1. Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros

2. Problem: Mapping urban social exclusion/inclusion in São José dos Campos, SP. Data – 8 socioeconomic indexes computed from raw IBGE dataset; Questions – How the dataset is distributed? – How each variable correlates with each other? – Is there some spatial correlation between the feature and physical spaces.

3. 342 urban census regions of São José dos Campos, São Paulo.

4. Socioeconomic data [-1,+1] 1. Familiar Income (IFH); 2. Educational Development (ED); 3. Educational Stimulus (ES); 4. Longevity (LONG); 5. Environmental Quality (EQ); 6. Home Quality (PQ); 7. Concentration of Family Headed by Women (CWFH); 8. Concentration of Family Headed by Illiterate Women (CIWFH); -1: Means high exclusion level;+1: Means high inclusion level

5. Neurocomputing

6. Self-Organizing Maps (SOM)

7. Unsupervised; Iterative; Batch (codevectors are updated after each iteraction) Gaussian neighborhood kernel function; SOM Learning process

8. Self-Organizing Maps (SOM) SOM Properties Raw dataset (each rectangle represents a feature vector (v i ) Learning {v 1, v 2... }

9. Relation between SOM and Spatial Map Neighborhood in the feature space Neighborhood in the physical space

10. Visualization Algorithms Unified Matrix Distance (U-matrix) U-matrix map the codevectors values into a 2D display.

11. Visualization Algorithms Component Planes (CP) For each variable

12. Results

13. Group2 20x15 Group1

14. Group 2 Detected Outliers

15. IFHED ESLONGEQ PQCIWFHCWFH High degree of similarity High degree of homogeinity

16. Vertical Horizontal Diagonal \ Diagonal / Social Exclusion Direction on SOM Map

17. Mapping SOM distribution into the Census Map

18. Comparing with previous statistical results Statistical clustering (IEX) Neuro-clustering (SOM) Center-to-peripherical direction of urban social exclusion

19. Tools CASAA (processing); SOM Toolbox Matlab (SOM’s visualization) TerraView (census map visualization) TerraLib (spatial data access library)

20. TerraView CASAA

21. Conclusions SOM worked well in the task of exploratory analysis of multivariated geospatial data; Component Planes can help us to discover spatial distribution of the phenomena; The size of SOM Map influences the final result learning process;

22. Marcos Aurélio Santos da Silva e-mail: aurelio@embrapa.br Thanks !!