548-0052/01 – Data and Model Uncertainty (NEDAM)

Gurantor departmentDepartment of GeoinformaticsCredits5
Subject guarantordoc. Ing. Jiří Horák, Dr.Subject version guarantordoc. Ing. Jiří Horák, Dr.
Study levelundergraduate or graduate
Study languageCzech
Year of introduction2010/2011Year of cancellation
Intended for the facultiesHGFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
HOR10 doc. 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
Combined Credit and Examination 6+6

Subject aims expressed by acquired skills and competences

The aim of the subject is to educate students basic concepts of uncertainty and enable them to apply appropriate methods for spatial analysis and to integrate knowledge from various fields with methods of control and management of uncertainty.

Teaching methods

Lectures
Tutorials

Summary

Terminology (uncertainty, ambiguity, vagueness, fuziness, quality, accuracy, errors, reliability), semantic issues. Dominant concepts in dealing with uncertainty (inheritent complexity and details of the world and phenomena, inheritent vagueness of definitions and concept, missing natural units for analysis, ambiguity of indirect indicators). Sources of errors and uncertainty. Introduction to application of Monte Carlo method. Geographical uncertainty (crisp boundaries, location etc.), attribute uncertainty. Ecological falacy, MAUP and data agreggation. Spatial autocorrelation. Errors of vector-raster conversions. Error propagation (statistical aproach, simulation aproach). Error balancing. Internal and external validation. Senstivity analysis. Methods based on simulations or decomposing the variance of the output. Reliability and Survival in Econometrics and Finance. Metadata. Bayesian theory, Bayesian belief networks. Dempster-Shafer theory. Techniques for reducing, quantifying and visually representing uncertainty. Cost and benefits of uncertainty decreasing. Uncertainty of decision making.

Compulsory literature:

Shi W.: Principles of Modeling Uncertainties in Spatial Data and Spatial Analysis. CRC Press (Taylor & Francis) 2010. Zhang J.X., Goodchild M.F. (2002): Uncertainty in Geographic Information. New York. Taylor & Francis.

Recommended literature:

Longley, P.A., Goodchild M.F., Maguire D.J., Rhind D.W.: Geographical Information Systems and Science. Wiley, 2005 (s. 127-153) Burrough P., McDonnell A.: Principles of Geographical Information Systems. Oxford University Press 1998, 333 stran. s.220-264. Maguire, DJ, Batty M, Goodchild MF: GIS, Spatial Analysis and Modeling. ESRI 2005. s. 68-129. Andrea Saltelli, Stefano Tarantola, Francesca Campolongo and Marco Ratto (2004) SENSITIVITY ANALYSIS IN PRACTICE. A GUIDE TO ASSESSING SCIENTIFIC MODELS. Wiley. ISBN 0-470-87093-1.

Way of continuous check of knowledge in the course of semester

E-learning

Další požadavky na studenta

No additional requirements are imposed on the student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Terminology (uncertainty, ambiguity, vagueness, fuziness, quality, accuracy, errors, reliability), semantic issues. Dominant concepts in dealing with uncertainty (inheritent complexity and details of the world and phenomena, inheritent vagueness of definitions and concept, missing natural units for analysis, ambiguity of indirect indicators). Sources of errors and uncertainty. Introduction to application of Monte Carlo method. Geographical uncertainty (crisp boundaries, location etc.), attribute uncertainty. Ecological falacy, MAUP and data agreggation. Spatial autocorrelation. Errors of vector-raster conversions. Error propagation (statistical aproach, simulation aproach). Error balancing. Internal and external validation. Senstivity analysis. Methods based on simulations or decomposing the variance of the output. Reliability and Survival in Econometrics and Finance. Metadata. Bayesian theory, Bayesian belief networks. Dempster-Shafer theory. Techniques for reducing, quantifying and visually representing uncertainty. Cost and benefits of uncertainty decreasing. Uncertainty of decision making.

Conditions for subject completion

Full-time form (validity from: 2016/2017 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Exercises evaluation and Examination Credit and Examination 100 (100) 51
        Exercises evaluation Credit 33  17
        Examination Examination 67 (67) 18
                written examination Written examination 50  18
                ústní zkouška Oral examination 17  0
Mandatory attendence parzicipation:

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Combined form (validity from: 2011/2012 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Exercises evaluation and Examination Credit and Examination 100 (100) 51
        Exercises evaluation Credit 33 (33) 17
                projekt Project 33  0
        Examination Examination 67 (67) 18
                písemná zkouška Written examination 40  0
                ústní zkouška Oral examination 27  0
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2017/2018 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 2 Choice-compulsory study plan
2015/2016 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Choice-compulsory study plan
2015/2016 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Choice-compulsory study plan
2014/2015 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Choice-compulsory study plan
2014/2015 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Choice-compulsory study plan
2013/2014 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Choice-compulsory study plan
2013/2014 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Choice-compulsory study plan
2012/2013 (N3646) Geodesy and Cartography (3602T002) Geoinformatics (10) P Czech Ostrava 1 Choice-compulsory study plan
2012/2013 (N3646) Geodesy and Cartography (3602T002) Geoinformatics P Czech Ostrava 1 Choice-compulsory study plan
2012/2013 (N3646) Geodesy and Cartography (3602T002) Geoinformatics (10) K Czech Ostrava 1 Choice-compulsory study plan
2012/2013 (N3646) Geodesy and Cartography (3602T002) Geoinformatics K Czech Ostrava 1 Choice-compulsory study plan
2012/2013 (N3646) Geodesy and Cartography (3602T002) Geoinformatics (20) K Czech Ostrava 1 Choice-compulsory study plan
2012/2013 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P Czech Ostrava 1 Choice-compulsory study plan
2012/2013 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics K Czech Ostrava 1 Choice-compulsory study plan
2011/2012 (N3646) Geodesy and Cartography (3602T002) Geoinformatics (10) P Czech Ostrava 1 Choice-compulsory study plan
2011/2012 (N3646) Geodesy and Cartography (3602T002) Geoinformatics (20) P Czech Ostrava 1 Choice-compulsory study plan
2011/2012 (N3646) Geodesy and Cartography (3602T002) Geoinformatics (10) K Czech Ostrava 1 Choice-compulsory study plan
2011/2012 (N3646) Geodesy and Cartography (3602T002) Geoinformatics (20) K Czech Ostrava 1 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner