• 14 Jan: Skim Bivand Chapter 1,
• 21 Jan: Skim Bivand section 3.1 and perhaps 3.2 or 3.3. Focus on 3.2 if prefer lattice graphics to base graphics. Focus on 3.3 if you prefer ggplot graphics.
• If you're new to R, read Bivand Sections 2.1 and 2.2 and the Introduction to R for Stat 406. Practice the R commands in section 2.2 and the Rintro document. Next week, be prepared to ask questions about anything in section that you don't understand.
• 28, 30 Jan: Bivand sections 8.1, 8.2, 8.3, 8.5 (Intro), 8.5.1
  We'll talk about variograms (section 8.4) after we talk about kriging.
• 6 Feb: Bivand section 8.4, omitting section 8.4.5 (multivariate) for now
• 6 Feb: Semivariogram models, pp. 84-97 in Webster and Oliver, Geostatistics for Environmental Scientists.
• 11-13 Feb: Bivand sections 8.5, 8.5.1, 8.6, 8.5.5, 8.5.6, 8.5.9 (in the order I will discuss them). Skim 8.5.2, 8.5.3, 8.5.7, 8.5.8 if interested.
• 18 Feb: The best discussion is chapter 5 of Oliver and Webster, 2015. Available online through the library: Link to the online book
• 20 Feb: Spatial Linear models: Bivand hardly discusses these and that discussion has a very different perspective than mine. I've posted more information:
  Spatial Linear Models. This is from the SAS Mixed Models book. Lots of mathematical details here, so skim for concepts
  Analysis of data from controlled experiments Chapter 16 (in part) from Plant, R.E. 2012. Spatial Data Analysis in Ecology and Agriculture using R. Again skim for concepts. gls() code for the "correlated errors model" is tucked inside the EMP.gls() function on page 528.
• 27 Feb: Areal data, Bivand chapter 9, sections 9.1-9.3
• 5 Mar: Modeling areal data, Bivand, sections 9.4, 9.4.1 (omit 9.4.2)
• Optional if interested in consequences of spatial adjustment:
  Hodges, J. and Reich, B. 2010. Adding spatially-correlated errors can mess up the fixed effect you love. Am. Stat. 64(4): 325-334.
• 12 Mar: Spatial smoothing, Bivand, intro to Chapter 10. Omit rest of chapter 10 unless you're interested in the many different approaches to the disease mapping problem.
• Spatial Point patterns: Bivand, sections 7.1, 7.2, 7.3 (skim 7.3.2, 7.3.3), 7.4.5
  Nearest Neighbor Methods, pages 1-6.
  Ripley's K, pages 1-4, first half of section 7.
• Spatial point patterns, Intensity: Bivand, 7.4.3, 7.4.4

Readings below here are for your information, in case interested.
They highlight where you can find more information about material I've discussed.

• Space-time analyses: Bivand chapter 6 covers data structures and visualization. Section 8.12 is a synopsis of prediction.
  If you want to do space-time areal or geostatistical analyses, Cressie and Wikle's book is the reference manual. See Diggle's book, 3rd ed., for space-time point pattern analysis.
• Space-time modeling example Example is precision agriculture. This is a chapter in a book on geostatistics for precision agriculture. G. Heuvelink has other papers on space-time geostats for environmental data.
• Combining data: Bivand section 5.1 discusses support and the modifiable areal unit problem (MAUP). The rest of chapter 5 discusses computing to combine data from different sorts of units.
• Geostatistical simulation: Very briefly discussed in Bivand section 8.8.1
  Concepts of geostatistical simulation (ArcGIS Help page)
  Overview of using simulation to characterize risk