Understanding Factors Affecting Tourist Distribution in Urban National Parks Based on Big Data and Machine Learning
Publication: Journal of Urban Planning and Development
Volume 150, Issue 3
Abstract
Urban national parks (UNPs) provide tourism services in cities worldwide. However, the factors affecting tourist distributions in UNP activity and path spaces remain uncertain. Using Web crawler technology, location big data were tracked and sampled in Donghu National Park in Wuhan, China, and 12 predictor variables were analyzed using a machine-learning method (i.e., random forest). The consistency of the big data compared to the population census and tourist observations was determined at 79.5% and 77.8%, respectively. The tourist number (p) and tourist density (p/ha) per day in the park space in Donghu National Park were 0–2,531 p and 0–198.0 p/ha, respectively. Peak tourist periods showed pressure flows of 0.3–34.5‰ between scenic areas in the park. An analytical framework was formulated for UNPs to link the urban environment, park attributes, and configurational attributes, which here explained 66.4%–72.5% of the tourist distribution in the path and activity spaces. Random forest models performed better than geographically weighted regression (GWR) or ordinary least squares (OLS) models, indicating a complex nonlinear relationship between the independent variables and tourist distribution in UNP spaces, rather than the linear relationship that has previously been found in urban parks. First, both activity and path spaces near developed urban environments or park entrances bore higher tourism pressure. Second, winding routes attracted tourists to path spaces, while water landscapes attracted tourists to both path and activity spaces. Third, tourism pressure in path spaces was determined by configurational attributes. These results are important reference points for the planning and management of UNPs.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The present study was supported by the Fundamental Research Funds for the Central Universities (No. BLX202207), the China Postdoctoral Science Foundation (No. 2023M740274) and the Postdoctoral Fellowship Program of CPSF (No. GZC20230245). We are grateful to the anonymous reviewers for their helpful comments.
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Published In
Journal of Urban Planning and Development
Volume 150 • Issue 3 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jun 21, 2023
Accepted: Mar 20, 2024
Published online: May 30, 2024
Published in print: Sep 1, 2024
Discussion open until: Oct 30, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Big data
- Buildings
- Computer programming
- Computing in civil engineering
- Ecosystems
- Engineering fundamentals
- Environmental engineering
- Facilities (by type)
- Forests
- Infrastructure
- Methodology (by type)
- Parks
- Recreation
- Research methods (by type)
- Stadiums and sport facilities
- Structural engineering
- Structures (by type)
- Tourism
- Urban and regional development
- Urban areas
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