GPS Tracking of Visitor Use: Factors Influencing Visitor Spatial Behavior on a Complex Trail System

Abstract

EXECUTIVE SUMMARY: There is currently a limited understanding of how visitors travel within and through a destination. Understanding what influences visitor travel patterns is an important aspect of tourism and recreation. Spacetime visitor travel patterns have been examined in a number of tourism studies; however, limited studies have focused on nature-based or trail system settings. Global Positioning System (GPS) tracking has been identified as an effective means of measuring visitor travel patterns. However, most GPS studies within these natural settings have focused on the utility of the method, not exploring the contribution that GPS can make to understanding what influences visitor travel patterns, practically and theoretically. Additionally, the examination of GPS data has rarely extended beyond visual analysis such as point densities and overlays. To better understand what influences visitor travel patterns, more in-depth analysis of these spatial data is needed, including identifying techniques for cleaning GPS data for more detailed level of analyses, operationalizing travel patterns (i.e. defining and measuring travel patterns), and examining variables that might influence travel patterns. The study first examined statistical differences between cleaned and uncleaned GPS data, finding that significant differences were present. Specifically, uncleaned data were found to report a significantly longer total distance traveled. Visitor reported total distance travel was also more similar to cleaned data. Travel patterns were then examined and tested using four different spatio-temporal operations: total distance traveled, number of zones (areas) encountered, distance from starting points, and time spent. The four different measures were found to measure conceptually and statistically different travel pattern aspects. Results generally suggest a more-toless order of mountain bikers, horseback riders, runners, then hikers with respect to total distance traveled, number of zones encountered, and distance from start. Knowledge of destination, visitor personal characteristics, and motivations were also examined for their influence on travel patterns. While each of these variables was found to predict travel patterns, generally each predictor was dependent on activity type with runners and mountain bikers providing the major contributions of these effects. In other words, these variables were good predictors of the spatio-temporal behavior of runners and mountain bikers, but less effective for horseback riders and hikers. The exception was knowledge of destination; higher knowledge results in greater use distribution regardless of activity type. These findings can assist trail managers in directing the spatial distribution of visitors and adapting visitor education and trail management policies to meet management directives.