Who is most likely to offend in my store now? Statistical steps towards retail crime prevention with Auror

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dc.citation.volume57
dc.contributor.authorMcDonald BW
dc.contributor.authorHall L
dc.contributor.authorZhang XP
dc.date.available2015
dc.date.issued14/08/2017
dc.description.abstractAuror is establishing itself both locally and internationally as a leader in retail crime solutions. In mid-2015 a study group of mathematicians and statisticians teamed up with Auror to analyse data from the first two and a half years of their venture to identify and prevent retail theft. The aim was to explore methods for nominating the top ten individuals most likely to offend in a particular store at a particular time. Various methods were employed to explore the relationships between retail crime incidents, including generalised linear models, regression trees and similarity matrices. The relationships identified were then used to inform predictions on individuals most likely to reoffend. The focus of the current analysis is to model the behaviour of reoffenders. At the time of the study group the project was still in the early phases of data collection. As data collection proceeds, prediction methods will likely give better and better intelligence to aid crime prevention efforts.
dc.description.publication-statusPublished
dc.format.extentM289 - M331
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000416173400011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationANZIAM JOURNAL, 2015, 57 pp. M289 - M331
dc.identifier.doi10.21914/anziamj.v57i0.10507
dc.identifier.eissn1446-8735
dc.identifier.elements-id370477
dc.identifier.harvestedMassey_Dark
dc.identifier.issn1446-1811
dc.identifier.urihttps://hdl.handle.net/10179/11805
dc.publisherAustralian Mathematical Society
dc.relation.isPartOfANZIAM JOURNAL
dc.relation.urihttps://journal.austms.org.au/ojs/index.php/ANZIAMJ/article/view/10507
dc.subject.anzsrc01 Mathematical Sciences
dc.subject.anzsrc09 Engineering
dc.titleWho is most likely to offend in my store now? Statistical steps towards retail crime prevention with Auror
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
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