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

Diggle, P.J. (2019) Problem-driven spatio-temporal analysis and implications for postgraduate statistics teaching. Spatial Statistics. ISSN 2211-6753

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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.

Item Type:
Journal Article
Journal or Publication Title:
Spatial Statistics
Additional Information:
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
Uncontrolled Keywords:
?? data scienceepidemiologypoint processteachingcomputers in earth sciencesstatistics and probabilitymanagement, monitoring, policy and law ??
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Deposited On:
27 Jan 2020 12:00
Last Modified:
19 Mar 2024 00:43