TY - JOUR

T1 - Problem-driven spatio-temporal analysis and implications for postgraduate statistics teaching

AU - Diggle, P.J.

N1 - 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

PY - 2019/12/28

Y1 - 2019/12/28

N2 - 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.

AB - 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.

KW - Data science

KW - Epidemiology

KW - Point process

KW - Teaching

U2 - 10.1016/j.spasta.2019.100401

DO - 10.1016/j.spasta.2019.100401

M3 - Journal article

JO - Spatial Statistics

JF - Spatial Statistics

SN - 2211-6753

ER -