Using Linear Tracking Data to Estimate Backcountry Recreation Popularity
Vincent Sutherland, David C. Folch
Geolocated data from smartphone apps are well-established resources for research. While most of that data come as points (e.g., geotagged photos), there are a growing number of apps that collect linear data from users activities (e.g., running, hiking, off-road driving). Using established ecological methods, shallow-machine learning packages, and multiprocessing we demonstrate a novel approach using mobile app data to estimate back-country recreation popularity at multiple scales. The topics covered include normalizing and thinning coordinate data, merging linear data from multiple sources, and accounting for spatial bias while preserving the integrity of the original data.
Social Science and the Digital Humanities
Grand Salon C