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).
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).