Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr

RESEARCH ARTICLE Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr Yihong Yuan*, Monica Medel Department of Geography, Texas State University, San Marcos, Texas, 78666, United States of America * Abstract a11111 OPEN ACCESS Citation: Yuan Y, Medel M (2016) Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr. PLoS ONE 11(5): e0154885. doi:10.1371/journal.pone.0154885 Editor: Ye Wu, Beijing University of Posts and Telecommunications, CHINA Recent advances in multimedia and mobile technologies have facilitated large volumes of travel photos to be created and shared online. Although previous studies have utilized geotagged photos to model travel patterns at individual locations, there is limited research on how these datasets can model international travel behavior and inter-country travel flows— a crucial indicator to quantify the interactions between countries in tourism economics. Realizing the necessity to investigate the potential of geotagged photos in tourism geography, this research investigates international travel patterns from two perspectives: 1) We apply a series of indicators (radius of gyration (ROG), number of countries visited, and entropy) to measure the descriptive characteristics of international travel in different countries; 2) By constructing a gravity model of trade, we investigate how distance decay influences the magnitude of international travel flow between geographic entities, and whether (or how much) the popularity of a given destination (defined as the percentage of tourist income in national gross domestic product (GDP)) affects travel choices in different countries. The results provide valuable input to various commercial applications such as individual travel planning and destination suggestions. Received: November 12, 2015 Accepted: April 20, 2016 Published: May 9, 2016 Copyright: © 2016 Yuan, Medel. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data are available from Yahoo Labs: https://webscope.sandbox.yahoo. com/catalog.php?datatype=i&did=67. Funding: The authors received no specific funding for this work. Competing Interests: The authors have declared that no competing interests exist. 1. Introduction Recent studies have investigated the usage of big data to generalize, model, and predict human mobility and travel behavior, including location-based social media (LBSM) [1, 2], mobile phone tracking [3, 4], Global Positioning System (GPS) logs, or a combination of the above [5]. Among these new big data sources, the usage of LBSM in modeling travel behavior has grown rapidly: these data are user-generated, geo-located, and contain varying types of contextual information (text, videos, images, etc.), therefore can be potential resources to characterize activities’ patterns in various temporal scales–from daily to yearly–and users’ social perceptions of place [6]. Specifically, researchers have explored the potential of employing geotagged photos to analyze individual and aggregated travel behaviors [7–9]. Recent advances in multimedia and PLOS ONE | DOI:10.1371/journal.pone.0154885 May 9, 2016 1 / 18 International Travel Behavior and Geotagged Photos mobile technologies have facilitated large volumes of travel photos to be created and shared online. Unlike traditional travel surveys or actively collected GPS logs (e.g., in human-participant experiments), these datasets often cover a large sample size and can easily be accessed through crowd-sourcing toolkits [8]. Hence, geotagged photos often provide information or solutions faster and in greater detail than traditional means for obtaining the spatio-temporal footprint of travelers [10]. Although previous studies have investigated utilizing geotagged photos to model travel patterns at individual locations (e.g., the study on Hong Kong tourists in [8]), as well as predicting individual travel behavior and providing future destination recommendations [11], there is limited research on how these datasets can model international travel behaviors and inter-country travel flows—a crucial indicator to measure the interactions between countries and model international capital flows in tourism economics [12]. Realizing the necessity to investigate the potential of geotagged photos in modeling intercountry travel behavior, this research aims to investigate international traveling from two perspectives: 1) we apply a series of indicators (radius of gyration (ROG), number of countries visited, and entropy) to measure the descriptive characteristics of international traveling in different countries. These three indicators measure both the “morphology” (e.g., the scale) of traveling and the internal structure of how travel interest distributes in different countries (e.g., do users in the United States (U.S.) upload similar number of photos in each visited country, or is the photo uploading activity less evenly distributed?); 2) the spatial decay effect has been a continuing topic in many research fields such as immigration, transportation, and international tourist studies (e.g., the decay of interaction flows between locations) [13]. A thorough understanding of these behavior patterns is crucial for promoting the development of the tourism industry and maintaining sustainable mobility. Hence, in this research, we also investigate how distance decay influences the magnitude of international traveling between geographic entities, and whether (or how much) the popularity of a certain destination (defined as the percentage of tourist income in national gross domestic product (GDP) of a certain country) affects travel choices in different countries. Among all potential models, we chose the gravity model of trade due to its effectiveness in predicting the degree of interaction, simplicity of equation, and its ability to deal with flows in both directions [14]. This study contributes to the field from the following perspectives: First, empirically, we analyze three aspects of international travel pattern (ROG, number of countries visited, and entropy), as well as how these patterns correlate with the spatial distance and the socio-economic factors of a certain country. Although similar studies have been conducted based on LBSM, such studies mainly focus on travel distances, and there has not been sufficient study on how to explore various aspects of international travel behaviors from user-generated geotagged photos. Second, methodologically, we demonstrate the effectiveness of employing a variation of the gravity model of trade in international traveling, where the travel behavior is bilateral. This paper is organized as follows: Section 2 describes related studies in the areas of travel behav (...truncated)