548-0115/04 – Uncertainty in Geoinformatics (NEGET)

Gurantor departmentDepartment of GeoinformaticsCredits5
Subject guarantorprof. Ing. Jiří Horák, Dr.Subject version guarantorprof. Ing. Jiří Horák, Dr.
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
HOR10 prof. Ing. Jiří Horák, Dr.
RUZ02 Ing. Kateřina Růžičková, 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 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



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:

CAERS J. Modeling uncertainty in the Earth Sciences. Wiley-Blackwell, 2011. ISBN 978-1-119-99263-9 DEVILLERS R., JEANSOULIN R. (Eds): Fundamentals of spatial data quality. London: ISTE. 2006. SHI W. Principles of Modeling Uncertainties in Spatial Data and Spatial Analysis. CRC Press (Taylor & Francis) 2010. WORBOYS M., DUCKHAM M. Geographic Information Systems: A Computing Perspective (2nd Edition), CRC Press, Boca Raton, Florida, 2004. ISBN 0415283752

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

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. Students also work on individual tasks. Tasks are frequently based on understanding of previous, simpler tasks.


Other requirements

No additional requirements are imposed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1. Definition of main concepts and ideas for understanding uncertainty in geoinformatics. 2. Imprecision, accuracy, vagueness, ambiguity 3. Error, reliability, error evaluation, sampling 4. Error propagation, Monte Carlo simulation. 5. Data quality and description, elements of data quality. 6. Standardization of data quality description, storage. 7. Organisation of data collection, methods. 8. Sources of uncertainty and methods of description. Sensitivity analysis. 9. Measurement of vagueness. Fuzzy set, fuzzy numbers, operations with fuzzy sets. 10. Spatially uncertain objects, fuzzy spatial operation. 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. Uncertainty visualization. UVI concept. Intrinsic and extrinsic variables.

Conditions for subject completion

Full-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 (67) 18 3
                písemná zkouška Written examination 50  18
                ústní zkouška Oral examination 17  0
Mandatory attendence participation: Continuous check of processing tasks during exercises. Written and oral examination.

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Conditions for subject completion and attendance at the exercises within ISP: Materials for an individual study are available at http://homel.vsb.cz/~hor10/Vyuka/ where you can find also topics for the exam. Consultations (both personal and online) with the lecturer are possible. The exercises are individual based on a semester project which has to be completed to the end of the exam period for the given semester. The exam is conducted only in person.

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Occurrence in study plans

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

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

Předmět neobsahuje žádné hodnocení.