Using social media data to estimate recreational travel costs: A case study from California - Charity Nyelele; Catherine Keske; Min Gon Chung; Han Guo; Benis N. Egoh (Ecological Indicators, 2023)
Understanding the economic value of ecosystem services is necessary to facilitate sustainable land use management, and to inform policy and decision making. However, valuing and monetizing ecosystem services remains challenging. Benefit-transfer and non-market valuation methods typically rely on administrative data and surveys, but this is time consuming, limited, and requires much more resources. Social media and other types of big data provide accessible and georeferenced data that can be incorporated into valuation approaches. We use recreation as an example and the Tahoe Central Sierra Initiative (TCSI) project area in California as a case study to explore the usefulness of such data in estimating travel costs that form an integral part of determining the value of recreational ecosystem services through the travel cost model. We estimated 6,951 person user days of recreation from 2,245 visitors who uploaded photographs to the Flickr photo-sharing application between 2005 and 2019. We used metadata from the images to infer visitor origins and estimate trip distance and costs of travel for visitors that took day trips (<500 miles (∼800 kms) roundtrip) to the area. Our results show that the most demand for recreational opportunities in the TCSI came from domestic visitors, particularly those from California and Nevada who took day trips. On average, visitors spent $156 per single day trip. The total cost of travel for recreational visits to the TCSI for the period was $491,500 (an average of $32,800 per year). However, when adjusted to align with actual visitation, the travel costs could range from $1.35 to $1.84 billion per year. Estimating recreational use and highlighting the travel cost for recreational opportunities illustrates how crowdsourced data can refine valuation approaches such as the widely used travel cost approach, which may fill in data gaps in valuing ecosystem services.