548-0926/04 – Spatial Statistics (PST)
| Gurantor department | Department of Geoinformatics | Credits | 10 |
| Subject guarantor | prof. Ing. Jiří Horák, Dr. | Subject version guarantor | prof. Ing. Jiří Horák, Dr. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2020/2021 | Year of cancellation | |
| Intended for the faculties | HGF | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The objective is to develop student’s knowledge of spatial statistics and increase his/her capability to apply advanced methods of spatial statistics in case studies with topics related to the theme of his/her PhD thesis.
Teaching methods
Lectures
Individual consultations
Summary
The course extends knowledge of spatial statistics. It contains explanation of theoretical models for dot spatial distribution, descriptive and inferentional statistical methods for main geometrical forms of representation of spatial objects with both single and multiple investigated phenomena, methods of hot/cold spot detection, spatial distribution optimization methods, methods of assessment of autocorrelation and anizotropy of spatial data, spatial regression models, multivariate techniques for spatial data and spatio-temporal methods.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Účast na konzultacích, seminární práce, ústní zkouška.
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:
Theoretical models for dot spatial distribution
Descriptive statistical methods for dots, lines, polygons
Inferentional statistical methods for 1 type of event
Inferentional statistical methods for multiple events
Identification and analysis of hot/cold spots, anomal places
Spatial distribution optimization methods, locational and allocation tasks.
Gravity theory and its utilization for spatial applications
Methods of assessment of autocorrelation and anizotropy of spatial data
Spatial regression models – global, local
Multivariate techniques for spatial data – usage of standard methods as well as spatial variants of methods.
Spatio-temporal predictions.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.