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Open AccessFeature PaperArticle

Evaluating Temporal Approximation Methods Using Burglary Data

1
Infineon Technologies IT-Services GmbH, 9500 Villach, Austria
2
Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 386; https://doi.org/10.3390/ijgi9060386
Received: 24 May 2020 / Revised: 8 June 2020 / Accepted: 9 June 2020 / Published: 10 June 2020
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
Law enforcement is very interested in knowing when a crime has happened. Unfortunately, the occurrence time of a crime is often not exactly known. In such circumstances, estimating the most likely time that a crime has happened is crucial for spatio-temporal analysis. The main purpose of this research is to introduce two novel temporal approximation methods, termed retrospective temporal analysis (RTA) and extended retrospective temporal analysis (RTAext). Both methods are compared to six existing temporal approximation methods and subsequently evaluated in order to identify the method that can most accurately estimate the occurrence time of crimes. This research is conducted with 100,000+ burglary crimes from the city of Vienna, Austria provided by the Criminal Intelligence Service Austria, from 2009–2015. The RTA method assumes that crimes in the immediate past occur at very similar times as in the present and in the future. Historical crimes with accurately known time stamps can therefore be applied to estimate when crimes occur in the present/future. The RTAext method enhances one existing temporal approximation method, aoristicext, with probability values derived from historical crime data with accurately known time stamps. The results show that the RTA method performs superiorly to all other temporal approximation methods, including the novel RTAext method, in two out of the three crime types analyzed. Additionally, the RTAext method shows very good results that are similar to the best performing existing approximation methods. View Full-Text
Keywords: spatio-temporal crime analysis; temporal approximation; retrospective temporal analysis; aoristic crimes; burglary; Vienna spatio-temporal crime analysis; temporal approximation; retrospective temporal analysis; aoristic crimes; burglary; Vienna
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Oswald, L.; Leitner, M. Evaluating Temporal Approximation Methods Using Burglary Data. ISPRS Int. J. Geo-Inf. 2020, 9, 386.

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