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Unsupervised learning of Swiss population spatial distribution

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  • Mikhail Kanevski

Abstract

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.

Suggested Citation

  • Mikhail Kanevski, 2021. "Unsupervised learning of Swiss population spatial distribution," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-24, February.
    • Handle: RePEc:plo:pone00:0246529
    DOI: 10.1371/journal.pone.0246529
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    References listed on IDEAS

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    1. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    2. Isabelle Thomas & Pierre Frankhauser & Benoit Frenay & Michel Verleysen, 2010. "Clustering Patterns of Urban Built-up Areas with Curves of Fractal Scaling Behaviour," Environment and Planning B, , vol. 37(5), pages 942-954, October.
    3. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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