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

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.

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

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

Neurocomputing

Self-Organizing Maps (SOM)

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

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

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

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

Visualization Algorithms Component Planes (CP) For each variable

Results

Group2 20x15 Group1

Group 2 Detected Outliers

IFHED ESLONGEQ PQCIWFHCWFH High degree of similarity High degree of homogeinity

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

Mapping SOM distribution into the Census Map

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

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

TerraView CASAA

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;

Marcos Aurélio Santos da Silva Thanks !!