548-0142/02 – Visual Analytics (VIAN)
| Gurantor department | Department of Geoinformatics | Credits | 4 |
| Subject guarantor | prof. Ing. Igor Ivan, Ph.D. | Subject version guarantor | prof. Ing. Igor Ivan, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | winter |
| | Study language | English |
| Year of introduction | 2021/2022 | Year of cancellation | |
| Intended for the faculties | HGF | Intended for study types | Follow-up Master |
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
Lectures
Tutorials
Summary
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:
Recommended literature:
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.
E-learning
Other requirements
No other requirements are defined.
Prerequisities
Subject has no prerequisities.
Co-requisities
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction