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SUMMARY:Using Linear Tracking Data to Estimate Backcountry Recreation Popu
 larity - Vincent Sutherland\, David C. Folch
DTSTART;TZID=America/Chicago:20230714T112500
DTEND;TZID=America/Chicago:20230714T115500
DTSTAMP:20261015T195432Z
UID:pretalx-2023-LMMPRP@cfp.scipy.org
DESCRIPTION:Geolocated data from smartphone apps are well-established reso
 urces for research. While most of that data come as points (e.g.\, geotagg
 ed photos)\, there are a growing number of apps that collect linear data f
 rom users activities (e.g.\, running\, hiking\, off-road driving). Using e
 stablished ecological methods\, shallow-machine learning packages\, and mu
 ltiprocessing we demonstrate a novel approach using mobile app data to est
 imate back-country recreation popularity at multiple scales. The topics co
 vered include normalizing and thinning coordinate data\, merging linear da
 ta from multiple sources\, and accounting for spatial bias while preservin
 g the integrity of the original data.
LOCATION:Grand Salon C
URL:https://cfp.scipy.org/2023/talk/LMMPRP/
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