Visualization of Geospatial Data Visualization of Geospatial Data by Component Planes and by Component Planes and

U-matrixU-matrixMarcos Aurélio Santos da SilvaMarcos Aurélio Santos da SilvaAntônio Miguel Vieira MonteiroAntônio Miguel Vieira Monteiro

José Simeão de MedeirosJosé Simeão de Medeiros

Problem: Mapping urban social Problem: Mapping urban social exclusion/inclusion in São José dos exclusion/inclusion in São José dos

Campos, SP.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]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

NeurocomputingNeurocomputing

Self-Organizing Maps (SOM)Self-Organizing Maps (SOM)

Self-Organizing Maps (SOM)Self-Organizing Maps (SOM)

Unsupervised;Iterative;Batch (codevectors are updated after each

iteraction)Gaussian neighborhood kernel function;

SOM Learning process

Self-Organizing Maps (SOM)Self-Organizing Maps (SOM)SOM Properties

Raw dataset(each rectangle represents

a feature vector (vi)

Learning

{v1, v2 ... }

Relation between SOM and Relation between SOM and Spatial MapSpatial Map

Neighborhood in the feature space Neighborhood

in the physical space

Visualization AlgorithmsVisualization Algorithms

Unified Matrix Distance (U-matrix)

U-matrix map the codevectors values into a 2D display.

Visualization AlgorithmsVisualization Algorithms

Component Planes (CP)

For each variable

ResultsResults

Group220x15

Group1

Group 1

Group 2

Detected Outliers

IFH ED

ES LONG EQ

PQ CIWFH CWFH

High degree of similarity

High degree of homogeinity

Vertical

Horizontal

Diagonal \

Diagonal /So

cial

Exc

lusi

on D

irect

ion

on S

OM

Map

Mapping SOM distribution into the Mapping SOM distribution into the Census MapCensus Map

Comparing with previous Comparing with previous statistical resultsstatistical results

Statistical clustering (IEX)Neuro-clustering (SOM)

Center-to-peripherical direction of urban social exclusion

ToolsTools

CASAA (processing);SOM Toolbox Matlab (SOM’s

visualization)TerraView (census map

visualization)TerraLib (spatial data access

library)

TerraView

CASAA

ConclusionsConclusions

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

Thanks !!