This course, offered by the University of Illinois at Urbana-Champaign, provides a comprehensive introduction to CyberGIS—Geospatial Information Science and Systems (GIS) based on advanced cyberinfrastructure. Through a series of modules, students will explore the state-of-the-art in high-performance computing, big data, and cloud computing in the context of geospatial data science, emphasizing cutting-edge advances in cyberGIS and its underlying principles.
The course comprises the following modules:
Students will delve into geographic information science and systems, cyberinfrastructure, geospatial big data, geospatial data science, scientific applications, geospatial visualization using Python, geospatial object manipulation, and theoretical foundations. Through hands-on activities and quizzes, learners will gain practical skills in geospatial data analysis, visualization, and manipulation using cutting-edge tools and technologies. Upon completion, students will be equipped to harness the power of cyberGIS and geospatial data science in various research and application domains.
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Get Started / More Info
Getting Started with CyberGIS consists of five modules that cover topics such as cyberinfrastructure, geospatial big data, geospatial visualization using Python, geospatial object manipulation, and theoretical foundations and future trends. Through hands-on activities and quizzes, students will gain practical skills in geospatial data analysis, visualization, and manipulation using cutting-edge tools and technologies.
Welcome to Getting Started with CyberGIS! This module provides an introduction to the course and includes a syllabus, discussion forums, profile updates, an orientation quiz, demographics survey, and general discussion forum. It sets the stage for the subsequent modules by familiarizing students with the course structure and expectations.
This module delves into the foundational concepts of CyberGIS, covering geographic information science and systems, cyberinfrastructure, geospatial big data, geospatial data science, and scientific applications. Learners will also explore module readings and resources, as well as test their knowledge through a module quiz.
Geospatial Visualization using Python introduces students to various tools and techniques for visualizing geospatial data, including plotting with Matplotlib, mapping with Cartopy and Basemap, web mapping with Mplleaflet and Folium, and geopandas plotting geometries and spatial operations. The module also includes readings, resources, and a quiz to reinforce learning.
This module focuses on geospatial object manipulation and an introduction to taming big data with Hadoop. Students will learn about vector operations with Shapely, raster manipulation with Raster.io, and the fundamentals of Hadoop, including the Hadoop Distributed File System, MapReduce paradigm, and Hadoop Streaming API. Readings, resources, and a quiz are provided for comprehensive learning.
Theoretical Foundations and Future Trends module covers theoretical foundations, applications, case studies, and future trends in CyberGIS and geospatial data science. Students will explore where the field is headed and the potential applications and impact of their learning. Readings, resources, and a final survey are included to gauge students' understanding and satisfaction with the course.
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