GGR376H5 Spatial Data Science II

GGR376 Course Desciption

Course Description

This course builds on spatial data analysis and quantitative methods introduced in GGR276, and aims to provide a broad study of advanced statistical methods and their use in a spatial context in physical, social and environmental sciences. The course covers theories, methods and applications geared towards helping students develop an understanding of the important theoretical concepts in spatial data analysis, and gain practical experience in application of spatial statistics to a variety of physical, social and environmental problems using advanced statistical software.

Credits: Science

Lecture hours: 24

Practicum hours: 24

Prerequisite: 9.0 credits including GGR276H5 or permission of instructor

Building on GGR276, this course is a deeper dive into regression and spatial analysis, which are highly powerful and flexible tools, but presents a variety of potential pitfalls. This course will challenge students to recognize and solve common statistical problems using advanced statistical software.

Dr. Matthew Adams

Core Skills Developed

  • data visualization
  • spatial regression
  • clustering
  • interpolation and spatial interaction
  • introduction to machine learning