548-0052/01 – Data and Model Uncertainty (NEDAM)
| 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 | Choice-compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2010/2011 | Year of cancellation | 2018/2019 |
| Intended for the faculties | HGF | Intended for study types | Follow-up Master |
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:
Additional study materials
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction