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基于网络信息的广东省风景名胜区吸引力空间格局与道路网络可达性

Spatial pattern of attractiveness and road network accessibility of scenic spots in Guangdong Province based on network information.

作者信息

Liao Zhenjie, Liang Shan

机构信息

School of Management, Guangzhou Huashang College, Guangzhou, China.

School of Economics, Guangzhou City University of Technology, Guangzhou, 510800, China.

出版信息

Sci Rep. 2025 Mar 7;15(1):7950. doi: 10.1038/s41598-025-91419-9.

DOI:10.1038/s41598-025-91419-9
PMID:40055441
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11889184/
Abstract

With the progress of the Internet, the limitations of traditional scenic spots research in data collection, market evaluation and tourist experience analysis can be broken through, and a new way to evaluate the attractiveness of scenic spots has been opened up. Scenic spots, as a key factor for tourists to choose a destination, has a profound impact on tourism behavior. Focusing on Guangdong Province, this study used GIS spatial analysis and geographic detector to analyze the spatial distribution, influencing factors and forming mechanism of scenic spots in Guangdong Province. It is found that at the significance level of 1%, Global Moran's I value is 0.1297 and Z value is 5.6028, indicating that the attractiveness distribution has a significant spatial agglomeration. The Pearl River Delta region, with its rich cultural resources, developed economy, convenient transportation and huge market demand, has become the most attractive tourist area, and eastern Guangdong followed; however, due to the complex terrain, inconvenient transportation and weak economic foundation, the scenic spots in northern and western Guangdong are not developed enough and the attractiveness is relatively weak. Revealing the spatial structure and influencing factors of scenic spots in Guangdong Province is of far-reaching significance for scientific integration of tourist resources, promoting regional cooperation, guiding the development and upgrading of scenic spots, optimizing resource allocation, enhancing tourism competitiveness and promoting sustainable development of tourism.

摘要

随着互联网的发展,传统景区研究在数据收集、市场评估和游客体验分析方面的局限性得以突破,开辟了一种评估景区吸引力的新途径。景区作为游客选择目的地的关键因素,对旅游行为有着深远影响。本研究以广东省为重点,运用GIS空间分析和地理探测器,对广东省景区的空间分布、影响因素及形成机制进行分析。研究发现,在1%的显著性水平下,全局莫兰指数(Global Moran's I)值为0.1297,Z值为5.6028,表明吸引力分布存在显著的空间集聚。珠江三角洲地区凭借丰富的文化资源、发达的经济、便捷的交通和巨大的市场需求,成为最具吸引力的旅游区域,粤东地区次之;然而,由于地形复杂、交通不便和经济基础薄弱,粤北和粤西地区的景区开发不足,吸引力相对较弱。揭示广东省景区的空间结构和影响因素,对于科学整合旅游资源、促进区域合作、引导景区发展升级、优化资源配置、提升旅游竞争力以及推动旅游业可持续发展具有深远意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/067e7225228e/41598_2025_91419_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/b1a90e83d115/41598_2025_91419_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/6cfe7977b3b4/41598_2025_91419_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/d62dcde04223/41598_2025_91419_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/7dc9a7bc7a37/41598_2025_91419_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/067e7225228e/41598_2025_91419_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/b1a90e83d115/41598_2025_91419_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/6cfe7977b3b4/41598_2025_91419_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/d62dcde04223/41598_2025_91419_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/7dc9a7bc7a37/41598_2025_91419_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d641/11889184/067e7225228e/41598_2025_91419_Fig5_HTML.jpg

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本文引用的文献

1
Spatial distribution characteristics and accessibility analysis of beautiful leisure villages in China.中国美丽乡村休闲旅游的空间分布特征及可达性分析。
PLoS One. 2022 Oct 26;17(10):e0276175. doi: 10.1371/journal.pone.0276175. eCollection 2022.
2
Spatial distribution evolution and accessibility of A-level scenic spots in Guangdong Province from the perspective of quantitative geography.基于数量地理学视角的广东省 A 级景区空间分布演化及可达性研究
PLoS One. 2021 Nov 15;16(11):e0257400. doi: 10.1371/journal.pone.0257400. eCollection 2021.