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Analysis of Clustering and Unsupervised Learning of Geospatial Demographic Data

Authors:
Mikhail Kanevski
Jean Golay

Keywords: geodemography; unsupervised learning; spatial data clustering

Abstract:
The research discusses the methodological framework of an application of a newly developed spatial clustering algorithm – the functional multipoint Morisita index (fm-Morisita) - and an unsupervised learning algorithm – self-organizing Kohonen maps (SOM), for the comprehensive exploratory analysis and quantification of patterns in high resolution geospatial demographic data in Switzerland. fm-Morisita is used to analyse the complex clustering of the spatial distribution of the population. The SOM are used to reveal regional patterns of similarity using detailed information about ageing groups.

Pages: 13 to 14

Copyright: Copyright (c) IARIA, 2014

Publication date: March 23, 2014

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-61208-326-1

Location: Barcelona, Spain

Dates: from March 23, 2014 to March 27, 2014