and between nodes, respectively. The recent work on acceleration made it necessary to revisit the fundamental algorithms of molecular simulation, including the concept of neighborsearching, and we discuss the present and future challenges we see for exascale simulation - in particular a very fine-grained task parallelism. We also discuss the software management, code peer review and continuous integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin

On the development and application of EMMIE: Insights from the What Works Centre for Crime Reduction

The What Works Centre for Crime Reduction was established in September 2013 with the aim of increasing the use of research evidence by decision-makers in policing and crime reduction. The EMMIE framework was developed to meet this aim. It encapsulates five broad categories of research evidence that are considered relevant to crime reduction, namely Effect size, Mechanism, Moderator (or context), Implementation and Economics. In this paper, we chart the origins and development of EMMIE. We also reflect on our experience of applying EMMIE both as a coding system to appraise systematic review evidence and as a framework to inform the design and conduct of systematic reviews in crime reduction. We conclude with a critique of EMMIE and with suggestions on how it might be developed and refined in the future