Spatial and spatio-temporal Log-Gaussian Cox processes:extending the geostatistical paradigm

Diggle, Peter and Moraga-Serrano, Paula and Rowlingson, Barry and Taylor, Benjamin (2013) Spatial and spatio-temporal Log-Gaussian Cox processes:extending the geostatistical paradigm. Statistical Science, 28 (4). pp. 542-563. ISSN 0883-4237

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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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Journal Article
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Statistical Science
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05 Mar 2014 09:10
Last Modified:
21 Nov 2022 23:31