| Course Unit Code | 548-0115/03 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit |
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| Name of Lecturer(s) | Personal ID | Name |
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| HOR10 | prof. Ing. Jiří Horák, Dr. |
| Summary |
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| 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. |
| Learning Outcomes of the Course Unit |
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| 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. |
| Course Contents |
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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. |
| Recommended or Required Reading |
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| Required Reading: |
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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 |
HORÁK J. Neurčitost v geoinformatice. Ostrava : Vysoká škola báňská - Technická univerzita Ostrava, 2020. 218 s. ISBN: 978-80-248-4378-0.
CAERS J. Modeling uncertainty in the Earth Sciences. Wiley-Blackwell, 2011. ISBN 978-1-119-99263-9
PÁSZTO V.: Prostorová informace a vybrané aspekty geocomputation pro její hodnocení. UP Olomouc, 2015. ISBN 978-80-244-4824-3
NOVÁK, V. Základy fuzzy modelování. Praha: BEN – technická literatura, 2002. 176 s. ISBN 80-7300-009-1 |
| Recommended Reading: |
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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 |
KUBÍČEK P.: Vybrané aspekty vizualizace nejistoty geografických dat. Habilitační práce. Univerzita obrany Brno, 2012.
LAMPART M., HORÁK J., IVAN I. Úvod do dynamických systémů: teorie a praxe v geoinformatice. VŠB-TU Ostrava, 2013. 200 s. ISBN 978-80-248-3185-5.
HENDL J.: Přehled statistických metod zpracování dat: analýza a metaanalýza dat. Portál, 2006. 80-7367-123-9
DEVILLERS R., JEANSOULIN R. (Eds): Fundamentals of spatial data quality. London: ISTE. 2006. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 33 | 17 |
| Examination | Examination | 67 (67) | 18 |
| písemná zkouška | Written examination | 50 | 18 |
| ústní zkouška | Oral examination | 17 | 0 |