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.

Recent years have seen an increase in the use of social me-dia for various decision-making purposes in the context ofurban computing and smart cities, including management ofpublic parks. However, as such decision-making tasks arebecoming more autonomous, a critical concern that arises isthe extent to which such analysis are fair and inclusive. Inthis article, we examine the biases that exist in social media analysis pipelines that focus on researching recreationalvisits to urban parks. More precisely, we demonstrate thepotential biases that exist in different data sources for esti-mating the number and demographics of visitors through acomparison of image content shared on Instagram and Flickrfrom 10 urban parks in Seattle, Washington. We draw a com-parison against a traditional intercept survey of park visitorsand a multi-modal city-wide survey of residents. We eval-uate the viability of using more complex AI facial recognition algorithms and its capabilities for removing some ofthe presented biases. We evaluate the AI algorithm throughthe lens of algorithmic fairness and its impact on sensitivedemographic groups. We show that despite the promisingresults, there are new sets of concerns regarding equity thatarise when we use AI algorithms.

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