Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82151
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorZhou, T-
dc.creatorLiu, X-
dc.creatorQian, Z-
dc.creatorChen, H-
dc.creatorTao, F-
dc.date.accessioned2020-05-05T05:58:52Z-
dc.date.available2020-05-05T05:58:52Z-
dc.identifier.urihttp://hdl.handle.net/10397/82151-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2019 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/).en_US
dc.rightsThe following publication Zhou T, Liu X, Qian Z, Chen H, Tao F. Automatic Identification of the Social Functions of Areas of Interest (AOIs) Using the Standard Hour-Day-Spectrum Approach. ISPRS International Journal of Geo-Information. 2020; 9(1):7, is available at https://doi.org/10.3390/ijgi9010007en_US
dc.subjectAOIen_US
dc.subjectLand use typeen_US
dc.subjectMachine learningen_US
dc.subjectSocial functionen_US
dc.subjectUrban functional zoningen_US
dc.titleAutomatic identification of the social functions of areas of interest (AOIS) using the standard hour-day-spectrum approachen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume9-
dc.identifier.issue1-
dc.identifier.doi10.3390/ijgi9010007-
dcterms.abstractThe social function of areas of interest (AOIs) is crucial to the identification of urban functional zoning and land use classification, which has been a hot topic in various fields such as urban planning and smart city fields. Most existing studies on urban functional zoning and land use classification either largely rely on low-frequency remote sensing images, which are constrained to the block level due to their spatial scale limitation, or suffer from low accuracy and high uncertainty when using dynamic data, such as social media and traffic data. This paper proposes an hour-day-spectrum (HDS) approach for generating six types of distribution waveforms of taxi pick-up and drop-off points which serve as interpretation indicators of the social functions of AOIs. To achieve this goal, we first performed fine-grained cleaning of the drop-off points to eliminate the spatial errors caused by taxi drivers. Next, buffer and spatial clustering were integrated to explore the associations between travel behavior and AOIs. Third, the identification of AOI types was made by using the standard HDS method combined with the k-nearest neighbor (KNN) algorithm. Finally, some matching tests were carried out by similarity indexes of a standard HDS and sample HDS, i.e., the Gaussian kernel function and Pearson coefficient, to ensure matching accuracy. The experiment was conducted in the Chongchuan and Gangzha Districts, Nantong, Jiangsu Province, China. By training 50 AOI samples, six types of standard HDS of residential districts, schools, hospitals, and shopping malls were obtained. Then, 108 AOI samples were tested, and the overall accuracy was found to be 90.74%. This approach generates value-added services of the taxi trajectory and provides a continuous update and fine-grained supplementary method for the identification of land use types. In addition, the approach is object-oriented and based on AOIs, and can be combined with image interpretation and other methods to improve the identification effect.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationISPRS international journal of geo-information, 2019, v. 9, no. 1, 7-
dcterms.isPartOfISPRS international journal of geo-information-
dcterms.issued2019-
dc.identifier.isiWOS:000514631100027-
dc.identifier.scopus2-s2.0-85077625764-
dc.identifier.eissn2220-9964-
dc.identifier.artn7-
dc.description.validate202006 bcma-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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