Courses¶
Cornell University (USA)¶
PLSCS/NTRES 6200: Spatial Modelling and Analysis¶
Intended for earlystage MSc and PhD students, and final semester undergraduates, who are using some kind of spatial or spatiotemporal analysis in their thesis projects.
Theory and practice of applying geospatial data for resource inventory and analysis, biophysical process modeling, and regional studies. Emphasizes use and evaluation of spatial analytical methods applied to agronomic and environmental systems and processes. Laboratory section is used to process, analyze, and visualize geospatial data of interest to the student, ending in a student project linked to the student’s ongoing or proposed thesis/exit project research.
Lecture slides used in the 2020 course – will be updated in May 2021 with the 2021 version (PDF)
Spatial analysis with the R Project for Statistical Computing
Conceptual basis of geostatistics
A universal model of spatial variation; detecting and modelling spatial autocorrelation; variogram models; spatial prediction; Ordinary Kriging; Universal Kriging; Kriging with External Drift
Introduction to Geographic Information Systems (GIS)
Includes a brief introduction to QGIS
Trend surfaces fitting by Ordinary and Generalized Least Squares and Generalized Additive Models
Regression Kriging (RK); Kriging with an External Drift (KED)
Remote sensing applied to agronomic and environmental systems
Principal Components Analysis with application to remote sensing image analysis
Sampling for natural resources and environmental modelling and monitoring

Modifiable unit area problem, the “ecological” fallacy, local spatial correlation (Moran’s I etc.), GeoDa, Spatial Autoregressive (SAR) models
Datadriven methods for predictive modelling
Modelling cultures; Classification and Regression Trees (CART); Random Forests (RF); Cubist; model tuning with
caret
Supplemental lecture slides (PDF)
Instrumental variables in linear regression
Some details not covered in “Areal Data Spatial Analysis” (see above)
Exercises
These use techniques covered in Tutorials: spatial analysis
Trend surfaces
Spatial Autocorrelation
Spatial sampling
Datasets used in this exercise
(RData, CSV; compressed)
SCAS 494: Land Suitability Evaluation¶
Land evaluation may be defined as the process of prediction of land performance when the land is used for specified purposes.
This course was last given in 1994; it is thus somewhat outdated but still can be a useful starting point. Algunos capítulos se han traducidos al Español.
南京师范大学 (Nanjing Normal University)¶
Frontiers in Geographical Information Science¶
An eightweek course for firstyear students in the GIS concentration) of the School of Geography (地理科学学院), four from me on mapping and four from Dick Brus on spatial sampling. My part uses parts of the above slides, but also (from 2020) emphasizes research skills.
University of Twente (NL)¶
Faculty of GeoInformation Science and Earth Observation
Geostatistics & Opensource statistical computing¶
Distance education course, equivalent to a fulltime 3week module, last given in 2015. Here are the lecture slides (including selfassessement questions) and the set computer exercises, a bit outdated but could still be useful. Most of the exercises use the Meuse soil geochemical dataset.
Lecture slides (PDF)
 How to read the materials
 Lecture 1: What is geostatistics; Geostatistical computing
 Lecture 2: Exploring and visualizing spatial data
 Lecture 3: Modelling spatial structure from point observations
 Lecture 4: Spatial prediction from point observations (Part 1)
 Lecture 5: Spatial prediction from point observations (Part 2)
 Lecture 6: Assessing the quality of spatial predictions
 Lecture 7: Geostatistical risk mapping
 Lecture 8: Spatial sampling
Computer exericses
Exercises
(set of PDF, compressed) ex0 Preparing the computing environment
 ex1 Using the R Environment for Statistical Computing
 ex1a Supplement to ex1: ggplot2 graphics
 ex2 Visualizing spatial structure
 ex3 Modelling spatial structure from point samples
 ex4 Predicting from point samples (Part 1): Ordinary Kriging
 ex4a Normalscore transformation
 ex5 Predicting from point samples (Part 2): Kriging weights, Block kriging, Universal Kriging
 ex5a Predicting from point samples (Part 3): Using secondary information
 ex6 Assessing the quality of spatial predictions; Geostatistical simulation
 ex7 Geostatistical risk mapping
 ex8 Spatial sampling
 ex8a Spatial sampling: simulated spatial annealing
 ex9 R and GIS
R code for exercises
(compressed)Datasets for exercises
(compressed)Jura heavy metals, Cameroon soil properties, Sandford transect,Kansas aquifer
Preparation for MSc research¶
This material is from a threeweek module, preparing students to enter the research phase of their MSc. These are from the last time I presented the course (Spring 2014), they may have been updated for the current course.
Lectures
(PDF, compressed)Science & research; Formulating research problems, objectives and questions; research frameworks; research methods; finding & evaluating information; citations & reference list; critical reading & abstracting; structured technical writing; technical English; ethics & professionalism in science; scientific inference; statistical inference for research; thesis quality;
Text
(PDF, compressed)Volume 1: Concepts; Volume 2: Skills
Exercises
(PDF, compressed)Literature review; Critical reading; Argumentation
Universidad Mayor de San Simón (Bolivia)¶
Preparación para la maestría¶
Herramientas para realizar una investigación
Enfoque a la realización de Tesis de Maestría del Centro de Levantamientos Aeroespaciales (CLAS). Material traducido y adaptado al Español por Ronald Vargas Rojas a partir del texto base en Inglés, ver arriba.
Last modified 30November2020