Applied Spatial Analysis for Sustainable Urban Development

Spatial analysis is a crucial part of projects carried out in urbanism and landscape architecture and a core application domain for the field of geomatics. After having encountered basic spatial analysis with short targeted workshops in the first three quarters of their MSc tracks, Urbanism and Landscape Architecture students following this intradisciplinary course work on an integrated, end-to-end analytical assignment, combining, advancing and extending their data-related skill- and tool-sets, while focusing on advanced workflows. The course offers the opportunity to MSc Urbanism Landscape Architecture, and Geomatics students to advance their technical skills required for scalable, automated and reproducible spatial analyses, practicing working in an intra-disciplinary setting with a real-world assignment. All students have the chance to learn skills from the other tracks and in the process strengthen their analytical skills for their upcoming thesis.

The course will start with an introduction to multi-criteria decision analysis, typology construction and their corresponding workflows suitable for a sustainable spatial development challenge. The higher-level principles of scalability, automation and reproducibility, as well as their implementation through computational notebooks, are also introduced early in the course. The course emphasizes data visualization as a way to explore data iteratively, generate new knowledge and communicate project results. Students learn about different types of thematic analysis, such as spatial analysis, network analysis, and landscape analysis which they can integrate in their analytical workflows. To apply the workflows and analytical methods introduced throughout the quarter, students work in intra-disciplinary groups on a given location.

The workflows presented in the course involve open-source tools using graphical user interfaces, such as QGIS, as well as programming in R and/or Python for spatial analysis. In the era of open data, the course emphasizes the importance of leveraging accessible and shared datasets to enhance the quality and depth of spatial analyses. In addition, the course underscores the significance of transparent and collaborative research practices by incorporating a dedicated segment on publishing datasets, workflows, and results. MSc Urbanism, Landscape Architecture and Geomatics students will be given the opportunity to contribute to the broader academic community through the dissemination of their work.

Students keep track of their progress and present their final products in a group report set up as a computational notebook in a version control system. This allows them to reflect on their process throughout the quarter and the instructors to assess individual contributions in detail. Peer feedback as an essential component of the course is employed in the midterm and the thematic workshops.

A rubric will be used for grading. The rubric will be available in the course guide or on the course specific Bright Space page.

Students should achieve a minimum grade of 6.0 to pass this course.

Grades 5,0, 5,5 may be considered for a Repair. A Repair is provided when only one specific part (one Learning Objective) of the assignment is missing or insufficient. The student receives a well described written repair assignment. The assignment must be handed in by the student on Friday 12.00 h. in the white week between Q2 and Q3, thus in the week between academic weeks 2.10 and 3.1, or the first week of summer, thus week 5.1. This will be mentioned in the written repair assignment. The result of the repair assignment may only be Pass or Fail. In case of Pass a V is registered in Osiris. In case of a Fail the original grade between 5,0 and 5,5 is registered in Osiris.

In case of a final grade below 6,0 the student needs to reenroll for the course.