Spatial Data Analysis
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 548-0044/08 |
| Abbreviation | PAD |
| Course title | Spatial Data Analysis |
| Credits | 5 |
| Coordinating department | Department of Geoinformatics |
| Course coordinator | prof. Ing. Jiří Horák, Dr. |
Subject syllabus
1. Spatial analysis. Objectives and types of analysis. Descriptive statistics for point pattern.
2. Circular statistics.
3. Modelling of point spatial patterns – theoretical models.
4. Inferential statistical tests for point pattern. Analysis of multivariable point events.
5. Introduction to the graph theory. Graph types, spatial structures.
6. Statistical description of graphs and networks. Transport accessibility.
7. Selected tasks in graphs (MST, Gabriel network, Steiner tree, optimal route, traveler salesman problem).
8. Theoretical models of networks
9. Location and allocation tasks. Gravity theory. Analysis of interaction data.
10. Selected analysis for polygons (Areal interpolation. Districting, regionalization. Smoothing. Regression).
11. Multivariate techniques for spatial data – PCA, FA, DA
12. Multivariate techniques for spatial data - hierarchical and non-hierarchical spatial clustering
13. Logistic regression
2. Circular statistics.
3. Modelling of point spatial patterns – theoretical models.
4. Inferential statistical tests for point pattern. Analysis of multivariable point events.
5. Introduction to the graph theory. Graph types, spatial structures.
6. Statistical description of graphs and networks. Transport accessibility.
7. Selected tasks in graphs (MST, Gabriel network, Steiner tree, optimal route, traveler salesman problem).
8. Theoretical models of networks
9. Location and allocation tasks. Gravity theory. Analysis of interaction data.
10. Selected analysis for polygons (Areal interpolation. Districting, regionalization. Smoothing. Regression).
11. Multivariate techniques for spatial data – PCA, FA, DA
12. Multivariate techniques for spatial data - hierarchical and non-hierarchical spatial clustering
13. Logistic regression
E-learning
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
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
Advised 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
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