A Comparison of Tourists’ Spatial–Temporal Behaviors Between Location-Based Service Data and Onsite GPS Tracks

Colby Parkinson, Bing Pan (), Sophie A. Morris, William L. Rice, B. Derrick Taff, Guangqing Chi and Peter Newman
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Colby Parkinson: Department of Park, Recreation, and Tourism Management, Pennsylvania State University, University Park, PA 16802, USA
Bing Pan: Department of Park, Recreation, and Tourism Management, Pennsylvania State University, University Park, PA 16802, USA
Sophie A. Morris: Department of Society and Conservation, University of Montana, Missoula, MT 59801, USA
William L. Rice: Department of Society and Conservation, University of Montana, Missoula, MT 59801, USA
B. Derrick Taff: Department of Park, Recreation, and Tourism Management, Pennsylvania State University, University Park, PA 16802, USA
Guangqing Chi: Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, University Park, PA 16802, USA
Peter Newman: Department of Park, Recreation, and Tourism Management, Pennsylvania State University, University Park, PA 16802, USA

Sustainability, 2025, vol. 17, issue 2, 1-22

Abstract: Tourism and recreation managers rely on spatial-temporal data to measure visitors’ behavior for gauging carrying capacity and sustainable management. Location-based service (LBS) data, which passively record location data based on mobile devices, may enable managers to measure behaviors while overcoming constraints in labor, logistics, and cost associated with in-person data collection. However, further validation of LBS data at more refined spatial and temporal scales within tourism attractions is needed. We compared observations of salient spatial–temporal measures from a stratified sample of onsite visitors’ GPS traces in a popular U.S. National Park during peak season over two years with a sample of visitors’ traces collected during the same period by a third-party LBS data provider. We described trip characteristics and behaviors within 34 points of interest (POIs) and then pre-processed both datasets into weighted, directed networks that treated POIs as nodes and flow between POIs as edges. Both datasets reported similar proportions of day-use visitors (~79%) and had moderate-to-strong correlations across networks depicting visitor flow ( r = 0.72–0.85, p < 0.001). However, relative to the onsite data, LBS data underestimated the number of POIs the visitors stopped by and differed in its rank of popular POIs, underestimating the length of time visitors spent in POIs (z = 1, p ≤ 0.001) and overestimating visitation to the most popular POIs (z = 180, p = 0.044). Our findings suggest that LBS data may be helpful for identifying trends or tracking tourist movement in aggregate and at crude spatial and temporal scales, but they are too sparse and noisy to reliably measure exact movement patterns, visitation rates, and stay time within attractions.

Keywords: big data; location-based service data; mobile-device data; GPS; nature-based tourism; outdoor recreation; tourism management; parks and protected areas; tourist mobility; carrying capacity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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