290002 PS Spatial Data Science (2024W)

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 clustering via DBSCAN, outline common methods in point pattern analysis, review simple features as the foundation of vector data, develop a first understanding of issues of computational complexity, and also briefly cover FAIR principles (findability, accessibility, interoperability, and reusability) in (research) data management.

Topics will include introductory materials in knowledge representation, data engineering, geographic information retrieval (GIR), clustering, classification, and so on, thereby providing an introduction for future class work, e.g., in GIR. We will also touch on several application areas, such as social sensing and place recommendations.