Detecting serial residential burglaries using clustering

Borg, Anton; Boldt, Martin; Lavesson, Niklas; Melander, Ulf; Boeva, Veselka

doi:10.1016/j.eswa.2014.02.035

Cited by 31 publications

(13 citation statements)

References 16 publications

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“…Today, there is an ongoing research collaborations between Blekinge Institute of Technology and the Swedish police regarding an implemented DSS, called SAMS, focusing on residential burglaries [6]. SAMS currently support, among other things, the collecting of spatial, temporal and behavioral characteristic data for residential burglaries.…”

Section: Samsmentioning

confidence: 99%

“…The officers are using a standardized form for every burglary incident. The form was designed in collaboration between Blekinge Institute of Technology and the Swedish law enforcement [6]. By having the form standardized, it makes the collecting process more unified across officers and across police districts in Sweden.…”

Section: Samsmentioning

confidence: 99%

“…Borg et al [6] built a DSS for the Swedish police to systematically collect, compare and analyze data from burglaries. With the help of the collected burglary data, they then applied a cut-cluster algorithm to groups crimes based on modus operandi, residential characteristics, stolen gods, spatial similarity or temporal similarity.…”

Section: B and Fosdickmentioning

confidence: 99%

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Detecting Crime Series Based on Route Estimation and Behavioral Similarity

Borg

Boldt

Eliasson

2017

2017 European Intelligence and Security Informatics Conference (EISIC)

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Context. A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. Objectives. Our objective was to investigate to what extent we could use route estimations and behavioral data to detect serial offenders. Method. To investigate if we could find crime routes with high similarity we ran experiments using behavioral data from authentic burglary reports. We also used used burglary reports from serial offenders to investigate to what extent we could detect serial offender crime routes. Results. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. We also found crime series with high similarity without a known offender which could be interesting for law enforcement to investigate further. Conclusion. Our results suggest that it is possible to detect crime series committed by the same offender using behavioral data from the burglary. There is also potential for the law enforcement to detect interesting crime series with high similarity using behavioral similarity.

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Section: Samsmentioning

confidence: 99%

Section: Samsmentioning

confidence: 99%

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Detecting Crime Series Based on Route Estimation and Behavioral Similarity

Borg

Boldt

Eliasson

2017

2017 European Intelligence and Security Informatics Conference (EISIC)

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“…Clustering crimes, finding links between crimes, profiling offenders and criminal network detection are some of the common areas where data mining is applied in crime analysis (Oatley & Ewwart, 2011;King & Sutton, 2013;Borg et al, 2014). Association analysis, classification and prediction, cluster analysis, and outlier analysis are some of the traditional data mining techniques which can be used to identify patterns in structured data.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy based binary feature profiling for modus operandi analysis

Chamikara

Galappaththi

Yapa

et al. 2016

PeerJ Computer Science

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It is a well-known fact that some criminals follow perpetual methods of operations known as modi operandi. Modus operandi is a commonly used term to describe the habits in committing crimes. These modi operandi are used in relating criminals to crimes for which the suspects have not yet been recognized. This paper presents the design, implementation and evaluation of a new method to find connections between crimes and criminals using modi operandi. The method involves generating a feature matrix for a particular criminal based on the flow of events of his/her previous convictions. Then, based on the feature matrix, two representative modi operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modi operandi are compared with the flow of events of the crime at hand, in order to generate two other outputs: completeness probability (CP) and deviation probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a score which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and ten other open data sets. In addition, comparison with nine other classification algorithms showed that the proposed method performs competitively with other related methods proving that the performance of the new method is at an acceptable level.

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“…1 shows two examples of the matrices above from an aoristic analysis of residential burglary data from the city of Malmö in Sweden between 2015 and 2016. The temporal heatmap plots are generated in a decision-support system that the authors have developed together with Swedish law enforcement [16]. In Fig.…”

Section: A Temporal Matrix Representationsmentioning

confidence: 99%

A Statistical Method for Detecting Significant Temporal Hotspots Using LISA Statistics

Boldt

Borg

2017

2017 European Intelligence and Security Informatics Conference (EISIC)

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Abstract-This work presents a method for detecting statistically significant temporal hotspots, i.e. the date and time of events, which is useful for improved planning of response activities. Temporal hotspots are calculated using Local Indicators of Spatial Association (LISA) statistics. The temporal data is in a 7x24 matrix that represents a temporal resolution of weekdays and hours-in-the-day. Swedish residential burglary events are used in this work for testing the temporal hotspot detection approach. Although, the presented method is also useful for other events as long as they contain temporal information, e.g. attack attempts recorded by intrusion detection systems. By using the method for detecting significant temporal hotspots it is possible for domain-experts to gain knowledge about the temporal distribution of the events, and also to learn at which times mitigating actions could be implemented.

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Detecting serial residential burglaries using clustering

Cited by 31 publications

References 16 publications

Detecting Crime Series Based on Route Estimation and Behavioral Similarity

Detecting Crime Series Based on Route Estimation and Behavioral Similarity

Fuzzy based binary feature profiling for modus operandi analysis

A Statistical Method for Detecting Significant Temporal Hotspots Using LISA Statistics

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