信息流视角下长株潭城市群关系网络特征辨析及规划启示——基于百度指数的分析
|
摘要
以长株潭城市群为研究区域,构建基于2015～2017年城市群内各城市间的百度搜索指数信息流网络,探讨城市群的空间网络特征及其时空演变,发现其关系网络呈现两大主要特征:一是"点—轴"结构明显:城市核心网络以长沙为中心点,长―株、长―岳、长―衡、长―常为主轴,呈"Y"型分布;二是"核心―外围"趋势强化:长沙市是强控制力和强主导型城市,益阳市、娄底市其城市控制力和主导性呈现逐年下降的趋势,且城市的离散程度不断增大,城市间不均衡程度呈逐年上升趋势。研究对规划的启示在于:在城市群空间组织的研究与规划中,应关注城市间关联的内涵,通过多视角、多层次、多维度对城市群空间关系进行定量分析与定性辨析,以构建一个更加立体、更加系统、更加开放的"网络化"城市群。
Based on the Changsha-Zhuzhou-Xiangtan urban agglomeration as the research area the Baidu search index information flow network between cities in the urban agglomerations in 2015, 2016 and 2017 is constructed to explore the spatial network characteristics of the urban agglomerations and their temporal and spatial evolution. The network presents two main features: Firstly, the "point-axis" structure is obvious: the urban core network is centered on Changsha, and the Changsha-Zhuzhou, Changsha-Yueyang, Changsha-Hengyang, Changsha-Changde, are the main axes, showing a "Y"distribution. Secondly, the "core-periphery" trend is strengthened: Changsha City is a strong control and strong-oriented city.The city's control and dominantness in Yiyang City and Loudi City are declining year by year, and the degree of dispersion of the city is increasing. The degree of imbalance between cities is increasing year by year. The enlightenment of the research on planning is that in the research and planning of the urban group space organization, we should pay attention to the connotation of inter-city association, and we should quantitatively analyze and qualitatively analyze the spatial relationship of the urban agglomeration through multi-perspective, multi-level and multi-dimensional to construct a more "dimensional", more systematic,and more open the "networked" urban agglomeration.
Based on the Changsha-Zhuzhou-Xiangtan urban agglomeration as the research area the Baidu search index information flow network between cities in the urban agglomerations in 2015, 2016 and 2017 is constructed to explore the spatial network characteristics of the urban agglomerations and their temporal and spatial evolution. The network presents two main features: Firstly, the "point-axis" structure is obvious: the urban core network is centered on Changsha, and the Changsha-Zhuzhou, Changsha-Yueyang, Changsha-Hengyang, Changsha-Changde, are the main axes, showing a "Y"distribution. Secondly, the "core-periphery" trend is strengthened: Changsha City is a strong control and strong-oriented city.The city's control and dominantness in Yiyang City and Loudi City are declining year by year, and the degree of dispersion of the city is increasing. The degree of imbalance between cities is increasing year by year. The enlightenment of the research on planning is that in the research and planning of the urban group space organization, we should pay attention to the connotation of inter-city association, and we should quantitatively analyze and qualitatively analyze the spatial relationship of the urban agglomeration through multi-perspective, multi-level and multi-dimensional to construct a more "dimensional", more systematic,and more open the "networked" urban agglomeration.
引文
[1]甄峰,刘晓霞,刘慧.信息技术影响下的区域城市网络:城市研究的新方向[J].人文地理,2007,22(2):76-80.
[2]PFLIEGER G,ROZENBLAT C.Urban networks and network theory:The city as the connector of multiple networks[J].Urban Studies,2010,47(13):2723-2735.
[3]MITCHELSON R,WHEELER J O.The flow of information in a global economy:The role of the American urban system in1990[J].Annals of the Association of American Geographers,1994,84(1):87-107.
[4]Townsend A M.Networked cities and the global structure of the Internet.American Behavioral Scientist,2001,44(10):1698-1717.
[5]Malecki E J.The economic geography of the Internet’s infrastructure[J].Economic Geography,2002,78(4):399-424.
[6]汪明峰,宁越敏.互联网与中国信息网络城市的崛起[J].地理学报,2004,59(3):446-454.
[7]孙中伟,贺军亮,金凤君.世界互联网城市网络的可达性与等级体系[J].经济地理,2010,30(9):1449-1455.
[8]甄峰,王波,陈映雪.基于网络社会空间的中国城市网络特征--以新浪微博为例[J].地理学报,2012,67(8):1031-1043.
[9]徐燕,黎明.基于新浪微博视角下的城市网络空间特征分析--以重庆市主城区为例[J].西南师范大学学报,2014,39(6):44-49.
[10]占自华.微博研究评述[J].济南大学学报(社会科学版),2011,21(1):34-37.
[11]陈晨,修春亮.流空间视角的东北地区城市网络研究[J].地域研究与开发,2014,33(4):82-89.
[12]熊丽芳,甄峰,王波,等.基于百度指数的长三角核心区城市网络特征研究[J].经济地理,2013,33(7):67-73.
[13]蒋大亮,孙烨,任航,等.基于百度指数的长江中游城市群城市网络特征研究[J].长江流域资源与环境,2015,24(10):1654-1664.
[14]赵映慧,高鑫,姜博.东北三省城市百度指数的网络联系层级结构[J].经济地理,2015,35(5):32-37.
[15]赵映慧,李佳谣,郭晶鹏.基于百度指数的成渝城市群网络联系格局研究[J].地域研究与开发,2017,36(4):55-59.
