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Abstract:
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National parks have tremendous cultural, ecological, and economic value to societies. In order to manage and maintain these public spaces, decision-makers rely on information about park use and park condition. Many parks lack precise visitor counts due to challenges associated with monitoring large and inaccessible areas with porous boundaries. To facilitate better management, we propose a method to estimate percentage changes in park visitation without using any on-site visitor counts. Using 20 national parks in the US, we develop a time series model for forecasting monthly changes in visitation based on the volume of social media images shared by visitors to parks. Forecasts are generated from historic park-level and national-level photo-user-days (PUD) of images posted to Flickr, using singular spectrum analysis (SSA). We account for the changes in popularity of the social media platform. We further propose an approach for augmenting existing on-site visitation data collected by the US National Park Service. Our model evaluations indicate that the proposed model which only uses social media data achieves competitive performance to the models which utilize on-site visitor counts.
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