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杭州湾城市群城乡融合发展的时空格局及其驱动机制分析。

Spatiotemporal Pattern of Urban-Rural Integration Development and Its Driving Mechanism Analysis in Hangzhou Bay Urban Agglomeration.

机构信息

Institute of Ecological Civilization, Zhejiang A&F University, Hangzhou 311300, China.

Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang A&F University, Hangzhou 311300, China.

出版信息

Int J Environ Res Public Health. 2022 Jul 9;19(14):8390. doi: 10.3390/ijerph19148390.

DOI:10.3390/ijerph19148390
PMID:35886243
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9320824/
Abstract

The quantitative analysis of the urban-rural integration development (URID) level and its driving factors is of great significance for the new-type urbanization of urban agglomerations. This study constructed a multidimensional framework in the perspective of a population-space-economy-society-ecology framework to measure the URID level from 2000 to 2020 and further explored the driving mechanism of the URID changes by a geographical detector model in the Hangzhou Bay urban agglomeration (HBUA). The results showed that the land-use change in the HBUA from 2000 to 2020 showed a typical characteristic of the transition between cultivated and construction land. The URID level in the HBUA improved from 0.294 in 2000 to 0.563 in 2020, and the year 2005 may have been the inflection point of URID in the HBUA. The URID level showed a significant spatial aggregation with high values. Hangzhou, Jiaxing, and Ningbo were hot spots since 2015, and the cold spots were Huzhou and Shaoxing. The population and spatial integration had more important impacts on URID levels in 2000, 2005, and 2020, while economic and social integration had more significant impacts on URID levels in 2010 and 2015. This study provided a deeper understanding of the evolution of URID in an urban agglomeration and could be used as a reference for decision makers.

摘要

城乡融合发展(URID)水平及其驱动因素的定量分析对城市群的新型城镇化具有重要意义。本研究从人口-空间-经济-社会-生态框架的角度构建了一个多维框架,从 2000 年到 2020 年衡量 URID 水平,并进一步通过地理探测器模型探讨了杭州湾城市群(HBUA)URID 变化的驱动机制。结果表明,2000 年至 2020 年 HBUA 的土地利用变化呈现出耕地与建设用地转换的典型特征。HBUA 的 URID 水平从 2000 年的 0.294 提高到 2020 年的 0.563,2005 年可能是 HBUA 中 URID 的转折点。URID 水平具有显著的空间聚集性,高值明显。自 2015 年以来,杭州、嘉兴和宁波一直是热点地区,湖州和绍兴则是冷点地区。人口和空间一体化对 2000 年、2005 年和 2020 年的 URID 水平有更重要的影响,而经济和社会一体化对 2010 年和 2015 年的 URID 水平有更显著的影响。本研究加深了对城市群 URID 演变的认识,可为决策者提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/b8422e386517/ijerph-19-08390-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/d14ea5107139/ijerph-19-08390-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/c2aa9522765e/ijerph-19-08390-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/3ba935cb38b3/ijerph-19-08390-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/3f99d3e98674/ijerph-19-08390-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/ad437b6b7c8d/ijerph-19-08390-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/89b808799eb8/ijerph-19-08390-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/715c9ec5a507/ijerph-19-08390-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/0f51bbdf8a3e/ijerph-19-08390-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/727292aff018/ijerph-19-08390-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/b8422e386517/ijerph-19-08390-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/d14ea5107139/ijerph-19-08390-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/c2aa9522765e/ijerph-19-08390-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/3ba935cb38b3/ijerph-19-08390-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/3f99d3e98674/ijerph-19-08390-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/ad437b6b7c8d/ijerph-19-08390-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/89b808799eb8/ijerph-19-08390-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/715c9ec5a507/ijerph-19-08390-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/0f51bbdf8a3e/ijerph-19-08390-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/727292aff018/ijerph-19-08390-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1ac/9320824/b8422e386517/ijerph-19-08390-g010.jpg

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