Spatial and spatio-temporal log-Gaussian Cox processes: Extending the geostatistical paradigm
Diggle, Peter J., Moraga, Paula, Rowlingson, Barry, & Taylor, Benjamin M. (2013) Spatial and spatio-temporal log-Gaussian Cox processes: Extending the geostatistical paradigm. Statistical Science, 28(4), pp. 542-563.
In this paper we first describe the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data. We discuss inference, with a particular focus on the computational challenges of likelihood-based inference. We then demonstrate the usefulness of the LGCP by describing four applications: estimating the intensity surface of a spatial point process; investigating spatial segregation in a multi-type process; constructing spatially continuous maps of disease risk from spatially discrete data; and real-time health surveillance. We argue that problems of this kind fit naturally into the realm of geostatistics, which traditionally is defined as the study of spatially continuous processes using spatially discrete observations at a finite number of locations. We suggest that a more useful definition of geostatistics is by the class of scientific problems that it addresses, rather than by particular models or data formats.
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|Item Type:||Journal Article|
|Keywords:||Cox process, epidemiology, geostatistics, Gaussian process, spatial point process|
|Divisions:||Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 Institute of Mathematical Statistics|
|Deposited On:||22 May 2016 22:22|
|Last Modified:||24 May 2016 23:39|
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