武汉城市圈资源集聚能力的空间关联研究
|
摘要
提升武汉城市圈各城市间资源集聚能力的空间关联程度,对于实现该区域的协调发展具有重要意义。基于此,运用修正的引力模型,采用社会网络分析法对1995～2015年武汉城市圈资源集聚能力的空间关联进行分析。研究表明:(1)武汉城市圈资源集聚能力的空间关联特征显著,网络密度呈现倒"U"型,网络等级逐年下降,网络效率呈现"U"型;(2)武汉城市圈资源集聚能力在网络中呈现"中心-边缘"的特征,武汉市的点度中心度最高,在网络中属于中心城市,而其它城市属于边缘城市;(3)板块一(武汉、孝感、天门)属于"双向溢出"板块,板块二(潜江、咸宁)属于"净受益"板块,板块三(仙桃、黄冈)属于"净溢出"板块,板块四(黄石、鄂州)属于"经纪人"板块。鉴于此,从多途径构建资源集聚能力空间关联"通道"、增强中心城市辐射带动功能和推动城市分工协作等3个方面提出了提升武汉城市圈资源集聚能力空间关联程度的政策建议。
Improving the spatial correlation degree of Wuhan Metropolitan Area's resources aggregating ability has great significance for realizing the coordinated development of the region. Based on this,this paper uses the modified gravity model and the social network analysis method to analyze the spatial correlation of the resources aggregating ability in Wuhan Metropolitan Area from 1995 to 2015,the study shows that:( 1) The spatial correlation characteristics of resources aggregating ability in Wuhan Metropolitan Area are significant, with an inverted U-shape network density,a slow decreasing network gradation through the years and an U-shape network efficiency;( 2) The resources aggregating ability in Wuhan Metropolitan Area presents the characteristics of"Center-Edge"in the Network,Wuhan has the highest degree of centrality,which belongs to the central city in the network,while the other cities belong to the edge city;( 3) The first plate( Wuhan,Xiaogan,Tianmen)belongs to "bidirectional spillover"plate,the second plate( Qianjiang,Xianning) belongs to "net beneficial"plate,the third plate( Xiantao,Huanggang) belongs to "net spillover",the fourth plate( Huangshi,Ezhou)belongs to "broker"plate. According to the above conclusions, we put forward three policy recommendations( multi-way constructs spatial correlation "channel"of resources aggregating ability,enhances radiant driving function of central city, promotes urban division of labor and cooperation) to improve the spatial correlation degree of the resources aggregating ability in Wuhan Metropolitan Area.
Improving the spatial correlation degree of Wuhan Metropolitan Area's resources aggregating ability has great significance for realizing the coordinated development of the region. Based on this,this paper uses the modified gravity model and the social network analysis method to analyze the spatial correlation of the resources aggregating ability in Wuhan Metropolitan Area from 1995 to 2015,the study shows that:( 1) The spatial correlation characteristics of resources aggregating ability in Wuhan Metropolitan Area are significant, with an inverted U-shape network density,a slow decreasing network gradation through the years and an U-shape network efficiency;( 2) The resources aggregating ability in Wuhan Metropolitan Area presents the characteristics of"Center-Edge"in the Network,Wuhan has the highest degree of centrality,which belongs to the central city in the network,while the other cities belong to the edge city;( 3) The first plate( Wuhan,Xiaogan,Tianmen)belongs to "bidirectional spillover"plate,the second plate( Qianjiang,Xianning) belongs to "net beneficial"plate,the third plate( Xiantao,Huanggang) belongs to "net spillover",the fourth plate( Huangshi,Ezhou)belongs to "broker"plate. According to the above conclusions, we put forward three policy recommendations( multi-way constructs spatial correlation "channel"of resources aggregating ability,enhances radiant driving function of central city, promotes urban division of labor and cooperation) to improve the spatial correlation degree of the resources aggregating ability in Wuhan Metropolitan Area.
