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吴冠秋，党安荣，田颖，阚长城.2021.基于时空大数据的粤港澳大湾区城镇群结构研究.遥感学报，25（2）： 665-676
Wu G Q，Dang A R，Tian Y and Kan C C. 2021. Study on the urban agglomerations structure of the Guangdong-Hong Kong-Macao Greater Bay Area based on spatiotemporal big data. National Remote Sensing Bulletin， 25（2）：665-676
粤港澳大湾区是中国重点建设的战略区域，其协同结构的有效认知是打造世界级湾区的核心研究内容。作为一种重要的城镇群发展模式，大湾区内部具有复杂的城镇协同关系，这一关系充分体现在城镇间的人群流动特性上，而跨城职住迁徙是区域人口流动的一种直观、稳定的表现，基于高精度跨城职住迁徙数据开展湾区协同结构的认知意义显著。文章在总结归纳国内外湾区协调发展研究、跨城职住综合应用研究的基础上，基于百度地图所识别的跨城职住时空大数据开展了粤港澳大湾区协同认知方法的研究与实践。研究构建了跨城职住交换网络，以统计单元为网络节点、以交换流量为连接权重，从加权连入连出度占比、加权中心度、迁徙平均距离3个方面认知跨城职住关系。研究进一步结合经济数据展开城镇群协同关系的聚类分析，将大湾区内各空间单元归纳为交流中心单元、优势单元及其特例、待发展单元、输出型单元、输入型单元6类。研究结果发现，当下粤港澳大湾区构建了广州—佛山、中山—珠海、深—莞—惠3处交换结构异质性组团，多中心发展结构明显。同时湾区协同不均衡的问题仍然存在，各类协同特征单元呈显著的圈层结构分布，东西岸城镇交换关系差异明显。最后，本文结合大湾区区域综合结构特征以及大湾区相关规划政策空间布局特征，阐述了大湾区内城镇群结构的发展状态、发展问题以及未来方向。指出未来粤港澳大湾区的发展需要进一步加强多中心协调机制优势，解决区域内东强西弱、周边滞后、核心北移等结构问题。梳理各类单元间的合作模式，强化协同网络中优势空间单元的贡献程度，巩固交流中心单元的参与程度，避免极核同周边形成单向的输入输出，充分利用广阔的湾区腹地促进区域功能的循环与互补，以期为粤港澳大湾区的协同发展提供支持。
The Guangdong-Hong Kong-Macao Greater Bay Area (the greater bay area) is the key strategic area in our country, and the cognition of the collaborative structure of the Greater Bay Area is an important research content for it to build a world-class bay area. As an important model of urban agglomerations, the Greater Bay Area has a complex structural relationship, which is fully reflected in the characteristics of the flow of people between cities and towns, yet inter-city job-housing migration behavior is an intuitive and stable manifestation of the regional population mobility. Therefore, it is significant to develop the cognition of the bay area collaborative structure based on high precision inter-city job-housing data.,Based on the summary of the research in Bay Areas around the world, the application research on job-housing spatiotemporal big data, the current status of the relationship between the Greater Bay Area, this paper carried out the research and practice of the collaborative cognitive methods in the Greater Bay Area based on inter-city job-housing spatiotemporal data which is identified by Baidu map. The study built an inter-city job-housing exchange network, using the statistical units as the network nodes and the migration flow as the connection weight, and recognize the inter-city job-housing relationship from three aspects, including the proportion of people moving in and out, weighted degree centrality, and average distance of migration. The study further carries out a cluster analysis on each unit combines with economic data and summarize the units into six types, including exchange center units, dominant units and their special cases, units to be developed, output units, and input units.The research results found that the Greater Bay Area has constructed three complex groups with different exchange structures including Guangzhou and Foshan, Zhongshan and Zhuhai, Shenzhen Dongguan and Huizhou, with obvious multi-center structure. Also, the problem of unbalanced regional coordination in the Bay Area still exists. The spatial distribution of all units’ type shows a pronounced circle structure, and the exchange relationship between the east and west banks is obviously different. Finally, combined with the analysis of spatial structural of relevant policies, the paper preliminarily expounds the development status, development problems and future direction of the urban agglomeration structure, and pointed out that the future development of the Greater Bay Area needs to strengthen the advantages of the multi-center development structure and needs to solve the structural problems in the region, such as the “strong east and weak west”, the lagging development of periphery, and the northward shift of the exchange core. By sorting out the cooperation modes between various units, the Greater Bay Area needs to strengthen the contribution of the dominant units, consolidate the participation of the exchange center units, and avoid the formation of one-way input and output between the polar cores and the surrounding area. Also, the Greater Bay Area needs to make full use of the vast hinterland area to promote the complementarity of all kinds of functions, in order to provide support for the collaborative development of the urban agglomerations.
