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This study of recreation in two geographically distinct regions of the United States indicates that social media can predict visitation at recreation sites on public land. We conclude that social media data can be applied with moderate success to estimate visitation at sites that are unmonitored or otherwise lack on-site counts, even in new regions. A basic visitation model that relies solely on generic predictors (e.g., weather, holidays, and seasonality) is only modestly successful due to regional differences in visitor behavior (Model 1). Performance is improved by including relationships between visitation and social media (Flickr, Instagram, and Twitter), even when these relationships are transferred from a different region (WWA, Model 2). These results are consistent with prior research findings that social media counts are correlated with on-site visitor counts from public lands8,9,24,32, and extend earlier findings by showing the potential for statistical models to estimate absolute numbers of visitors at unmonitored sites with parameters derived from social media. This is evidence of patterns in how visitors use and share social media. Furthermore, it suggests that measurable variables associated with social media use could support transferable models for accurately estimating visitation to public lands across large geographies.
We expected to detect regional differences in social media use given regional distinctions in climate, land management, population density, mobile phone signal coverage, and the demographics of visitors. Contrary to our expectations, we observed that the rate of posting to social media about recreation visits is similar across sites in NNM and WWA, and both regions displayed positive correlations between each of these social media data sources and observed visitation (Fig. 3). Furthermore, a model parameterized with social media use in WWA (Model 2) explains 45% of the variation in visitation across all 13 sites in NNM and 79% of the variability in visitation at the subset of sites that had social media posts. Re-parameterizing Model 2 with a portion of NNM visitation data does not improve its performance (Model 3) until these data are considered at the site level (Model 5). There is a noisy but consistent relationship between a destination’s popularity with visitors and its popularity on social media, regardless of whether the site is in NNM or WWA. We interpret this as evidence that visitors are equally likely to share their recreation experiences in NNM or WWA, despite the regional differences in the types of recreation opportunities and the people who are visiting.
Although there are consistent relationships between social media use and visitation at a regional scale, we see large site-to-site variability in how visitors use social media within both regions. Beyond simple differences in numbers of posts by site, the proportion of people who post about their visits varies by destination, ranging from 7% of visitors to Kasha–Katuwe Tent Rocks National Monument posting on Instagram to zero recorded social media user-days at several trails in the Valles Caldera National Preserve. As a result, models with random effects that allow sites to have unique relationships between social media, weather, and visitation (Models 4–5) perform substantially better than models that assume social media use is consistently related to visitation for all types of sites (Models 1–3). This is especially true at the most and least visited sites (Figs. 4, 5), where visitors may be sharing social media differently than they do at moderately visited sites, and responding differently to other conditions such as weather or holidays. These results indicate that while data on social media use are helpful for predicting visitation with moderate certainty in an otherwise unknown region (Model 1 vs. 2), their utility for estimating visitation is less clear when local data on the effects of environmental and institutional conditions such as weather and holidays are available to parameterize site-specific models.
A primary goal of this study is to test approaches for estimating visitation over relatively small areas in order to explore the limits of the data and methods. We find that six of the 13 sites in NNM—representing individual trails or groups of trails within a larger park—lack social media during the study period. Model 2, which depends on social media data to estimate visitation at unmonitored sites, consequently performs relatively poorly at these six sites (Fig. 4). Generally, sites that have sparse social media data tend to receive few visitors, but there are exceptions. Alcove House in Bandelier National Monument, for instance, is a very highly visited site that lacks Instagram images in our study because the site does not appear as a prescribed location for Instagram users. Our informal observation is that many visitors instead share untagged photographs of Alcove House or assign their images to other relevant place names such as Bandelier National Monument. Clearly, there are thresholds to where and how social media can be leveraged for visitor estimation. Our research suggests that future studies and visitation models could be improved by accounting for the popularity of sites on social media in the study design12. Visitation models that include predictors derived from social media (Models 2, 3, and 5, here) will likely out-perform alternative models for estimating use at popular sites or when longer time series are available. At locations with low or no posting activity, where social media contributes less to visitor estimates, it could be more useful to collect on-site data such as vehicle and pedestrian counts. Further research is necessary to understand what combination of on-site, social media, environmental, and other data is most valuable at different spatial and temporal scales.
These observations suggest that variability in correlations between social media and on-site visitor counts seen here and in previous studies8,12,20 is derived from local factors influencing visitors’ day-to-day decisions about whether and how to share a destination on social media. Choices about whether to post to social media are likely influenced by the characteristics of the local site—perhaps its topography, amenities, predominant activity, or unique natural features—and the ways that people relate to these features33. The characteristics of the visitors and the relative contributions of the natural versus the social experience in the motivation for the trip may play a role. For example, if a given type of site attracts visitors wishing to “unplug” and have a nature-based outing (a calm forest glade, say), there may be fewer posts per visit than for a site attracting visitors who desire a social experience within a natural setting (a famous scenic overlook, for instance). Another possibility is that the prevailing popularity of certain destinations on social media creates a positive feedback, whereby new visitors feel compelled to share content about their visit in response to the posts of others or the local hashtags that may make it easier or more enticing to post. Variability could also arise from the recent trend towards discouraging visitors from posting geolocated content and attracting attention to less popular or back-country sites that are not equipped to sustain higher use, although this is probably of minor importance, currently.
This is the first study to our knowledge that develops and tests models for estimating absolute numbers of visitors at unmonitored recreation sites or times using multiple social media data sources with differential effects. Building on earlier research exploring relationships of park visitation with numbers of posts to multiple social media platforms9,12,20,27, the present study tests whether models with a mixture of predictors to represent varying effects of three online platforms can estimate visitation in novel situations. We find that each social media data source contributes information that explains a statistically significant portion of the variability in visitation and improves the accuracy of the estimate. This is the case not only for Instagram, which captures 3–4% of visitor-days at our research sites in WWA and NNM, but also for Flickr and Twitter, with relatively small amounts of content shared ( More

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