Spatial and spatio-temporal log-gaussian cox processes: Extending the geostatistical paradigm

Peter J. Diggle, Paula Moraga, Barry Rowlingson, Benjamin M. Taylor

Research output: Contribution to journalArticlepeer-review

160 Scopus citations


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. © Institute of Mathematical Statistics, 2013.
Original languageEnglish (US)
Pages (from-to)542-563
Number of pages22
JournalStatistical Science
Issue number4
StatePublished - Nov 1 2013
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-03-16

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Mathematics(all)


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