Course description: This course covers foundational concepts and theories in spatial statistics, key methodologies widely used in applications, machine and deep learning approaches for complex spatial and spatio-temporal data, and cutting-edge applications of spatial statistics in climate science, environmental studies, and related fields.
Students are required to complete assignments involving the analysis of spatial data using the methods and software packages introduced in class. In addition, each student will give a paper presentation on a topic related to spatial statistics.
Through this course, students will learn the core principles of spatial statistics and develop practical coding skills for visualizing, modeling, and analyzing spatial data. Moreover, they are encouraged to connect the methodologies and theories introduced in this course with knowledge gained from other courses and with their own research interests.
Students are required to complete assignments involving the analysis of spatial data using the methods and software packages introduced in class. In addition, each student will give a paper presentation on a topic related to spatial statistics.
Through this course, students will learn the core principles of spatial statistics and develop practical coding skills for visualizing, modeling, and analyzing spatial data. Moreover, they are encouraged to connect the methodologies and theories introduced in this course with knowledge gained from other courses and with their own research interests.
Dates offered:
-
Session time: 2025 Winter
Term: 2
Course outline:
STAT547P_Spatial_Statistics.pdf
Instructors: