Auror 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. References http://www.auror.co/we-have-exciting-news/ (Accessed 9 Apr 2017). http://www.stuff.co.nz/national/crime/9806920/High-tech-blitz-on-2m-a-day-shoplifters (Accessed 9 Apr 2017). http://theregister.co.nz/news/2015/06/retail-crimewatch (Accessed 9 Apr 2017). http://www.saynotoshoplifting.org/the-issue-why-care (Accessed 9 Apr 2017). http://www.auror.co/the-global-picture-of-shoplifting/ (Accessed 9 Apr 2017). http://www.scoop.co.nz/stories/BU1410/S00104/connecting-the-dots-on-retail-theft.htm (Accessed 9 Apr 2017). http://www.auror.co/two-at-a-time-is-great-prevention/ (Accessed 9 Apr 2017). http://www.auror.co/collaboration_success/ (Accessed 9 Apr 2017). http://www.innovators.org.nz/winners-a-finalists/winners-2015 (Accessed 9 Apr 2017) Seni, G. and Elder J. F. (2010) Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions Morgan and Claypool, California. doi:10.2200/S00240ED1V01Y200912DMK002 Powers, D. M. W. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies 2 (1) 37–63. Ricci F., Rokach, L. and Shapira, B. (2015) Recommender Systems Handbook. Springer, New York. ISBN 978-1-4899-7637-6 McGloin J. M. and Kirk D. S. (2010) An Overview of Social Network Analysis Journal of Criminal Justice Education 21 (2) 169–181 doi:10.1080/10511251003693694 McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models (second edition) Chapman and Hall/CRC London. ISBN 9780412317606 Akaike, H. (1974), A new look at the statistical model identification IEEE Transactions on Automatic Control 19 (6) 716–723. doi:10.1109/TAC.1974.1100705 Moore, D. F. (2016) Applied Survival Analysis Using R Springer International, Switzerland. eBook ISBN 9783319312453 Hand, D. J. and Yu K. (2001) Idiot's Bayes: Not So Stupid after All? International Statistical Review 69 385–398
@article{10507,
title = {Who is most likely to offend in my store now? Statistical steps towards retail crime prevention with Auror},
journal = {ANZIAM Journal},
volume = {58},
year = {2017},
doi = {10.21914/anziamj.v57i0.10507},
language = {EN},
url = {http://dml.mathdoc.fr/item/10507}
}
McDonald, Barry William; Hall, Lisa; Zhang, Xingyou Philip. Who is most likely to offend in my store now? Statistical steps towards retail crime prevention with Auror. ANZIAM Journal, Tome 58 (2017) . doi : 10.21914/anziamj.v57i0.10507. http://gdmltest.u-ga.fr/item/10507/