About the content
Sources, schedule, badges, and what is still to come
Where the content comes from
The app is built from the course’s own material:
- the session slides by Qinyun Lin (sessions 1–4, preliminary “v0” versions);
- the instructor’s Canvas posts for each session: topics, learning objectives and key questions;
- the course syllabus (learning outcomes, examination) and the literature list;
- the links in the Canvas posts and slides, collected on Resources (videos, books, web pages);
- lecture notes taken in class (topics marked ✏️; they also record that the exam tests concepts, not programming).
Explanations, quizzes, drills, simulated figures, R cells, why-questions and written model answers were written for this app. Simulations use made-up data. The course books are cited, not reproduced:
- Lawson, Banerjee, Haining & Ugarte (eds.) (2016). Handbook of Spatial Epidemiology. CRC Press. Main textbook.
- Anselin (2024). An Introduction to Spatial Data Science with GeoDa, Volume 1: Exploring Spatial Data. Chapman & Hall/CRC. Chapters 1–7, 10, 13, 16.
Neither book has been used for this version of the app yet.
How the badges were assigned
| Badge | Rule |
|---|---|
| 🎯 Instructor learning objective | The section teaches something stated as a learning objective: in the course syllabus (Session 1: role of place, spatial dependence and heterogeneity, bias from aggregation, measurement and missing data), the Canvas post (Sessions 2 and 4) or the “Learning goals” slide (Session 3) |
| ❓ Instructor’s key question | The section answers one of the key questions in the Session 4 Canvas post, or a question the instructor asked on a slide (for example “which is the box map?”, “is π̂ the average of all rates?”, the SAR plausibility and scenario slides) |
| ✏️ Emphasised in class | Written down in the lecture notes: spatial file types, DeSO, geocoding, polygon/area, GIS and GeoDa, the social-ecological and life-course models. Where a noted topic also matches an objective (MAUP, the definition), 🎯 is used |
| 📌 Likely exam-relevant (AI-inferred) | Inferred from definitions, formulas and worked examples on the slides. Not confirmed |
| 📎 Optional depth | Marked “optional depth” on the slide (PC priors), listed as not covered (Session 3 scope slide), or stated as not expected (INLA’s computational details) |
Course facts
STA530 Spatial Epidemiology, 3.5 credits, Sahlgrenska Academy, University of Gothenburg. Autumn 2026, 1 October – 3 November, 50% pace, taught in English.
| Examination | Credits | Grades |
|---|---|---|
| Individual written exam | 2.5 | U / G / VG |
| Active participation in a seminar | 1.0 | U / G |
To pass the course you need G on both; VG on the course needs VG on the written exam.
Software: R (with RStudio or Positron) and GeoDa, to be installed before 13 October.
Sessions so far
| Session | Date | Slide file | App page |
|---|---|---|---|
| 1 · Introduction to spatial epidemiology | 6 Oct, 13:15–15:00 | session_1_v0-1.pdf | Session 1 |
| 2 · Disease mapping and spatial patterns | 6 Oct, 15:15–17:00 | session_2_v0.pdf | Session 2 |
| 3 · Introduction to spatial regression | 9 Oct, 10:15–12:00 | session_3_v0-1.pdf | Session 3 |
| 4 · Introduction to Bayesian disease mapping | 9 Oct, 13:15–15:00 | session_4_v0.pdf | Session 4 |
Notes on the slides
- Session 3, slide 12 (toy SAR example): the slide shows a direct impact of about 1.019 and indirect impacts of about 0.116. The exact inverse of (I − 0.2W) gives 1.023 and 0.114. The total, 1.25 = β/(1 − ρ), agrees. The app uses the exact values.
- The slides are preliminary (“v0”) versions. If updated slides are posted, sections may be revised.
Still to come
- Later sessions, computer labs (GeoDa, R) and the seminar. Labs will be covered as concepts on made-up data, not as solutions.
- Readings from the two course books, if specific chapters are assigned per session.
- Any example exam questions from the instructor.
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