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Introduction to Geospatial Data Visualization II
Course Description

The recent introduction of mapping skills courses in numerous academic programs across various disciplines worldwide highlights the increasing importance of mapping as a fundamental literacy skill for a growing number of students. To optimize the learning outcomes for the CEU community, the 2-credit IGDV course is divided into two interconnected modules: IGDV-I (Basic) and IGDV-II (Advanced), each worth 1 credit. This flexible structure allows students to enroll in either or both of the modules.

These courses are available to both undergraduate and graduate students, with the workload adjusted accordingly. For MAs/PhDs, the workload consists of 600 classroom minutes per credit, while for BAs, it amounts to 720 minutes per credit.

Successful completion of these course is a prerequisite for the Syslab's winter semester courses, "Introduction to Geospatial Analysis" and "Earth Observations for Monitoring Sustainable Development Goals", as well as Syslab’s internship and Summer University programs.

The course will feature collaboration activities with relevant courses at the American University of Central Asia and Bard College through the OSUN Network Collaborative Course program.

Please note that no audit option is available for this course.


 Geospatial methods and mapping have gained popularity across various research areas due to their cross-disciplinary recognition and availability. The advancements in hardware and software have opened up new possibilities for researchers in different disciplines to enhance their traditional research methods. This course aims to build upon the students' basic understanding of GIS applications developed in the previous IGDV course (IGDV-I) and further advance their skills in the use of geospatial data and GIS. The sessions will provide both theoretical understanding and practical use of geospatial data and technologies for mapping societal and environmental phenomena. 

The course will primarily utilize qGIS, the most popular open-source GIS package, to showcase modern techniques of geospatial data collection, generation, and visualization. Students will learn advanced features of qGIS and how to utilize them for their own research. The course will also include guest lectures and workshops on mapping methods and applications. 


Learning Outcomes

By the end of the course, students should be able to:

  • Understand the variety of data mapping approaches, their principles, and the benefits of their usage.
  • Develop their own datasets based on different data sources such as statistics or expert knowledge.
  • Create detailed topical maps using advanced features of qGIS.
  • Consider alternative ways of collecting geospatial data, including online data mining and participatory science (geospatial data crowdsourcing).

The course assessment is based on the following criteria:

  • 30% Practical Sessions: Completion of several in-class exercises;
  • 70% Graded Individual Project: Development and presentation of a mapping project.

Students are required to select a topic for their mapping project from a provided list or suggest their own.


Attending the Introduction to Geospatial Data Visualization I, unless a student can demonstrate prior experience in geospatial visualization.

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