引文
[1]方创琳,张舰.中国城市群形成发育的政策保障机制与对策建议[J].中国人口·资源与环境,2011(10):107-113.FANG C L, ZHANG J. Urban agglomerations formation and development in China:Policy guarantee mechanism, countermeasures and suggestions[J]. China Population, Resources and Environment,2011(10):107-113.
[2]方创琳.中国城市群研究取得的重要进展与未来发展方向[J].地理学报,2014,69(8):1130-1144.FANG C L. Progress and the future direction of research into urban agglomeration in China[J]. Journal of Geographical Sciences,2014,69(8):1130-1144.
[3]郭庆宾,许泱,刘承良.长江中游城市群资源集聚能力影响因素与形成机理[J].中国人口·资源与环境,2018,28(2):151R157.GUO Q B,XU Y,LIU C L. Influencing factors and formation mechanism of resources aggregating ability in urban agglomeration in the middle reaches of the Yangtze River[J]. China Population,Resources and Environment,2018, 28(2):151-157.
[4]贾春梅,葛扬.城市行政级别、资源集聚能力与房价水平差异[J].财经问题研究,2015(10).JIA C M,GE Y. Urban administrative level resource gathering ability and housing price difference[J]. Research on Financial and Economic Issues,2015(10).
[5]季伟杰,翟伟峰,郑安安.基于金融资源论的金融产业集聚研究[J].中国城市经济,2011(3):82-83.JI W J,ZHAI W F,ZHENG A A. Research on financial industry agglomeration based on the theory of financial resources[J]. China Urban Economy,2011(3):82-83.
[6]王修华,黄明.金融资源空间分布规律:一个金融地理学的分析框架[J].经济地理,2009,29(11):1808-1815.WANG X H,HUANG M. Spatial distribution regularity of finance resource:An analytical framework of finance geography[J]. Economic Geography,2009,29(11):1808-1815.
[7] GEHRIG THOMAS. Cities&the geography of financial centers[J]. Centre for economic policy research,1998.
[8]覃剑.城市群金融资源空间配置研究进展[J].区域金融研究,2013(4):35-39.QIN J. The research progress of financial resource spatial allocation in urban agglomeration[J]. Journal of Regional Financial Research,2013(4):35-39.
[9]茹乐峰,苗长虹,王海江.我国中心城市金融集聚水平与空间格局研究[J].经济地理,2014,34(2):58-66.RU L F,MIAO C H,WANG H J. Research on the level and spatial pattern of financial agglomeration on central cities in China[J]. Economic Geography,2014,34(2):58-66.
[10] AUDRESS. D,FELDMAN M. Spillovers and the geography of innovation and production[J]. American economic review,2006,86(3).
[11] BALDWIN R.,P M. Global income divergence,trade and industrialization:The geography of growth take off[J]. Journal of economic growth,2001(6).
[12]陈俊,胡宗义,刘亦文.金融集聚的区域差异及影响因素的空间计量分析[J].财经理论与实践,2013,34(6):21-24.CHEN J,HU Z Y,LIU Y W. Spatial econometric analysis on the influencing factors of China’s financial industry regional clusters[J]. The Theory and Practice of Finance and Economics,2013,34(6):21-24.
[13]王奋,韩伯棠.科技人力资源区域集聚效应的实证研究[J].中国软科学,2006(3):91-99.WANG F,HAN B T. An empirical study on the regional economic agglomeration effect of the science and geography technology human resource[J]. China Soft Science,2006(3):91-99.
[14]陈菲琼,韩莹.创新资源集聚的自组织机制研究[J].科学学研究,2009,27(8):1246-1254.CHEN F Q,HAN Y. Research on self organization of innovative clusters[J]. Studies in Science of Science, 2009,27(8):1246-1254.
