290002 PS Spatial Data Science (2023W)

This course will introduce students to Spatial Data Science as a "fourth paradigm" of science. The course will outline all stages from the initial conceptualization and representation of data to their creation, entry, cleaning, and analysis, e.g., using clustering or classification. We will discuss classics such as DBSCAN, develop a first understanding of issues of computational complexity, and also cover FAIR principles (findability, accessibility, interoperability, and reusability) in (research) data management.

The course will also introduce foundational literature about different kinds of regions in geography and then introduce a novel, data-synthesis-based method to replicate the findings of these classical papers in order to show applications of Spatial Data Science.

Topics will include introductory materials in knowledge representation, data engineering, geographic information retrieval, clustering, classification, and so on. We will also touch on several application areas, such as social sensing and place recommendations.

Students will then develop their own experiments (as a series of assignments) and present them during the class.