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342 urban census regions of São José dos Campos, São Paulo.
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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
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: [email protected] e-mail: [email protected]
Thanks !!