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Evaluating Temporal Analysis Methods Using Residential Burglary Data
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-9316-4842
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2016 (English)In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 5, no 9Article in journal (Refereed) Published
Abstract [en]

Law enforcement agencies, as well as researchers rely on temporal analysis methods in many crime analyses, e.g., spatio-temporal analyses. A number of temporal analysis methods are being used, but a structured comparison in different configurations is yet to be done. This study aims to fill this research gap by comparing the accuracy of five existing, and one novel, temporal analysis methods in approximating offense times for residential burglaries that often lack precise time information. The temporal analysis methods are evaluated in eight different configurations with varying temporal resolution, as well as the amount of data (number of crimes) available during analysis. A dataset of all Swedish residential burglaries reported between 2010 and 2014 is used (N = 103,029). From that dataset, a subset of burglaries with known precise offense times is used for evaluation. The accuracy of the temporal analysis methods in approximating the distribution of burglaries with known precise offense times is investigated. The aoristic and the novel aoristic ext" style="position: relative;" tabindex="0" id="MathJax-Element-1-Frame" class="MathJax">ext method perform significantly better than three of the traditional methods. Experiments show that the novel aoristic ext" style="position: relative;" tabindex="0" id="MathJax-Element-2-Frame" class="MathJax">ext method was most suitable for estimating crime frequencies in the day-of-the-year temporal resolution when reduced numbers of crimes were available during analysis. In the other configurations investigated, the aoristic method showed the best results. The results also show the potential from temporal analysis methods in approximating the temporal distributions of residential burglaries in situations when limited data are available.

Place, publisher, year, edition, pages
2016. Vol. 5, no 9
Keyword [en]
temporal analysis; aoristic analysis; crime analysis; residential burglaries
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-12946DOI: 10.3390/ijgi5090148OAI: oai:DiVA.org:bth-12946DiVA: diva2:955640
Note

Open access

Available from: 2016-08-25 Created: 2016-08-25 Last updated: 2016-08-30Bibliographically approved

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Publisher's full texthttp://dx.doi.org/10.3390/ijgi5090148

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Boldt, MartinBorg, Anton
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