Courses

Cornell University (USA)

PLSCS/NTRES 6200: Spatial Modelling and Analysis

Intended for early-stage MSc and PhD students, and final semester undergraduates, who are using some kind of spatial or spatio-temporal analysis in their thesis projects.

Theory and practice of applying geo-spatial 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 geo-spatial data of interest to the student, ending in a student project linked to the student’s on-going or proposed thesis/exit project research.

• Course introduction and syllabus 2021 (PDF)

• Lecture slides used in the 2021 course (PDF)

  • Concepts of Space and Spatial Modelling

  • Introduction to the R Project for Statistical Computing

  • 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

  • Introduction to Raster GIS

  • Trend surfaces fitting by Ordinary and Generalized Least Squares and Generalized Additive Models

  • Empirical interpolation: thin plate splines

  • 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

  • Uncertainty and data quality in spatial modelling

  • Introduction to Geospatial Metadata

  • Sampling for natural resources and environmental modelling and monitoring

  • Time-series analyis; Space-time geostatistics

  • Areal Data Spatial Analysis

    Modifiable unit area problem, the “ecological” fallacy, local spatial correlation (Moran’s I etc.), GeoDa, Spatial Autoregressive (SAR) models

  • Data-driven methods for predictive modelling

    Modelling cultures; Classification and Regression Trees (CART); Random Forests (RF); Cubist; model tuning with caret

  • Point-pattern analysis

  • Model evaluation

  • Spatial stochastic simulation

  • Geographically Weighted Models

  • Big Data, Open Data

• Supplemental lecture slides (PDF)

  • Coördinate Reference Systems

  • Theory of Kriging

  • Introduction to Bayesian (geo)-statistical modelling

  • Clustering

  • Mapping from point observations

  • Instrumental variables in linear regression

    Some details not covered in “Areal Data Spatial Analysis” (see above)

  • Introduction to Correlation and Regression

  • Data Analysis Strategy

• Exercises

  These use techniques covered in Tutorials: spatial analysis

  • Trend surfaces

    • Northeast (USA) Climate R Dataset (RData)

  • 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.

• Lecture notes

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.

• Exercise: regional mapping of climate variables from point samples: Northeast China

University of Twente (NL)

Faculty of Geo-Information Science and Earth Observation

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)

  1. How to read the materials

  2. Lecture 1: What is geostatistics; Geostatistical computing

  3. Lecture 2: Exploring and visualizing spatial data

  4. Lecture 3: Modelling spatial structure from point observations

  5. Lecture 4: Spatial prediction from point observations (Part 1)

  6. Lecture 5: Spatial prediction from point observations (Part 2)

  7. Lecture 6: Assessing the quality of spatial predictions

  8. Lecture 7: Geostatistical risk mapping

  9. 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 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.

• 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 06-July-2021