Statistics C173/C273: Applied Geostatistics
- First lecture is on Monday, 03 January 2022
Location: MW Young Hall 2200, F Haines Hall 39
Day/time: MWF 11:00 - 11:50
See you then!
- For the course syllabus click
here.
Useful links:
- http://www.socr.ucla.edu
-
http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials
- Download R and packages.
- Download RStudio.
Data sets
Copy and paste each line below at the R command line to acces the data:
a1 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/wolfcamp.txt", header=TRUE)
a2 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/walker_lake_v.txt", header=TRUE)
a3 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/walker_lake_u.txt", header=TRUE)
a4 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/soil.txt", header=TRUE)
a5 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/o3.txt", header=TRUE)
a6 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/soil_complete.txt", header=TRUE)
a7 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/coal_ash.txt", header=TRUE)
a8 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/broom_barn_data.txt", header=TRUE)
a9 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/nc_sids.txt", header=TRUE)
a10 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/unemp_data.txt", header=TRUE)
a11 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/nc_sids_data.txt", header=TRUE)
a12 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/nc_seats.txt", header=TRUE)
a13 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/parana.txt", header=TRUE)
a14 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/jura.txt", header=TRUE)
a15 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/o3.txt", header=TRUE)
a16 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/coal_ash.txt", header=TRUE)
a17 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/elevation.txt", header=TRUE)
a18 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/kruger_park_rainfall.txt", header=TRUE)
a19 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/ca_elections_results_2012.txt", header=TRUE)
a20 <- read.table("http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/la_data.txt", header=TRUE)
Handouts
- 1. Types of spatial
data.
- 2. Types of spatial data -
examples.
- 3. Spatial data sources.
- 4. Introduction.
- 5. Matrix and vector
differentiation.
- 6. Random vectors
and properties.
- 7. Parana data: h-scatterplots.
- 7. Testing for spatial
correlation - paper 1.
- 8. Testing for spatial
correlation - paper 2: Binary transformation of the data.
- 9. Testing for clustering.
- 10. Lattices: BB and BW
join counts, Moran's and Geary's test statistics.
- 11. Notes on Continuous Stochastic Phenomena:
Paper by Moran.
- 12. The Contiguity Ratio and Statistical
Mapping: Paper by Geary.
- 13. Matrix for independence
in the i.i.d. case of normally distributed random variables.
- 14. h-scatterplots, variogram.
- 15. Parana data: h-scatterplots.
- 16. The variogram.
- 17.
Application of geostatistics in Political Science.
- 18. Simulations using Cholesky decomposition - example.
- 19. Simulations using the grf function of geoR - example.
- 20. Simulations using geoR and the maps
packages.
- 21. Simulating
geostatistical data.
- 22. Robust estimator paper.
- 23. Computing the variogram using geoR.
- 24. More on variogram.
- 25. More variograms.
- 26. Fitting a model variogram - Noel Cressie (1985).
- 27. Fitting a model variogram.
- 28. Fitting a model variogram - R code.
- 29. R code to graph some model variograms.
- 30. Data with trend.
- 31. Working with gstat.
- 32. Variogram models - summary.
- 33. Geometric anisotropy.
- 34. Anisotropy plot.
- 35. Geometric anisotropy R.
- 36. Geometric anisotropy - calculations.
- 37. Anisotropy paper.
- 38. Linear model of
regionalization - example in R.
- 39. Example of the linear model of
regionalization (see also #38).
- 40. Multivariatre normal distribution.
- 41. Spatial prediction.
- 42. Inverse distance
interpolation - with contours.
- 43. Ordinary kriging in terms of variogram.
- 44. Ordinary kriging in terms of covariance.
- 45. Ordinary kriging using geoR and gstat.
- 46. Short R code for ordinary kriging in
terms of variogram.
- 47. Short R code for ordinary kriging in
terms of covariance.
- 48. Data file kriging_1.txt for handouts 46 and 47.
- 49. Data file kriging_11.txt for handouts 46 and 47.
- 50. Ordinary kriging - R code using
geoR and gstat.
- 51. Ordinary kriging in matrix form.
- 52. Kriging is an
exact interpolator.
- 53. Simple kriging - R code.
- 54. Simple kriging - with geoR and gstat.
- 55. Kriging is an exact
interpolator.
- 56. Check geoR and gstat for exact interpolation calculations!
- 57. Lognormal
simple kriging.
- 58. Lognormal ordinary kriging - R code.
- 59. Effect of variogram parameters on kriging weights.
- 60. Cross validation.
- 61. Cross-validation - examples I.
- 62. Cross-validation -
examples II.
- 63. Assign "NA" values.
- 64. Universal kriging.
- 65. Cross-validation: comparing
ordinary with univesrsal kriging - example.
- 66. Cokriging.
- 67. Cokriging - extra notes.
- 68. Linear model of
coregionalization.
- 69. Block kriging.
- 70. One-dimensional ordinary kriging -
screen and relay effect.
- 71. Introduction to point pattern
data.
- 72.
Testing for complete spatial randomness (CSR) hypothesis using ecdf and cdf.
- 73. Testing for complete
spatial randomness - examples in R.
Labs
- 1. Lab 1 - Try this on your computer.
- California adjacency
matrix - R file (by Conor Kresin - graduate student).
- Variogram calculations
using geoR.
- Variogram calculations
using geoR: Variogram cloud, robust estimator.
- Fitting a model variogram
using the geoR package.
- An example using the
gstat package.
- Linear model of
regionalization.
- Geometric anisotropy.
- Inverse distance interpolation.
- Ordinary kriging
using geoR and gstat.
- Universal kriging
using geoR and gstat.
- Co-kriging
using gstat - one colocated variable.
- Co-kriging
using gstat - two colocated variable.
- Cross validation:
Compare ordinary with co-kriging.
- Testing for complete
spatial randomness - examples in R.
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