An Approach for Mapping Ecotourism Suitability Using Machine Learning: A Case Study of Zhangjiajie, China

Qin Huang, Chen Zhou (), Manchun Li (), Yu Ma and Song Hua
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Qin Huang: School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Chen Zhou: School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Manchun Li: School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Yu Ma: School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Song Hua: School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

Land, 2024, vol. 13, issue 8, 1-22

Abstract: The assessment of ecotourism suitability is crucial for sustainable regional development and is seen as an effective strategy to achieve both environmental protection and economic growth. One of the key challenges in land research is effectively identifying potential ecotourism resources while balancing regional protection and development. This study mapped the suitability of ecotourism in Zhangjiajie, China, using a combination of various geospatial data sources and four machine-learning techniques. Additionally, an indicator system was developed, covering the ecological environment, geological geomorphology, socioeconomics, and resource availability. The prediction results for suitability classified the area into four categories: highly suitable, moderately suitable, marginally suitable, and unsuitable; based on the ensemble results generated by the four algorithms, these categories accounted for 19.34%, 28.78%, 23.87%, and 28.01% of the total area, respectively. This study’s findings illustrate the spatial distribution of ecotourism suitability in Zhangjiajie, providing valuable insights for identifying potential ecotourism resources as well as informing regional planning and policy-making.

Keywords: ecotourism; sustainable development; suitability evaluation; machine learning; multi-source geospatial data; land use (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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