Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors

Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and...
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Autor*in:	Law, Jane [verfasserIn] Chan, Ping W. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Binomial, Spatial logistic regression Unexplained spatial autocorrelation WinBUGS Built environment Local planning and policies

Übergeordnetes Werk:	Enthalten in: Applied spatial analysis and policy - Dordrecht : Springer Netherlands, 2008, 5(2011), 1 vom: 23. Feb., Seite 73-96
Übergeordnetes Werk:	volume:5 ; year:2011 ; number:1 ; day:23 ; month:02 ; pages:73-96

Links:	Volltext

DOI / URN:	10.1007/s12061-011-9060-1

Katalog-ID:	SPR024170402

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520			\|a Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed.
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matchkey_str	article:18744621:2011----::aeinptarnoefcmdligoaayigugayikcnrligoofnesc
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allfields	10.1007/s12061-011-9060-1 doi (DE-627)SPR024170402 (SPR)s12061-011-9060-1-e DE-627 ger DE-627 rakwb eng 910 ASE Law, Jane verfasserin aut Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed. Binomial, Spatial logistic regression (dpeaa)DE-He213 Unexplained spatial autocorrelation (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 Built environment (dpeaa)DE-He213 Local planning and policies (dpeaa)DE-He213 Chan, Ping W. verfasserin aut Enthalten in Applied spatial analysis and policy Dordrecht : Springer Netherlands, 2008 5(2011), 1 vom: 23. Feb., Seite 73-96 (DE-627)564750840 (DE-600)2422325-6 1874-4621 nnns volume:5 year:2011 number:1 day:23 month:02 pages:73-96 https://dx.doi.org/10.1007/s12061-011-9060-1 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_187 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_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_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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 AR 5 2011 1 23 02 73-96
spelling	10.1007/s12061-011-9060-1 doi (DE-627)SPR024170402 (SPR)s12061-011-9060-1-e DE-627 ger DE-627 rakwb eng 910 ASE Law, Jane verfasserin aut Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed. Binomial, Spatial logistic regression (dpeaa)DE-He213 Unexplained spatial autocorrelation (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 Built environment (dpeaa)DE-He213 Local planning and policies (dpeaa)DE-He213 Chan, Ping W. verfasserin aut Enthalten in Applied spatial analysis and policy Dordrecht : Springer Netherlands, 2008 5(2011), 1 vom: 23. Feb., Seite 73-96 (DE-627)564750840 (DE-600)2422325-6 1874-4621 nnns volume:5 year:2011 number:1 day:23 month:02 pages:73-96 https://dx.doi.org/10.1007/s12061-011-9060-1 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_187 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_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_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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 AR 5 2011 1 23 02 73-96
allfields_unstemmed	10.1007/s12061-011-9060-1 doi (DE-627)SPR024170402 (SPR)s12061-011-9060-1-e DE-627 ger DE-627 rakwb eng 910 ASE Law, Jane verfasserin aut Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed. Binomial, Spatial logistic regression (dpeaa)DE-He213 Unexplained spatial autocorrelation (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 Built environment (dpeaa)DE-He213 Local planning and policies (dpeaa)DE-He213 Chan, Ping W. verfasserin aut Enthalten in Applied spatial analysis and policy Dordrecht : Springer Netherlands, 2008 5(2011), 1 vom: 23. Feb., Seite 73-96 (DE-627)564750840 (DE-600)2422325-6 1874-4621 nnns volume:5 year:2011 number:1 day:23 month:02 pages:73-96 https://dx.doi.org/10.1007/s12061-011-9060-1 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_187 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_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_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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 AR 5 2011 1 23 02 73-96
allfieldsGer	10.1007/s12061-011-9060-1 doi (DE-627)SPR024170402 (SPR)s12061-011-9060-1-e DE-627 ger DE-627 rakwb eng 910 ASE Law, Jane verfasserin aut Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed. Binomial, Spatial logistic regression (dpeaa)DE-He213 Unexplained spatial autocorrelation (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 Built environment (dpeaa)DE-He213 Local planning and policies (dpeaa)DE-He213 Chan, Ping W. verfasserin aut Enthalten in Applied spatial analysis and policy Dordrecht : Springer Netherlands, 2008 5(2011), 1 vom: 23. Feb., Seite 73-96 (DE-627)564750840 (DE-600)2422325-6 1874-4621 nnns volume:5 year:2011 number:1 day:23 month:02 pages:73-96 https://dx.doi.org/10.1007/s12061-011-9060-1 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_187 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_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_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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 AR 5 2011 1 23 02 73-96
