No abstract

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-today 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.