[15]焦继文,郭宝洁.中心城市科技资源集聚辐射力评价[J].统计与决策,2015(23):56-59.JIAO J W,GUO B J. Evaluation of radiant power of scientific and technological resources agglomeration in central cities[J].Statistics&Decision,2015(23):56-59.
[16]米娟.中国区域要素集聚状况探析[J].改革与战略,2008(11):133-144.MI J. An analysis of the status of regional factors agglomeration in China[J]. Reform and Strategy,2008(11):133-144.
[17]郭庆宾,张中华.长江中游城市群要素集聚能力的时空演变[J].地理学报,2017(10):1746-1761.GUO Q B,ZHANG Z H. Spatial-temporal evolution of factors aggregating ability in agglomeration in the middle reaches of the Yangtze River[J]. Economic Geography,2017(10):1746-1761.
[18]江永真.区域企业创新要素集聚效益评价[J].福州大学学报,2015(3):43-50.JIANG Y Z. Evaluation of innovation factor agglomeration benefit of regional enterprises[J]. Journal of Fuzhou University,2015(3):43-50.
[19]刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究[J].中国工业经济,2015(5):83-95.LIU H J, LIU C M, SUN Y N. Spatial correlation network structure of energy consumption and its effect in China[J].China Industrial Economics,2015(5):83-95.
[20]张翼.基于空间关联网络结构的中国省域协同碳减排研究[J].统计与信息论坛,2017(2):63-69.ZHANG Y. Study on Chinese provincial collaborative carbon reduction based on spatial correlation network structure[J]. Statistics&Information Forum,2017(2):63-69.
[21]逯苗苗,孙涛.我国雾霾污染空间关联性及其驱动因素分析——基于社会网络分析方法[J].宏观质量研究,2017(4):66-75.LU M M,SUN T. An analysis of the spatial correlation and the driving factors of haze pollution in China-An SNA based approach[J]. Journal of Macro-Quality Research, 2017(4):66-75.
[22]方大春,裴梦迪.房价空间关联网络结构实证分析[J].上海经济研究,2018(1):63-73.FANG D C, PEI M D. The empirical analysis of the spatial correlation network structure of housing price[J]. Shanghai Economic Review,2018(1):63-73.
[2]方创琳.中国城市群研究取得的重要进展与未来发展方向[J].地理学报,2014,69(8):1130-1144.FANG C L. Progress and the future direction of research into urban agglomeration in China[J]. Journal of Geographical Sciences,2014,69(8):1130-1144.
[3]郭庆宾,许泱,刘承良.长江中游城市群资源集聚能力影响因素与形成机理[J].中国人口·资源与环境,2018,28(2):151R157.GUO Q B,XU Y,LIU C L. Influencing factors and formation mechanism of resources aggregating ability in urban agglomeration in the middle reaches of the Yangtze River[J]. China Population,Resources and Environment,2018, 28(2):151-157.
[4]贾春梅,葛扬.城市行政级别、资源集聚能力与房价水平差异[J].财经问题研究,2015(10).JIA C M,GE Y. Urban administrative level resource gathering ability and housing price difference[J]. Research on Financial and Economic Issues,2015(10).
[5]季伟杰,翟伟峰,郑安安.基于金融资源论的金融产业集聚研究[J].中国城市经济,2011(3):82-83.JI W J,ZHAI W F,ZHENG A A. Research on financial industry agglomeration based on the theory of financial resources[J]. China Urban Economy,2011(3):82-83.
[6]王修华,黄明.金融资源空间分布规律:一个金融地理学的分析框架[J].经济地理,2009,29(11):1808-1815.WANG X H,HUANG M. Spatial distribution regularity of finance resource:An analytical framework of finance geography[J]. Economic Geography,2009,29(11):1808-1815.
[7] GEHRIG THOMAS. Cities&the geography of financial centers[J]. Centre for economic policy research,1998.
