Standardizing the Procedure for Cleaning GPS Device Data for Recreation and Tourism Research

Spatial-temporal methods have grown rapidly in prominence over the preceding two decades in outdoor recreation and tourism research. However, there are limited well-documented, formal procedures for cleaning GPS device data despite its significance as the most valid tool for measuring intra-attraction tourist mobility. To address this limitation, we present open-source Python code that automates downloading GPS device data to ArcGIS Pro and establishes boundaries and parameters for extracting erroneous points. We will use the code to clean GPS device data from a random sample of visitors to Grand Canyon National Park and examine differences in data dimensionality as well as spatial, temporal, and behavioral outcomes between cleaned and uncleaned data. Our research may help reduce bias and increase replicability to improve the standard of our field’s research and complement other works that have provided guidance regarding GPS device purchasing and methods for minimizing researcher and device error.