Crime Science最新文献

{"title":"Learning to rank spatio-temporal event hotspots","authors":"George Mohler, Michael Porter, Jeremy Carter, Gary LaFree","doi":"10.1186/s40163-020-00112-x","DOIUrl":"https://doi.org/10.1186/s40163-020-00112-x","url":null,"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.","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"21 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140882404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constellations of youth criminogenic factors associated with young adult violent criminal behavior","authors":"Menno Segeren, Thijs Fassaert, Matty de Wit, Arne Popma","doi":"10.1186/s40163-020-0111-2","DOIUrl":"https://doi.org/10.1186/s40163-020-0111-2","url":null,"abstract":"This study identified constellations of childhood risk factors associated with violent criminal behavior in early adulthood. Police data were used to sample violent and nonviolent offenders from a population of young adult males with a history of juvenile probation. Risk factors were retrieved from their juvenile probation files. A single classification tree analysis organized these into a decision tree for violent criminal behavior with good predictive accuracy. Two constellations of risk factors were associated with a high risk of violent criminal behavior. The first consisted of juvenile delinquents who had been moderately involved with criminal peers, who had committed offenses under the influence of drugs, and who came from a dysfunctional family. The second was characterized by having been severely involved with criminal peers and having had criminal family members. Presenting with depressive symptoms in childhood was associated with a low risk of violent criminal behavior. These constellations bear clinical importance as they provide targets for personalized interventions.","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"111 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140882613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is security worth to consumers? Investigating willingness to pay for secure Internet of Things devices","authors":"John M. Blythe, Shane D. Johnson, Matthew Manning","doi":"10.1186/s40163-019-0110-3","DOIUrl":"https://doi.org/10.1186/s40163-019-0110-3","url":null,"abstract":"The Internet of Things (IoT) is considered the next technological revolution. IoT devices include once everyday objects that are now internet connected, such as smart locks and smart fridges, but also new types of devices to include home assistants. However, while this increased interconnectivity brings considerable benefits, it can and does increase people’s exposure to crime risk. This is particularly the case as most devices are developed without security in mind. One reason for this is that there is little incentive for manufacturers to make devices secure by design, and the costs of so doing do not encourage it. The principle aim of the current paper was to estimate the extent to which consumers are willing to pay for improved security in internet connected products. The second aim was to examine whether this is conditioned by their exposure to security-related information. Using an experimental design, and a contingent valuation method, we find that people are willing to pay for improved security and that for some devices, this increases if they are exposed to information about security prior to stating their willingness to pay. The implications of our findings for industry and the secure by design agenda are discussed.","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"47 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2020-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140882406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Somehow I always end up alone: COVID-19, social isolation and crime in Queensland, Australia.","authors":"Martin A Andresen,&nbsp;Tarah Hodgkinson","doi":"10.1186/s40163-020-00135-4","DOIUrl":"https://doi.org/10.1186/s40163-020-00135-4","url":null,"abstract":"<p><p>The COVID-19 pandemic has dramatically affected social life. In efforts to reduce the spread of the virus, countries around the world implemented social restrictions, including social distancing, working from home, and the shuttering of numerous businesses. These social restrictions have also affected crime rates. In this study, we investigate the impact of the COVID-19 pandemic on the frequency of offending (crimes include property, violent, mischief, and miscellaneous) in Queensland, Australia. In particular, we examine this impact across numerous settings, including rural, regional and urban. We measure these shifts across the restriction period, as well as the staged relaxation of these restrictions. In order to measure impact of this period we use structural break tests. In general, we find that criminal offences have significantly decreased during the initial lockdown, but as expected, increased once social restrictions were relaxed. These findings were consistent across Queensland's districts, save for two areas. We discuss how these findings are important for criminal justice and social service practitioners when operating within an extraordinary event.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"9 1","pages":"25"},"PeriodicalIF":6.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40163-020-00135-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38651721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime.","authors":"Eric Halford,&nbsp;Anthony Dixon,&nbsp;Graham Farrell,&nbsp;Nicolas Malleson,&nbsp;Nick Tilley","doi":"10.1186/s40163-020-00121-w","DOIUrl":"https://doi.org/10.1186/s40163-020-00121-w","url":null,"abstract":"<p><p>Governments around the world restricted movement of people, using social distancing and lockdowns, to help stem the global coronavirus (COVID-19) pandemic. We examine crime effects for one UK police force area in comparison to 5-year averages. There is variation in the onset of change by crime type, some declining from the WHO 'global pandemic' announcement of 11 March, others later. By 1 week after the 23 March lockdown, all recorded crime had declined 41%, with variation: shoplifting (- 62%), theft (- 52%), domestic abuse (- 45%), theft from vehicle (- 43%), assault (- 36%), burglary dwelling (- 25%) and burglary non-dwelling (- 25%). We use Google Covid-19 Community Mobility Reports to calculate the mobility elasticity of crime for four crime types, finding shoplifting and other theft inelastic but responsive to reduced retail sector mobility (MEC = 0.84, 0.71 respectively), burglary dwelling elastic to <i>increases</i> in residential area mobility (- 1), with assault inelastic but responsive to reduced workplace mobility (0.56). We theorise that crime rate changes were primarily caused by those in mobility, suggesting a mobility theory of crime change in the pandemic. We identify implications for crime theory, policy and future research.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"9 1","pages":"11"},"PeriodicalIF":6.