Spatial Data Science

Catalog description

Spatial Data Science explores the application of statistical, computational, and geographic principles to analyze and derive insights from spatial data. This class serves as an advanced Geographic Information Science (GIS) course focusing on three important stages of geographic information: modelling, analyzing, and visualization of spatial information. This course is designed for students interested in leveraging spatial data science techniques to analyze, visualize, and interpret spatial data for decision-making and problem-solving in diverse fields such as geography, environmental science, urban planning, and public health. Through lab work and foundational lectures, this course covers the guiding principles behind various facets of Spatial Data Science including big data, cluster analysis, geosimulation, pattern recognition, deep learning, network analysis, and geovisualization. You will have the opportunity to explore cutting-edge spatial data science techniques and apply them to real-world problems across multiple disciplines