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Destination, as a key concept in tourism geography, has largely determined the scale at which tourist activity space was modeled and studied. Existing studies usually focused on investigating tourists' activities and movements either at the intradestination (e.g., within a city) or interdestination scale. Although useful in numerous research contexts, these models based on fixed spatial scales are incapable of portraying the complex spatial structure of tourist activity spaces, which sometimes exhibit hierarchical structures, and could span across different spatial scales. In this study, we propose a new representation of tourist activity space to bridge these gaps. The representation takes tourists' accommodation locations as key reference points. At the macroscale, the sequence of accommodation locations forms the backbone of tourist activity space, denoted as itinerary type. At the microscale, we introduce the concept of territory to describe how individuals organize activities around these overnight "base camps" (i.e., accommodation locations). We apply this representation over a large-scale mobile phone data set of international travelers visiting South Korea to demonstrate its capability. Results show that four generic itinerary types capture the activity space structure of 89 percent of the tourists. The interrelationships of territories and their topological structures further categorize activity spaces into subtypes, leading to a new method of tourist classification based on their spatiotemporal activity patterns. We believe the proposed representation could enrich new perspectives and debates on how tourist activities can be studied. The representation can also be extended as a generic framework to delineate complex forms of human activity space. [ABSTRACT FROM AUTHOR]

• A quantitative analysis of the movement predictability of urban tourists and visitors using a nationwide mobile phone dataset. • A Markov chain model and a recurrent neural network are used to assess travelers' movement predictability. • Travelers' movement predictability is impacted by length of stay and activeness in travel patterns. • A notable geographic variation is observed: travelers' movements are more predictable in some cities, but less in others. The abilities to predict tourist movements are critical to many urban applications, such as travel recommendations, targeted advertising, and infrastructure planning. Despite its importance, our understanding on the movement predictability of urban tourists and visitors is still limited, partially due to difficulties in accessing large scale mobility observations. In this study, we aim to bridge this gap by analyzing a nationwide mobile phone dataset. The dataset captures movement traces of a large number of international travelers who visited South Korea in 2018. By introducing two prediction models, one being Markov chain and the other with a recurrent neural network architecture, we assess how well travelers' movements can be predicted under different model settings, and examine how predictability relates to travelers' length of stay and activeness in travel patterns. Since travelers' destination choices are quite diverse in South Korea, this enables us to further investigate the geographic variation of the models' performance. Results show that the Markov chain model achieves an overall accuracy between 33.4% (@Acc1 metric) and 64.2% (@Acc5 metric), compared to 41.9% (@Acc1) and 67.7% (@Acc5) for the recurrent neural network model. The prediction capabilities of both models are largely unequal across individuals, with active travelers being more predictable in general. There is a notable geographic variation in the models' performance, meaning that travelers' movements are more predictable in some cities, but less in others. We believe this study represents a new effort in portraying the movement predictability of urban tourists and visitors. The analytical framework can be applied to assist tourism planning and service deployment in cities. [ABSTRACT FROM AUTHOR]

This study characterizes a destination network based on travel flow. Although the tourism literature has mostly discussed the static configurations of network structures, this study presents a dynamic destination network considering four seasons and daily periods. Given the advancement of connected technology, mobile sensor big data collected from international travelers visiting Korea were analyzed to explore movement behaviors across 250 cities in the country. Results demonstrate the dynamics of complex network systems in tourism destinations over time, such as seasons and diurnal variations. Its findings are crucial for developing vibrant destination management and contextual marketing. [ABSTRACT FROM AUTHOR]

The last decade has witnessed a wealth of studies on characterizing human mobility patterns using movement datasets. Such efforts have highlighted a few salient dimensions of individual travel behavior relevant to urban planning and policy analysis. Despite the fruitful research outcomes, most of the findings are drawn upon urban residents. The behavioral characteristics of other population groups, such as tourists, remain underexplored. In this study, we introduce an analytical framework to gain insights into tourist mobility patterns. By analyzing mobile phone trajectories of international travelers to three different cities in South Korea, we introduce nine mobility indicators to capture different facets of tourist travel behavior (e.g., duration of stay in a city, spatial extent of activities, location visited and trips conducted, and mobility diversity), and examine their statistical properties across cities. An eigendecomposition approach is then introduced to better understand the interdependency of these mobility indicators and inherent variations among individual travelers. Based on the eigendecomposition results, we further employ a dimension reduction technique to describe the key characteristics of each traveler. Since the mobile phone dataset captures the nationality of tourists, we use such information to quantify the behavioral heterogeneity of travelers across countries and regions. Finally, we select a few traveler groups with distinctive mobility patterns in each city and examine the spatial patterns of their activities. Substantial differences are observed among traveler groups in their spatial preferences. The implications for location recommendation and deployment of tourism services (e.g., transportation) are discussed. We hope the study brings a synergy between classic human mobility analysis and the emerging field of tourism big data. The framework can be applied or extended to compatible datasets to understand travel behavior of tourists, residents, and special population groups in cities. • A cross-city comparison of tourist mobility patterns using mobile phone data • Nine mobility indicators for quantifying important aspects of tourist travel behavior • An eigen decomposition approach for characterizing inherent mobility variations among individual travelers • The behavioral characteristics of travelers from different foreign countries to South Korea are analyzed. • Travelers with different mobility characteristics have different spatial preferences when visiting a city. [ABSTRACT FROM AUTHOR]