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...
Ausführliche Beschreibung
Autor*in: |
Lukas Oswald [verfasserIn] Michael Leitner [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020 |
---|
Schlagwörter: |
spatio-temporal crime analysis |
---|
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: |
---|
DOI / URN: |
10.3390/ijgi9060386 |
---|
Katalog-ID: |
DOAJ051659425 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ051659425 | ||
003 | DE-627 | ||
005 | 20240412231941.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230227s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/ijgi9060386 |2 doi | |
035 | |a (DE-627)DOAJ051659425 | ||
035 | |a (DE-599)DOAJ842ad577e4434ddcab1d936899193b82 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a G1-922 | |
100 | 0 | |a Lukas Oswald |e verfasserin |4 aut | |
245 | 1 | 0 | |a Evaluating Temporal Approximation Methods Using Burglary Data |
264 | 1 | |c 2020 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a 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. | ||
650 | 4 | |a spatio-temporal crime analysis | |
650 | 4 | |a temporal approximation | |
650 | 4 | |a retrospective temporal analysis | |
650 | 4 | |a aoristic crimes | |
650 | 4 | |a burglary | |
650 | 4 | |a Vienna | |
653 | 0 | |a Geography (General) | |
700 | 0 | |a Michael Leitner |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t ISPRS International Journal of Geo-Information |d MDPI AG, 2012 |g 9(2020), 6, p 386 |w (DE-627)689130961 |w (DE-600)2655790-3 |x 22209964 |7 nnns |
773 | 1 | 8 | |g volume:9 |g year:2020 |g number:6, p 386 |
856 | 4 | 0 | |u https://doi.org/10.3390/ijgi9060386 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/842ad577e4434ddcab1d936899193b82 |z kostenfrei |
856 | 4 | 0 | |u https://www.mdpi.com/2220-9964/9/6/386 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2220-9964 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4392 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 9 |j 2020 |e 6, p 386 |
author_variant |
l o lo m l ml |
---|---|
matchkey_str |
article:22209964:2020----::vlaigeprlprxmtomtos |
hierarchy_sort_str |
2020 |
callnumber-subject-code |
G |
publishDate |
2020 |
allfields |
10.3390/ijgi9060386 doi (DE-627)DOAJ051659425 (DE-599)DOAJ842ad577e4434ddcab1d936899193b82 DE-627 ger DE-627 rakwb eng G1-922 Lukas Oswald verfasserin aut Evaluating Temporal Approximation Methods Using Burglary Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. spatio-temporal crime analysis temporal approximation retrospective temporal analysis aoristic crimes burglary Vienna Geography (General) Michael Leitner verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 9(2020), 6, p 386 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:9 year:2020 number:6, p 386 https://doi.org/10.3390/ijgi9060386 kostenfrei https://doaj.org/article/842ad577e4434ddcab1d936899193b82 kostenfrei https://www.mdpi.com/2220-9964/9/6/386 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2020 6, p 386 |
spelling |
10.3390/ijgi9060386 doi (DE-627)DOAJ051659425 (DE-599)DOAJ842ad577e4434ddcab1d936899193b82 DE-627 ger DE-627 rakwb eng G1-922 Lukas Oswald verfasserin aut Evaluating Temporal Approximation Methods Using Burglary Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. spatio-temporal crime analysis temporal approximation retrospective temporal analysis aoristic crimes burglary Vienna Geography (General) Michael Leitner verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 9(2020), 6, p 386 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:9 year:2020 number:6, p 386 https://doi.org/10.3390/ijgi9060386 kostenfrei https://doaj.org/article/842ad577e4434ddcab1d936899193b82 kostenfrei https://www.mdpi.com/2220-9964/9/6/386 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2020 6, p 386 |
allfields_unstemmed |
10.3390/ijgi9060386 doi (DE-627)DOAJ051659425 (DE-599)DOAJ842ad577e4434ddcab1d936899193b82 DE-627 ger DE-627 rakwb eng G1-922 Lukas Oswald verfasserin aut Evaluating Temporal Approximation Methods Using Burglary Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. spatio-temporal crime analysis temporal approximation retrospective temporal analysis aoristic crimes burglary Vienna Geography (General) Michael Leitner verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 9(2020), 6, p 386 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:9 year:2020 number:6, p 386 https://doi.org/10.3390/ijgi9060386 kostenfrei https://doaj.org/article/842ad577e4434ddcab1d936899193b82 kostenfrei https://www.mdpi.com/2220-9964/9/6/386 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2020 6, p 386 |
allfieldsGer |
10.3390/ijgi9060386 doi (DE-627)DOAJ051659425 (DE-599)DOAJ842ad577e4434ddcab1d936899193b82 DE-627 ger DE-627 rakwb eng G1-922 Lukas Oswald verfasserin aut Evaluating Temporal Approximation Methods Using Burglary Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. spatio-temporal crime analysis temporal approximation retrospective temporal analysis aoristic crimes burglary Vienna Geography (General) Michael Leitner verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 9(2020), 6, p 386 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:9 year:2020 number:6, p 386 https://doi.org/10.3390/ijgi9060386 kostenfrei https://doaj.org/article/842ad577e4434ddcab1d936899193b82 kostenfrei https://www.mdpi.com/2220-9964/9/6/386 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2020 6, p 386 |
allfieldsSound |
10.3390/ijgi9060386 doi (DE-627)DOAJ051659425 (DE-599)DOAJ842ad577e4434ddcab1d936899193b82 DE-627 ger DE-627 rakwb eng G1-922 Lukas Oswald verfasserin aut Evaluating Temporal Approximation Methods Using Burglary Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. spatio-temporal crime analysis temporal approximation retrospective temporal analysis aoristic crimes burglary Vienna Geography (General) Michael Leitner verfasserin aut In ISPRS International Journal of Geo-Information MDPI AG, 2012 9(2020), 6, p 386 (DE-627)689130961 (DE-600)2655790-3 22209964 nnns volume:9 year:2020 number:6, p 386 https://doi.org/10.3390/ijgi9060386 kostenfrei https://doaj.org/article/842ad577e4434ddcab1d936899193b82 kostenfrei https://www.mdpi.com/2220-9964/9/6/386 kostenfrei https://doaj.org/toc/2220-9964 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2020 6, p 386 |
language |
English |
source |
In ISPRS International Journal of Geo-Information 9(2020), 6, p 386 volume:9 year:2020 number:6, p 386 |
sourceStr |
In ISPRS International Journal of Geo-Information 9(2020), 6, p 386 volume:9 year:2020 number:6, p 386 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