遥感粤港澳大湾区城镇群结构跨城职住时空大数据
remote sensingthe Greater Bay Areaurban agglomerations structureinter-city job-housingspatiotemporal big data
Alina S,Jonathan D and Boris A.P.2018.Environmental risk factors associated with low birth weight: The case study of the Haifa Bay Area in Israel.Environmental Research, 165:337-348 [DOI:10.1016/j.envres.2018.05.006http://dx.doi.org/10.1016/j.envres.2018.05.006]
Chen G H, Chen J Q, Song Z and Han X.2018.Research on Factors and Prediction Model about the Operation of Labor Crowdsourcing APP Platform in Big Data Environment:Part of Cities from the Pearl River Delta as an Example.Statistics & Information Forum,7:116-122
陈光慧,陈嘉倩,宋祯,韩潇.2018.大数据环境下劳务众包APP平台运营的影响因素及预测研究——以珠三角部分城市为例.统计与信息论坛,7:116-122
Chen Z X and Wu Z B.2019.Land use structure and land use efficiency evaluation on Guangdong Hong-kong Macao Greater Bay Area Urban Agglomeration.Urban Problems.4:29-35
陈章喜,吴振帮.2019.粤港澳大湾区城市群土地利用结构与效率评价.城市问题,4:29-35 [DOI:10.13239/j.bjsshkxy.cswt.190404http://dx.doi.org/10.13239/j.bjsshkxy.cswt.190404]
Chen S, Yang S and Yu T F.2019.Infrastructure Construction and Regional Driving Performance in Circum-Bohai Bay Region.Planners,7:41-47
陈颂,杨烁,于涛方.2019.环渤海大湾区基础设施建设及区域带动绩效研究.规划师,7:41-47
Dang A R, Yuan M, Shen Z J and Wang P.2015.Rational Planning and Urban Governance Based on Smart City and Big Data.Construction Science and Technology,5:64-66
党安荣,袁牧,沈振江,王鹏.2015.基于智慧城市和大数据的理性规划与城乡治理思考.建设科技,5:64-66 [DOI:10.16116/j.cnki.jskj.2015.05.008http://dx.doi.org/10.16116/j.cnki.jskj.2015.05.008]
Guo L, Bi Y F, Huang J Z, Zheng C Y, Hu G Y and Wang G.2018.Multi-scale Comparison and Analysis of Jobs-housing Spatiale Characteristics in Big Cities——Taking Wuhan as an Example.Urban Planning Forum,5:88-97
郭亮,毕瑜菲,黄建中,郑朝阳,胡刚钰,王冠.2018.大城市职住空间特征的多尺度比较与分析——以武汉为例.城市规划学刊,5:88-97 [DOI:10.16361/j.upf.201805010http://dx.doi.org/10.16361/j.upf.201805010]
Gu C L, Guo J, Yun Y X, Bao L, Zhang Z X, Hou C L, Zheng Y, Li M Y, Niu P Y, Zhang Z X and Li H C.2015.Research on Urban Spatial Layout in Beijing-Tianjin-Hebei Region.Journal of Urban and Regional Planning,1:88-131
顾朝林,郭婧,运迎霞,鲍龙,张兆欣,侯春蕾,郑毅,李明玉,牛品一,张朝霞,李洪澄.2015.京津冀城镇空间布局研究.城市与区域规划研究,1:88-131
Jean G, LI H and Chen X Y.2009.Megalopolis:or the Urbanization of the Northeastern Seaboard.Urban Planning International,1:305-311
J.戈特曼,李浩,陈晓燕.2009.大城市连绵区:美国东北海岸的城市化.国际城市规划,1:305-311
Long Y and Mao Q Z.2019.Theory and Method of Urban Planning Big Data.Beijing: China Architecture & Building Press
龙瀛,毛其智.2019.城市规划大数据理论与方法.北京:中国建筑工业出版社
Meng M X, Zhang X L and Pan Z.2019.Planning practice of metropolitan areas crossing administrative boundaries:The implication of New York Metropolitan Area's four overall regional planning on China.Journal of Chongqing University(Social Science Edition),4:22-37
孟美侠,张学良,潘洲.2019.跨越行政边界的都市区规划实践——纽约大都市区四次总体规划及其对中国的启示.重庆大学学报(社会科学版), 4:22-37
Matt B and Nicole M.A.Community context, human needs, and transportation choices: A view across San Francisco Bay Area communities.Journal of Transport Geography,2017,60:189-199.[DOI:10.1016/j.jtrangeo.2017.03.005http://dx.doi.org/10.1016/j.jtrangeo.2017.03.005]
Ma R F.2018.Analysis of the spatial expansion pattern and its driving factors of Pearl River Delta urban-metropolitan region on the basis of DMSP/OLS and NPP/VIIRS image.Journal of Baoji University of Arts and Sciences(Natural Science Edition),3:68-75
马瑞峰.2018.珠三角城市群空间扩展格局及其驱动力因子分析——基于DMSP/OLS和NPP/VIIRS影像.宝鸡文理学院学报(自然科学版),3:68-75 [DOI:10.13467/j.cnki.jbuns.2018.03.007http://dx.doi.org/10.13467/j.cnki.jbuns.2018.03.007]
