
Course Overview
This class will focus on data analytics and professional practice in Geographic Information Systems. Students will participate in a collaborative data challenge project to engage with graduate students on a global-scale geospatial analysis problem. Penn State MGIS students will collaborate with graduate students from ITC - University of Twente located in Enschede, Netherlands to develop solutions to analyze spatio-temporal patterns in refugee migration data. Students will have the opportunity to present their work and develop new connections with EU geospatial professionals via site visits to European national mapping agencies. Students will work in teams to tackle this global-scale data set and use geospatial analytics to arrive at a solution to visualize patterns over space and time.
Course Instructors
What will be expected of you?
This course follows a blended learning environment. The first two weeks of the course feature collaborative learning at a distance. Penn State students will spend those weeks engaged in problem-solving activities via an online presence. The next two weeks feature Penn State students traveling to Enschede, Netherlands to spend time in an immersive environment collaborating with ITC students. The first week will be spent traveling to national mapping agencies of the EU. The second week will be spent collaborating with graduate students at ITC. Following this two-week travel abroad, Penn State students will return to their respective homes and further develop the capstone deliverable in both written and video format.
Course Objectives
At the successful completion of this course, you should be able to:
- independently develop a workable solution to a geospatial problem
- work with a large geospatial database
- integrate powerful visualization and computational tools (such as Geospatial Analytics) to help provide a solution
- synchronously and asynchronously collaborate with peers to provide feedback and listen to critiques
- formally present a solution to a large-scale geospatial problem
