Spatial Data Science

Knowledge

None

Description

Students learn how to handle geospatial data, including spatial data formats, analysis methods, and map visualization. These topics are elaborated in the course of real-world examples in order to illustrate their practical relevance.

Learning objectives

After successfully completing the course, students will be able to

•    Explain spatial data structures, standards, spatial as-sociation and autocorrelation
•    Apply exploratory spatial data analysis (ESDA) and spatial statistical analysis
•    Explain map design: How to build appropriate and beautiful maps
•    Critically evaluate the interdisciplinary nature of geospatial analysis and visualization 

Comment

Content:
•    How spatial is special: spatial association and autocorrelation
•    Basic spatial analysis methods and spatial statistics
•    Spatial data integration, service-based data access, spatial data types
•    Map design: How to build correct and beautiful maps (map design principles, visual perception)
•    Real-world research examples: spatial analysis of human-generated data (geo-social media posts, physiological sensor data, etc.)
•    Discussion: the interdisciplinary, cross-cutting nature to geospatial analysis and visualization