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Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

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  • David Wheeler
  • Lance Waller

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  • David Wheeler & Lance Waller, 2009. "Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests," Journal of Geographical Systems, Springer, vol. 11(1), pages 1-22, March.
    • Handle: RePEc:kap:jgeosy:v:11:y:2009:i:1:p:1-22
    DOI: 10.1007/s10109-008-0073-5
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    References listed on IDEAS

    as
    1. David Wheeler & Catherine Calder, 2007. "An assessment of coefficient accuracy in linear regression models with spatially varying coefficients," Journal of Geographical Systems, Springer, vol. 9(2), pages 145-166, June.
    2. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    3. Scribner, R.A. & MacKinnon, D.P. & Dwyer, J.H., 1995. "The risk of assaultive violence and alcohol availability in Los Angeles county," American Journal of Public Health, American Public Health Association, vol. 85(3), pages 335-340.
    4. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
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    Citations

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    Cited by:

    1. Mulley, Corinne & Ma, Liang & Clifton, Geoffrey & Yen, Barbara & Burke, Matthew, 2016. "Residential property value impacts of proximity to transport infrastructure: An investigation of bus rapid transit and heavy rail networks in Brisbane, Australia," Journal of Transport Geography, Elsevier, vol. 54(C), pages 41-52.
    2. David C. Wheeler & Joseph Boyle & D. Jeremy Barsell & Trevin Glasgow & F. Joseph McClernon & Jason A. Oliver & Bernard F. Fuemmeler, 2022. "Spatially Varying Associations of Neighborhood Disadvantage with Alcohol and Tobacco Retail Outlet Rates," IJERPH, MDPI, vol. 19(9), pages 1-13, April.
    3. Peter Congdon, 2020. "Geographical Aspects of Recent Trends in Drug-Related Deaths, with a Focus on Intra-National Contextual Variation," IJERPH, MDPI, vol. 17(21), pages 1-18, November.
    4. Tara A. Smith & J. S. Onésimo Sandoval, 2019. "Examining the Local Spatial Variability of Robberies in Saint Louis Using a Multi-Scale Methodology," Social Sciences, MDPI, vol. 8(2), pages 1-25, February.
    5. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    6. Danlin Yu & Chuanglin Fang, 2022. "How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    7. Lin, Fangzheng & Tang, Yanlin & Zhu, Huichen & Zhu, Zhongyi, 2022. "Spatially clustered varying coefficient model," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    8. Ashley N. Arnio & Eric P. Baumer, 2012. "Demography, foreclosure, and crime:," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(18), pages 449-488.
    9. Jinjun Tang & Fan Gao & Fang Liu & Wenhui Zhang & Yong Qi, 2019. "Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM," Sustainability, MDPI, vol. 11(19), pages 1-19, October.
    10. Hongbo Li & Yali Liu & Anlu Zhang, 2018. "Spatially varying associations between creative worker concentrations and social diversity in Shenzhen, China," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 85-99, January.
    11. Saravanan Veluswami Subramanian & Min Jung Cho & Fotima Mukhitdinova, 2018. "Health Risk in Urbanizing Regions: Examining the Nexus of Infrastructure, Hygiene and Health in Tashkent Province, Uzbekistan," IJERPH, MDPI, vol. 15(11), pages 1-16, November.
    12. Jane Law & Matthew Quick, 2013. "Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach," Journal of Geographical Systems, Springer, vol. 15(1), pages 89-113, January.
    13. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    14. Antonio Páez & Steven Farber & David Wheeler, 2011. "A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships," Environment and Planning A, , vol. 43(12), pages 2992-3010, December.
    15. Maria Terres & Alan Gelfand, 2015. "Using spatial gradient analysis to clarify species distributions with application to South African protea," Journal of Geographical Systems, Springer, vol. 17(3), pages 227-247, July.
    16. David C. Wheeler & Antonio Páez & Jamie Spinney & Lance A. Waller, 2014. "A Bayesian approach to hedonic price analysis," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 663-683, August.
    17. Wheeler, Andrew Palmer & Steenbeek, Wouter, 2020. "Mapping the risk terrain for crime using machine learning," SocArXiv xc538, Center for Open Science.
    18. Jeffrey M. Switchenko & Jacky M. Jennings & Lance A. Waller, 2020. "Exploring spatially varying demographic associations with gonorrhea incidence in Baltimore, Maryland, 2002–2005," Journal of Geographical Systems, Springer, vol. 22(2), pages 201-216, April.
    19. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
    20. Jakob A. Dambon & Stefan S. Fahrländer & Saira Karlen & Manuel Lehner & Jaron Schlesinger & Fabio Sigrist & Anna Zimmermann, 2022. "Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-14, December.

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    More about this item

    Keywords

    GWR; Bayesian regression; Collinearity; Penalization methods; C11; C13; C21;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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