548-0952/02 – Spatio-temporal Data Analysis (CSAD)
| Gurantor department | Department of Geoinformatics | Credits | 10 |
| Subject guarantor | prof. Ing. Igor Ivan, Ph.D. | Subject version guarantor | prof. Ing. Igor Ivan, Ph.D. |
| 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
Knowledge demonstrated at the end of the course:
Orientation in methods of time series analysis, spatiotemporal analysis and spatiotemporal clustering.
Skills demonstrated at the end of the course:
Ability to apply time series analysis and spatiotemporal analysis and clustering in solving practical tasks.
Teaching methods
Lectures
Individual consultations
Other activities
Summary
The aim of this course is to present the processing of spatiotemporal data. The introductory part of the course presents the problems of time series and selected methods of their visualization, analysis and modeling (decomposition, regression models, exponential models, ARIMA and SARIMA). Furthermore, key aspects of spatiotemporal data and methods of their exploratory analysis are presented. Methods of analysis of spatiotemporal data and spatiotemporal clustering are presented, which form a key part of this course. Lectures are also devoted to the issue of visualization of spatiotemporal data.
Compulsory literature:
Recommended literature:
CRESSIE, N., WINKLE, C.K. Statistics for Spatio-Temporal Data. Wiley, 2011, 624 p.
SHERMAN, M. Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties. Wiley, 2010, 294 p.
LEVINE, N. CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations (v 4.02). Ned Levine & Associates, Houston, Texas, and the National Institute of Justice, Washington, D.C. August. 2015.
ANSARI, M.Y., AHMAD, A., KHAN, S.S., BHUSHAN, G., MAINUDDIN. Spatiotemporal clustering: a review. Artificial Intelligence Review, 2020, 53:2381–2423, https://doi.org/10.1007/s10462-019-09736-1.
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:
Introduction to time series.
Decomposition of time series.
Methods of time series analysis - regression modeling.
Methods of time series analysis - exponential modeling.
Methods of time series analysis - ARIMA.
Methods of time series analysis - SARIMA models.
Introduction to spatiotemporal data.
Exploratory analysis of spatiotemporal data.
Spatio-temporal analyzes.
Spatio-temporal clustering.
Visualization of spatiotemporal data.
New directions in spatio-temporal analyzes.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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