全域旅游空间潜力预测,让AI助力乡村振兴(5月31日)

全域旅游空间潜力预测,让AI助力乡村振兴(5月31日)

2023-05-31 08:12:50  浏览:75  作者:管理员

在建设“全域旅游”的背景下,区域尺度的游憩服务发展将从单一景点景区的建设转向旅游目的地的综合统筹,助力乡村振兴和区域协调发展。然而,在全域旅游“连点成片”的过程中,如何根据本地独特的环境禀赋识别出具有较高游憩潜力的区域并据此评估发展的优先程度,仍是研究与实践的热点和难点。本研究以鄂西地区为例,引入生态系统文化服务理论中潜力评估的研究方法,运用社会-生态多源数据构建了结合集成机器学习的SDM模型,对研究区域内336个已知游憩服务热点的环境特征进行了剖析,并预测了连续空间中高游憩潜力区域的概率分布。本研究提供了一条从环境特征变量数值关系角度理解区域尺度游憩空间规律的技术路径,旨在为全域旅游和乡村振兴的空间发展策略提供参考。

关键词

全域旅游;游憩服务;生态系统文化服务;空间潜力预测;机器学习;鄂西

“全域旅游”导向下鄂西游憩服务的

空间潜力预测研究

——基于集成模型的机器学习方法

Spatial Potential of Recreational Services in Western Hubei Region in Light of the “All-for-One Tourism” Development

—A Machine Learning Approach Based on Ensemble Model

01

背景概述

02

文献综述

03

研究资料与方法

研究区域

研究场地位于湖北西部——鄂西区域全境,覆盖人口超2730万,主要为山地。鄂西既是CES多样且丰富的地区,也是发展旅游扶贫的重点区域。

研究场地内受认证的游憩服务发生点 

鄂西游憩服务点示例 © Rural Construction Center of Hubei Province (RCC-HB)

机器学习数据集构建

融合集合机器学习框架的SDM建模

04

研究结果

发生数据和环境特征数据分析结果

环境特征的空间建模结果 © 文晨,茶静,徐利权,徐海韵

环境特征的数值分布 © 文晨,茶静,徐利权,徐海韵

研究使用10个主成分提取了原始数据中累计达95%的可解释变异,并作为自变量参与模型构建。在三组模型中,纳入了所有环境特征变量的模型三性能表现最好,将以此作为空间可视化的对象。

基于不同算法的游憩服务潜力预测结果 © 文晨,茶静,徐利权,徐海韵

基于模型三的全域游憩潜力的预测概率地图和二元适应性地图 © 文晨,茶静,徐利权,徐海韵

预测结果和模型表现

05

讨论

理论和方法论意义

06

结论

部分参考文献

[1] Yang, Z., (2016). The connotation of all-for-one tourism and its development stages. Tourism Tribune, 31(12), 1–3.

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[3] Wen, C., Albert, C., & von Haaren, C. (2022). Nature-based recreation for the elderly in urban areas: Assessing opportunities and demand as planning support. Ecological Processes, (11), 44.

[4] Fish, R., Church, A., & Winter, M. (2016). Conceptualising cultural ecosystem services: A novel framework for research and critical engagement. Ecosystem Services, (21), 208–217.

[5] Cheng, X., Van Damme, S., Li, L., & Uyttenhove, P. (2019). Evaluation of cultural ecosystem services: A review of methods. Ecosystem services, (37), 100925.

[6] Paracchini, M. L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J. P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P. A., & Bidoglio, G. (2014). Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU. Ecological Indicators, (45), 371–385.

[7] Pérez-Valladares, C. X., Moreno-Calles, A. I., Mas, J. F., & Velazquez, A. (2022). Species distribution modeling as an approach to studying the processes of landscape domestication in central southern Mexico. Landscape Ecology, (37), 461–476.

[8] Zhang, J., & Li, S. (2017, December). A review of machine learning based species’ distribution modelling. In: 2017 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) (pp. 199–206). IEEE.

[9] Li, Y., Fei, T., Huang, Y., Li, J., Li, X., Zhang, F., Kang, Y., & Wu, G. (2021). Emotional habitat: Mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model. International Journal of Geographical Information Science, 35(2), 227–249.

[10] Hermes, J., Albert, C., & von Haaren, C. (2018). Assessing the aesthetic quality of landscapes in Germany. Ecosystem Services, (31), 296–307.

[11] People’s Government of Hubei Province. (2016, May 31). Notice of Provincial People’s Government on Issuance of the 13th Five-Year Plan for Tourism Development in Hubei Province.

[12] Jin, C., Fan, L., & Lu, Y. (2010). Spatial distribution of agricultural tourism based on accessibility in case of Jiangsu Province. Journal of Natural Resources, 25(9), 1506–1518.

[13] Weyland, F., & Laterra, P. (2014). Recreation potential assessment at large spatial scales: A method based in the ecosystem services approach and landscape metrics. Ecological Indicators, (39), 34–43.

[14] Gosal, A. S., Giannichi, M. L., Beckmann, M., Comber, A., Massenberg, J. R., Palliwoda, J., Roddis, P., Schägner, J. P., Wilson, J., & Ziv, G. (2021). Do drivers of nature visitation vary spatially? The importance of context for understanding visitation of nature areas in Europe and North America. Science of the Total Environment, (776), 145190.

[15] Chhetri, P., & Arrowsmith, C. (2008). GIS-based modelling of recreational potential of nature-based tourist destinations. Tourism Geographies, 10(2), 233–257.

[16] Chan, N. W., & Wichman, C. J. (2020). Climate change and recreation: Evidence from North American cycling. Environmental and Resource Economics, (76), 119–151.

[17] Rugel, E. J., Henderson, S. B., Carpiano, R. M., & Brauer, M. (2017). Beyond the Normalized Difference Vegetation Index (NDVI): Developing a natural space index for population-level health research. Environmental Research, (159), 474–483.

[18] Velazco, S. J. E., Rose, M. B., de Andrade, A. F. A., Minoli, I., & Franklin, J. (2022). FLEXSDM: An R package for supporting a comprehensive and flexible species distribution modelling workflow. Methods in Ecology and Evolution, 13(8), 1661-1669.

[19] Martin, C. L., Momtaz, S., Gaston, T., & Moltschaniwskyj, N. A. (2016). A systematic quantitative review of coastal and marine cultural ecosystem services: Current status and future research. Marine Policy, (74), 25–32.

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