Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/118444
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorLiao, M-
dc.creatorLiu, X-
dc.date.accessioned2026-04-15T02:05:00Z-
dc.date.available2026-04-15T02:05:00Z-
dc.identifier.issn1874-463X-
dc.identifier.urihttp://hdl.handle.net/10397/118444-
dc.language.isoenen_US
dc.publisherSpringer Dordrechten_US
dc.rights© The Author(s) 2026en_US
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rightsThe following publication Liao, M., Liu, X. Beyond Mobility Volumes: Origin-City-Level Tourist Visitation Patterns Across Urban Attractions. Appl. Spatial Analysis 19, 63 (2026) is available at https://doi.org/10.1007/s12061-026-09844-w.en_US
dc.subjectData-driven clusteringen_US
dc.subjectMultilayer network analysisen_US
dc.subjectOrigin-city-level mobilityen_US
dc.subjectTourist visitation patternsen_US
dc.titleBeyond mobility volumes : origin-city-level tourist visitation patterns across urban attractionsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume19-
dc.identifier.issue1-
dc.identifier.doi10.1007/s12061-026-09844-w-
dcterms.abstractUnderstanding tourist behavior at the origin-city level is essential for destination planning and regional tourism management. Most existing studies focus on aggregate intercity mobility volumes, overlooking how tourists from different cities distribute their visits across various attractions. This study addresses this gap by analyzing origin-city-level visitation patterns to three attraction categories (i.e., cultural-historic, natural scenery, and leisure) using weekly mobility data from over 300 Chinese cities to Nanjing during 2018–2019. First, the spatiotemporal heterogeneity of visitation to three attraction categories is analyzed. Second, a data-driven regionalization is conducted based on intercity similarities in attraction visitation. Logistic regression is then employed to examine how city-level characteristics, including socioeconomic, demographic, geographic, and functional attributes, are associated with these visitation patterns. Results reveal pronounced spatial heterogeneity and two dominant visitation regimes covering approximately 92% of cities: one favoring cultural-historic attractions in northern China and Guangdong, and another exhibiting more balanced visitation between cultural-historic and nature-scenery attractions in southern regions. Regression analyses highlight the importance of demographic composition, geographic orientation, urban functions, and digital attention in shaping intercity visitation differences. These findings provide empirical support for behavior-informed tourism planning, enabling policymakers to align destination management and resource allocation with the visitation patterns of different origin cities.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationApplied spatial analysis and policy, Mar. 2026, v. 19, no. 1, 63-
dcterms.isPartOfApplied spatial analysis and policy-
dcterms.issued2026-03-
dc.identifier.scopus2-s2.0-105033496459-
dc.identifier.eissn1874-4621-
dc.identifier.artn63-
dc.description.validate202604 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_TAen_US
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.TASpringer Nature (2026)en_US
dc.description.oaCategoryTAen_US
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