140.660.89
Introduction to Geospatial Statistics
Location
Internet
Term
Summer Institute
Department
Biostatistics
Credit(s)
1
Academic Year
2026 - 2027
Instruction Method
Online Asynchronous
Start Date
Monday, June 8, 2026
End Date
Friday, June 26, 2026
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Yes
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
Familiarity with basic statistical analysis and probability concepts, R programming
Enrollment Restriction
This course is not restricted.
This course is timely and essential due to the increasing reliance on geospatial data across fields such as climate science, epidemiology, and urban planning. Many students and practitioners who use spatial data would benefit from formal training in its core concepts, data structures, and analytical methods. This course fills that gap by providing a comprehensive foundation in geospatial data handling, visualization, and basic spatial analysis. It equips learners with practical skills and conceptual tools needed to work confidently with contemporary geospatial datasets in research and applied settings.
Provides a practical introduction to geospatial data and methods, with an emphasis on understanding and analyzing real-world spatial datasets. Begins with the basics of geospatial data types and formats, coordinate systems, map projections, and spatial data structures. Covers exploratory spatial data analysis and visualization, including map-making, spatial summaries, and detecting spatial patterns. Introduces core methods for geospatial analysis such as spatial smoothing and interpolation (e.g., variograms, Gaussian process and basic kriging ideas), spatial regression, Bayesian methods, and methods for large spatial data. Includes examples of usage of spatial software such as R, giving students practical skills to import, clean, visualize, and analyze geospatial data for applications in environmental science, public health, and urban studies.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Identify the types of spatial data and unique challenges of analyzing spatial data.
- Gain proficiency in core statistical methods for geospatial data visualization and analysis
- Conduct a wide variety of geospatial analyses in R
Upon successfully completing this course, students will be able to:
Methods of Assessment
This course is evaluated as follows:
- 20% Participation
- 80% Final Project