548-0952/01 – Spatio-temporal Data Analysis (CSAD)

Gurantor departmentDepartment of GeoinformaticsCredits10
Subject guarantorprof. Ing. Igor Ivan, Ph.D.Subject version guarantorprof. Ing. Igor Ivan, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageCzech
Year of introduction2020/2021Year of cancellation
Intended for the facultiesHGFIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
IVA026 prof. Ing. Igor Ivan, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 20+0
Part-time Examination 20+0

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

Individual consultations
Other activities


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:

KULLDORFF, M. SaTScan User Guide. 2021. 119 p. http://www.satscan.org/ SHMUELI, G., LICHTENDAHL, K.C. Practical Time Series Forecasting with R: A Hands-On Guide, Axelrod Schnall Publishers, 2nd edition, 2016, ISBN 978-0997847918. SHI, Z., PUN-CHENG, L.S.C. Spatiotemporal Data Clustering: A Survey of Methods. ISPRS International Journal of Geo-Information, 2019, 8, 112; doi:10.3390/ijgi8030112. ROGERSON, P., YAMADA, I. Statistical Detection and Surveillance of Geographic Clusters. Chapman and Hall/CRC, 2008, 324 p.

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.

Way of continuous check of knowledge in the course of semester

Účast na konzultacích, seminární práce, ústní zkouška


Other requirements

No additional requirements are imposed on the student.


Subject has no prerequisities.


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

Part-time form (validity from: 2020/2021 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Examination Examination   3
Mandatory attendence participation:

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Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2023/2024 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan
2023/2024 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2022/2023 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2022/2023 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0532D330037) Geoinformatics P Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0532D330037) Geoinformatics K Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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