When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns
Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
van Sleeuwen, Sabine E. M. [verfasserIn] Steenbeek, Wouter [verfasserIn] Ruiter, Stijn [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of quantitative criminology - Getzville, NY : HeinOnline, 1985, 37(2020), 4 vom: 31. Aug., Seite 863-889 |
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| Übergeordnetes Werk: |
volume:37 ; year:2020 ; number:4 ; day:31 ; month:08 ; pages:863-889 |
| Links: |
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| DOI / URN: |
10.1007/s10940-020-09470-w |
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SPR045400962 |
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| 520 | |a Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. | ||
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| 700 | 1 | |a Ruiter, Stijn |e verfasserin |4 aut | |
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10.1007/s10940-020-09470-w doi (DE-627)SPR045400962 (SPR)s10940-020-09470-w-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl van Sleeuwen, Sabine E. M. verfasserin aut When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. Repeat offenders (dpeaa)DE-He213 Temporal consistency (dpeaa)DE-He213 Hour of day (dpeaa)DE-He213 Hour of week (dpeaa)DE-He213 Monte Carlo permutation (dpeaa)DE-He213 Steenbeek, Wouter verfasserin aut Ruiter, Stijn verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 37(2020), 4 vom: 31. Aug., Seite 863-889 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:37 year:2020 number:4 day:31 month:08 pages:863-889 https://dx.doi.org/10.1007/s10940-020-09470-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 37 2020 4 31 08 863-889 |
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10.1007/s10940-020-09470-w doi (DE-627)SPR045400962 (SPR)s10940-020-09470-w-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl van Sleeuwen, Sabine E. M. verfasserin aut When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. Repeat offenders (dpeaa)DE-He213 Temporal consistency (dpeaa)DE-He213 Hour of day (dpeaa)DE-He213 Hour of week (dpeaa)DE-He213 Monte Carlo permutation (dpeaa)DE-He213 Steenbeek, Wouter verfasserin aut Ruiter, Stijn verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 37(2020), 4 vom: 31. Aug., Seite 863-889 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:37 year:2020 number:4 day:31 month:08 pages:863-889 https://dx.doi.org/10.1007/s10940-020-09470-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 37 2020 4 31 08 863-889 |
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10.1007/s10940-020-09470-w doi (DE-627)SPR045400962 (SPR)s10940-020-09470-w-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl van Sleeuwen, Sabine E. M. verfasserin aut When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. Repeat offenders (dpeaa)DE-He213 Temporal consistency (dpeaa)DE-He213 Hour of day (dpeaa)DE-He213 Hour of week (dpeaa)DE-He213 Monte Carlo permutation (dpeaa)DE-He213 Steenbeek, Wouter verfasserin aut Ruiter, Stijn verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 37(2020), 4 vom: 31. Aug., Seite 863-889 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:37 year:2020 number:4 day:31 month:08 pages:863-889 https://dx.doi.org/10.1007/s10940-020-09470-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 37 2020 4 31 08 863-889 |
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10.1007/s10940-020-09470-w doi (DE-627)SPR045400962 (SPR)s10940-020-09470-w-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl van Sleeuwen, Sabine E. M. verfasserin aut When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. Repeat offenders (dpeaa)DE-He213 Temporal consistency (dpeaa)DE-He213 Hour of day (dpeaa)DE-He213 Hour of week (dpeaa)DE-He213 Monte Carlo permutation (dpeaa)DE-He213 Steenbeek, Wouter verfasserin aut Ruiter, Stijn verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 37(2020), 4 vom: 31. Aug., Seite 863-889 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:37 year:2020 number:4 day:31 month:08 pages:863-889 https://dx.doi.org/10.1007/s10940-020-09470-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 37 2020 4 31 08 863-889 |
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10.1007/s10940-020-09470-w doi (DE-627)SPR045400962 (SPR)s10940-020-09470-w-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl van Sleeuwen, Sabine E. M. verfasserin aut When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. Repeat offenders (dpeaa)DE-He213 Temporal consistency (dpeaa)DE-He213 Hour of day (dpeaa)DE-He213 Hour of week (dpeaa)DE-He213 Monte Carlo permutation (dpeaa)DE-He213 Steenbeek, Wouter verfasserin aut Ruiter, Stijn verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 37(2020), 4 vom: 31. Aug., Seite 863-889 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:37 year:2020 number:4 day:31 month:08 pages:863-889 https://dx.doi.org/10.1007/s10940-020-09470-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 37 2020 4 31 08 863-889 |
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M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. 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van Sleeuwen, Sabine E. M. |
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van Sleeuwen, Sabine E. M. ddc 340 bkl 86.00 misc Repeat offenders misc Temporal consistency misc Hour of day misc Hour of week misc Monte Carlo permutation When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns |
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340 ASE 86.00 bkl When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns Repeat offenders (dpeaa)DE-He213 Temporal consistency (dpeaa)DE-He213 Hour of day (dpeaa)DE-He213 Hour of week (dpeaa)DE-He213 Monte Carlo permutation (dpeaa)DE-He213 |
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ddc 340 bkl 86.00 misc Repeat offenders misc Temporal consistency misc Hour of day misc Hour of week misc Monte Carlo permutation |
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ddc 340 bkl 86.00 misc Repeat offenders misc Temporal consistency misc Hour of day misc Hour of week misc Monte Carlo permutation |
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When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns |
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When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns |
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van Sleeuwen, Sabine E. M. |
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Journal of quantitative criminology |
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van Sleeuwen, Sabine E. M. Steenbeek, Wouter Ruiter, Stijn |
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when do offenders commit crime? an analysis of temporal consistency in individual offending patterns |
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When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns |
| abstract |
Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. © The Author(s) 2020 |
| abstractGer |
Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. © The Author(s) 2020 |
| abstract_unstemmed |
Objectives Building on Hägerstrand’s time geography, we expect temporal consistency in individual offending behavior. We hypothesize that repeat offenders commit offenses at similar times of day and week. In addition, we expect stronger temporal consistency for crimes of the same type and for crimes committed within a shorter time span. Method We use police-recorded crime data on 28,274 repeat offenders who committed 152,180 offenses between 1996 and 2009 in the greater The Hague area in the Netherlands. We use a Monte Carlo permutation procedure to compare the overall level of temporal consistency observed in the data to the temporal consistency that is to be expected given the overall temporal distribution of crime. Results Repeat offenders show strong temporal consistency: they commit their crimes at more similar hours of day and week than expected. Moreover, the observed temporal consistency patterns are indeed stronger for offenses of the same type of crime and when less time has elapsed between the offenses, especially for offenses committed within a month after the prior offense. Discussion The results are consistent with offenders having recurring rhythms that shape their temporal crime pattern. These findings might prove valuable for improving predictive policing methods and crime linkage analysis as well as interventions to reduce recidivism. © The Author(s) 2020 |
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When Do Offenders Commit Crime? An Analysis of Temporal Consistency in Individual Offending Patterns |
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https://dx.doi.org/10.1007/s10940-020-09470-w |
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Steenbeek, Wouter Ruiter, Stijn |
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Steenbeek, Wouter Ruiter, Stijn |
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10.1007/s10940-020-09470-w |
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2024-07-03T15:45:13.831Z |
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| score |
7.3988037 |