Syllabus
Registration via LPIS
| Day | Date | Time | Room |
|---|---|---|---|
| Wednesday | 10/07/26 | 10:00 AM - 01:00 PM | D4.0.133 |
| Wednesday | 10/14/26 | 10:00 AM - 01:00 PM | D5.1.002 |
| Wednesday | 10/21/26 | 10:15 AM - 01:15 PM | D3.0.222 |
| Wednesday | 10/28/26 | 10:00 AM - 01:00 PM | D5.1.002 |
| Wednesday | 11/11/26 | 10:00 AM - 01:00 PM | EA.5.030 |
| Wednesday | 11/18/26 | 10:00 AM - 01:00 PM | TC.4.14 |
| Wednesday | 11/25/26 | 10:00 AM - 01:00 PM | EA.5.030 |
| Wednesday | 12/02/26 | 10:00 AM - 01:00 PM | TC.4.16 |
| Wednesday | 12/09/26 | 10:00 AM - 01:00 PM | TC.4.16 |
| Wednesday | 12/16/26 | 10:00 AM - 01:00 PM | EA.5.044 |
| Wednesday | 01/13/27 | 10:00 AM - 01:00 PM | TC.3.06 |
| Wednesday | 01/20/27 | 10:00 AM - 01:00 PM | TC.3.07 |
This course critically engages with topics on transforming cities and regions and provides the methodological tools to visualize and analyze spatial data. The course will show how changing historical-economic contexts such as globalization, theoretical developments in the fields of urban and regional economics and economic geography, as well as empirical analyses and results have influenced spatial policies.
A key feature of this course is the translation of theoretical concepts and ideas into empirical research. Students will learn and perform applied spatial analysis! The course will introduce students to publicly available spatial data from different sources, as well as software tools (R, GeoDa, GIS, etc.) for obtaining and manipulating spatial boundary (map) files. Furthermore, students will be taught to independently apply various techniques (e.g. cluster mapping, spatial econometrics) to empirically investigate topics on transforming cities and regions (transport, inequality, etc.).
- Understand the relevance of a spatial approach to social sciences
- Evaluate the strengths and weaknesses of various methodologies
- Present and discuss relevant data sources and papers
- Understand the uniqueness of spatial data
- Learn about and work with publicly available geospatial data
- Learn and apply spatial methodological tools to visualize, identify, and explain spatial patterns and processes
Attendance is mandatory: All classes in continuous assessment (PI) courses must be attended. Students must attend at least 9 of the 12 sessions, including mandatory presence during the first and final unit. Absences should be communicated in advance. Documentation (such as a doctor's note) will be required. However, missing more than three classes (with an excuse) or more than one class (without an excuse) will result in failure of the course. Absences due to work commitments are not considered valid reasons for missing classes.
The course includes lectures, discussions, student presentations, and exercises in data handling and applied spatial analysis in R and GeoDa. Students are required to bring their own laptops to every class.
In order to pass the course, students are expected to attend and participate in all lectures and seminars. The total grade will consist of
- 30% Readings and presentations
- 10% Participation in class (e.g. in discussions and seminars)
Empirical group work
- 5% Research Proposal I: one-paragraph summary of idea and research question
- 5% Research Proposal II: two-page summary (including topic, literature, data, method, sources)
- 10% Final Report Presentation
- 40% Research Report
Grading:
| points | grade |
|---|---|
| ≥87.5 | excellent |
| 75.0 - <87.5 | good |
| 62.5 - <75.0 | satisfactory |
| 50.0 - <62.5 | sufficient |
| <50 | fail |
There is NO EXAM for this module. Instead, there are a number of assessed components, including participation in discussions, brief summaries of readings, presentations, a final group presentation, and a group research report.
Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.
Back