Spatializing GIS Commands with Self-Organizing Maps. Jochen Wendel Barbara P. Buttenfield Roland J. Viger Jeremy M. Smith

Transcription

1 Spatializing GIS Commands with Self-Organizing Maps Jochen Wendel Barbara P. Buttenfield Roland J. Viger Jeremy M. Smith

2 Outline Introduction Characterizing GIS Commands Implementation Interpretation of SOM outputs Analyzing the SOM output Summary and further work

3 Introduction Majority of spatialization research uses documents or data files Important aspect in this research descriptive metadata about each GIS operator to the SOM analysis Does not attempt to provide a universal classification system Context-specific semantic reference system Can spatialization methods be applied to GIS commands?

4 Characterizing GIS commands List of 100+ GIS functions compiled Attributes assigned to describe the operation of each command Boolean matrix Tried to describe dependencies between GIS commands

5 Characterizing GIS commands Raster Only Data Management Atom (1st) Prerequisites (2nd) 1 indicates task is raster data only, 0 indicates its either vector only or raster and vector 1 indicates this task is a data mgt. function, 0 is does not 1 indicates it as atom, 0 describes it as a molecular Indicates that prior tasks are required to run this operation Geometric 1 describes if task modifies the geometry of a GIS layer, 0 indicates this task modifies attributes only, or both Terrain Flow Local Regional CSR 1 indicates this task deals with terrain data an can be used for calculation flow across terrain surface, 0 is does not 1 describes that the GIS operation works based on the properties of each individual pixel, 0 it does not 1 indicates GIS operation works based on properties of more than one cell, 0 it does not 1 describes it changes spatial relation, 0 it does not

6 Implementation Input Data SOM Visualization Boolean Matrix of GIS Commands SOM Analyst ArcGIS (André Skupin) SOM Toolbox MatLab (CIS) Adobe Illustrator Analysis MatLab Code modification Stata

7 Implementation (Learning process) SOM learning process Learning process of the SOM was tested at 10, 50, 100, 500, 1 000, 5 000, and iterations 1000 learning iteration are sufficient for our data input data After iterations terrain_flow

8 Implementation (Animation) terrain_flow

9 Interpretation of SOM outputs The SOM is displayed on matrix of hexagonal cells ArcGIS: generated SOMs of 5x3, 10x3, 20x3 cells Matlab: generated SOMs of 8x8, 16x16, 32x32 cells # of Iterations Learning = 1000 fine-tuning = 200 initial weighting radius = 1/2 output SOM size

10 Interpretation of SOM outputs (ArcGIS) 3 x 5 SOM cell grid

11 Interpretation of SOM outputs (MatLab) 8 x 8 matrix

12 Interpretation of SOM outputs (MatLab 16 x 16 matrix

13 Interpretation of SOM outputs (MatLab) 32 x 32 matrix

14 Interpretation of SOM outputs (Comparison) 8 x 8 16 x x 32

15 Analysing the SOM output Principal components/correlation Number of obs = 256 Number of comp. = 8 Trace = 8 Rotation: (unrotated = principal) Rho = Component Eigenvalue Difference Proportion Cumulative Comp Comp Comp Comp Comp Comp Comp Comp Principal components (eigenvectors) Variable Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 Comp7 Comp8 Unexplained var var var var var var var var

16 Analysing the SOM Comparing the results of all matrices and the input data input matrix 8 x 8 matrix 32 x 32 matrix 16 x 16 matrix

17 Summary and futher work Spatialization of GIS commands appears to be feasible Use of PCA to interpret SOM axes helpful The analysis gives insight to needed dimensionality of input matrix Stability of the crispness doesn t change across iterations but the shape of cluster does

18 Summary and futher work Add a prerequisite column to matrix Convert to non-binary matrix Tune the number of columns for our matrix