File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Detecting latent urban mobility structure using mobile phone data

TitleDetecting latent urban mobility structure using mobile phone data
Authors
KeywordsLatent structure
mobile phone data
network detection method
community structure
visualization
Issue Date2020
PublisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/mplb/mplb.shtml
Citation
Modern Physics Letters B, 2020, v. 34 n. 30, p. article no. 2050342 How to Cite?
AbstractThe spatial heterogeneity of land use patterns and residents’ corresponding economic activities give rise to urban mobility’s latent structure, which is of great importance for urban planning and transport infrastructure investment but cannot be readily captured using conventional data sources. We developed a methodological framework for detecting urban mobility structure at the transportation analysis zone (TAZ) level in Beijing using mobile phone signal data. First, we derived origin–destination data at the TAZ level from mobile phone data and visualized them in ArcGIS. Next, we improved community detecting algorithms generally used in social networks by reversing distance weight, such as by dividing ODs by 1, and used the results to reveal hidden clustering features of TAZs, according ODs between them. We visualized and analyzed population density, OD spatial distribution at different times, and ratio of daytime to nighttime population using the GIS platform; all showed some spatial cluster features. We then applied a structure detection algorithm using ODs between TAZ pairs to identify the hidden structure of urban mobility extracted from phone data. For Beijing, the identified mobility structure contains 27 clusters, with those in suburban areas tending to match administrative boundaries well but those in the developed center areas showing complex distributions and matching administrative boundaries poorly. Authorities that provide mobility infrastructure can use the resulting insights into urban planning and transportation planning to inform policy decisions at the local and city levels.
Persistent Identifierhttp://hdl.handle.net/10722/289957
ISSN
2023 Impact Factor: 1.8
2023 SCImago Journal Rankings: 0.334
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, ZJ-
dc.contributor.authorChen, ZX-
dc.contributor.authorWU, JY-
dc.contributor.authorYu, HW-
dc.contributor.authorYao, XM-
dc.date.accessioned2020-10-22T08:19:53Z-
dc.date.available2020-10-22T08:19:53Z-
dc.date.issued2020-
dc.identifier.citationModern Physics Letters B, 2020, v. 34 n. 30, p. article no. 2050342-
dc.identifier.issn0217-9849-
dc.identifier.urihttp://hdl.handle.net/10722/289957-
dc.description.abstractThe spatial heterogeneity of land use patterns and residents’ corresponding economic activities give rise to urban mobility’s latent structure, which is of great importance for urban planning and transport infrastructure investment but cannot be readily captured using conventional data sources. We developed a methodological framework for detecting urban mobility structure at the transportation analysis zone (TAZ) level in Beijing using mobile phone signal data. First, we derived origin–destination data at the TAZ level from mobile phone data and visualized them in ArcGIS. Next, we improved community detecting algorithms generally used in social networks by reversing distance weight, such as by dividing ODs by 1, and used the results to reveal hidden clustering features of TAZs, according ODs between them. We visualized and analyzed population density, OD spatial distribution at different times, and ratio of daytime to nighttime population using the GIS platform; all showed some spatial cluster features. We then applied a structure detection algorithm using ODs between TAZ pairs to identify the hidden structure of urban mobility extracted from phone data. For Beijing, the identified mobility structure contains 27 clusters, with those in suburban areas tending to match administrative boundaries well but those in the developed center areas showing complex distributions and matching administrative boundaries poorly. Authorities that provide mobility infrastructure can use the resulting insights into urban planning and transportation planning to inform policy decisions at the local and city levels.-
dc.languageeng-
dc.publisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/mplb/mplb.shtml-
dc.relation.ispartofModern Physics Letters B-
dc.rightsFor preprints : Preprint of an article published in [Journal, Volume, Issue, Year, Pages] [Article DOI] © [copyright World Scientific Publishing Company] [Journal URL] For postprints : Electronic version of an article published as [Journal, Volume, Issue, Year, Pages] [Article DOI] © [copyright World Scientific Publishing Company] [Journal URL]-
dc.subjectLatent structure-
dc.subjectmobile phone data-
dc.subjectnetwork detection method-
dc.subjectcommunity structure-
dc.subjectvisualization-
dc.titleDetecting latent urban mobility structure using mobile phone data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1142/S021798492050342X-
dc.identifier.scopuseid_2-s2.0-85095129044-
dc.identifier.hkuros317138-
dc.identifier.volume34-
dc.identifier.issue30-
dc.identifier.spagearticle no. 2050342-
dc.identifier.epagearticle no. 2050342-
dc.identifier.isiWOS:000587733700013-
dc.publisher.placeSingapore-
dc.identifier.issnl0217-9849-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats