Supersampling and Network Reconstruction of Urban Mobility.
Identifieur interne : 000076 ( PubMed/Corpus ); précédent : 000075; suivant : 000077Supersampling and Network Reconstruction of Urban Mobility.
Auteurs : Oleguer Sagarra ; Michael Szell ; Paolo Santi ; Albert Díaz-Guilera ; Carlo RattiSource :
- PloS one [ 1932-6203 ] ; 2015.
English descriptors
- KwdEn :
- MESH :
- geographic : New York City.
- statistics & numerical data : Motor Vehicles.
- Algorithms, Geographic Information Systems, Humans, Sampling Studies, Spatio-Temporal Analysis, Urban Population.
Abstract
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.
DOI: 10.1371/journal.pone.0134508
PubMed: 26275237
Links to Exploration step
pubmed:26275237Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Supersampling and Network Reconstruction of Urban Mobility.</title><author><name sortKey="Sagarra, Oleguer" sort="Sagarra, Oleguer" uniqKey="Sagarra O" first="Oleguer" last="Sagarra">Oleguer Sagarra</name><affiliation><nlm:affiliation>Departament de Física Fonamental, Universitat de Barcelona, Barcelona, Spain; Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Szell, Michael" sort="Szell, Michael" uniqKey="Szell M" first="Michael" last="Szell">Michael Szell</name><affiliation><nlm:affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America; Center for Complex Network Research, Northeastern University, Boston, Massachusetts, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Santi, Paolo" sort="Santi, Paolo" uniqKey="Santi P" first="Paolo" last="Santi">Paolo Santi</name><affiliation><nlm:affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America; Istituto di Informatica e Telematica del CNR, Pisa, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Diaz Guilera, Albert" sort="Diaz Guilera, Albert" uniqKey="Diaz Guilera A" first="Albert" last="Díaz-Guilera">Albert Díaz-Guilera</name><affiliation><nlm:affiliation>Departament de Física Fonamental, Universitat de Barcelona, Barcelona, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Ratti, Carlo" sort="Ratti, Carlo" uniqKey="Ratti C" first="Carlo" last="Ratti">Carlo Ratti</name><affiliation><nlm:affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2015">2015</date><idno type="doi">10.1371/journal.pone.0134508</idno><idno type="RBID">pubmed:26275237</idno><idno type="pmid">26275237</idno><idno type="wicri:Area/PubMed/Corpus">000076</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000076</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Supersampling and Network Reconstruction of Urban Mobility.</title><author><name sortKey="Sagarra, Oleguer" sort="Sagarra, Oleguer" uniqKey="Sagarra O" first="Oleguer" last="Sagarra">Oleguer Sagarra</name><affiliation><nlm:affiliation>Departament de Física Fonamental, Universitat de Barcelona, Barcelona, Spain; Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Szell, Michael" sort="Szell, Michael" uniqKey="Szell M" first="Michael" last="Szell">Michael Szell</name><affiliation><nlm:affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America; Center for Complex Network Research, Northeastern University, Boston, Massachusetts, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Santi, Paolo" sort="Santi, Paolo" uniqKey="Santi P" first="Paolo" last="Santi">Paolo Santi</name><affiliation><nlm:affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America; Istituto di Informatica e Telematica del CNR, Pisa, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Diaz Guilera, Albert" sort="Diaz Guilera, Albert" uniqKey="Diaz Guilera A" first="Albert" last="Díaz-Guilera">Albert Díaz-Guilera</name><affiliation><nlm:affiliation>Departament de Física Fonamental, Universitat de Barcelona, Barcelona, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Ratti, Carlo" sort="Ratti, Carlo" uniqKey="Ratti C" first="Carlo" last="Ratti">Carlo Ratti</name><affiliation><nlm:affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Geographic Information Systems</term><term>Humans</term><term>Motor Vehicles (statistics & numerical data)</term><term>New York City</term><term>Sampling Studies</term><term>Spatio-Temporal Analysis</term><term>Urban Population</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>New York City</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Motor Vehicles</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Geographic Information Systems</term><term>Humans</term><term>Sampling Studies</term><term>Spatio-Temporal Analysis</term><term>Urban Population</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">26275237</PMID><DateCreated><Year>2015</Year><Month>08</Month><Day>15</Day></DateCreated><DateCompleted><Year>2016</Year><Month>05</Month><Day>12</Day></DateCompleted><DateRevised><Year>2015</Year><Month>08</Month><Day>21</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>8</Issue><PubDate><Year>2015</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Supersampling and Network Reconstruction of Urban Mobility.