Meets weekly, 2017 Sep 3 to Oct 8, 18:15-20:45 Eastern Time (UTC-5)
Location: *Kendall ???? (first and last sessions) and online via
|*Sep 10||1. Intro to R||bestiary||1, 2, 4
|Sep 17||2. Points||Redwoods||3.4.4, 6||1. Maps and stats|
|Sep 24||3. Polygon||Montgomery County||2.4, 3.1-4|
|Oct 1||4. Raster||landsat||5.8-9||2. Point pattern|
|Oct 8||5. Graphs||TBA||7|
|*Oct 15||6. In-class presentations||3. Submit by Oct 6|
Lee De Cola
DATA to Insight, Reston VA
the R programming environment,
about the mathematics of space,
the fundamentals of spatial data structures,
to do quantitative analysis of spatial processes,
to conduct and present original research.
Lab exercises using R interactively and collaboratively
Readings, including significant coding exercises
Three assignments, including a final project presented in the last session
**Brunsdon, C. and L. Comber (2015) An introduction to R for spatial analysis & mapping Los Angeles, SAGE. The book covers a lot more material than we can, so i'll suggest sections that you should code.
Adler, J. (2010). R in a nutshell Sebastopol, CA, O'Reilly. There are many intro R books around; this is one I like.
*Bivand, Roger, et al. (2013) Applied spatial data analysis with R New York, Springer. An excellent but quite technical review of our topics.
Waller, L. A. and C. A. Gotway (2004) Applied spatial statistics for public health data Hoboken, N.J, John Wiley & Sons.
Cressie, N. A. C. (1991) Statistics for spatial data New York, Wiley.