Theories and Applications of Spatial-Temporal Data Mining and Knowledge Discovery Yee Leung

4. a) b)

5. a) b)

9. Daily rainfall data of two stations in Pearl River basin of China

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

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, horizontal wind, etc Data: Hundreds and thousands of gigabytes within a specific time interval

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 Data Information Knowledge Decision

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

27. If the training samples are treated as an imaginary image with expression: Then the corresponding blurred image f (x, σ , D l ) at scale σ can be specified by

44. a) b) Ms-time plot of clustering results for earthquakes (Ms≥4.7): a) 2 clusters in the 74th~112th scale range; b) 18 clusters at the 10th scale step

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

57. Two ellipsoidal feature extraction

58. General Curvilinear Structure

59. Complex Shape Structure

61. Geographically Weighted Regression Hypothesis testing 1. Ho: No difference between OLR and GWR 2. Ho: a 1k = a 2k = … = a nk

64. (Regression-Classes Decomposition Method)

66. Information Extraction and Classification Neural Networks for Classification--MLP-BP

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

84. Schematic representation of cascade (adopted from Puente and Lopez, 1995, Physical Letters A)

102. Daily rainfall data of two stations in Pearl River basin of China

103. Log-log plots of F q (s) versus s for the daily rainfall time series of station 56691 in Pearl River basin (left) and Station Chuantang in East River basin (right) with q =2.

104. The h ( q ) curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right).

105. The curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right).

106. The curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right)

107. The curves of daily rainfall time series of 5 stations in the Pearl River basin

108. The curves of daily rainfall time series of stations in the Pearl River basin (left) and stations in the East River basin (right).

109. The curves 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 the D (2) value of the rainfall time series.

111. Elevation of rainfall stations in the East River basin with the D2 values of their rainfall data. Elevation (m above MSL)

115. Spatial Concept/Class and Data Encapsulation

116. Concept Hierarchy

117. Inheritance

118. Generalization and Specialization

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/