Short Introduction to Geostatistical and Spatial Data Analysis

with GRASS and R statistical data language

by Markus Neteler

ITC-irst/MPA, Trento, Italy

Data Analysis with R statistical data analysis language provides:

R-stats is an ideal extension for GRASS. R is described in greater detail here: What are R and CRAN. You can freely download the R software (MS-Windows/Linux/MacOSX/etc binaries are provided). A general web portal related to geostatistics is found at AI-GEOSTATS.

Table of contents

Part I:
  1. Installation of R and R/GRASS 5 interface
  2. Installation of R and R/GRASS 6 interface
  3. Learning R
    1. Basics
    2. Visualization
    3. Import/export using ASCII-files (tables), manual table entry
    4. Plotting to file (EPS, PS)
    5. Graphical user interface: R Commander

Part II:

  1. Distribution Tests
  2. Working with elevation data (DEM)
  3. Interpolation methods/Spatial statistics
    1. Constant and Linear interpolation (approxfun) [base package]
    2. Trend surface fitting = polynomial regression surface by least squares (surf.ls, trmat) [spatial package]
    3. Local polynomial regression fitting with error prediction (loess, predict.loess) [modreg package]
    4. Trend surface fitting by generalized least squares (surf.gls, trmat) [spatial]
    5. Kriging with automated variogram model estimation

Part III:

  1. Connecting R to RDBMS
  2. Using GRASS and R
    1. Using R/GRASS with Project ASSIST/Leics dataset
    2. Analysing the Maas contaminated soils dataset
    3. Running R/GRASS with own data (in your own location)
    4. Synthetic data for tests using R and GRASS

References and further links:

Online Tutorials:

Graphical Interfaces:

Some examples on these pages are based on:
© 2000-2007 Markus Neteler
Last update: $Date: 2007-02-09 10:42:53 +0100 (Fri, 09 Feb 2007) $