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Lecturer(s)
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Fačevicová Kamila, Mgr. Ph.D.
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Course content
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- Types of spatial dependent data and its preprocessing with a statistical software - Visualization of spatial data - Spatial correlation - Spatial point patterns - Areal data - Basics of spatio-temporal data analysis - Additional topics related to spatial dependency
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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The aim is to acquaint students with the basic procedures in the analysis of spatial dependent data.
The student will be able to independently analyze spatial dependent data, to visualize and interpret the results.
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Prerequisites
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Basics of probability theory, statistics, and work with a statistical software.
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Assessment methods and criteria
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Oral exam, Student performance, Seminar Work
Seminar work and an oral exam. Active participation.
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Recommended literature
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Brundson, C., Comber, L. (2019). An Introduction to R for Spatial Analysis and Mapping. Sage.
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Fischer, M. M., Getis, A., eds. (2010). Handbook of Applied Spatial Analysis.
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Fotheringham, A. S., Brunsdon, C., Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships.
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Haining, R., Li, G. (2020). Modelling Spatial and Spatial-Temporal Data - A Bayesian Approach.
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R. S. Bivand, E. J. Pebesma, V. Gómez-Rubio. (2013). Applied Spatial Data Analysis with R.
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