Developing a Locally Adaptive Spatial Multilevel Logistic Model to Analyze Ecological Effects on Health Using Individual Census Records.
Geographical variable distributions often exhibit both macroscale geographic smoothness and microscale discontinuities or local step changes. Nonetheless, accounting for both effects in a unified statistical model is challenging, especially when the data under study involve a multiscale structure an...
| Published in: | Annals of the American Association of Geographers Vol. 110; no. 3; pp. 739 - 758 |
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| Main Authors: | , , , , , |
| Format: | Article |
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Taylor & Francis Ltd
May2020
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=142554914&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 142554914 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: May2020 vid: 110 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 142554914 10.1080/24694452.2019.1644990 ppf: 739 ppct: 19 formats: tig: atl: Developing a Locally Adaptive Spatial Multilevel Logistic Model to Analyze Ecological Effects on Health Using Individual Census Records. aug: au: Dong, Guanpeng Ma, Jing Lee, Duncan Chen, Mingxing Pryce, Gwilym Chen, Yu affil: Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center for Yellow River Civilization, Henan University Department of Geography & Planning, University of Liverpool Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University School of Mathematics & Statistics, University of Glasgow Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences Sheffield Methods Institute, Faculty of Social Sciences, University of Sheffield School of East Asian Studies, University of Sheffield su: Econometrics Markov chain Monte Carlo Gaussian Markov random fields Environmental research Autocorrelation (Statistics) sug: subj: Econometrics Markov chain Monte Carlo Gaussian Markov random fields Environmental research Autocorrelation (Statistics) ab: Geographical variable distributions often exhibit both macroscale geographic smoothness and microscale discontinuities or local step changes. Nonetheless, accounting for both effects in a unified statistical model is challenging, especially when the data under study involve a multiscale structure and non-Gaussian response variables. This study develops a locally adaptive spatial multilevel logistic model to examine binomial response variables that integrates an innovative locally adaptive spatial econometric model with a multilevel model. It takes into account global spatial autocorrelation, local step changes, and vertical dependence effects arising from the multiscale data structure. Another appealing feature is that the spatial correlation structure, implied by a spatial weights matrix, is learned along with other model parameters via an iterative estimation algorithm, rather than being presumed to be invariant. Bayesian Markov chain Monte Carlo (MCMC) samplers are derived to implement this new spatial multilevel logistic model. A data augmentation approach, drawing on recently devised Pólya-gamma distributions, is adopted to reduce computational burdens of calculating binomial likelihoods with a logit link function. The validity of the developed model is evaluated by a set of simulation experiments, before being applied to analyze self-rated health for the elderly in Shijiazhuang, the capital city of Hebei Province, China. Model estimation results highlight a nuanced geography of self-rated health and identify a range of individual- and area-level correlates of health for the elderly. Key Words: geography of health, local spatial modeling, multilevel models, spatial autocorrelation, spatial econometrics. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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