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Modelling spatially varying impacts of socioeconomic predictors on mortality outcomes

Author

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  • P. Congdon

Abstract

A methodology is proposed for modelling spatially varying predictor effects on a disease or mortality count outcome. The methodology may be extended to multivariate outcomes, so that one may assess the similarity of spatial patterning of regression effects between outcomes. Another extension involves longitudinal data, where a number of modelling structures are possible. The methodology is illustrated by suicide mortality in 32 London Boroughs over the period 1979–1993, in terms of area deprivation and a measure of social fragmentation. Copyright Springer-Verlag Berlin Heidelberg 2003

Suggested Citation

  • P. Congdon, 2003. "Modelling spatially varying impacts of socioeconomic predictors on mortality outcomes," Journal of Geographical Systems, Springer, vol. 5(2), pages 161-184, August.
    • Handle: RePEc:kap:jgeosy:v:5:y:2003:i:2:p:161-184
    DOI: 10.1007/s10109-003-0099-7
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    Citations

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    Cited by:

    1. David C Wheeler, 2009. "Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso," Environment and Planning A, , vol. 41(3), pages 722-742, March.
    2. Yu, Danlin & Zhang, Yaojun & Wu, Xiwei & Li, Ding & Li, Guangdong, 2021. "The varying effects of accessing high-speed rail system on China’s county development: A geographically weighted panel regression analysis," Land Use Policy, Elsevier, vol. 100(C).
    3. Marcia Castro, 2007. "Spatial Demography: An Opportunity to Improve Policy Making at Diverse Decision Levels," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(5), pages 477-509, December.
    4. Rodrigues, Alexandre & Assunção, Renato, 2008. "Propriety of posterior in Bayesian space varying parameter models with normal data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2408-2411, October.
    5. 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.
    6. Congdon, Peter, 2006. "A model for non-parametric spatially varying regression effects," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 422-445, January.
    7. Matthew Quick, 2019. "Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach," Journal of Geographical Systems, Springer, vol. 21(3), pages 339-365, September.

    More about this item

    Keywords

    spatial heterogeneity; suicide; random effects; epidemiology; health resourcing; C21; C23; C25; C11; I12;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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