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Ranking places in attributed temporal urban mobility networks
Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can furt...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556537/ https://www.ncbi.nlm.nih.gov/pubmed/33052916 http://dx.doi.org/10.1371/journal.pone.0239319 |
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author | Nanni, Mirco Tortosa, Leandro Vicent, José F. Yeghikyan, Gevorg |
author_facet | Nanni, Mirco Tortosa, Leandro Vicent, José F. Yeghikyan, Gevorg |
author_sort | Nanni, Mirco |
collection | PubMed |
description | Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of “hotspots” of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of “hotspots” and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities. |
format | Online Article Text |
id | pubmed-7556537 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-75565372020-10-21 Ranking places in attributed temporal urban mobility networks Nanni, Mirco Tortosa, Leandro Vicent, José F. Yeghikyan, Gevorg PLoS One Research Article Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of “hotspots” of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of “hotspots” and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities. Public Library of Science 2020-10-14 /pmc/articles/PMC7556537/ /pubmed/33052916 http://dx.doi.org/10.1371/journal.pone.0239319 Text en © 2020 Nanni et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Nanni, Mirco Tortosa, Leandro Vicent, José F. Yeghikyan, Gevorg Ranking places in attributed temporal urban mobility networks |
title | Ranking places in attributed temporal urban mobility networks |
title_full | Ranking places in attributed temporal urban mobility networks |
title_fullStr | Ranking places in attributed temporal urban mobility networks |
title_full_unstemmed | Ranking places in attributed temporal urban mobility networks |
title_short | Ranking places in attributed temporal urban mobility networks |
title_sort | ranking places in attributed temporal urban mobility networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556537/ https://www.ncbi.nlm.nih.gov/pubmed/33052916 http://dx.doi.org/10.1371/journal.pone.0239319 |
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