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Spatial Statistics

With the rise and wide availability of geographic information system (GIS), it has become increasingly more common for researchers to collect spatially-referenced data. Spatial data typically display dependence and thus, spatial statistical methods intend to extend the scope of classical models developed for independent data to account for the spatial correlation in the observations.

This track presents current work by prominent spatial statisticians and epidemiologists on spatial data that has arisen from several applications, ranging from atmospheric sciences to epidemiological and environmental health studies.

The statistical methods presented in the talks in the 5 sessions span the three categories in which spatial data is often classified: point-referenced data, or continuous spatial variation data, for which the spatial dependence is modeled via correlation or covariance functions; areal data, or discrete spatial variation data, more commonly encountered in epidemiological studies where the outcome is the number of cases of a disease in an areal unit; and point process or point pattern data, where inferring upon the random process that drives the location of the events is the main interest.

Veronica Berrocal, University of Michigan, United States
Andrew Lawson, Medical University of South Carolina, United States
Organized Sessions associated with this Track