The course will provide an overview of current ideas in statistical inference methods appropriate for analysing various types of spatially point referenced data, some of which may also vary temporally.
The course begins with an outline of the three types of spatial data: point-level (geostatistical), areal (lattice) and spatial point process, illustrated with examples from environmental pollution monitoring and epidemiological disease mapping.
Exploratory data analysis tools and traditional geostatistical modelling approaches (variogram fitting, kriging, and so forth) are described for point referenced data, along with similar presentations for areal data models. These start with choropleth maps and other displays and progress towards more formal statistical concepts, such as the conditional, intrinsic, and simultaneous autoregressive (CAR, IAR, and SAR) models so often used in conjunction with spatial disease mapping.
The heart of the course will cover hierarchical modelling for spatial response data, including Bayesian kriging and lattice modelling. More advanced issues will also be covered, such as nonstationarity (mean level depending on location) and anisotropy (spatial correlation depending on direction as well as distance). Bayesian methods will also be discussed for modelling data that are spatially misaligned (say, with one variable measured by post-code and another by census tract), since they are particularly well-suited to sorting out complex interrelationships and constraints.
The course concludes with a brief discussion of spatio-temporal and spatial survival models, both illustrated in the context of cancer control and epidemiology. Computer implementations for the models via the WinBUGS and R packages will be described throughout.
Participants are encouraged to buy the book Hierarchical Modelling and Analysis for Spatial Data, co-authored by Professor Gelfand.
These courses are likely to be very popular, as when these were run biennially since 2005.
On the first day, the course will start with registration and coffee at 9.00am with formal teaching starting at 9.30 am On the last day, formal teaching will end at about 5.00pm. Afterwards there will be an opportunity for participants to ask questions about the course.
Southampton Statistical Sciences Research Institute
Building 39, University of Southampton
Southampton
SO17 1BJ
Prof Sujit K Sahu
3rd June – 4th June 2015
£60 for DTC students
£200 for registered students
£250 for staff from academic institutions (including research centres)
£300 for all other participants
The course fee includes course materials, lunches and morning and afternoon refreshments. Travel and accommodation are to be arranged and paid for by the participant.