548-0115/01 – Uncertainty in Geoinformatics (NEGET)
| Gurantor department | Department of Geoinformatics | Credits | 5 |
| Subject guarantor | prof. Ing. Jiří Horák, Dr. | Subject version guarantor | prof. Ing. Jiří Horák, Dr. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2016/2017 | Year of cancellation | 2022/2023 |
| Intended for the faculties | HGF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The objective is to explain basic concepts of uncertainty, the role of uncertainty during spatial data processing and spatial modelling, to learn how to apply suitable methods for performing spatial data analysis, to be able to integrate information from other application field with approaches recommended for control and management of uncertainty, and to evaluate the data quality.
Teaching methods
Lectures
Tutorials
Summary
Introduction to typology of uncertainty and its application in geoinformatics. Eplanation of basic concept such as imprecision, vagueness, ambiguity. Explanation of error types, reliability and its measurement, error evaluation for quantitative and qualitative data, error propagation. Description and explanation of data quality which are used in metadata. Dealing with sources of uncertainty and methods of description. Explanation of fuzziness (fuzzy set, operation, fuzzy region, topological and other spatial operation. Dealing with qualitative measurements of uncertainty, multivalue logic, endorsement theory, Bayes theory and Dempster-Shafer theory. Introduction to uncertainty visualization.
Compulsory literature:
Recommended literature:
CAHA, J. Uncertainty Propagation in Fuzzy Surface Analysis. PhD thesis, Palacky University in Olomouc, 2014
HEUVELINK, G.B.M., BROWN, J.D., VAN LOON, E.E. A probabilistic framework for representing and simulating uncertain environmental variables. International Journal of Geographic Information Science, 2006, 2/5, p. 497-513.
KINKELDEY, C., SENARATNE, H. Representing Uncertainty. The Geographic Information Science & Technology Body of Knowledge (2nd Quarter 2018 Edition), John P. Wilson (ed.). DOI:10.22224/gistbok/2018.2.3
MASON J.S., RETCHLESS D., KLIPPEL A. Domains of uncertainty visualization research: a visual summary approach, Cartography and Geographic Information Science, 2016. DOI: 10.1080/15230406.2016.1154804
Additional study materials
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
No additional requirements are applied.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Definition of main concepts and ideas for understanding uncertainty in geoinformatics.
2. Error, reliability, error evaluation, sampling
3. Error propagation, Monte Carlo simulation.
4. Data quality and description, elements of data quality.
5. Standardization of data quality description, storage.
6. Organisation of data collection.
7. Sources of uncertainty and methods of description.
8. Sensitivity analysis.
9. Fuzzy set, fuzzy numbers, region, fuzzy spatial operation
10. Rough sets theory.
11. Qualitative measurements of uncertainty - Revision of belief. Multivalue logic. Endorsement theory.
12. Quantitative measurements of uncertainty - Bayes theory. Dempster-Shafer theory.
13. Validity and objectiveness.
14. Uncertainty visualization
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction