AbstractsComputer Science

Filtering estimated series of residential burglaries using spatio-temporal route calculations

by Jaswanth Bala

Institution: DIVA
Year: 2016
Keywords: Residential Burglaries; Serial Crimes; Spatial Analysis; Online Direction Services; Swedish police; Natural Sciences; Computer and Information Science; Computer Science; Naturvetenskap; Data- och informationsvetenskap; Datavetenskap (datalogi); DVAXA Masterprogram i Datavetenskap; DVAXA Master of Science Programme in Computer Science; DV2566 Master's Thesis (120 credits) in Computer Science; DV2566 Masterarbete i datavetenskap
Posted: 02/05/2017
Record ID: 2102580
Full text PDF: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-11822


Context. According to Swedish National Council for Crime Prevention, there is an increase of 19% in residential burglary crimes in Sweden over the last decade and only 5% of the total crimes reported were actually solved by the law enforcement agencies. In order to solve these cases quickly and efficiently, the law enforcement agencies has to look into the possible linked serial crimes. Many studies have suggested to link crimes based on Modus Operendi and other characteristic. Sometimes crimes which are not possible to travel spatially with in the reported times but have similar Modus Operendi are also grouped as linked crimes. Investigating such crimes could possibly waste the resources of the law enforcement agencies. Objectives. In this study, we investigate the possibility of the usage of travel distance and travel duration between different crime locations while linking the residential burglary crimes. A filtering method has been designed and implemented for filtering the unlinked crimes from the estimated linked crimes by utilizing the distance and duration values. Methods. The objectives in this study are satisfied by conducting an experiment. The travel distance and travel duration values are obtained from various online direction services. The filtering method was first validated on ground truth represented by known linked crime series and then it was used to filter out crimes from the estimated linked crimes. Results. The filtering method had removed a total of 4% unlinked crimes from the estimated linked crime series when the travel mode is considered as driving. Whereas it had removed a total of 23% unlinked crimes from the estimated linked crime series when the travel mode is considered as walking. Also it was found that a burglar can take an average of 900 seconds (15 minutes) for committing a burglary. Conclusions. From this study it is evident that the usage of spatial and temporal values in linking residential burglaries gives effective crime links in a series. Also, the usage of Google Maps for getting distance and duration values can increase the overall performance of the filtering method in linking crimes.