Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorZhou, T-
dc.creatorLiu, X-
dc.creatorQian, Z-
dc.creatorChen, H-
dc.creatorTao, F-
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 (
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
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
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-
dc.description.validate202006 bcma-
dc.description.oaVersion of Recorden_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Zhou_Automatic_identification_social.pdf14.45 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

Last Week
Last month
Citations as of Oct 1, 2023


Citations as of Oct 1, 2023


Citations as of Sep 28, 2023


Citations as of Sep 28, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.