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2. 3. 4. a) b) 5. a) b) 6. 7. 8. 9. Daily rainfall data of two stations in Pearl River basin of China 10. 11. The monthly sunspot time series. 12. The Portuguese Stock Index PSI-20 evolution from 1993 to 2002 (adopted from J.A.O. Matos et al. / Physica A 342 (2004) 665 676) 13. Outbreak of Avian Flu in different regions 14. 15. What are the structures and processes hidden in spatial data?
16. Typhoon Tracks Adapted from Wang and Chan 17. Typhoon/Hurricane Tracking Objective:Intensity, track (land falling, recurvature) Object: The space-time track of unusually low sea- surface air pressure in the x-y-z plane Data: potential temperature, horizontal velocity,vertical velocity, relative humidity,horizontalwind, etc Data: Hundreds and thousands of gigabytes within aspecific time interval 18. 19. 20. 21. Data Mining in Hyperspectral Images 1. Objective Classification, Pattern Recognition 2. Object Spectral Signatures of Objects 3. Data Spectral, Non-spectral Data 4. Data Volume e.g. AVIRIS from 0.4 to 2.45 micrometers, 224 bands HYDICE from 0.4 to 2.5 micrometers, 210 bands Hyperion from 0.4 to 2.5 micrometers, 220 bands, 30 meter resolution 22. The Objective of Knowledge Discovery and Data Mining Fayyad:The discovery of non-trivial, novel,potentially useful and interpretable knowledge/information from data DataInformationKnowledgeDecision 23. Characteristics of Spatial Data
2. Sparse 3. Diversity 4. Complex 5. Dynamic 6. Redundant 7. Imperfect (random fuzzy granular incomplete noisy)8. Multi-scale 24. Main Tasks of Spatial Knowledge Discovery and Data Mining 1. Clustering 3. Association 2. Classification Spatial Relations Temporal Relations Spatial-temporal Relations *In particular the local-global issue 4. Processes 25. CLUSTERING
26. Scale Space Theory
The solution of the above equation is explicitly expressed as where denotes the convolution operation, g (x,) is the Gaussian function 27. If the training samples are treated as an imaginary image with expression: Then the corresponding blurred imagef (x, , D l ) at scale can be specified by 28. Essentials of Clustering byScale-space Filtering
2. Cluster validity check 3. Clustering validity check 4. Relevant concepts (a) life time of a cluster (b) life time of a clustering (c) compactness (d) isolatedness 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41.
a) b) 42. Temporal segmentation of Strong Earthquakes (Ms6.0) of 1290A.D. - 2000A.D.
43.
a) b) 44. a) b) Ms-time plot of clustering results for earthquakes (Ms4.7):a) 2 clusters in the 74th~112th scale range; b) 18 clusters at the 10th scale step 45. Temporal Segmentation of Strong Earthquakes (Ms4.7) of 1484A.D. - 2000A.D.
a) b) 46.
47. 48. Advantages of Scale-space Filtering
49. 5. Scale Space Clustering Scale-Space Filtering for Simulated Data 50. 5. Scale Space Clustering Scale-Space Filtering for Remote-Sensing Data Clustering Tree Quasi-Light 51. Clustering by Regression-Classes Decomposition Method 52. Simple Gaussian Class 53. Linear Structure 54. Identification of line objects in remotely sensed data 55. Ellipsoidal Structure 56. 57. Two ellipsoidal feature extraction 58. General Curvilinear Structure 59. Complex Shape Structure 60. ANALYSIS OF SPATIAL RELATIONSHIP
61. Geographically Weighted Regression Hypothesis testing 1.Ho: No difference between OLR and GWR 2.Ho: a 1k= a 2k= = a nk 62. 63. 64. (Regression-Classes Decomposition Method) 65. CLASSIFICATION
66. Information Extraction and Classification Neural Networks for Classification--MLP-BP 67. Some Typical Feedforward Neural Networks
Figure 8. Perceptrons 68.
Some Typical Feedforward Neural Networks (con t) Fig. 13. A 2-layer feedforward network for the restaurant problem. 69. 70. 71. 72. 73. 74. 75. 76.
77. 78. Typhoon Tracks Adapted from Wang and Chan 79. Trees by Classification and Regression Tree (CART)MSW 6/12/18: Maximum Sustained Wind of TC 6/12/18 hours before recurvature.0: Recurve,1: Straight 80.
Rules by CART 81. DISCOVERY OF TEMPORAL PROCESSES
82.
83. Multiplicative Cascade
84. Schematic representation of cascade (adopted from Puente and Lopez, 1995, Physical Letters A) 85. 86. TEMPORAL ANALYSIS
87. The Multifractal Approach
88. MF-DFA
89. MF-DFA
90. MF-DFA
91. MF-DFA
92. MF-DFA
93.
94. 95. 96. 97. 98. 99. 100. 101. 102. Daily rainfall data of two stations in Pearl River basin of China 103. Log-log plots ofF q(s)versussfor the daily rainfall time seriesof station 56691 in Pearl River basin (left) and Station Chuantang in East River basin (right) withq =2. 104. Theh ( q ) curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right). 105. Thecurves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right). 106. Thecurves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right) 107. Thecurves of daily rainfall time series of 5 stations in the Pearl River basin 108. Thecurves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right). 109. Thecurves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right). The real lines are their cascade model fitting. 110. The correlation relationship between the altitude of the rainfall stations in the East River basin and theD (2) value of the rainfall time series. 111. Elevation of rainfall stations in the East River basin with theD2values of their rainfall data.Elevation (m above MSL) 112. DISCOVERY OF KNOWLEDGE STRUCTURES
113.
114. 115. Spatial Concept/Class and Data Encapsulation 116. Concept Hierarchy 117. Inheritance 118. Generalization and Specialization 119. Summary
120. Yee Leung. Knowledge Discovery in Spatial Data. Berlin: Springer-Verlag, 2010. [email_address] IGU-Commission on Modeling Geographical Systems http://www.science.mcmaster.ca/~igu~cmgs/