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