Recreation and tourism are important ways that people interact with and derive benefits from natural environments. Understanding how and where nature provides recreational opportunities and benefits is necessary for management decisions that impact the environment. This study develops and tests an approach for mapping tourism patterns, and assessing people’s preferences for cultural and natural landscapes, using user-generated geographic content. The volume of geotagged images and tweets shared publicly on Flickr and Twitter and proprietary mobile phone traffic provided by a telecommunications company, are used to map visitation rates to potential tourist destinations across Jeju Island, South Korea. We find that densities of social media posts and mobile phone traffic are all correlated with ticket sales and counts of gate entries at tourist sites. Using multivariate linear regression, we measure the degree to which attributes of the natural and built environment explain variation in visitation rates, and find that tourists to Jeju Island prefer to recreate near beaches, sea cliffs, golf courses and hiking trails. We conclude that high-resolution and spatially-explicit visitation data provided by user-generated content open the door for statistical models that can quantify recreation demand. Managers and practitioners could combine these flexible and relatively inexpensive user-generated data with more traditional survey data to inform sustainable tourism development plans and policy decisions. These methods are especially useful in the context of landscape or regional-scale ecosystem service assessments, where there is a need to map the multiple ecological, economic, and cultural benefits of the environment.

Sustainable tourism involves increasingly attracting visitors while preserving the natural capital of a destination for future generations. To foster tourism while protecting sensitive environments, coastal managers, tourism operators, and other decision-makers benefit from information about where tourists go and which aspects of the natural and built environment draw them to particular locations. Yet this information is often lacking at management-relevant scales and in remote places. We tested and applied methods using social media as data on tourism in The Bahamas. We found that visitation, as measured by numbers of geolocated photographs, is well correlated with counts of visitors from entrance surveys for islands and parks. Using this relationship, we predicted nearly 4 K visitor-days to the network of Bahamian marine protected areas annually, with visitation varying more than 20-fold between the most and least visited parks. Next, to understand spatial patterns of tourism for sustainable development, we combined social media-based data with entrance surveys for Andros, the largest island in The Bahamas. We estimated that tourists spend 125 K visitor-nights and more than US$45 M in the most highly visited district, five times that of the least visited district. We also found that tourists prefer accessible, natural landscapes—such as reefs near lodges—that can be reached by air, roads, and ferries. The results of our study are being used to inform development and conservation decisions, such as where to invest in infrastructure for visitor access and accommodation, siting new marine protected areas, and management of established protected areas. Our work provides an important example of how to leverage social media as a source of data to inform strategies that encourage tourism, while conserving the environments that draw visitors to a destination in the first place.

Decision makers need information on the use of, and demand for, public recreation and transportation facilities. Innovations in monitoring technologies and diffusion of social media enable new approaches to estimation of demand. We assess the feasibility of using geo-tagged photographs uploaded to the image-sharing website Flickr and tweets from Twitter as proxy measures for urban trail use. We summarize geo-tagged Flickr uploads and tweets along 80 one-mile segments of the multiuse trail network in Minneapolis, Minnesota, and correlate results with previously published estimates of annual average daily trail traffic derived from infrared trail monitors. Although heat maps of Flickr images and tweets show some similarities with maps of variation in trail traffic, the correlation between photographs and trail traffic is moderately weak (0.43), and there is no meaningful statistical correlation between tweets and trail traffic. Use of a simple log-log bivariate regression to estimate trail traffic from photographs results in relatively high error. The predictor variables included in published demand models for the same trails explain roughly the same amount of variation in photo-derived use, but some of the neighborhood socio-demographic and built-environment independent variables have different effects. Taken together, these findings show that both Flickr images and tweets have limitations as proxies for demand for urban trails, and that neither can be used to develop valid, reliable estimates of trail use. These results differ from previously published results that indicate social media may be useful in assessing relative demand for recreational destinations. This difference may be because urban trails are used for multiple purposes, including routine commuting and shopping, and that trail users are less inclined to use social media on trips for these purposes.