Evaluating Temporal Approximation Methods Using Burglary Data

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 resear...
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Gespeichert in:

Autor*in:	Lukas Oswald [verfasserIn] Michael Leitner [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	spatio-temporal crime analysis temporal approximation retrospective temporal analysis aoristic crimes burglary Vienna

Übergeordnetes Werk:	In: ISPRS International Journal of Geo-Information - MDPI AG, 2012, 9(2020), 6, p 386
Übergeordnetes Werk:	volume:9 ; year:2020 ; number:6, p 386

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ijgi9060386

Katalog-ID:	DOAJ051659425

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callnumber-first	G - Geography, Anthropology, Recreation
author	Lukas Oswald
spellingShingle	Lukas Oswald misc G1-922 misc spatio-temporal crime analysis misc temporal approximation misc retrospective temporal analysis misc aoristic crimes misc burglary misc Vienna misc Geography (General) Evaluating Temporal Approximation Methods Using Burglary Data
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topic_title	G1-922 Evaluating Temporal Approximation Methods Using Burglary Data spatio-temporal crime analysis temporal approximation retrospective temporal analysis aoristic crimes burglary Vienna
topic	misc G1-922 misc spatio-temporal crime analysis misc temporal approximation misc retrospective temporal analysis misc aoristic crimes misc burglary misc Vienna misc Geography (General)
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title	Evaluating Temporal Approximation Methods Using Burglary Data
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title_full	Evaluating Temporal Approximation Methods Using Burglary Data
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title_sort	evaluating temporal approximation methods using burglary data
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title_auth	Evaluating Temporal Approximation Methods Using Burglary Data
abstract	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 (RTA<sub<ext</sub<). 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 RTA<sub<ext</sub< method enhances one existing temporal approximation method, aoristic<sub<ext</sub<, 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 RTA<sub<ext</sub< method, in two out of the three crime types analyzed. Additionally, the RTA<sub<ext</sub< method shows very good results that are similar to the best performing existing approximation methods.
abstractGer	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 (RTA<sub<ext</sub<). 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 RTA<sub<ext</sub< method enhances one existing temporal approximation method, aoristic<sub<ext</sub<, 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 RTA<sub<ext</sub< method, in two out of the three crime types analyzed. Additionally, the RTA<sub<ext</sub< method shows very good results that are similar to the best performing existing approximation methods.
abstract_unstemmed	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 (RTA<sub<ext</sub<). 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 RTA<sub<ext</sub< method enhances one existing temporal approximation method, aoristic<sub<ext</sub<, 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 RTA<sub<ext</sub< method, in two out of the three crime types analyzed. Additionally, the RTA<sub<ext</sub< method shows very good results that are similar to the best performing existing approximation methods.
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title_short	Evaluating Temporal Approximation Methods Using Burglary Data
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