Niu X Y, Ding L and Song X D.2017.Analyzing Suburban New Town Development in Shanghai from the Perspective of Jobs-Housing Spatial Relationship.City Planning Review,8:47-53+126
钮心毅,丁亮,宋小冬.2017.基于职住空间关系分析上海郊区新城发展状况.城市规划,8:47-53+126
Peter H, Kathy P, Luo Z D.2010.Polycentric Metropolises: Experience From The European Mega-Urban Region.Beijing:
China building industry press (Peter Hall, Kathy Pain, 罗震东译.2010.多中心大都市:来自欧洲巨型城市区域的经验.北京:中国建筑工业出版社
Qiu J J, Liu Y H, Chen H R and Gao F.2019.Urban Network Structure of Guangdong-Hong Kong-Macao Greater Bay Area with the View of Space of Flows:A Comparison between Information Flow and Transportation Flow.Economic Geography,6:7-15
邱坚坚,刘毅华,陈浩然,高枫.2019.流空间视角下的粤港澳大湾区空间网络格局——基于信息流与交通流的对比分析.经济地理,6:7-15 [DOI:10.15957/j.cnki.jjdl.2019.06.002http://dx.doi.org/10.15957/j.cnki.jjdl.2019.06.002]
Qiang H H, Wu X, Wang H and Li Q B.2016.Analysis of the Job-Housing Balance for the Newly Employed_Take the Main City of Nanjing as an Example.Urban Planning Forum,4:75-81
强欢欢,吴晓,王慧,李泉葆.2016.“职住平衡”视角下新就业人员的职住空间平衡关系及其内在影响因素——以南京市主城区为例.城市规划学刊,4:75-81 [DOI:10.16361/j.upf.201604010http://dx.doi.org/10.16361/j.upf.201604010]
Shi Y L.1998.“Desakota” Model and Its Effects on the Model of China’s Urban Economic Organization.Urban Development Studies, 5:3-5
史育龙.1998.Desakota模式及其对我国城乡经济组织方式的启示.城市发展研究,5:3-5
Wu W J, Wu L Y, Wang K, Shi S, Shi N, Zhang Q, Liu Q Z, Lyu C T, Mao Q Z, Li L and Wu T H.2018.How Should Metropolitan Areas Take the Lead To Realize The“Centenary Goal”.City Planning Review,3:87-94
吴唯佳,吴良镛,王凯,石崧,石楠,张勤,刘奇志,吕传廷,毛其智,李亮,武廷海.2018.特大城市地区如何引领实现百年目标.城市规划,3:87-94
Wu T H and Zhang N.2015.Spatial Pattern And Development Of China's Urban Agglomerations: An Interpretation Of The Sciences Of Human Settlements.City Planning Review,6:14-25+36
武廷海,张能.作为人居环境的中国城市群——空间格局与展望.城市规划,6:14-25+36
Wu D G, Zhu W and Wang D.2013.The Process and Inspiration of Plan for the Randstad in Netherlands.Modern Urban Research,1:39-46
吴德刚,朱玮,王德.2013.荷兰兰斯塔德地区的规划历程及启示.现代城市研究,1:39-46
Xu J, Dang A R and Li T.2016.Research on Urban Planning Method from Big Data Perspective.Geomatics World,3:1-4+12
许剑,党安荣,李涛.2016.大数据视角的城市规划编制方法研究.地理信息世界,3:1-4+12
Yan Y G.2019.A Thought on Building the Guangdong-Hong Kong-Macao Greater Bay Area into a World-class Area for Innovative Talents.Macroeconomic Management,9:59-65
阎豫桂.2019.粤港澳大湾区打造世界一流创新人才高地的思考.宏观经济管理,9:59-65 [DOI:10.19709/j.cnki.11-3199/f.2019.09.010http://dx.doi.org/10.19709/j.cnki.11-3199/f.2019.09.010]
Zhen M C, Dang A R, Wu G Q and Ma Q W.2020.The Demand of Spatiotemporal Big Data for Quantitative Research on New Urbanization.Geomatics World,27(02):1-8+14
甄茂成,党安荣,吴冠秋,马琦伟.2020.新型城镇化定量研究的时空大数据应用需求分析.地理信息世界,27(02):1-8+14
Zhen M C, Dang A R and Xu J.2019.Research Progress on the Applications of Big Data to Urban Planning.Geomatics World,26(01):6-12+24
甄茂成,党安荣,许剑.2019.大数据在城市规划中的应用研究综述.地理信息世界,26(01):6-12+24
Zhen M C, Dang A R and Kan C C.2019.Study on the Identification of Urban Agglomerations in the Yangtze River Delta Based on Big Data and Network Analysis.Shanghai Urban Planning Review,6:8-16
甄茂成,党安荣,阚长城.2019.基于大数据与网络分析的长三角城市群识别研究.上海城市规划,6:8-16
Zhou L, Zhao Q and Yang F.2019.Identification of urban agglomeration boundary based on POI and NPP/VIIRS night light data.Progress in Geography,6:840-850
周亮,赵琪,杨帆.2019.基于POI与NPP/VIIRS灯光数据的城市群边界定量识别.地理科学进展,6:840-850
Zheng L.2017.Big Data Brings Social Network Analysis into A Golden Age.Social Science Research,2:72-80
郑路.2017.大数据为社会网络分析带来“黄金时代”.实证社会科学,2:72-80
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