Migue final presentation_v28

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Bio-inspired computational techniques applied to the clustering and visualization of spatio-temporal geospatial data

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Migue final presentation_v28

  1. 1. Bio-inspired computational techniquesapplied to the clustering and visualization of spatio-temporal geospatial data Miguel BARRETO-SANZ June 27, 2011 1
  2. 2. More data has been createdsince 2005 than in the previous40,000 years 2
  3. 3. Geospatial data timeline 2010 Social networks Geotag 1992 Internet 2006 explosion GPS receiver 1993 2000 built into It is 1997 2005 cell Civilian 1980 launched Tropical demand Google phones First the 24th Rainfall Earth for GPS commercial Navstar Measuring products1972 vendors of satellite MissionLandsat 1, Geographical completing (TRMM)1st civilian information the GlobalEarth Systems (GIS) Positioningobservation software Systemsatellite 3
  4. 4. These data are critical fordecision support, but theirvalue depends on our abilityto extract useful information 4
  5. 5. ChallengesNASA earth observatory • Highly-dimensional(Information from several missionse.g. Terra, TRMM, SRTM) • Large quantity of data • Unlabeled samples (labeling is expensive and time consuming process) Worldclim (climate data from weather stations) Derivate variables Elevation Slope Aspect Moisture Mean annual temperature (ºC) -30.1 30.5 Landscape Solar Class Exposure Radiation Curvature Annual precipitation (mm) 0 12084 5
  6. 6. Spatio-temporal challengesSpatio-temporal representations Variables and clusters evolved inat several levels a temporal context Hours Days Months Years Fuzzy boundaries in Visualization of clusters in geographical geographical space and feature space 6
  7. 7. Thesis Tree-structured SOM FGHSON component planes SOMColombia (Ecoregions) GHSOMSouth America (Ecoregions) Colombia (agroecozones, ecoregions) Clustering Visualization and projection Spatio-temporal data 7
  8. 8. Visualization and projection 8
  9. 9. Visualization by using Self-organizing MapsData set SOM training Visualization 3 2 3 1 9
  10. 10. Visualization by using Self-organizing Maps Exploration Correlation hunting Partial Similar correlations 10
  11. 11. A real world problem: Classification of agro-ecological variables related with productivity in the sugar cane culture.Climate variables.• Average Temperature (TempAvg) Total 54 variables• Average Relative Humidity (RHAvg)• Radiation (Rad)• Precipitation (Prec)Soil variables.• Order (Ord)• Texture (Tex)• Deep (Dee)Topographic variables.• Landscape (Ls)• Slope (Sl).Other variables.• Water Balance (WB)• Variety (Var)Production 11
  12. 12. Classical approach: scatter plot matrix 5 Variables 12
  13. 13. Classical approach: scatter plot matrix23 Variables 13
  14. 14. Classical approach: scatter plot matrix54 Variables 14
  15. 15. SOM component planes5 Variables 15
  16. 16. SOM component planes23 Variables 16
  17. 17. 54 Variables SOM component planes 17
  18. 18. SOM component planes 54 Variables 18
  19. 19. Correlation Hunting 19
  20. 20. SOM of component planes 20
  21. 21. Tree-structured SOM component planes 21
  22. 22. Tree-structured SOM component planes 54 Variables 22
  23. 23. Tree-structured SOM component planes 23
  24. 24. Clustering 24
  25. 25. Hierarchical Self-organizing Structures• It combines the advantages of the Hierarchical representation and Soft Competitive Learning• In the state of the art all the methods are crisp approaches• In geospatial applications crisp memberships are not the optimal representation of clusters. 25
  26. 26. Real world data and its fuzzy nature Crisp Fuzzy 26
  27. 27. An approach to tackle thisproblem consists in allowinga fuzzy representation in thehierarchical structures 27
  28. 28. Fuzzy Growing Hierarchical Self-Organizing Networks FGHSON Breadth grow processDepth grow process α-cut α-cut α-cut Hierarchy Fuzzy membership 28
  29. 29. Case study-South America Cali Colombia Temperature Similar Zones Precipitation 29
  30. 30. Case study-South America Cali Colombia 30
  31. 31. Case study-South America Cali Colombia To finding the right prototype 31
  32. 32. Level 1 32
  33. 33. Level 2 33
  34. 34. Fortaleza Brazil Level 3Cali Colombia 34
  35. 35. Spatio-Temporal Clustering 35
  36. 36. Spatio-Temporal Clustering Time – WhenSpace - Where Homologues places for Colombian coffee production. Brazil, Equator, East Africa, and New Guinea. 36
  37. 37. Spatio-Temporal Clustering Space and time – Where and when ArgentinaMaize (Zea maize L.) United States 37
  38. 38. Spatio-Temporal ClusteringObjective: to find similar environmental zones trough time in South America.In these experience we are looking for regions with similar patterns in timewindows of three months. 38
  39. 39. Spatio-Temporal Clustering 39
  40. 40. Spatio-Temporal Clustering Temperature Similar Zones to Cali in the period Precipitation jan-feb-mar? 40
  41. 41. Spatio-Temporal Clustering 41
  42. 42. Conclusions1. Original contributionsFGHSON• Capability to reflect the underlying structure of a dataset in ahierarchical fuzzy way• It does not require an a-priory definition of the number ofclusters.•The algorithm executes self-organizing processes in parallel.•Only three parameters are necessary to the setup of thealgorithm. 42
  43. 43. ConclusionsTree-structured SOM component planes• It creates structures that allow the visual exploratory dataanalysis of large high-dimensional datasets.• Similarities on variables’ behavior can be easilydetected (e.g. local correlations, maximal and minimal valuesand outliers). 43
  44. 44. Conclusions2. Test of methodologies for clustering andvisualization of georeferenced data• GHSOM• SOM• FGHSON3. Methodology contributions• Clustering of spatio-temporal datasets through time by usingFGHSON. 44
  45. 45. Conclusions4. Agroecological knowledge contribution• In sugar cane productivity• In sugar cane agroecoregionalizacion• In Andean blackberry production The COCH project 45
  46. 46. Questions 46

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