spatio-temporal crime analysis temporal approximation retrospective temporal analysis aoristic crimes burglary Vienna Geography (General) |
isfreeaccess_bool |
true |
container_title |
ISPRS International Journal of Geo-Information |
authorswithroles_txt_mv |
Lukas Oswald @@aut@@ Michael Leitner @@aut@@ |
publishDateDaySort_date |
2020-01-01T00:00:00Z |
hierarchy_top_id |
689130961 |
id |
DOAJ051659425 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ051659425</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412231941.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/ijgi9060386</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ051659425</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ842ad577e4434ddcab1d936899193b82</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">G1-922</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Lukas Oswald</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evaluating Temporal Approximation Methods Using Burglary Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">spatio-temporal crime analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">temporal approximation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">retrospective temporal analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">aoristic crimes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">burglary</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vienna</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geography (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Michael Leitner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">ISPRS International Journal of Geo-Information</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">9(2020), 6, p 386</subfield><subfield code="w">(DE-627)689130961</subfield><subfield code="w">(DE-600)2655790-3</subfield><subfield code="x">22209964</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:6, p 386</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/ijgi9060386</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/842ad577e4434ddcab1d936899193b82</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2220-9964/9/6/386</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2220-9964</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2020</subfield><subfield code="e">6, p 386</subfield></datafield></record></collection>
|
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 |
authorStr |
Lukas Oswald |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)689130961 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
G1-922 |
illustrated |
Not Illustrated |
issn |
22209964 |
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) |
topic_unstemmed |
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) |
topic_browse |
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) |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
ISPRS International Journal of Geo-Information |
hierarchy_parent_id |
689130961 |
hierarchy_top_title |
ISPRS International Journal of Geo-Information |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)689130961 (DE-600)2655790-3 |
title |
Evaluating Temporal Approximation Methods Using Burglary Data |
ctrlnum |
(DE-627)DOAJ051659425 (DE-599)DOAJ842ad577e4434ddcab1d936899193b82 |
title_full |
Evaluating Temporal Approximation Methods Using Burglary Data |
author_sort |
Lukas Oswald |
journal |
ISPRS International Journal of Geo-Information |
journalStr |
ISPRS International Journal of Geo-Information |
callnumber-first-code |
G |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
txt |
author_browse |
Lukas Oswald Michael Leitner |
container_volume |
9 |
class |
G1-922 |
format_se |
Elektronische Aufsätze |
author-letter |
Lukas Oswald |
doi_str_mv |
10.3390/ijgi9060386 |
author2-role |
verfasserin |
title_sort |
evaluating temporal approximation methods using burglary data |
callnumber |
G1-922 |
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. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 |
container_issue |
6, p 386 |
title_short |
Evaluating Temporal Approximation Methods Using Burglary Data |
url |
https://doi.org/10.3390/ijgi9060386 https://doaj.org/article/842ad577e4434ddcab1d936899193b82 https://www.mdpi.com/2220-9964/9/6/386 https://doaj.org/toc/2220-9964 |
remote_bool |
true |
author2 |
Michael Leitner |
author2Str |
Michael Leitner |
ppnlink |
689130961 |
callnumber-subject |
G - General Geography |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/ijgi9060386 |
callnumber-a |
G1-922 |
up_date |
2024-07-03T21:35:20.414Z |
_version_ |
1803595305885630464 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ051659425</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412231941.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/ijgi9060386</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ051659425</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ842ad577e4434ddcab1d936899193b82</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">G1-922</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Lukas Oswald</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evaluating Temporal Approximation Methods Using Burglary Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">spatio-temporal crime analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">temporal approximation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">retrospective temporal analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">aoristic crimes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">burglary</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vienna</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geography (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Michael Leitner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">ISPRS International Journal of Geo-Information</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">9(2020), 6, p 386</subfield><subfield code="w">(DE-627)689130961</subfield><subfield code="w">(DE-600)2655790-3</subfield><subfield code="x">22209964</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:6, p 386</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/ijgi9060386</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/842ad577e4434ddcab1d936899193b82</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2220-9964/9/6/386</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2220-9964</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2020</subfield><subfield code="e">6, p 386</subfield></datafield></record></collection>
|
score |
7.400485 |