<?xml version='1.0' encoding='UTF-8'?><xml><records><record><source-app name="HighWire" version="7.x">Drupal-HighWire</source-app><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Duff, Cameron</style></author><author><style face="normal" font="default" size="100%">Antia, Alice</style></author><author><style face="normal" font="default" size="100%">Hilger, James</style></author><author><style face="normal" font="default" size="100%">Horsch, Eric</style></author><author><style face="normal" font="default" size="100%">Merrill, Nathaniel</style></author><author><style face="normal" font="default" size="100%">Murray, Jason</style></author><author><style face="normal" font="default" size="100%">Winder, Sama</style></author><author><style face="normal" font="default" size="100%">Wood, Spencer</style></author><author><style face="normal" font="default" size="100%">Zafonte, Matthew</style></author></authors><secondary-authors></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluating Mobility Data for Recreation Monitoring</style></title><secondary-title><style face="normal" font="default" size="100%">Land Economics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2026-03-11 12:54:46</style></date></pub-dates></dates><elocation-id><style  face="normal" font="default" size="100%">011626-0006</style></elocation-id><doi><style  face="normal" font="default" size="100%">10.3368/le.102.3.011626-0006</style></doi><volume><style face="normal" font="default" size="100%"></style></volume><issue><style face="normal" font="default" size="100%"></style></issue><abstract><style  face="normal" font="default" size="100%">Mobile device location data (or mobility data, MD) are a novel and exciting source of information for recreational monitoring. In this paper we analyze datasets from four commercial vendors across three case studies to illustrate some important challenges. Our results show inconsistent spatial-temporal patterns and correlations with on-site counts. Given this evidence, we describe a conceptual model of the potential sources of error and structural variability in visitation estimates derived from MD. We discuss approaches for evaluation of data quality and suggest a range of supplemental data products that vendors could provide to support recreation analysis.</style></abstract></record></records></xml>