In recent years there has been enormous growth in interest in the analysis of spatial data, and the development of statistical methodologies that interact with geographical information systems (GISs). This course is about spatial data, spatial statistical models, and their proper fitting, summary, and interpretation. It is designed to introduce students to the nature of spatial data and the special analysis tools that help analyze such data. The course covers a blend of theory, applications, and software.

The following are useful text books for spatial statistics and hierarchical models:

  • Banerjee, S., Carlin, B.P. and Gelfand, A.E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Publisher: CRC/Chapman and Hall.
  • Carlin, B.P. and Louis, T.A. (2000).Bayes and Empirical Bayes Methods for Data Analysis. Second Edition. Publisher: CRC/Chapman and Hall.
  • Dalgaard, P. (2002). Introductory Statistics with R.
  • Diggle, P.J. and Ribeiro Jr., P.J. (2007). Model-based Geostatistics. Publisher: Springer.
  • Faraway, J.J. (2005). Linear Models with R. Publisher: CRC/Chapman and Hall.
  • Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (2004). Bayesian Data Analysis. Second Edition. Publisher: CRC/Chapman and Hall.
  • Lee, P. M. (2004). Bayesian Statistics Publisher: Hodder Arnold
  • Venables, W.N., Smith, D.M. and the R Development Core Team (2002). An Introduction to R: Revised and Updated.
  • Waller, L. and Gotway, C. (2004). Applied Spatial Statistics for Public Health Data. Publishers: John Wiley and Sons.

  • Some notes on the basic principles of Bayesian inference and the Bayesian linear regression model.

    The web sites for the two softwares we will use:

  • WinBUGS or OpenBUGS
  • You can download the new registration key for WinBUGS from HERE. NOTE THAT YOU DO NOT REQUIRE ANY REGISTRATION KEY FOR OPENBUGS.
  • R