Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data
Submitted: 12 November 2015
Accepted: 5 February 2016
Published: 31 May 2016
Accepted: 5 February 2016
Abstract Views: 6258
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APPENDIX: 1009
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Mathematical Sciences School, Queensland University of Technology, Brisbane;
Cooperative Research Centre Programme for Spatial Information, Melbourne, Australia.
Mathematical Sciences School, Queensland University of Technology, Brisbane; Viertel
Centre for Research in Cancer Control, Cancer Council Queensland, Brisbane, Australia.
Mathematical Sciences School, Queensland University of Technology, Brisbane;
Cooperative Research Centre Programme for Spatial Information, Melbourne, Australia.
School of Nursing, Midwifery and Paramedicine, Faculty of Health Sciences, Curtin
University, Perth, Australia.
Mathematical Sciences School, Queensland University of Technology, Brisbane;
Cooperative Research Centre Programme for Spatial Information, Melbourne, Australia.
How to Cite
Kang, S. Y., Cramb, S. M., White, N. M., Ball, S. J., & Mengersen, K. L. (2016). Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data. Geospatial Health, 11(2). https://doi.org/10.4081/gh.2016.428
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