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Lecturer(s)
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Macků Karel, Mgr. Ph.D.
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Dobešová Zdena, doc. Ing. Ph.D.
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Course content
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1. Advanced exploratory data anylsis 2. Spatial statistics 3. Geographically weighted methods 4. Spatial regression models 5. Logistic regression 6. Application of fuzzy sets theory in GI 6. Application of information theory in GI 7. Shape and spatial metrics for data analysis in GIS 8. Application of fractal geometry in GI 9. Kernel density estimation
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Laboratory Work
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Learning outcomes
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The course provides basic information in the field of geodata preparation and advanced geodata processing using various computational methods. The presented theoretical knowledge and procedures will be the basis for independent experiments of students with knowledge discovery in databases.
Application of advanced data mining and analytical methods of spatial data.
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Prerequisites
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Basic knowledge of KGI / DATAM Data Mining course.
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Assessment methods and criteria
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Seminar Work, Written exam
Theoretical and practical knowledge of presented topics.
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Recommended literature
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Lampart, M, Horák, J, Igor I. (2013). Úvod do dynamických systémů: teorie a praxe v geoinformatice. Ostrava.
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Pászto V. (2015). Prostorová informace a vybrané metody geocomputation pro její hodnocení, doktorská práce. Olomouc.
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Shekhar S., Xiong H., Zhou X. (2017). Encyclopedia of GIS. Springer.
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Witten IH, Frank F, Hall AH. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kauf.
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