ADVANCED GEOCOMPUTING AND GEOSPATIAL BIG DATA ANALYTICS
Geospatial big data is an extension of big data with an emphasis on the geospatial component. It is used to describe large volumes of georeferenced data about various aspects of the environment and society captured by millions of environmental and human sensors. An introduction to the theory, techniques, and analytical methods for geospatial big data. Methods for storing, processing, analyzing, and visualizing various types of geospatial big data using advanced Python programming will be introduced. Designed for students who have programming experience and want to reinforce their programming skills and learn AI and machine learning methods for solving geospatial big data problems.
Prerequisites & course information
Requisites: GEOG 378 , COMP SCI 220 , or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness + Lab Breadth - Physical Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Summarize the fundamental concepts of geospatial big data. Audience: Both Grad & Undergrad 2. Describe how to develop computational models and analytical methods for geospatial big data. Audience: Both Grad & Undergrad 3. Identify the challenges in storing, managing, processing, analyzing, visualizing, and verifying the quality of data. Audience: Both Grad & Undergrad 4. Recall the major high-performance platforms for big data processing. Audience: Both Grad & Undergrad 5. Use Python programming for (spatiotemporal) data analysis and machine-learning tasks. Audience: Both Grad & Undergrad 6. Collaborate in teams and complete tasks under time pressure. Audience: Both Grad & Undergrad 7. Lead efforts on teamwork and geospatial problem solving skill development. Audience: Graduate 8. Demonstrate higher-order synthesis and spatial analysis skills in spatial data science. Audience: Graduate Learning Outcomes: 1. Summarize the fundamental concepts of geospatial big data. Audience: Both Grad & Undergrad 2. Describe how to develop computational models and analytical methods for geospatial big data. Audience: Both Grad & Undergrad 3. Identify the challenges in storing, managing, processing, analyzing, visualizing, and verifying the quality of data. Audience: Both Grad & Undergrad 4. Recall the major high-performance platforms for big data processing. Audience: Both Grad & Undergrad 5. Use Python programming for (spatiotemporal) data analysis and machine-learning tasks. Audience: Both Grad & Undergrad 6. Collaborate in teams and complete tasks under time pressure. Audience: Both Grad & Undergrad 7. Lead efforts on teamwork and geospatial problem solving skill development. Audience: Graduate 8. Demonstrate higher-order synthesis and spatial analysis skills in spatial data science. Audience: Graduate
Sources
- Course catalogue ↗2026-2027