548-0954/01 – Visual Analytics (VISA)

Gurantor departmentDepartment of GeoinformaticsCredits10
Subject guarantorprof. Ing. Igor Ivan, Ph.D.Subject version guarantorprof. Ing. Igor Ivan, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageCzech
Year of introduction2020/2021Year of cancellation
Intended for the facultiesHGFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
IVA026 prof. Ing. Igor Ivan, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 20+0
Part-time Examination 20+0

Subject aims expressed by acquired skills and competences

- the student demonstrates knowledge of: • issues of visual analytics, • existing channels and marks in visualization, • suitable visualization tools according to the type of data, • interactivity options for data visualization, • automatic analytics support; - the student is able to: • choose a suitable visualization method for multivariate, temporal, spatial, textual and graph data, • evaluate a suitable visualization technique, • analyze large data using interactive visualization tools; - the student is able to: • decide on a suitable procedure based on the analyzed data and knowledge of visualization techniques, • interpret the results achieved.

Teaching methods

Individual consultations
Other activities


The course will explain to students the issue of visual analytics, which is becoming a highly attractive topic due to the development of large data sets. Students are acquainted with the history, development and current trends in this area. Possibilities of interactive data visualization based on their type using more common and non-traditional and complex approaches are presented. Emphasis is placed on determining the suitability of individual visualization methods and evaluating weaknesses and possible problems. Students are introduced to 2D and 3D space-time visualization methods and their use in geoinformatics.

Compulsory literature:

Andrienko N., Andrienko G., Fuchs G., Slingsby A., Turkay C., Wrobel S. Visual Analytics for Understanding Temporal Distributions and Variations. In: Visual Analytics for Data Scientists. Springer, Cham, 2020. https://doi.org/10.1007/978-3-030-56146-8_8 Munzner, T. Visualization Analysis and Design. A K Peters/CRC Press, 1 edition, 2014, ISBN 9781466508910. Ward, M. O.,‎ Grinstein, G.,‎ Keim, D. Interactive Data Visualization: Foundations, Techniques, and Applications. A K Peters/CRC Press, Second Edition, 2015, ISBN 9781482257373. Loth, A. Visual Analytics with Tableau, Wiley, 1st edition, 2019, ISBN: 9781119560203.

Recommended literature:

Andrienko, N., Andrienko, A. Visual Analytics of Movement. Springer, ISBN 978-3642375828. Andrienko, N., Andrienko, A. Exploratory Analysis of Spatial and Temporal Data. A Systematic Approach. Springer, 2006, ISBN 978-3-540-25994-7. Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F. Mastering the Information Age. Solving Problems with Visual Analytics. Eurographics Association, 2010, ISBN 978-3-905673-77-7. Tominski, C., Schumann, H. Interactive Visual Data Analysis. A K Peters/CRC Press, 2020, ISBN 9780367898755.

Way of continuous check of knowledge in the course of semester

Účast na konzultacích, seminární práce, ústní zkouška.


Other requirements

No additional requirements are imposed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

Introduction to visual analytics. Basic concepts. Summary of acquired knowledge in the field of visualization. Basic principles of interactive visualization. Computational methods in visual analytics. Visual analytics for data investigating and processing. Visual analytics for understanding multivariate and graph data. Visual analytics for understanding temporal and spatial data. Visual analytics for understanding spatial events data Visual analytics for understanding spatial time series Visual analytics for understanding trajectories and mobility data Visual analytics for understanding text, images and video. Design, comparison and evaluation of visualization techniques. Interacting with visualization. Advanced concepts in visual analytics.

Conditions for subject completion

Full-time form (validity from: 2020/2021 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Examination Examination   3
Mandatory attendence participation:

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Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan
2024/2025 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2023/2024 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan
2023/2024 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2022/2023 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2022/2023 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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