IEEE International Conference on Data Mining Workshops 2009 Hai Jin and Diansheng Guo Department of Geography University of South Carolina Columbia

 Introduction  Self-Organizing Map  U-Matrix  Parallel Coordinate Plot  Example  Conclusion

 Climate change has been a challenging and urgent research problem for many related research fields. ◦ most existing visualization and mapping approaches for climate data analysis are limited to one variable or one perspective at a time  This paper introduces the application of a multivariate geovisualization approach ◦ to explore and understand complex climate change patterns across multiple perspectives  including the geographic space, time, and multiple variables.

 SOMs were developed by Kohonen in the early 1980's  SOM 的基本原理源於大腦結構的特性，因為大腦具 有相同功能的腦細胞會聚集在一起的特性，例如： 大腦中有專司味覺、視覺等的區塊。 ◦ input data ∈ℛ n ◦ weight: W i (t) (reference vector) ∈ℛ n ◦ physical space: neurons (low-dimension)

A 3D structure of a diverging–diverging color scheme from an ellipsoid model. A 3D structure of a diverging–diverging scheme from a bell-shaped model.

 The climate data used in this research is a spatiotemporal data set of monthly mean surface air temperature for 60 years (Jan. 1948—Dec. 2007). ◦ the 10-year average temperature for each 10-year period ( , , , , , ) ◦ 2664 spatial objects (grid cells) ◦ 12 variables(monthly anomaly) for 6 decades.

 This paper presents a preliminary application of an integrated approach to multivariate clustering and geovisualization to explore climate change patterns.  The analysis and visualization of climate change patterns presented in the paper focus on fixed spatial (grid cells) and temporal resolutions (monthly and decadal aggregations).  The software for the presented approach is available at