Hierarchical Bayesian spatial models for alcohol availability, drug "hot spots" and violent crime

被引：45

作者：

Zhu L. ^{[1
]}

Gorman D.M. ^{[1
]}

Horel S. ^{[1
]}

机构：

[1] Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A and M Health Science Center, College Station

来源：

International Journal of Health Geographics | / 5卷 / 1期

关键词：

Census Tract; Spatial Dependence; Violent Crime; Standard Incidence Ratio; Deviance Information Criterion;

D O I：

10.1186/1476-072X-5-54

中图分类号：

学科分类号：

摘要：

Background: Ecologic studies have shown a relationship between alcohol outlet densities, illicit drug use and violence. The present study examined this relationship in the City of Houston, Texas, using a sample of 439 census tracts. Neighborhood sociostructural covariates, alcohol outlet density, drug crime density and violent crime data were collected for the year 2000, and analyzed using hierarchical Bayesian models. Model selection was accomplished by applying the Deviance Information Criterion. Results: The counts of violent crime in each census tract were modelled as having a conditional Poisson distribution. Four neighbourhood explanatory variables were identified using principal component analysis. The best fitted model was selected as the one considering both unstructured and spatial dependence random effects. The results showed that drug-law violation explained a greater amount of variance in violent crime rates than alcohol outlet densities. The relative risk for drug-law violation was 2.49 and that for alcohol outlet density was 1.16. Of the neighbourhood sociostructural covariates, males of age 15 to 24 showed an effect on violence, with a 16% decrease in relative risk for each increase the size of its standard deviation. Both unstructured heterogeneity random effect and spatial dependence need to be included in the model. Conclusion: The analysis presented suggests that activity around illicit drug markets is more strongly associated with violent crime than is alcohol outlet density. Unique among the ecological studies in this field, the present study not only shows the direction and magnitude of impact of neighbourhood sociostructural covariates as well as alcohol and illicit drug activities in a neighbourhood, it also reveals the importance of applying hierarchical Bayesian models in this research field as both spatial dependence and heterogeneity random effects need to be considered simultaneously. © 2006 Zhu et al; licensee BioMed Central Ltd.

引用

共 40 条

[1]

Besag J., York J., Mollie A., Bayesian image restoration, with two applications in spatial statistics (with discussion), Annals of the Institute of Statistical Mathematics, 43, pp. 1-59, (1991)

[2]

Waller L.A., Carlin B.P., Xia H., Gelfand A.E., Hierarchical spatio-temporal mapping of disease rates, Journal of the American Statistical Association, 92, pp. 607-617, (1997)

[3]

Lawson A., Statistical Methods in Spatial Epidemiology, (2001)

[4]

Waller L.A., Gotway C.A., Applied Spatial Statistics for Public Health Data, (2004)

[5]

Banerjee S., Carlin B.P., Gelfand A.E., Hierarchical Modeling and Analysis for Spatial Data, (2004)

[6]

Spiegelhalter D.J., Thomas A., Best N., Gilks W.R., BUGS: Bayesian Inference Using Gibbs Sampling, Version 0.50, (1995)

[7]

Carlin B.P., Louis T.A., Bayes and Empirical Boyes Methods for Data Analysis, (2000)

[8]

Berry D., Evett I., Pinchin R., Statistical inference in crime investigations using deoxyribonucleic acid profiling, Journal of the Royal Statistical Society C, 41, pp. 499-531, (1992)

[9]

Cohen J., Nagin D., Wallstrom G., Wasserman L., Hierarchical Bayesian analysis of arrest rates, Journal of the American Statistical Association, 93, pp. 1260-1270, (1998)

[10]

Law J., Haining R., A Bayesian approach to modeling binary data: The case of high-intensity crime areas, Geographical Analysis, 36, pp. 197-215, (2004)

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