Applied Spatial Data Science.

Catalog description

Students will learn to apply numerous spatial data science skills and tools to solve real-world problems and model geographic phenomena. Material will include theoretical and conceptual underpinnings of the methods and techniques, as well as hands on application in R. Topics include R as a GIS, spatial neighborhoods, spatial autocorrelation, regression, clustering, interpolation, and geovisualization. Real world examples and data spanning physical, social, and health sciences are used throughout the course to promote contextual learning.