Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78715
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorJia, T-
dc.creatorJi, Z-
dc.date.accessioned2018-09-28T01:17:22Z-
dc.date.available2018-09-28T01:17:22Z-
dc.identifier.urihttp://hdl.handle.net/10397/78715-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).-
dc.rightsThe following publication Jia, T., & Ji, Z. (2017). Understanding the functionality of human activity hotspots from their scaling pattern using trajectory data. ISPRS International Journal of Geo-Information, 6(11), 341 is available at https://doi.org/10.3390/ijgi6110341-
dc.subjectTrajectory dataen_US
dc.subjectHuman activity hotspotsen_US
dc.subjectScalingen_US
dc.subjectUrban functionalityen_US
dc.subjectBayesian inference modelen_US
dc.titleUnderstanding the functionality of human activity hotspots from their scaling pattern using trajectory dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume6-
dc.identifier.issue11-
dc.identifier.doi10.3390/ijgi6110341-
dcterms.abstractHuman activity hotspots are the clusters of activity locations in space and time, and a better understanding of their functionality would be useful for urban land use planning and transportation. In this article, using trajectory data, we aim to infer the functionality of human activity hotspots from their scaling pattern in a reliable way. Specifically, a large number of stopping locations are extracted from trajectory data, which are then aggregated into activity hotspots. Activity hotspots are found to display scaling patterns in terms of the sublinear scaling relationships between the number of stopping locations and the number of points of interest (POIs), which indicates economies of scale of human interactions with urban land use. Importantly, this scaling pattern remains stable over time. This finding inspires us to devise an allometric ruler to identify the activity hotspots, whose functionality could be reliably estimated using the stopping locations. Thereafter, a novel Bayesian inference model is proposed to infer their urban functionality, which examines the spatial and temporal information of stopping locations covering 75 days. Experimental results suggest that the functionality of identified activity hotspots are reliably inferred by stopping locations, such as the railway station.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationISPRS international journal of geo-information, Nov. 2017, v. 6, no. 11, 341-
dcterms.isPartOfISPRS international journal of geo-information-
dcterms.issued2017-
dc.identifier.isiWOS:000416779300022-
dc.identifier.scopus2-s2.0-85044528900-
dc.identifier.eissn2220-9964-
dc.identifier.artn341-
dc.description.validate201809 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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