Tourism Geography through the Lens of Time Use

A Computational Framework Using Fine-Grained Mobile Phone Data

Bibliographic Data

ID3775313
AuthorsYang Xu (0000-0003-3898-022X, Hong Kong Polytechnic University), Jingyan Li (0000-0002-5805-6520, Hong Kong Polytechnic University), Jiaying Xue (0009-0007-3696-2585, Hong Kong Polytechnic University), Sangwon Park (0000-0003-3021-1987, Hong Kong Polytechnic University), Qingquan Li (0000-0002-2438-6046, Shenzhen University)
Year2021
Volume111
Issue5
Pages1420-1444
Publication date2021-07-29
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnals of the American Association of Geographers (JOURNAL)
Journal identifiersISSN: 2469-4452 • E-ISSN: 2469-4460
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/24694452.2020.1812372
OpenAlexW3092785986
LanguageEN
Citations received18
References cited64

Location-aware technologies and big data are transforming the ways we capture and analyze human activities. This has particularly affected tourism geography, which aims to study tourist activities within the context of space and places. In this study, we argue that the tourism geography of cities can be better understood through the time use of tourists captured by fine-grained human mobility observations. By using a large-scale mobile phone data set collected in three cities in South Korea (Gangneung, Jeonju, and Chuncheon), we develop a computational framework to enable accurate quantification of tourist time use, the visualization of their spatiotemporal activity patterns, and systematic comparisons across cities. The framework consists of several approaches for the extraction and semantic labeling of tourist activities, visual-analytic tools (time use diagram, time-activity diagram) for examining their time use, as well as quantitative measures that facilitate day-to-day comparisons. The feasibility of the framework is demonstrated by performing a comparative analysis in three cities during representative days when tourists tended to show more regular patterns. The framework is also employed to examine tourist time use during special events, using Gangneung during the 2018 Winter Olympics (WO) as an example. The findings are validated by comparing the spatiotemporal patterns with the WO calendar of events. The study provides a new perspective that connects time geography and tourism through the usage of spatiotemporal big data. The computational framework can be applied to compatible data sets to advance time geography, tourism, and urban mobility research

Big data · Cartography · Context (archaeology · Data mining · Data science · Economic geography · Geography · Historical geography · Human geography · Mobile phone · Perspective (graphical · Regional science · Scale (ratio · Set (abstract data type · Time geography · Tourism · Computer Science · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban Transport and Accessibility · Artificial Intelligence

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