English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper we first describe the class of log-Gaussian Cox processes(LGCPs) as models for spatial and spatio-temporal point process data. Wediscuss inference, with a particular focus on the computational challenges oflikelihood-based inference. We then demonstrate the usefulness of the LGCP bydescribing four applications: estimating the intensity surface of a spatialpoint process; investigating spatial segregation in a multi-type process;constructing spatially continuous maps of disease risk from spatially discretedata; and real-time health surveillance. We argue that problems of this kindfit naturally into the realm of geostatistics, which traditionally is definedas the study of spatially continuous processes using spatially discreteobservations at a finite number of locations. We suggest that a more usefuldefinition of geostatistics is by the class of scientific problems that itaddresses, rather than by particular models or data formats.

Spatial and Spatio-Temporal Log-Gaussian Cox Processes: Extending the Geostatistical Paradigm