Touristic site attractiveness seen through Twitter

Traveling is getting more accessible in the present era of progressive globalization. It has never been easier to travel, resulting in a significant increase of the volume of leisure trips and tourists around the world (see, for instance, the statistics of the last UNWTO reports [1]). Over the last fifty years, this increasing importance of the economic, social and environmental impact of tourism on a region and its residents has led to a considerable number of studies in the so-called geography of tourism [2]. In particular, geographers and economists have attempted to understand the contribution of tourism to global and regional economy [3‐7] and to assess the impact of tourism on local people [8‐13].

These researches on tourism have traditionally relied on surveys and economic datasets, generally composed of small samples with a low spatio-temporal resolution. However, with the increasing availability of large databases generated by the use of geolocated information and communication technologies (ICT) devices such as mobile phones, credit or transport cards, the situation is now changing. Indeed, this flow of information has notably allowed researchers to study human mobility patterns at an unprecedented scale [14‐19]. In addition, once these data are recorded, they can be aggregated in order to analyze the city’s spatial structure and function [20‐27] and they have also been successfully tested against more traditional data sources [28‐30]. In the field of tourism geography, these new data sources have offered the possibility to study tourism behavior at a very high spatio-temporal resolution [18, 31‐37].

In this work, we propose a ranking of touristic sites worldwide based on their attractiveness measured with geolocated data as a proxy for human mobility. Many different rankings of most visited touristic sites exist but they are often based on the number of visitors, which does not really tell us much about their attractiveness at a global scale. Here we apply an alternative method proposed in [38] to measure the influence of cities. The purpose of this method is to analyze the influence and the attractiveness of a site based on the average radius traveled and the area covered by individuals visiting this site. More specifically, we select 20 out of the most popular touristic sites of the world and analyze their attractiveness using a dataset containing about 10 million geolocated tweets, which have already demonstrated their efficiency as useful source of data to study mobility at a world scale [18, 38]. In particular, we propose three rankings of the touristic sites’ attractiveness based on the spatial distribution of the visitors’ place of residence, we show that the Taj Mahal, the Pisa Tower and the Eiffel Tower appear always in the top 5. Then, we study the touristic site’s visiting figures by country of residence, demonstrating that the Eiffel Tower, Times Square and the London Tower attract the majority of the visitors. To close the analysis, we focus on users detected in more than one site and explore the relationships between the 20 touristic sites by building a network of undirected trips between them.

The purpose of this study is to measure the attractiveness of 20 touristic sites taking into account the spatial distribution of their visitors’ places of residence. To do so, we analyze a database containing 9.6 million geolocated tweets worldwide posted in the period between September 10, 2010 and October 21, 2015. The dataset was built by selecting the geolocated tweets sent from the touristic places in the general streaming and requesting the time-lines of the users posting them. The touristic sites boundaries have been identified manually. Collective accounts and user exhibiting non-human behaviors have been removed from the data by identifying users tweeting too quickly from the same place, with more than 9 tweets during the same minute and from places separated in time and space by a distance larger than what is possible to be covered by a commercial flight (with an average speed of 750 km/h). Their spatial distributions and that of the touristic sites can be seen in Figure 1.

In order to measure the site attractiveness, we need to identify the place of residence of every user who have been at least once in one of the touristic sites. First, we discretize the space by dividing the world into squares of equal area (\(100 \times100~\mbox{km}^{2}\)) using a cylindrical equal-area projection. Then, we identify the place most frequented by a user as the cell from which he or she has spent most of his/her time. To ensure that this most frequented location is the actual user’s place of residence the constraint that at least one third of the tweets has been posted from this location is imposed. The resulting dataset contains about \(59{,}000\) users’ places of residence. The number of valid users is shown in Table 1 for each touristic site. In the same way, we identify the country of residence of every user who have posted a tweets from one of the touristic sites during the time period.

Two metrics have been considered to measure the attractiveness of a touristic site based on the spatial distribution of the places of residence of users who have visited this site:

To fairly compare the different touristic sites which may have different number of visitors, the two metrics are computed with 200 users’ place of residence selected at random and averaged over 100 independent extractions. Note that unlike the coverage the radius does not depend on the sample size but, to be consistent, we decided to use the same sampling procedure for both indicators.

We study the global attractiveness of 20 touristic sites worldwide taking into account the spatial distribution of the place of residence of the visitors as detected from Twitter. Instead of studying the most visited places, the focus of the analysis is set on the sites attracting visitors from most diverse parts of the world. A first ranking of the sites is obtained based on cells of residence of the users at a geographical scale of 100 by 100 kilometers. Both the radius of attraction and the coverage of the visitors’ origins consistently point toward the Taj Mahal, the Eiffel tower and the Pisa tower as top rankers. When the users’ place of residence is scaled up to country level, these sites still appear on the top and we are also able to discover particular cases such as the Grand Canyon and the Niagara Falls that are most visited by users residing in their hosting countries. At country level, the top rankers are the Taj Mahal and the Pyramids of Giza exhibiting a low cell coverage but with residence cells distributed in many different countries.

Our method to use social media as a proxy to measure human mobility lays the foundation for even more involved analysis. For example, when we cluster the sites by the country of the origin of their visitors, two main clusters emerge: one including the Americas and the Far East and the other with Europe, Oceania and South Africa. The relations between sites have been also investigated by considering users who visited more than one place. An undirected network was built connecting sites visited by the same users. The Eiffel Tower, Times Square, Zocalo and the London Tower are the most central sites of the network.

In summary, this manuscript serves to illustrate the power of geolocated data to provide world wide information regarding leisure related mobility. The data and the method are completely general and can be applied to a large range of geographical locations, travel purposes and scales. We hope thus that this work contribute toward a more agile and cost-efficient characterization of human mobility.