Multifractal portrayal of the Swiss population

Fractal geometry is a fundamental approach for describing the complex irregularities of the spatial structure of point patterns. The present research characterizes the spatial structure of the Swiss population distribution in the three Swiss geographical regions (Alps, Plateau and Jura) and at the e...

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Main Authors: Carmen Delia Vega Orozco, Jean Golay, Mikhail Kanevski
Format: Article
Language:deu
Published: Unité Mixte de Recherche 8504 Géographie-cités 2015-03-01
Series:Cybergeo
Subjects:
Online Access:http://journals.openedition.org/cybergeo/26829
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spelling doaj-aa3a1fc951564ad294ddaae9b01b37bb2021-10-05T13:17:41ZdeuUnité Mixte de Recherche 8504 Géographie-citésCybergeo1278-33662015-03-0110.4000/cybergeo.26829Multifractal portrayal of the Swiss populationCarmen Delia Vega OrozcoJean GolayMikhail KanevskiFractal geometry is a fundamental approach for describing the complex irregularities of the spatial structure of point patterns. The present research characterizes the spatial structure of the Swiss population distribution in the three Swiss geographical regions (Alps, Plateau and Jura) and at the entire country level. The analyses were carried out by means of a fractal measure (the box-counting dimension) and two multifractal measures (the Rényi generalized dimensions and the multifractal spectrum) for point patterns, which enabled the estimation of the spatial degree of clustering of a distribution at different scales. The Swiss population dataset is presented on a grid of points and thus it can be modelled as a realization of a "point process" where each point is characterized by its spatial location (geometrical support) and a number of inhabitants (measured variable). Results showed that the four patterns are multifractals and their population distribution present different clustering behaviours. Thus, applying multifractal and fractal methods at different geographical regions and at different scales allowed us characterising the degree of clustering of the population distribution in Switzerland and quantifying their dissimilarities. This paper is the first Swiss geodemographic study applying multifractal methods using high resolution data.http://journals.openedition.org/cybergeo/26829fractal dimensionbox-countingmultifractal dimensionRényi dimensionssingularity spectrumgeodemographics
collection DOAJ
language deu
format Article
sources DOAJ
author Carmen Delia Vega Orozco
Jean Golay
Mikhail Kanevski
spellingShingle Carmen Delia Vega Orozco
Jean Golay
Mikhail Kanevski
Multifractal portrayal of the Swiss population
Cybergeo
fractal dimension
box-counting
multifractal dimension
Rényi dimensions
singularity spectrum
geodemographics
author_facet Carmen Delia Vega Orozco
Jean Golay
Mikhail Kanevski
author_sort Carmen Delia Vega Orozco
title Multifractal portrayal of the Swiss population
title_short Multifractal portrayal of the Swiss population
title_full Multifractal portrayal of the Swiss population
title_fullStr Multifractal portrayal of the Swiss population
title_full_unstemmed Multifractal portrayal of the Swiss population
title_sort multifractal portrayal of the swiss population
publisher Unité Mixte de Recherche 8504 Géographie-cités
series Cybergeo
issn 1278-3366
publishDate 2015-03-01
description Fractal geometry is a fundamental approach for describing the complex irregularities of the spatial structure of point patterns. The present research characterizes the spatial structure of the Swiss population distribution in the three Swiss geographical regions (Alps, Plateau and Jura) and at the entire country level. The analyses were carried out by means of a fractal measure (the box-counting dimension) and two multifractal measures (the Rényi generalized dimensions and the multifractal spectrum) for point patterns, which enabled the estimation of the spatial degree of clustering of a distribution at different scales. The Swiss population dataset is presented on a grid of points and thus it can be modelled as a realization of a "point process" where each point is characterized by its spatial location (geometrical support) and a number of inhabitants (measured variable). Results showed that the four patterns are multifractals and their population distribution present different clustering behaviours. Thus, applying multifractal and fractal methods at different geographical regions and at different scales allowed us characterising the degree of clustering of the population distribution in Switzerland and quantifying their dissimilarities. This paper is the first Swiss geodemographic study applying multifractal methods using high resolution data.
topic fractal dimension
box-counting
multifractal dimension
Rényi dimensions
singularity spectrum
geodemographics
url http://journals.openedition.org/cybergeo/26829
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AT jeangolay multifractalportrayaloftheswisspopulation
AT mikhailkanevski multifractalportrayaloftheswisspopulation
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