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Conference Paper: The Uncertain Geographic Context Problem in Identifying Activity Centers Using Mobile Phone Positioning Data and Point of Interest Data

TitleThe Uncertain Geographic Context Problem in Identifying Activity Centers Using Mobile Phone Positioning Data and Point of Interest Data
Authors
Zhou, XLiu, JYeh, AGOYue, YLi, W
KeywordsActivity center
Mobile phone positioning data
Point of interest
UGCoP
Issue Date2015
PublisherSpringer International Publishing.
Citation
16th International Symposium on Spatial Data Handling (SDH 2014), Toronto, Canada, 6-8 October 2014. In Harvey, F & Leung, Y (Eds.). Advances in Spatial Data Handling and Analysis: Select Papers from the 16th IGU Spatial Data Handling Symposium, p. 107-119. Cham: Springer International Publishing, 2015 How to Cite?
DOI: http://dx.doi.org/10.1007/978-3-319-19950-4_7
AbstractPeople aggregate at different areas in different times of the day, thus forming different activity centers. The identification of activity centers faces the uncertain geographic context problem (UGCoP) because people go to different places to conduct different activities, and also go to the same place for carrying out different activities in different times of the day. In this paper, we employ two kinds of novel dynamic data, namely mobile phone positioning data and Point of Interest (POI) data to identify the activity centers in a city in China. Then mobile phone positioning data is utilized to identify the activity centers in different times of a working day, and POI data are used to show the activity density variations at these activity centers to explain the temporal dynamics of geographic context. We find that mobile phone positioning data and POI data as two kinds of spatial-temporal data demonstrate people’s activity patterns from different perspectives. Mobile phone positioning data provide a proxy to represent the activity density variations. POI data can be used to identify activity centers of different categories. These two kinds of data can be integrated to identify the activity centers and clarify the UGCoP.
Persistent Identifierhttp://hdl.handle.net/10722/235684
ISBN
978-3-319-19949-8
ISSN
1867-2434
2020 SCImago Journal Rankings: 0.196

 

DC FieldValueLanguage
dc.contributor.authorZhou, X-
dc.contributor.authorLiu, J-
dc.contributor.authorYeh, AGO-
dc.contributor.authorYue, Y-
dc.contributor.authorLi, W-
dc.date.accessioned2016-10-14T13:54:47Z-
dc.date.available2016-10-14T13:54:47Z-
dc.date.issued2015-
dc.identifier.citation16th International Symposium on Spatial Data Handling (SDH 2014), Toronto, Canada, 6-8 October 2014. In Harvey, F & Leung, Y (Eds.). Advances in Spatial Data Handling and Analysis: Select Papers from the 16th IGU Spatial Data Handling Symposium, p. 107-119. Cham: Springer International Publishing, 2015-
dc.identifier.isbn978-3-319-19949-8-
dc.identifier.issn1867-2434-
dc.identifier.urihttp://hdl.handle.net/10722/235684-
dc.description.abstractPeople aggregate at different areas in different times of the day, thus forming different activity centers. The identification of activity centers faces the uncertain geographic context problem (UGCoP) because people go to different places to conduct different activities, and also go to the same place for carrying out different activities in different times of the day. In this paper, we employ two kinds of novel dynamic data, namely mobile phone positioning data and Point of Interest (POI) data to identify the activity centers in a city in China. Then mobile phone positioning data is utilized to identify the activity centers in different times of a working day, and POI data are used to show the activity density variations at these activity centers to explain the temporal dynamics of geographic context. We find that mobile phone positioning data and POI data as two kinds of spatial-temporal data demonstrate people’s activity patterns from different perspectives. Mobile phone positioning data provide a proxy to represent the activity density variations. POI data can be used to identify activity centers of different categories. These two kinds of data can be integrated to identify the activity centers and clarify the UGCoP.-
dc.languageeng-
dc.publisherSpringer International Publishing.-
dc.relation.ispartofAdvances in Spatial Data Handling and Analysis: Select Papers from the 16th IGU Spatial Data Handling Symposium-
dc.subjectActivity center-
dc.subjectMobile phone positioning data-
dc.subjectPoint of interest-
dc.subjectUGCoP-
dc.titleThe Uncertain Geographic Context Problem in Identifying Activity Centers Using Mobile Phone Positioning Data and Point of Interest Data-
dc.typeConference_Paper-
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hk-
dc.identifier.emailLi, W: wfli@hku.hk-
dc.identifier.authorityYeh, AGO=rp01033-
dc.identifier.authorityLi, W=rp01507-
dc.identifier.doi10.1007/978-3-319-19950-4_7-
dc.identifier.scopuseid_2-s2.0-84947222461-
dc.identifier.hkuros269942-
dc.identifier.spage107-
dc.identifier.epage119-
dc.identifier.eissn1867-2442-
dc.publisher.placeCham-
dc.identifier.issnl1867-2434-

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