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.
|Number of pages||29|
|Journal||International Journal Of Geographical Information Science|
|Publication status||Published - 1 Apr 2009|
- Kohonen self-organizing maps
- Neural networks