[16]于欣洺,吴相利.基于百度指数的东北地区城市网络特征研究[J].哈尔滨师范大学自然科学学报,2015,31(6):114-118.
[17]陈震,赵映慧,陈琪.长江中游城市群网络联系能级研究--基于百度指数的探索[J].现代城市,2016(2):34-39.
[18]郝修宇,徐培玮.基于百度指数和引力模型的城市网络对比--以京津冀城市群为例[J].北京师范大学学报(自然科学版),2017,53(4):479-485.
[19]刘望保,石恩名.基于ICT的中国城市间人口日常流动空间格局--以百度迁徙为例[J].地理学报,2016,71(10):1667-1679.
[20]蒋小荣,汪胜兰.中国地级以上城市人口流动网络研究--基于百度迁徙大数据的分析[J].中国人口科学,2017(2):35-46.
[21]叶强,张俪璇,彭鹏,等.基于百度迁徙数据的长江中游城市群网络特征研究[J].经济地理,2017,37(8):53-59.
[22]董超,修春亮,魏冶.基于通信流的吉林省流空间网络格局[J].地理学报,2014,69(4):510-519.
[23]邓楚雄,宋雄伟,谢炳庚等.基于百度贴吧数据的长江中游城市群城市网络联系分析[J].地理研究,2018,37(6):1181-1192.
[24]熊丽芳,甄峰,席广亮,等.我国三大经济区城市网络变化特征--基于百度信息流的实证研究[J].热带地理,2014,34(1):34-43.
[25]WHEELER J O,MITCHELSON R L.Information flows among major metropolitan areas in the united states[J].Annals of the Association of American Geographers,1989,79(4):523-543.
[2]PFLIEGER G,ROZENBLAT C.Urban networks and network theory:The city as the connector of multiple networks[J].Urban Studies,2010,47(13):2723-2735.
[3]MITCHELSON R,WHEELER J O.The flow of information in a global economy:The role of the American urban system in1990[J].Annals of the Association of American Geographers,1994,84(1):87-107.
[4]Townsend A M.Networked cities and the global structure of the Internet.American Behavioral Scientist,2001,44(10):1698-1717.
[5]Malecki E J.The economic geography of the Internet’s infrastructure[J].Economic Geography,2002,78(4):399-424.
[6]汪明峰,宁越敏.互联网与中国信息网络城市的崛起[J].地理学报,2004,59(3):446-454.
[7]孙中伟,贺军亮,金凤君.世界互联网城市网络的可达性与等级体系[J].经济地理,2010,30(9):1449-1455.
[8]甄峰,王波,陈映雪.基于网络社会空间的中国城市网络特征--以新浪微博为例[J].地理学报,2012,67(8):1031-1043.
[9]徐燕,黎明.基于新浪微博视角下的城市网络空间特征分析--以重庆市主城区为例[J].西南师范大学学报,2014,39(6):44-49.
[10]占自华.微博研究评述[J].济南大学学报(社会科学版),2011,21(1):34-37.
[11]陈晨,修春亮.流空间视角的东北地区城市网络研究[J].地域研究与开发,2014,33(4):82-89.
[12]熊丽芳,甄峰,王波,等.基于百度指数的长三角核心区城市网络特征研究[J].经济地理,2013,33(7):67-73.
[13]蒋大亮,孙烨,任航,等.基于百度指数的长江中游城市群城市网络特征研究[J].长江流域资源与环境,2015,24(10):1654-1664.
[14]赵映慧,高鑫,姜博.东北三省城市百度指数的网络联系层级结构[J].经济地理,2015,35(5):32-37.
[15]赵映慧,李佳谣,郭晶鹏.基于百度指数的成渝城市群网络联系格局研究[J].地域研究与开发,2017,36(4):55-59.
[16]于欣洺,吴相利.基于百度指数的东北地区城市网络特征研究[J].哈尔滨师范大学自然科学学报,2015,31(6):114-118.
[17]陈震,赵映慧,陈琪.长江中游城市群网络联系能级研究--基于百度指数的探索[J].现代城市,2016(2):34-39.
[18]郝修宇,徐培玮.基于百度指数和引力模型的城市网络对比--以京津冀城市群为例[J].北京师范大学学报(自然科学版),2017,53(4):479-485.
[19]刘望保,石恩名.基于ICT的中国城市间人口日常流动空间格局--以百度迁徙为例[J].地理学报,2016,71(10):1667-1679.
[20]蒋小荣,汪胜兰.中国地级以上城市人口流动网络研究--基于百度迁徙大数据的分析[J].中国人口科学,2017(2):35-46.
[21]叶强,张俪璇,彭鹏,等.基于百度迁徙数据的长江中游城市群网络特征研究[J].经济地理,2017,37(8):53-59.
[22]董超,修春亮,魏冶.基于通信流的吉林省流空间网络格局[J].地理学报,2014,69(4):510-519.
[23]邓楚雄,宋雄伟,谢炳庚等.基于百度贴吧数据的长江中游城市群城市网络联系分析[J].地理研究,2018,37(6):1181-1192.
[24]熊丽芳,甄峰,席广亮,等.我国三大经济区城市网络变化特征--基于百度信息流的实证研究[J].热带地理,2014,34(1):34-43.
[25]WHEELER J O,MITCHELSON R L.Information flows among major metropolitan areas in the united states[J].Annals of the Association of American Geographers,1989,79(4):523-543.