</ArticleTitle><Pagination><MedlinePgn>e0134508</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0134508</ELocationID><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sagarra</LastName><ForeName>Oleguer</ForeName><Initials>O</Initials><AffiliationInfo><Affiliation>Departament de Física Fonamental, Universitat de Barcelona, Barcelona, Spain; Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Szell</LastName><ForeName>Michael</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America; Center for Complex Network Research, Northeastern University, Boston, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Santi</LastName><ForeName>Paolo</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America; Istituto di Informatica e Telematica del CNR, Pisa, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Díaz-Guilera</LastName><ForeName>Albert</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Departament de Física Fonamental, Universitat de Barcelona, Barcelona, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ratti</LastName><ForeName>Carlo</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>08</Month><Day>14</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2004 Oct 19;101(42):15124-9</RefSource><PMID Version="1">15477600</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nature. 2006 Jan 26;439(7075):462-5</RefSource><PMID Version="1">16437114</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2006 Feb 14;103(7):2015-20</RefSource><PMID Version="1">16461461</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nature. 2008 Jun 5;453(7196):779-82</RefSource><PMID Version="1">18528393</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2009 Jul 14;106(28):11433-8</RefSource><PMID Version="1">19571013</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):21484-9</RefSource><PMID Version="1">20018697</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Science. 2010 Feb 19;327(5968):1018-21</RefSource><PMID Version="1">20167789</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2010;5(7):e11596</RefSource><PMID Version="1">20657651</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2010;5(11):e15422</RefSource><PMID Version="1">21124970</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2011;6(1):e15923</RefSource><PMID Version="1">21249210</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Nature. 2012 Apr 5;484(7392):96-100</RefSource><PMID Version="1">22367540</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2012;7(4):e34487</RefSource><PMID Version="1">22529917</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Sci Rep. 2012;2:457</RefSource><PMID Version="1">22708055</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2012;7(10):e45985</RefSource><PMID Version="1">23049691</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS One. 2014;9(8):e105184</RefSource><PMID Version="1">25133549</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D040362">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D018986">Motor Vehicles</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000706">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" Type="Geographic" UI="D009519">New York City</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012494">Sampling Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D062211">Spatio-Temporal Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014505">Urban Population</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4537279</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="ecollection"><Year>2015</Year><Month></Month><Day></Day></PubMedPubDate><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>4</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>7</Month><Day>9</Day></PubMedPubDate><PubMedPubDate PubStatus="epublish"><Year>2015</Year><Month>8</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>8</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>8</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>5</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1371/journal.pone.0134508</ArticleId><ArticleId IdType="pii">PONE-D-15-17090</ArticleId><ArticleId IdType="pubmed">26275237</ArticleId><ArticleId IdType="pmc">PMC4537279</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/TelematiV1/Data/PubMed/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000076 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd -nk 000076 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Ticri/CIDE
|area= TelematiV1
|flux= PubMed
|étape= Corpus
|type= RBID
|clé= pubmed:26275237
|texte= Supersampling and Network Reconstruction of Urban Mobility.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i -Sk "pubmed:26275237" \
| HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd \
| NlmPubMed2Wicri -a TelematiV1
| This area was generated with Dilib version V0.6.31. |  |