allfieldsSound	10.1007/s12061-011-9060-1 doi (DE-627)SPR024170402 (SPR)s12061-011-9060-1-e DE-627 ger DE-627 rakwb eng 910 ASE Law, Jane verfasserin aut Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed. Binomial, Spatial logistic regression (dpeaa)DE-He213 Unexplained spatial autocorrelation (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 Built environment (dpeaa)DE-He213 Local planning and policies (dpeaa)DE-He213 Chan, Ping W. verfasserin aut Enthalten in Applied spatial analysis and policy Dordrecht : Springer Netherlands, 2008 5(2011), 1 vom: 23. Feb., Seite 73-96 (DE-627)564750840 (DE-600)2422325-6 1874-4621 nnns volume:5 year:2011 number:1 day:23 month:02 pages:73-96 https://dx.doi.org/10.1007/s12061-011-9060-1 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_187 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_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_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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 AR 5 2011 1 23 02 73-96
language	English
source	Enthalten in Applied spatial analysis and policy 5(2011), 1 vom: 23. Feb., Seite 73-96 volume:5 year:2011 number:1 day:23 month:02 pages:73-96
sourceStr	Enthalten in Applied spatial analysis and policy 5(2011), 1 vom: 23. Feb., Seite 73-96 volume:5 year:2011 number:1 day:23 month:02 pages:73-96
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Binomial, Spatial logistic regression Unexplained spatial autocorrelation WinBUGS Built environment Local planning and policies
dewey-raw	910
isfreeaccess_bool	false
container_title	Applied spatial analysis and policy
authorswithroles_txt_mv	Law, Jane @@aut@@ Chan, Ping W. @@aut@@
publishDateDaySort_date	2011-02-23T00:00:00Z
hierarchy_top_id	564750840
dewey-sort	3910
id	SPR024170402
language_de	englisch
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Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. 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author	Law, Jane
spellingShingle	Law, Jane ddc 910 misc Binomial, Spatial logistic regression misc Unexplained spatial autocorrelation misc WinBUGS misc Built environment misc Local planning and policies Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors
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topic_title	910 ASE Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors Binomial, Spatial logistic regression (dpeaa)DE-He213 Unexplained spatial autocorrelation (dpeaa)DE-He213 WinBUGS (dpeaa)DE-He213 Built environment (dpeaa)DE-He213 Local planning and policies (dpeaa)DE-He213
topic	ddc 910 misc Binomial, Spatial logistic regression misc Unexplained spatial autocorrelation misc WinBUGS misc Built environment misc Local planning and policies
topic_unstemmed	ddc 910 misc Binomial, Spatial logistic regression misc Unexplained spatial autocorrelation misc WinBUGS misc Built environment misc Local planning and policies
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title	Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors
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title_full	Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors
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title_sort	bayesian spatial random effect modelling for analysing burglary risks controlling for offender, socioeconomic, and unknown risk factors
title_auth	Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors
abstract	Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed.
abstractGer	Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed.
abstract_unstemmed	Abstract This paper adopts a Bayesian spatial random effect modelling approach to analyse the risk of domestic burglary in Cambridgeshire, England, at the census output area level (OA). The model, in the form of Binomial spatial logistic regression, integrates offence and offender based theories and takes into account unknown local risk factors (represented as unexplained spatial autocorrelation in the model). A score of ‘proximity to offenders’ was calibrated for each OA based on the number of likely offenders in the county, the OAs they reside, and their proximities. Our results indicate that areas that have a score higher than the average score were at higher risks of being burgled. Household occupied by non-couple and economically inactivity are positively associated confounders. Household occupied by owner is a negatively associated confounder. These confounders diminish the effect of high score of proximity to offenders, which, however, remains positively associated with the risk of burglary. Bayesian spatial random effect modelling, which adds to the traditional (non-spatial) regression model a spatial random effect term, stabilizes estimated risks and remarkably improves model fit and causation inference. Mapping the results of spatial random effect reveals locations of high risk of burglary after controlling for offender and socioeconomic factors. Limitations of the study and strategies to deter burglaries based on the results of spatial random effect modelling are discussed.
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title_short	Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors
url	https://dx.doi.org/10.1007/s12061-011-9060-1
remote_bool	true
author2	Chan, Ping W.
author2Str	Chan, Ping W.
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doi_str	10.1007/s12061-011-9060-1
up_date	2024-07-03T23:46:09.708Z
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Bayesian Spatial Random Effect Modelling for Analysing Burglary Risks Controlling for Offender, Socioeconomic, and Unknown Risk Factors

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