The Effect of Commuting on City-Level Crime Rates
Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, an...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Stults, Brian J. [verfasserIn] Hasbrouck, Matthew [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of quantitative criminology - Getzville, NY : HeinOnline, 1985, 31(2015), 2 vom: 04. Feb., Seite 331-350 |
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| Übergeordnetes Werk: |
volume:31 ; year:2015 ; number:2 ; day:04 ; month:02 ; pages:331-350 |
| Links: |
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| DOI / URN: |
10.1007/s10940-015-9251-z |
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| Katalog-ID: |
SPR014782227 |
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| 245 | 1 | 4 | |a The Effect of Commuting on City-Level Crime Rates |
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| 520 | |a Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. | ||
| 650 | 4 | |a Crime rate |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Routine activities |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Commuting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Macro-level |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cities |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Hasbrouck, Matthew |e verfasserin |4 aut | |
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10.1007/s10940-015-9251-z doi (DE-627)SPR014782227 (SPR)s10940-015-9251-z-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Stults, Brian J. verfasserin aut The Effect of Commuting on City-Level Crime Rates 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. Crime rate (dpeaa)DE-He213 Routine activities (dpeaa)DE-He213 Commuting (dpeaa)DE-He213 Macro-level (dpeaa)DE-He213 Cities (dpeaa)DE-He213 Hasbrouck, Matthew verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 31(2015), 2 vom: 04. Feb., Seite 331-350 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:31 year:2015 number:2 day:04 month:02 pages:331-350 https://dx.doi.org/10.1007/s10940-015-9251-z lizenzpflichtig 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_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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2015 2 04 02 331-350 |
| spelling |
10.1007/s10940-015-9251-z doi (DE-627)SPR014782227 (SPR)s10940-015-9251-z-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Stults, Brian J. verfasserin aut The Effect of Commuting on City-Level Crime Rates 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. Crime rate (dpeaa)DE-He213 Routine activities (dpeaa)DE-He213 Commuting (dpeaa)DE-He213 Macro-level (dpeaa)DE-He213 Cities (dpeaa)DE-He213 Hasbrouck, Matthew verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 31(2015), 2 vom: 04. Feb., Seite 331-350 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:31 year:2015 number:2 day:04 month:02 pages:331-350 https://dx.doi.org/10.1007/s10940-015-9251-z lizenzpflichtig 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_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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2015 2 04 02 331-350 |
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10.1007/s10940-015-9251-z doi (DE-627)SPR014782227 (SPR)s10940-015-9251-z-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Stults, Brian J. verfasserin aut The Effect of Commuting on City-Level Crime Rates 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. Crime rate (dpeaa)DE-He213 Routine activities (dpeaa)DE-He213 Commuting (dpeaa)DE-He213 Macro-level (dpeaa)DE-He213 Cities (dpeaa)DE-He213 Hasbrouck, Matthew verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 31(2015), 2 vom: 04. Feb., Seite 331-350 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:31 year:2015 number:2 day:04 month:02 pages:331-350 https://dx.doi.org/10.1007/s10940-015-9251-z lizenzpflichtig 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_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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2015 2 04 02 331-350 |
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10.1007/s10940-015-9251-z doi (DE-627)SPR014782227 (SPR)s10940-015-9251-z-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Stults, Brian J. verfasserin aut The Effect of Commuting on City-Level Crime Rates 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. Crime rate (dpeaa)DE-He213 Routine activities (dpeaa)DE-He213 Commuting (dpeaa)DE-He213 Macro-level (dpeaa)DE-He213 Cities (dpeaa)DE-He213 Hasbrouck, Matthew verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 31(2015), 2 vom: 04. Feb., Seite 331-350 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:31 year:2015 number:2 day:04 month:02 pages:331-350 https://dx.doi.org/10.1007/s10940-015-9251-z lizenzpflichtig 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_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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2015 2 04 02 331-350 |
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10.1007/s10940-015-9251-z doi (DE-627)SPR014782227 (SPR)s10940-015-9251-z-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Stults, Brian J. verfasserin aut The Effect of Commuting on City-Level Crime Rates 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. Crime rate (dpeaa)DE-He213 Routine activities (dpeaa)DE-He213 Commuting (dpeaa)DE-He213 Macro-level (dpeaa)DE-He213 Cities (dpeaa)DE-He213 Hasbrouck, Matthew verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 31(2015), 2 vom: 04. Feb., Seite 331-350 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:31 year:2015 number:2 day:04 month:02 pages:331-350 https://dx.doi.org/10.1007/s10940-015-9251-z lizenzpflichtig 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_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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2015 2 04 02 331-350 |
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Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. 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| author |
Stults, Brian J. |
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Stults, Brian J. ddc 340 bkl 86.00 misc Crime rate misc Routine activities misc Commuting misc Macro-level misc Cities The Effect of Commuting on City-Level Crime Rates |
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340 ASE 86.00 bkl The Effect of Commuting on City-Level Crime Rates Crime rate (dpeaa)DE-He213 Routine activities (dpeaa)DE-He213 Commuting (dpeaa)DE-He213 Macro-level (dpeaa)DE-He213 Cities (dpeaa)DE-He213 |
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ddc 340 bkl 86.00 misc Crime rate misc Routine activities misc Commuting misc Macro-level misc Cities |
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The Effect of Commuting on City-Level Crime Rates |
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effect of commuting on city-level crime rates |
| title_auth |
The Effect of Commuting on City-Level Crime Rates |
| abstract |
Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. |
| abstractGer |
Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. |
| abstract_unstemmed |
Objective To examine the effect of commuting rates on crime rate estimates in US cities, and to observe potential changes in the effects of other common crime rate correlates after accounting for commuting. Methods Crimes evaluated include homicide, aggravated assault, robbery, burglary, larceny, and auto theft. The sample includes US cities with a population of at least 100,000. The analysis first compares crime rankings using a rate based on the residential population and an alternative rate that takes into account daytime population changes due to commuting. Next, multivariate random effects panel models are used to evaluate the effect of commuting on crime rates, and to examine the extent to which the effects of other predictors change after controlling for commuting. Results A city’s ranking can vary considerably depending on which denominator is used. Multivariate findings suggest that daily commuting rates are a significant, strong predictor of crime rates, and that controlling for commuting yields important changes in the effects of concentrated disadvantage, concentrated affluence, racial composition and residential instability. Conclusions The impact of the commuting population on crime rate rankings underscores the importance of viewing crime rankings with great caution. Specifically, the residential crime rate overestimates relative risk for cities that attract a large daily population from outside the city limits. Findings provide support for the routine activities perspective, and suggest that future research examining city-level crime rates should control for commuting. Limitations to the study and directions for future research are discussed. |
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2 |
| title_short |
The Effect of Commuting on City-Level Crime Rates |
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https://dx.doi.org/10.1007/s10940-015-9251-z |
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true |
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Hasbrouck, Matthew |
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Hasbrouck, Matthew |
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| doi_str |
10.1007/s10940-015-9251-z |
| up_date |
2024-07-04T03:02:18.752Z |
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| score |
7.399207 |