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A Machine Learning Approach for Studying Linked Residential Burglaries
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2014 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

Context. Multiple studies demonstrate that most of the residential burglaries are committed by a few offenders. Statistics collected by the Swedish National Council for Crime Prevention show that the number of residential burglary varies from year to year. But this value normally increases. Besides, around half of all reported burglaries occur in big cities and only some burglaries occur in sparsely-populated areas. Thus, law enforcement agencies need to study possible linked residential burglaries for their investigations. Linking crime-reports is a difficult task and currently there is not a systematic way to do it. Objectives. This study presents an analysis of the different features of the collected residential burglaries by the law enforcement in Sweden. The objective is to study the possibility of linking crimes depending on these features. The characteristics used are residential features, modus operandi, victim features, goods stolen, difference of days and distance between crimes. Methods. To reach the objectives, quasi experiment and repeated measures are used. To obtain the distance between crimes, routes using Google maps are used. Different cluster methods are investigated in order to obtain the best cluster solution for linking residential burglaries. In addition, the study compares different algorithms in order to identify which algorithm offers the best performance in linking crimes. Results. Clustering quality is measured using different methods, Rule of Thumb, the Elbow method and Silhouette. To evaluate these measurements, ANOVA, Tukey and Fisher’s test are used. Silhouette presents the greatest quality level compared to other methods. Other clustering algorithms present similar average Silhouette width, and therefore, similar quality clustering. Results also show that distance, days and residential features are the most important features to link crimes. Conclusions. The clustering suggestion denotes that it is possible to reduce the amount of burglaries cases. This reduction is done by finding linked residential burglaries. Having done the clustering, the results have to be investigated by law enforcement.

Place, publisher, year, edition, pages
2014. , 31 p.
Keyword [en]
k-means algorithm, residential burglaries, cluster analysis below the abstract.
National Category
Computer Science
URN: urn:nbn:se:bth-4280Local ID: diva2:831610
Available from: 2015-04-22 Created: 2014-12-11 Last updated: 2015-06-30Bibliographically approved

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