548-0044/07 – Spatial Data Analysis (PAD)

Gurantor departmentDepartment of GeoinformaticsCredits5
Subject guarantorprof. Ing. Jiří Horák, Dr.Subject version guarantorprof. Ing. Jiří Horák, Dr.
Study levelundergraduate or graduateRequirementCompulsory
Study languageEnglish
Year of introduction2018/2019Year of cancellation
Intended for the facultiesHGFIntended for study typesFollow-up Master, Bachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
HOR10 prof. Ing. Jiří Horák, Dr.
JUR02 Ing. Lucie Orlíková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 6+6

Subject aims expressed by acquired skills and competences

The objective is to learn student how to utilize selected methods of spatial analysis not included in other courses. It is focused on circular statistics, modelling of spatial distribution of events and relevant inferential methods to analyse their randomness, including multiple events. Large attention is dedicated to graph theory and its application for spatial tasks, statistical description of networks (local and global measures), selected tasks in graphs. Students get acqauinted with locational and alocational tasks, utilization of gravity theory, selected analysis for areal data, multivariate techniques for spatial data and a logistic regression.

Teaching methods



The subject represents an advanced course of spatial analytical methods. It contains descriptive statistics for dots, circular statistics, methods of modelling of spatial distribution of events, inferential methods to analyse randomness of single dots as well as multiple events, explains basic terms of the graph theory, indicators used for local and global description of networks namely social networks, it introduces to the evaluation of transport accessibility, it explains selected tasks in graphs, basic methods for locational and alocational tasks, utilization of gravity theory, introduces selected analysis for areal data and multivariate techniques for spatial data, a basic predictive model for categorised variables using the logistic regression.

Compulsory literature:

Hilbe, J.M. Practical guide to logistic regression. CRC Press/Taylor & Francis, Boca Raton, 2016. S. 158. ISBN 978-1-4987-0957-6 Newman, M. Networks: an introduction. Oxford University Press. 2010 Rogerson, P. Statistical methods for geography, 5th ed. SAGE, LA, 2019. S. 405. ISBN 978-1-5264-9880- Smith M.J., Goodchild M.F., Longley P.A. Geospatial Analysis. 2011. Dostupné na http://www.spatialanalysisonline.com

Recommended literature:

Anselin L., Florax R., Rey S. (Eds.): Advances in Spatial Econometrics. Springer, 2004, pp. 51, 3ISBN 3540437290. Barabási, A.: Network Science. The Barabási-Albert Model. 2012. Dostupné na http://barabasi.com/f/622.pdf Batschelet, E.: Circular Statistics in Biology. Academic Press, 1981, London. Hosmer, D.W., Lemeshow, S., Sturdivant, R.X.. Applied logistic regression, Third edition. ed, Wiley series in probability and statistics. Wiley, 2013. S. 528. ISBN 978-1-118-54835-6

Way of continuous check of knowledge in the course of semester


Other requirements

No additional requirements are imposed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1) Definition, history and objectives of spatial analysis, review of usual methods, review of spatial statistical methods. 2) Points. Spatial statistics for point pattern. Inferential statistical tests for point pattern. Quadrat tests. Nearest neighbor methods. Ripley’ K function. 3) Modelling of point spatial patterns – theoretical models. 4) Point pattern transformation into a continuos field (raster model, kernel functions). 5) Analysis of multivariable point events. 6) Space-temporal analysis. 7) Line. Statistical description. Introduction to graph theory. Optimal path searching. Transport accessibility. Location and allocation tasks. 8) Gravity theory. Analysis of interaction data. 9) Polygons. Areal interpolation. Districting, regionalization. 10) Smoothing of areal data. Multivariate techniques. Regression modelling (including spatial regression).

Conditions for subject completion

Full-time form (validity from: 2018/2019 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 33  17
        Examination Examination 67  18 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 yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2022/2023 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Compulsory study plan
2021/2022 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Compulsory study plan
2020/2021 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Compulsory study plan
2019/2020 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Compulsory study plan
2018/2019 (N3654) Geodesy, Cartography and Geoinformatics (3608T002) Geoinformatics P English Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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