Unsupervised learning of Swiss population spatial distribution

The paper deals with the analysis of spatial distribution of Swiss population using fractal concepts and unsupervised learning algorithms. The research methodology is based on the development of a high dimensional feature space by calculating local growth curves, widely used in fractal dimension estimation and on the application of clustering algorithms in order to reveal the patterns of spatial population distribution. The notion unsupervised also means, that only some general criteria-density, dimensionality, homogeneity, are used to construct an input feature space, without adding any supervised/expert knowledge. The approach is very powerful and provides a comprehensive local information about density and homogeneity/fractality of spatially distributed point patterns.

Automated summary

This paper analyzes the spatial distribution of the Swiss population using fractal concepts and unsupervised learning algorithms. By calculating local growth curves to develop a high-dimensional feature space, and using clustering algorithms to reveal spatial population distribution patterns, the approach provides comprehensive local information on the density and homogeneity/fractality of spatially distributed point patterns.