visualization of geospatial data by component planes and u-matrix marcos aurélio santos da silva...
DESCRIPTION
342 urban census regions of São José dos Campos, São Paulo.TRANSCRIPT
![Page 1: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/1.jpg)
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
![Page 2: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/2.jpg)
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.
![Page 3: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/3.jpg)
342 urban census regions of São José dos Campos, São Paulo.
![Page 4: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/4.jpg)
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
![Page 6: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/6.jpg)
Self-Organizing Maps (SOM)Self-Organizing Maps (SOM)
![Page 7: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/7.jpg)
Self-Organizing Maps (SOM)Self-Organizing Maps (SOM)
Unsupervised;Iterative;Batch (codevectors are updated after each
iteraction)Gaussian neighborhood kernel function;
SOM Learning process
![Page 8: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/8.jpg)
Self-Organizing Maps (SOM)Self-Organizing Maps (SOM)SOM Properties
Raw dataset(each rectangle represents
a feature vector (vi)
Learning
{v1, v2 ... }
![Page 9: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/9.jpg)
Relation between SOM and Relation between SOM and Spatial MapSpatial Map
Neighborhood in the feature space Neighborhood
in the physical space
![Page 10: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/10.jpg)
Visualization AlgorithmsVisualization Algorithms
Unified Matrix Distance (U-matrix)
U-matrix map the codevectors values into a 2D display.
![Page 11: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/11.jpg)
Visualization AlgorithmsVisualization Algorithms
Component Planes (CP)
For each variable
![Page 12: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/12.jpg)
ResultsResults
![Page 13: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/13.jpg)
Group220x15
Group1
![Page 14: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/14.jpg)
Group 1
Group 2
Detected Outliers
![Page 15: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/15.jpg)
IFH ED
ES LONG EQ
PQ CIWFH CWFH
High degree of similarity
High degree of homogeinity
![Page 16: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/16.jpg)
Vertical
Horizontal
Diagonal \
Diagonal /So
cial
Exc
lusi
on D
irect
ion
on S
OM
Map
![Page 17: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/17.jpg)
Mapping SOM distribution into the Mapping SOM distribution into the Census MapCensus Map
![Page 18: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/18.jpg)
Comparing with previous Comparing with previous statistical resultsstatistical results
Statistical clustering (IEX)Neuro-clustering (SOM)
Center-to-peripherical direction of urban social exclusion
![Page 19: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/19.jpg)
ToolsTools
CASAA (processing);SOM Toolbox Matlab (SOM’s
visualization)TerraView (census map
visualization)TerraLib (spatial data access
library)
![Page 20: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/20.jpg)
TerraView
CASAA
![Page 21: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/21.jpg)
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;
![Page 22: Visualization of Geospatial Data by Component Planes and U-matrix Marcos Aurélio Santos da Silva Antônio Miguel Vieira Monteiro José Simeão de Medeiros](https://reader036.vdocuments.site/reader036/viewer/2022062401/5a4d1b1c7f8b9ab059993b42/html5/thumbnails/22.jpg)
Marcos Aurélio Santos da Silva Marcos Aurélio Santos da Silva e-mail: [email protected] e-mail: [email protected]
Thanks !!