[8]覃剑.城市群金融资源空间配置研究进展[J].区域金融研究,2013(4):35-39.QIN J. The research progress of financial resource spatial allocation in urban agglomeration[J]. Journal of Regional Financial Research,2013(4):35-39.
[9]茹乐峰,苗长虹,王海江.我国中心城市金融集聚水平与空间格局研究[J].经济地理,2014,34(2):58-66.RU L F,MIAO C H,WANG H J. Research on the level and spatial pattern of financial agglomeration on central cities in China[J]. Economic Geography,2014,34(2):58-66.
[10] AUDRESS. D,FELDMAN M. Spillovers and the geography of innovation and production[J]. American economic review,2006,86(3).
[11] BALDWIN R.,P M. Global income divergence,trade and industrialization:The geography of growth take off[J]. Journal of economic growth,2001(6).
[12]陈俊,胡宗义,刘亦文.金融集聚的区域差异及影响因素的空间计量分析[J].财经理论与实践,2013,34(6):21-24.CHEN J,HU Z Y,LIU Y W. Spatial econometric analysis on the influencing factors of China’s financial industry regional clusters[J]. The Theory and Practice of Finance and Economics,2013,34(6):21-24.
[13]王奋,韩伯棠.科技人力资源区域集聚效应的实证研究[J].中国软科学,2006(3):91-99.WANG F,HAN B T. An empirical study on the regional economic agglomeration effect of the science and geography technology human resource[J]. China Soft Science,2006(3):91-99.
[14]陈菲琼,韩莹.创新资源集聚的自组织机制研究[J].科学学研究,2009,27(8):1246-1254.CHEN F Q,HAN Y. Research on self organization of innovative clusters[J]. Studies in Science of Science, 2009,27(8):1246-1254.
[15]焦继文,郭宝洁.中心城市科技资源集聚辐射力评价[J].统计与决策,2015(23):56-59.JIAO J W,GUO B J. Evaluation of radiant power of scientific and technological resources agglomeration in central cities[J].Statistics&Decision,2015(23):56-59.
[16]米娟.中国区域要素集聚状况探析[J].改革与战略,2008(11):133-144.MI J. An analysis of the status of regional factors agglomeration in China[J]. Reform and Strategy,2008(11):133-144.
[17]郭庆宾,张中华.长江中游城市群要素集聚能力的时空演变[J].地理学报,2017(10):1746-1761.GUO Q B,ZHANG Z H. Spatial-temporal evolution of factors aggregating ability in agglomeration in the middle reaches of the Yangtze River[J]. Economic Geography,2017(10):1746-1761.
[18]江永真.区域企业创新要素集聚效益评价[J].福州大学学报,2015(3):43-50.JIANG Y Z. Evaluation of innovation factor agglomeration benefit of regional enterprises[J]. Journal of Fuzhou University,2015(3):43-50.
[19]刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究[J].中国工业经济,2015(5):83-95.LIU H J, LIU C M, SUN Y N. Spatial correlation network structure of energy consumption and its effect in China[J].China Industrial Economics,2015(5):83-95.
[20]张翼.基于空间关联网络结构的中国省域协同碳减排研究[J].统计与信息论坛,2017(2):63-69.ZHANG Y. Study on Chinese provincial collaborative carbon reduction based on spatial correlation network structure[J]. Statistics&Information Forum,2017(2):63-69.
[21]逯苗苗,孙涛.我国雾霾污染空间关联性及其驱动因素分析——基于社会网络分析方法[J].宏观质量研究,2017(4):66-75.LU M M,SUN T. An analysis of the spatial correlation and the driving factors of haze pollution in China-An SNA based approach[J]. Journal of Macro-Quality Research, 2017(4):66-75.
[22]方大春,裴梦迪.房价空间关联网络结构实证分析[J].上海经济研究,2018(1):63-73.FANG D C, PEI M D. The empirical analysis of the spatial correlation network structure of housing price[J]. Shanghai Economic Review,2018(1):63-73.