Extending Kohonen's self-organizing mapping algorithms to learn ballistic movements
Proceedings of the NATO Advanced Research Workshop on Neural computers
Parallel digital implementations of neural networks
Parallel digital implementations of neural networks
Self-organizing maps
Continuous cartogram construction
Proceedings of the conference on Visualization '98
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Self-Organizing Maps
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Efficient Cartogram Generation: A Comparison
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
CartoDraw: A Fast Algorithm for Generating Contiguous Cartograms
IEEE Transactions on Visualization and Computer Graphics
RecMap: Rectangular Map Approximations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
IEEE Computer Graphics and Applications
Semiology of graphics
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Data Mining: um Guia Prático
Controlling the magnification factor of self-organizing feature maps
Neural Computation
Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data
IEEE Transactions on Neural Networks
Asymptotic level density in topological feature maps
IEEE Transactions on Neural Networks
International Journal of Geographical Information Science
Cartograms, Self-Organizing Maps, and Magnification Control
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
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The basic idea of a cartogram is to distort a geographical map by substituting the geographic area of a region by some other variable of interest. The objective is to rescale each region according to the value of the variable of interest while keeping the map, as much as possible, recognizable. There are several algorithms for building cartograms. None of these methods has proved to be universally better than any other, since the trade-offs made to get the correct distortion vary. In this paper we present a new method for building cartograms, based on self-organizing neural networks (Kohonen's self-organizing maps or SOM). The proposed method is widely available and is easy to carry out, and yet has several appealing properties, such as easy parallelization, making up a good tool for geographic data presentation and analysis. We present a series of tests on different problems, comparing the new algorithm with existing ones. We conclude that it is competitive and, in some circumstances, can perform better then existing algorithms.