Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Linking Residential Burglaries using the Series Finder Algorithm in a Swedish Context
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Context. A minority of criminals performs a majority of the crimes today. It is known that every criminal or group of offenders to some extent have a particular pattern (modus operandi) how crime is performed. Therefore, computers' computational power can be employed to discover crimes that have the same model and possibly are carried out by the same criminal. The goal of this thesis was to apply the existing Series Finder algorithm to a feature-rich dataset containing data about Swedish residential burglaries.

Objectives. The following objectives were achieved to complete this thesis: Modifications performed on an existing Series Finder implementation to fit the Swedish police forces dataset and MatLab code converted to Python. Furthermore, experiment setup designed with appropriate metrics and statistical tests. Finally, modified Series Finder implementation's evaluation performed against both Spatial-Temporal and Random models.

Methods. The experimental methodology was chosen in order to achieve the objectives. An initial experiment was performed to find right parameters to use for main experiments. Afterward, a proper investigation with dependent and independent variables was conducted.

Results. After the metrics calculations and the statistical tests applications, the accurate picture revealed how each model performed. Series Finder showed better performance than a Random model. However, it had lower performance than the Spatial-Temporal model. The possible causes of one model performing better than another are discussed in analysis and discussion section.

Conclusions. After completing objectives and answering research questions, it could be clearly seen how the Series Finder implementation performed against other models. Despite its low performance, Series Finder still showed potential, as presented in future work.

Place, publisher, year, edition, pages
2017. , 38 p.
Keyword [en]
Crime linklage, Modus Operandi, Series Finder, Residential Burglaries
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-14033OAI: oai:DiVA.org:bth-14033DiVA: diva2:1083898
Subject / course
DV2566 Master's Thesis (120 credits) in Computer Science
Educational program
DVACS Master of Science Programme in Computer Science
Supervisors
Examiners
Available from: 2017-03-24 Created: 2017-03-22 Last updated: 2017-03-24Bibliographically approved

Open Access in DiVA

fulltext(387 kB)114 downloads
File information
File name FULLTEXT02.pdfFile size 387 kBChecksum SHA-512
1963ec70b37ccaec7b3ac2df2db79c54ba41abbaa5b866810e8bb70fb1b1d56751ea99aa3c0eebdf4a109a279353cda8240f7ffa9f5d3f97cbdeb3261ac0cb0d
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science and Engineering
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 114 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 976 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf