SemesterFall Semester, 2025
DepartmentJunior Class of Department of Land Economics-Land Management Program Junior Class of Department of Land Economics-Land Resources Planning Program Junior Class of Department of Land Economics-Geomatics Program
Course NameSpatial Decision Making
InstructorSTEPHAN VAN GASSELT
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule

About This Schedule

The semester is structured in two parts.

The first half builds a strong foundation in decision theory — moving from certainty and uncertainty into multi‑criteria decision‑making (MCDM) methods, weighting criteria, advanced approaches, and robustness testing.



The second half applies those concepts to spatial decision-making, introducing GIS, data integration, spatial modeling, workflow design, and an applied real‑world project.



Classes take place in the GIS lab (270610) for 3 hours per session. The hybrid format integrates theory and hands-on practice, supporting learners of varied backgrounds. All course content is in English; while prior experience with GIS and spatial data is helpful, curiosity and an analytical mindset are the most valuable prerequisites.















































































































Week Topic Content & Reading Assignment Teaching Activities & Homework
1 Course Introduction Overview, aims, and decision principles. Certainty-based decision problems. Guided certainty decision exercises.
2 Decision Under Certainty Classic frameworks, payoff tables, optimal strategies. Apply certainty techniques to practical problems.
3 Decision Under Uncertainty Maximin, Minimax, Laplace, Hurwicz criteria. Explore uncertainty approaches with problem sets.
4 Multi-Criteria Decision Making I: WSM, WPM, WASPAS Introduction to core additive and multiplicative MCDM methods. Concepts, strengths, and limitations. Work through example problems and compare results.
5 Public Holiday — —
6 Public Holiday — —
7 Criteria Weight Calculation + Sensitivity Analysis Weighting methods: AHP, Entropy, statistical approaches. Sensitivity testing with WSM to explore robustness. Compute weights and test effect on rankings.
8 Advanced MCDM Methods: A(H/N)P, TOPSIS, ELECTRE, PROMETHEE Advanced MCDM methods, outranking approaches; comparison of application contexts and strengths. Structured case study analysis.
9 Midterm Exam Week — —
10 Introduction to GIS and Spatial Data Principles of GIS, spatial data types, and sources. Basics of combining datasets. Practical spatial data integration tasks.
11 Data Integration and Spatial Joins Combining spatial and attribute data; spatial join techniques. Lab: integrate datasets and perform joins.
12 Spatial Weighted Modeling Applying weighted decision models to spatial problems; building custom solutions with GIS software. Develop and critique spatial models.
13 Complex Spatial Workflows & Extensions Designing advanced spatial workflows; addressing software limitations; custom modeling solutions. Develop and refine multi‑step workflows.
14 Assessments, Optimization & Machine Learning Evaluate spatial models; optimization concepts; intro to clustering, decision trees, and regression as analytic extensions. Discuss and trial explainable ML tools.
15 Project Use Case Environmental Impact Assessment (EIA) — applying integrated methods to a real‑world decision problem. Project lab work.
16 Final Exam Week Completion and presentation of the spatial decision-making project. Present and submit final project outcomes.




 




Teaching Methods
Teaching Assistant

The teaching assistant for Spatial Decision Making will be announced in due time.


Requirement/Grading

This course has a midterm and a final exam.

Homework assignments and bonus exercises will help to consolidate the obtained knowledge.




  • The midterm examination will be a portfolio of online questions and/or a project covering the theoretical foundations.

  • The final exam will be a project. In order to complete the final project successfully, students will set up a spatial decision-making project, and produce a workflow with evaluation criteria.


Textbook & Reference

All relevant material will be distributed during class.

 

There is currently no suitable textbook on the market for spatial decision making (plenty for decision making in general), and those that provide some background detail on this dynamic topic are outdated. The following contribution comes closest to the course aims and if you find it online or as hardcopy in a library, it does not hurt to take a look. All other material will be provided in class.



Sugumaran R, Degroote, J (2010): Spatial Decision Support Systems: Principles and Practices. - 469 pp, CRC Press. ISBN: 9781420062120.


Urls about Course
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