2020 / Public land, Recreation, Social media

Next-Generation Visitation Models using Social Media to Estimate Recreation on Public Lands

Citation

Wood, S. A., Winder, S. G., Lia, E. H., White, E. M., Crowley, C. S. L., & Milnor, A. A. (2020). Next-generation visitation models using social media to estimate recreation on public lands. Scientific Reports, 10(1). doi.org/10.1038/s41598-020-70829-x

Link to file

Outdoor recreation offers a lot of benefits to people and communities. But land managers often do not have good information about where and when people are actually visiting public lands.

Social media could help fill that gap. Past research has shown that the number of people visiting a place is linked to how much social media activity happens there. That connection suggests social media data could be used to estimate visitor numbers. But until now, no one had created a model that could work across different social media sources and different locations.

This study looked at whether social media data from multiple sources could help estimate visitation at sites that are not directly monitored. Researchers used a new dataset of more than 30,000 social media posts and 286,000 observed visits from two regions in the United States. They tested several statistical models to see which ones worked best.

The results showed that social media data significantly improved visitor estimates at unmonitored sites — even when a model was built using data from a different region. Estimates got even better when the model also included some on-the-ground visitor counts.

While social media data cannot fully replace on-site counting, it can be a powerful tool for recreation research on public lands.

Abstract

Figure 1. Locations of geotagged social media posts made by visitors to public lands in WWA and NNM. Points represent the latitude and longitude where a Flickr photograph (purple) or tweet (green) was created. For Instagram, points represent places to which images were assigned by users (blue). Larger points represent a greater number of Instagram images from the location. Study sites are contained within the management units (shaded grey). Figure created using R48 version 3.5.3.

Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.