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40163-020-00121-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38297536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disentangling community-level changes in crime trends during the COVID-19 pandemic in Chicago.","authors":"Gian Maria Campedelli, Serena Favarin, Alberto Aziani, Alex R Piquero","doi":"10.1186/s40163-020-00131-8","DOIUrl":"10.1186/s40163-020-00131-8","url":null,"abstract":"<p><p>Recent studies exploiting city-level time series have shown that, around the world, several crimes declined after COVID-19 containment policies have been put in place. Using data at the community-level in Chicago, this work aims to advance our understanding on how public interventions affected criminal activities at a finer spatial scale. The analysis relies on a two-step methodology. First, it estimates the community-wise causal impact of social distancing and shelter-in-place policies adopted in Chicago via Structural Bayesian Time-Series across four crime categories (i.e., burglary, assault, narcotics-related offenses, and robbery). Once the models detected the direction, magnitude and significance of the trend changes, Firth's Logistic Regression is used to investigate the factors associated to the statistically significant crime reduction found in the first step of the analyses. Statistical results first show that changes in crime trends differ across communities and crime types. This suggests that beyond the results of aggregate models lies a complex picture characterized by diverging patterns. Second, regression models provide mixed findings regarding the correlates associated with significant crime reduction: several relations have opposite directions across crimes with population being the only factor that is stably and positively associated with significant crime reduction.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"9 1","pages":"21"},"PeriodicalIF":6.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40163-020-00131-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38555802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"Show this thread\": policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic.","authors":"Manja Nikolovska, Shane D Johnson, Paul Ekblom","doi":"10.1186/s40163-020-00129-2","DOIUrl":"10.1186/s40163-020-00129-2","url":null,"abstract":"<p><p>Crisis and disruption are often unpredictable and can create opportunities for crime. During such times, policing may also need to meet additional challenges to handle the disruption. The use of social media by officials can be essential for crisis mitigation and crime reduction. In this paper, we study the use of Twitter for crime mitigation and reduction by UK police (and associated) agencies in the early stages of the Covid-19 pandemic. Our findings suggest that whilst most of the tweets from our sample concerned issues that were not specifically about crime, especially during the first stages of the pandemic, there was a significant increase in tweets about fraud, cybercrime and domestic abuse. There was also an increase in retweeting activity as opposed to the creation of original messages. Moreover, in terms of the impact of tweets, as measured by the rate at which they are retweeted, followers were more likely to 'spread the word' when the tweet was content-rich (discussed a crime specific matter and contained media), and account holders were themselves more active on Twitter. Considering the changing world we live in, criminal opportunity is likely to evolve. To help mitigate this, policy makers and researchers should consider more systematic approaches to developing social media communication strategies for the purpose of crime mitigation and reduction during disruption and change more generally. We suggest a framework for so doing.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"9 1","pages":"20"},"PeriodicalIF":3.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38531950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Initial evidence on the relationship between the coronavirus pandemic and crime in the United States.","authors":"Matthew P J Ashby","doi":"10.1186/s40163-020-00117-6","DOIUrl":"https://doi.org/10.1186/s40163-020-00117-6","url":null,"abstract":"<p><p>The COVID-19 pandemic led to substantial changes in the daily activities of millions of Americans, with many businesses and schools closed, public events cancelled and states introducing stay-at-home orders. This article used police-recorded open crime data to understand how the frequency of common types of crime changed in 16 large cities across the United States in the early months of 2020. Seasonal auto-regressive integrated moving average (SARIMA) models of crime in previous years were used to forecast the expected frequency of crime in 2020 in the absence of the pandemic. The forecasts from these models were then compared to the actual frequency of crime during the early months of the pandemic. There were no significant changes in the frequency of serious assaults in public or (contrary to the concerns of policy makers) any change to the frequency of serious assaults in residences. In some cities, there were reductions in residential burglary but little change in non-residential burglary. Thefts of motor vehicles decreased in some cities while there were diverging patterns of thefts from motor vehicles. These results are used to make suggestions for future research into the relationships between the coronavirus pandemic and different crimes.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"9 1","pages":"6"},"PeriodicalIF":6.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40163-020-00117-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37978110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Routine activity effects of the Covid-19 pandemic on burglary in Detroit, March, 2020.","authors":"Marcus Felson, Shanhe Jiang, Yanqing Xu","doi":"10.1186/s40163-020-00120-x","DOIUrl":"10.1186/s40163-020-00120-x","url":null,"abstract":"<p><p>The spread of the coronavirus has led to containment policies in many places, with concomitant shifts in routine activities. Major declines in crime have been reported as a result. However, those declines depend on crime type and may differ by parts of a city and land uses. This paper examines burglary in Detroit, Michigan during the month of March, 2020, a period of considerable change in routine activities. We examine 879 block groups, separating those dominated by residential land use from those with more mixed land use. We divide the month into three periods: pre-containment, transition period, and post-containment. Burglaries increase in block groups with mixed land use, but not blocks dominated by residential land use. The impact of containment policies on burglary clarifies after taking land use into account.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"9 1","pages":"10"},"PeriodicalIF":6.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38297535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review on spatial crime forecasting.","authors":"Ourania Kounadi, Alina Ristea, Adelson Araujo, Michael Leitner","doi":"10.1186/s40163-020-00116-7","DOIUrl":"10.1186/s40163-020-00116-7","url":null,"abstract":"<p><strong>Background: </strong>Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects.</p><p><strong>Methods: </strong>We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics.</p><p><strong>Results: </strong>The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1-Score. Finally, the most common validation approach was the train-test split while other approaches include the cross-validation, the leave one out, and the rolling horizon.</p><p><strong>Limitations: </strong>Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems.</p><p><strong>Conclusions: </strong>There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real-time prediction.</p><p><strong>Implications: </strong>Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66). To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study's key data items.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"9 1","pages":"7"},"PeriodicalIF":6.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38126484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}