A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal

In recent years several studies have examined changes in the distribution of poverty in the North American cities, with most empirical work assessing neighborhood change between two time points. This paper aims to make a methodological contribution to the study of neighborhood change, by comparing t...

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Autores principales:	Philippe Apparicio, Mylène Riva, Anne-Marie Séguin
Formato:	article
Lenguaje:	DE EN FR IT PT
Publicado:	Unité Mixte de Recherche 8504 Géographie-cités 2015
Materias:	latent class growth modeling/modelling k-means clustering neighborhood/neighbourhood poverty trajectories Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/c822b85e968f4dd7b9a14dbd351d9e8f
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id	oai:doaj.org-article:c822b85e968f4dd7b9a14dbd351d9e8f
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spelling	oai:doaj.org-article:c822b85e968f4dd7b9a14dbd351d9e8f2021-12-02T11:08:32ZA comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal1278-336610.4000/cybergeo.27035https://doaj.org/article/c822b85e968f4dd7b9a14dbd351d9e8f2015-06-01T00:00:00Zhttp://journals.openedition.org/cybergeo/27035https://doaj.org/toc/1278-3366In recent years several studies have examined changes in the distribution of poverty in the North American cities, with most empirical work assessing neighborhood change between two time points. This paper aims to make a methodological contribution to the study of neighborhood change, by comparing two classification methods, one classical (k-means clustering) the other more novel (Latent Class Growth Modelling; LCGM) to identify groups of census tracts having followed similar trajectories of poverty in the Montreal metropolitan area, Canada. Here trajectories of poverty are measured over a twenty-year period, using five time points. The relative performance of the LCGM vs. the k-means clustering was assessed using a series of multinomial logistic regressions examining how different socioeconomic variables were associated with the trajectories of poverty. Results showed that k-means and LCGM identified similar groups of census tracts characterized by ascending, descending, or stable poverty levels throughout the period, with LGCM only marginally outperforming k-means clustering.Philippe ApparicioMylène RivaAnne-Marie SéguinUnité Mixte de Recherche 8504 Géographie-citésarticlelatent class growth modeling/modellingk-meansclusteringneighborhood/neighbourhoodpovertytrajectoriesGeography (General)G1-922DEENFRITPTCybergeo (2015)
institution	DOAJ
collection	DOAJ
language	DE EN FR IT PT
topic	latent class growth modeling/modelling k-means clustering neighborhood/neighbourhood poverty trajectories Geography (General) G1-922
spellingShingle	latent class growth modeling/modelling k-means clustering neighborhood/neighbourhood poverty trajectories Geography (General) G1-922 Philippe Apparicio Mylène Riva Anne-Marie Séguin A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal
description	In recent years several studies have examined changes in the distribution of poverty in the North American cities, with most empirical work assessing neighborhood change between two time points. This paper aims to make a methodological contribution to the study of neighborhood change, by comparing two classification methods, one classical (k-means clustering) the other more novel (Latent Class Growth Modelling; LCGM) to identify groups of census tracts having followed similar trajectories of poverty in the Montreal metropolitan area, Canada. Here trajectories of poverty are measured over a twenty-year period, using five time points. The relative performance of the LCGM vs. the k-means clustering was assessed using a series of multinomial logistic regressions examining how different socioeconomic variables were associated with the trajectories of poverty. Results showed that k-means and LCGM identified similar groups of census tracts characterized by ascending, descending, or stable poverty levels throughout the period, with LGCM only marginally outperforming k-means clustering.
format	article
author	Philippe Apparicio Mylène Riva Anne-Marie Séguin
author_facet	Philippe Apparicio Mylène Riva Anne-Marie Séguin
author_sort	Philippe Apparicio
title	A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal
title_short	A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal
title_full	A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal
title_fullStr	A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal
title_full_unstemmed	A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal
title_sort	comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in montreal
publisher	Unité Mixte de Recherche 8504 Géographie-cités
publishDate	2015
url	https://doaj.org/article/c822b85e968f4dd7b9a14dbd351d9e8f
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A comparison of two methods for classifying trajectories: a case study on neighborhood poverty at the intra-metropolitan level in Montreal

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