Cornell University (USA)¶
PLSCS/NTRES 6200: Spatial Modelling and Analysis applied to agronomic and environmental systems¶
Intended for early-stage MSc and PhD students, and final semester undergraduates, who are using some kind of spatial analysis in their thesis projects.
Theory and practice of applying geo-spatial data for resource inventory and analysis, biophysical process modeling, and land surveys. 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 geo-spatial data of interest to the student, ending in a comprehensive student project.
Lecture slides used in the current course (PDF)
A universal model of spatial variation; detecting and modelling spatial autocorrelation; variogram models; spatial prediction; Ordinary Kriging; Universal Kriging; Kriging with External Drift
Includes a brief introduction to QGIS
Modifiable unit area problem, the “ecological” fallacy, local spatial correlation (Moran’s I etc.), GeoDa, Spatial Autoregressive (SAR) models
Modelling cultures; Classification and Regression Trees (CART); Random Forests (RF); Cubist; model tuning with
Supplemental lecture slides (PDF)
Some details not covered in “Areal Data Spatial Analysis” (see above)
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This course was last given in 1994; it is thus somewhat outdated but still can be a useful starting point.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 eight-week course for first-year 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)¶
Geostatistics & Open-source statistical computing¶
Distance education course, equivalent to a full-time 3-week module, last given in 2015. Here are the lecture slides (including self-assessement questions) and the set computer exercises, a bit outdated but could still be useful.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
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 Normal-score 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 three-week 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.
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;
Volume 1: Concepts; Volume 2: Skills
Literature review; Critical reading; Argumentation
Universidad Mayor de San Simón (Bolivia)¶
Preparación para la maestría¶
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 04-May-2020