Crime Science最新文献_第10页

To SPB or not to SPB? A mixed methods analysis of self-protective behaviours to prevent repeat victimisation from cyber abuse SPB 还是不 SPB？用混合方法分析自我保护行为，防止网络虐待的重复伤害

IF 6.1

Crime Science Pub Date : 2020-11-11 DOI: 10.1186/s40163-020-00134-5

Zarina I. Vakhitova, Rob I. Mawby, Clair L. Alston-Knox, Callum A. Stephens

{"title":"To SPB or not to SPB? A mixed methods analysis of self-protective behaviours to prevent repeat victimisation from cyber abuse","authors":"Zarina I. Vakhitova, Rob I. Mawby, Clair L. Alston-Knox, Callum A. Stephens","doi":"10.1186/s40163-020-00134-5","DOIUrl":"https://doi.org/10.1186/s40163-020-00134-5","url":null,"abstract":"This paper presents the findings from a mixed-methods examination of self-protective behaviours (SPBs) adopted by victims of cyber abuse from the rational choice perspective. The data from a sample of the U.S. adults ( $$N = 746$$ N = 746 ), members of an online opt-in panel, were analysed to first distinguish the types of SPBs adopted by victims of cyber abuse using a thematic analysis of open-ended responses. We then identified the factors associated with an increased likelihood of adopting SPBs and the specific identified types of SPBs using logistic regression with Bayesian variable selection and a stochastic search algorithm. Of the six identified types of SPBs, adjusting privacy settings was the most commonly reported response, and improving security (e.g. changing passwords, etc.) was the least common SPB. Older victims who reported higher than the average perceived impact from victimisation, were abused by a stranger and experienced either surveillance of their online activities or multiple types of abuse, were significantly more likely to adopt an SPB. Our findings inform strategies for both Internet user education and for preventing cyber abuse victimisation.","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140882438","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}

引用次数: 0

A framework for estimating crime location choice based on awareness space 基于感知空间的犯罪地点选择估计框架

IF 6.1

Crime Science Pub Date : 2020-11-04 DOI: 10.1186/s40163-020-00132-7

Sophie Curtis-Ham, W. Bernasco, O. Medvedev, D. Polaschek

引用次数: 12

A machine learning analysis of serious misconduct among Australian police 对澳大利亚警方严重不当行为的机器学习分析

IF 6.1

Crime Science Pub Date : 2020-10-31 DOI: 10.1186/s40163-020-00133-6

Timothy I. C. Cubitt, K. Wooden, K. Roberts

引用次数: 10

Unsupervised identification of crime problems from police free-text data 从警方自由文本数据中无监督识别犯罪问题

IF 6.1

Crime Science Pub Date : 2020-10-07 DOI: 10.1186/s40163-020-00127-4

Daniel Birks, Alex Coleman, David Jackson

{"title":"Unsupervised identification of crime problems from police free-text data","authors":"Daniel Birks, Alex Coleman, David Jackson","doi":"10.1186/s40163-020-00127-4","DOIUrl":"https://doi.org/10.1186/s40163-020-00127-4","url":null,"abstract":"We present a novel exploratory application of unsupervised machine-learning methods to identify clusters of specific crime problems from unstructured modus operandi free-text data within a single administrative crime classification. To illustrate our proposed approach, we analyse police recorded free-text narrative descriptions of residential burglaries occurring over a two-year period in a major metropolitan area of the UK. Results of our analyses demonstrate that topic modelling algorithms are capable of clustering substantively different burglary problems without prior knowledge of such groupings. Subsequently, we describe a prototype dashboard that allows replication of our analytical workflow and could be applied to support operational decision making in the identification of specific crime problems. This approach to grouping distinct types of offences within existing offence categories, we argue, has the potential to support crime analysts in proactively analysing large volumes of modus operandi free-text data—with the ultimate aims of developing a greater understanding of crime problems and supporting the design of tailored crime reduction interventions.","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140882402","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}

引用次数: 0

Unsupervised identification of crime problems from police free-text data 警方自由文本数据中犯罪问题的无监督识别

IF 6.1

Crime Science Pub Date : 2020-08-19 DOI: 10.31235/osf.io/8w73n

Daniel Birks, A. Coleman, Donald A. Jackson

引用次数: 6

Too far for comfort? Situational access to emergency medical care and violent assault lethality 太远而不舒适？紧急情况下获得紧急医疗救助与暴力袭击的致命性

IF 6.1

Crime Science Pub Date : 2020-08-17 DOI: 10.1186/s40163-020-00124-7

Lucia Summers, Tiffany Gentry Rogers

引用次数: 0

AI-enabled future crime 人工智能驱动的未来犯罪