Urban parks provide substantial environmental, social, and health benefits, yet their recreational economic value often remains unquantified due to limitations in traditional data collection methods. Although previous studies have estimated park recreational value, they rarely capture how this value varies across neighborhoods and seasons because fine-grained, time-sensitive data are typically unavailable. This study addresses this gap by applying large-scale smartphone mobility data and the Travel Cost Method (TCM) analysis to estimate the recreational use value of Dick Nichols District Park, which is one of the most visited District parks in Austin, Texas. Using consumer surplus as a monetary measure of recreational benefits, the analysis combines visitation rates, travel distances, and opportunity costs with neighborhood-level sociodemographic indicators to reveal spatial and temporal patterns in park use. Our results show that the Park’s 2019 (Before COVID-19) annual recreational use value ranges from 306, 983. 80 to 307, 067. 72. Spatially, block groups in Austin’s northeastern quadrant exhibit higher recreational value, likely reflecting stronger connectivity and moderate travel distances. Our temporal analysis shows that June has the highest monthly recreational value. This study illustrates how large-scale mobility data can be integrated into recreational value assessment, offering greater visibility, scalability, and cost-effectiveness than traditional approaches. By leveraging these richer data streams, planners and policymakers can conduct more equitable, evidence-based park planning and resource allocation.
Zipeng et al. (2026) studied this question.