548-0142/02 – Visual Analytics (VIAN)

Gurantor departmentDepartment of GeoinformaticsCredits4
Subject guarantorprof. Ing. Igor Ivan, Ph.D.Subject version guarantorprof. Ing. Igor Ivan, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Study languageEnglish
Year of introduction2021/2022Year of cancellation
Intended for the facultiesHGFIntended for study typesFollow-up Master
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 Credit and Examination 2+2
Part-time Credit and Examination 8+8

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



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

Students are asked about knowledge from areas that they should have already known from previous lectures. They also work on individual tasks. They must pass writing and oral exam.


Other requirements

No other requirements are defined.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1) Introduction to visual analytics. Basic concepts. 2) Summary of acquired knowledge in the field of visualization. 3) Basic principles of interactive visualization. 4) Computational methods in visual analytics. 5) Visual analytics for data investigating and processing. 6) Visual analytics for understanding multivariate and graph data. 7) Visual analytics for understanding temporal and spatial data. 8) Visual analytics for understanding spatial events data 9) Visual analytics for understanding spatial time series 10) Visual analytics for understanding trajectories and mobility data 11) Visual analytics for understanding text, images and video. 12) Design, comparison and evaluation of visualization techniques. 13) Interacting with visualization.

Conditions for subject completion

Part-time form (validity from: 2021/2022 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 33  17
        Examination Examination 67  34 3
Mandatory attendence participation: Attendance in at least 80 % of seminars according to Study and Examination Regulations for Study in Bachelor’s and Master’s Degree Programmes.

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Conditions for subject completion and attendance at the exercises within ISP: Instead of attending lectures, it is necessary to study the materials that are given for the course. In order to receive credit, the student must complete two separate tasks (according to the lecturer's assignment)

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0532A330039) Geoinformatics AGI K Czech Ostrava 1 Compulsory study plan
2024/2025 (N0532A330039) Geoinformatics AGI P Czech Ostrava 1 Compulsory study plan
2024/2025 (N0532A330044) Geoinformatics AGI P English Ostrava 1 Compulsory study plan
2023/2024 (N0532A330039) Geoinformatics AGI K Czech Ostrava 1 Compulsory study plan
2023/2024 (N0532A330039) Geoinformatics AGI P Czech Ostrava 1 Compulsory study plan
2023/2024 (N0532A330044) Geoinformatics AGI P English Ostrava 1 Compulsory study plan
2022/2023 (N0532A330039) Geoinformatics AGI P Czech Ostrava 1 Compulsory study plan
2022/2023 (N0532A330039) Geoinformatics AGI K Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

2023/2024 Winter
2022/2023 Winter