Tourism Geography through the Lens of Time Use: A Computational Framework Using Fine-Grained Mobile Phone Data

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-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.

位置技术和大数据正在改变我们获取和分析人类活动的方式, 尤其影响了旅游地理。旅游地理研究时空环境下游客的活动。本研究认为, 人类流动性观测数据中游客对时间的使用, 可以用来更好地理解城市旅游地理。利用韩国三个城市（Gangneung, Jeonju, Chuncheon）大尺度手机数据, 我们开发了一个概念框架, 可以准确地量化游客对时间的使用, 对时空活动模式进行可视化, 并系统性比较不同城市。该框架包括：提取和标注游客活动的方法, 时间利用的可视化和分析工具（时间利用图, 时间和活动图）, 以及用于逐日对比的定量测量。通过选取具有规律性模式的代表性时间, 对三个城市进行比较分析, 展示了框架的可行性。针对2018年冬奥会期间的Gangneung, 本框架讨论了特定事件中游客对时间的使用。通过对比冬奥会事件和游客时空模式, 对本文的结果进行了验证。采用时空大数据, 本研究为连接时间地理和旅游提供了新观点。本概念框架可以用于类似数据, 推动了时间地理、旅游和城市流动性的研究。

Las tecnologías y los big data estrechamente ligados con localización están transformando las maneras como captamos y analizamos las actividades humanas. En particular, esto ha afectado la geografía del turismo, que apunta a estudiar las actividades turísticas dentro del contexto de espacio y lugares. El este estudio sostenemos que la geografía del turismo en las ciudades puede entenderse mejor a través del uso del tiempo de los turistas captado con observaciones muy detalladas de la movilidad humana. Con el uso de un conjunto de datos a gran escala de teléfonos móviles recogido en tres ciudades de Corea del Sur (Gangneung, Jeonju y Chuncheon), desarrollamos un marco computacional que permite la cuantificación exacta del uso del tiempo por el turista, la visualización de los patrones de su actividad espaciotemporal y comparaciones sistemáticas a través de las ciudades. El marco consta de varios enfoques para la extracción y el rotulado semántico de las actividades turísticas, herramientas analítico-visuales (diagrama del tiempo–uso, diagrama de tiempo–actividad) para examinar su uso del tiempo, lo mismo que medidas cuantitativas que faciliten hacer comparaciones de día a día. La viabilidad del marco se demuestra representando un análisis comparativo en tres ciudades durante días representativos, cuando los turistas tienden a mostrar patrones más regulares. El marco también se emplea para examinar el uso del tiempo por el turista durante eventos especiales, usando como ejemplo a Gangneung durante los Olímpicos de Invierno (WO) de 2018. la validación de los descubrimientos logrados se hace comparando los patrones espaciotemporales con el calendario WO de los eventos. El estudio provee una nueva perspectiva que conecta la geografía del tiempo y el turismo por medio del uso de big data espaciotemporales. El marco computacional puede aplicarse a conjuntos de datos compatibles para fomentar la geografía del tiempo, el turismo y la investigación sobre movilidad urbana.

Key Words:

• human mobility
• mobile phone data
• time geography
• time use
• tourism geography

关键词:

• 人类流动性
• 手机数据
• 时间地理
• 时间的使用
• 旅游地理

Palabras clave:

• datos de teléfonos móviles
• geografía del tiempo
• geografía del turismo
• movilidad humana
• uso del tiempo

Acknowledgments

The authors thank Dr. Ling Bian and the reviewers for their valuable comments and suggestions that improved this article. We sincerely thank Korea Tourism Organization (particularly to Tourism Big Data Center) for their full support in initiating the project and conducting the data collection.

Notes

1 The cellular operator has differentiated international travelers from local residents. The fundamental idea is that the mobile data used in this research refer to inbound tourists who have used mobile roaming services in South Korea.

2 Defined as the number of phone users with at least one record during the specified day.

3 Note that individual activities (e.g., NAP, OTHERS) are identified at the level of activity anchor points, which could consist of one or several MBS locations. This introduces an issue when we want to map the density of activities at the level of MBS. To simplify the mapping process, for each activity anchor (of each individual), we identify its representative MBS, defined as the MBS in the anchor point with the highest stay duration. The activity is always allocated to the representative MBS when counting the frequency of activities.

Additional information

Funding

This article and research project (Project Account Code: 5-ZJLW) are jointly funded by Research Grant of Hospitality and Tourism Research Centre (HTRC Grant) of the School of Hotel and Tourism Management and PTeC (Project Code P18-0015) at The Hong Kong Polytechnic University, and the Hong Kong Polytechnic University Start-Up Grant (Grant no. 1-BE0J).

Notes on contributors

Yang Xu

YANG XU is an Assistant Professor in the Department of Land Surveying and Geo-Informatics at the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. E-mail: [email protected]. His research interests include GIScience, human mobility, and urban informatics.

Jingyan Li

JINGYAN LI is a Research Assistant in the Department of Land Surveying and Geo-Informatics at the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. E-mail: [email protected]. Her research interests include GIScience, spatiotemporal data mining, and mobility science.

Jiaying Xue

JIAYING XUE is a Research Assistant in the Department of Land Surveying and Geo-Informatics at the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. E-mail: [email protected]. Her research interests include urban mobility, GIScience, and spatial analysis.

Sangwon Park

SANGWON PARK is Associate Professor at the School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. E-mail: [email protected]. His research interests include tourism big data, information technology, and travel behavior.

Qingquan Li

QINGQUAN LI is a Professor in the Guangdong Key Laboratory of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, China. E-mail: [email protected]. His research interests include 3D and dynamic modeling in GIS; location-based services; surveying engineering; integration of GIS, GPS, and remote sensing; and intelligent transportation systems.