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Supersampling and Network Reconstruction of Urban Mobility
Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically on...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537279/ https://www.ncbi.nlm.nih.gov/pubmed/26275237 http://dx.doi.org/10.1371/journal.pone.0134508 |
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author | Sagarra, Oleguer Szell, Michael Santi, Paolo Díaz-Guilera, Albert Ratti, Carlo |
author_facet | Sagarra, Oleguer Szell, Michael Santi, Paolo Díaz-Guilera, Albert Ratti, Carlo |
author_sort | Sagarra, Oleguer |
collection | PubMed |
description | Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess the accuracy of the predicted vehicle flows. Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is required. |
format | Online Article Text |
id | pubmed-4537279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45372792015-08-20 Supersampling and Network Reconstruction of Urban Mobility Sagarra, Oleguer Szell, Michael Santi, Paolo Díaz-Guilera, Albert Ratti, Carlo PLoS One Research Article Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess the accuracy of the predicted vehicle flows. Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is required. Public Library of Science 2015-08-14 /pmc/articles/PMC4537279/ /pubmed/26275237 http://dx.doi.org/10.1371/journal.pone.0134508 Text en © 2015 Sagarra 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Sagarra, Oleguer Szell, Michael Santi, Paolo Díaz-Guilera, Albert Ratti, Carlo Supersampling and Network Reconstruction of Urban Mobility |
title | Supersampling and Network Reconstruction of Urban Mobility |
title_full | Supersampling and Network Reconstruction of Urban Mobility |
title_fullStr | Supersampling and Network Reconstruction of Urban Mobility |
title_full_unstemmed | Supersampling and Network Reconstruction of Urban Mobility |
title_short | Supersampling and Network Reconstruction of Urban Mobility |
title_sort | supersampling and network reconstruction of urban mobility |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537279/ https://www.ncbi.nlm.nih.gov/pubmed/26275237 http://dx.doi.org/10.1371/journal.pone.0134508 |
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