Suitability Assessment Method of Red Tourism Development Using Geospatial and Social Humanity Data: A Case Study of Ruijin City, East China

Yaozu Qin (), Li Cao (), Wenjing Li, Ali Darvishi Boloorani, Yuan Li, Xinxin Ke, Masoud Soleimani, Qian Yu and Cuimin Zhou
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Yaozu Qin: The Key Laboratory of Digital Land and Resources, East China University of Technology, Nanchang 330013, China
Li Cao: Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 430103, China
Wenjing Li: Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Ali Darvishi Boloorani: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
Yuan Li: Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Xinxin Ke: Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Masoud Soleimani: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
Qian Yu: Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Cuimin Zhou: Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China

Sustainability, 2023, vol. 15, issue 11, 1-18

Abstract: It is important to analyze the trend in land use changes and assess the suitability of resource development for protecting natural resources, developing ecological industries, and land use planning issues. Ruijin City is located in South Jiangxi and has abundant resources for red tourism development. By analyzing the landscape changes in land use and the spatial distribution characteristics of local red culture resources, a supervised machine learning-based prediction model was constructed to quantitatively assess the suitability of red tourism development in a geographic information system (GIS) and the R language environment using geographical, economical, and human-related datasets. The results revealed that: (i) the increasing of human activities and economic vitality provide a beneficial social environment for the development of tourism resources; (ii) highly concentrated red resources, or those with special significance, are conducive to developing red tourism resources; (iii) preferentially, central–eastern Ruijin was followed by the extension areas to peripheral towns, which are potentially suitable areas for the development of red scenic spots. Generally, the findings of this study were consistent with the conventional cognitions and lessons on tourism development, and the constructed evaluation system is expected to be promoted to similar research.

Keywords: suitability assessment; machine learning; landscape changes; red tourism; Ruijin City (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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