A large and growing body of research demonstrates the value of local parks to mental and physical well-being. Recently, researchers have begun using passive digital data sources to investigate equity in usage; exactly who is benefiting from parks? Early studies suggest that park visitation differs according to demographic features, and that the demographic composition of a park's surrounding neighborhood may be related to the utilization a park receives. Employing a data set of park visitations generated by observations of roughly 50 million mobile devices in the US in 2019, we assess the ability of the demographic composition of a park's walkshed to predict its yearly visitation. Predictive models are constructed using Support Vector Regression, LASSO, Elastic Net, and Random Forests. Surprisingly, our results suggest that the demographic composition of a park's walkshed demonstrates little to no utility for predicting visitation.

翻译：大量且不断增长的研究证明了本地公园对心理和身体健康的价值。近年来，研究者开始利用被动数字数据源调查使用的公平性——究竟哪些人群从公园中受益？早期研究表明，公园访问量因人口特征而异，且公园周边社区的人口构成可能与公园的使用情况相关。基于2019年美国约5000万移动设备观测生成的公园访问量数据集，我们评估了公园步行范围内人口构成对其年度访问量的预测能力。我们采用支持向量回归、LASSO、弹性网络和随机森林构建预测模型。令人意外的是，研究结果表明，公园步行范围内的人口构成对预测访问量几乎没有或完全没有效用。