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    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, ?, ?, 2020 DOI: 10.1016/j.spasta.2019.100401

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Problem-driven spatio-temporal analysis and implications for postgraduate statistics teaching

Research output: Contribution to Journal/Magazine › Journal article › peer-review

E-pub ahead of print
<mark>Journal publication date</mark>28/12/2019
<mark>Journal</mark>Spatial Statistics
Publication StatusE-pub ahead of print
Early online date28/12/19
<mark>Original language</mark>English

Abstract

The paper uses two case-studies, one in public health surveillance the other in veterinary epidemiology, to argue that the analysis strategy for spatio-temporal point process data should be guided by the scientific context in which the data were generated and, more particularly, by the objectives of the data analysis. This point of view is not specific to the point process setting and, in the author’s opinion, should influence the way that statistics is taught at postgraduate level in response to the emergence and rapid growth of data science.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, ?, ?, 2020 DOI: 10.1016/j.spasta.2019.100401