基于POI数据的北京市文化创意产业空间集聚特征与影响机制研究
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摘要
在文化与经济相互渗透的发展模式中,文化创意产业已成为北京市经济发展的支柱产业之一.本研究基于北京市97 555条文化创意产业POI数据,利用核密度分析和空间自相关分析法,对北京市文化创意产业空间集聚特征进行探究,并总结了集聚产生的主要影响机制.结果表明:1)北京市文化创意产业集聚水平较高,以东、西城区为核心呈现圈层式发展结构;2)各区文化创意产业发展水平及主导产业类型差别较大,九大类文化创意产业集聚特征各有不同;3)影响文化产业集聚的动力机制主要可分为市场和资源两大因素.研究可为优化北京市文化创意产业空间布局,促进生产要素协调统一发展提供一定科学参考.
Cultural and creative industries have become the pillar industries of Beijing′s economic development. Studying the spatial agglomeration characteristics of cultural and creative industries in Beijing has great significance for optimzing the layout of industrial space and promoting agglomeration of production factors and the overall socio-economic development of Beijing. Based on the POI data of Beijing culture creative industries,the methods of kernel density analysis and spatial autocorrelation are used to identify the center of cultural and creative industry in Beijing, and to explore its spatial agglomeration characteristics and impact mechanism. The results show that:1) The level of Beijing cultural and creative industries agglomeration is relatively high, and it presents a layered development structure with Dongcheng District and Xicheng District as the core.2) The development level of cultural and creative industries and the distribution of resources are relatively unbalanced between different regions in Beijing. Besides, the characteristics of nine categories of cultural and creative industries are different.3) The motivation mechanism that affects the gathering of cultural and creative industries can be divided into market-oriented mechanism and resource-oriented mechanism.
Cultural and creative industries have become the pillar industries of Beijing′s economic development. Studying the spatial agglomeration characteristics of cultural and creative industries in Beijing has great significance for optimzing the layout of industrial space and promoting agglomeration of production factors and the overall socio-economic development of Beijing. Based on the POI data of Beijing culture creative industries,the methods of kernel density analysis and spatial autocorrelation are used to identify the center of cultural and creative industry in Beijing, and to explore its spatial agglomeration characteristics and impact mechanism. The results show that:1) The level of Beijing cultural and creative industries agglomeration is relatively high, and it presents a layered development structure with Dongcheng District and Xicheng District as the core.2) The development level of cultural and creative industries and the distribution of resources are relatively unbalanced between different regions in Beijing. Besides, the characteristics of nine categories of cultural and creative industries are different.3) The motivation mechanism that affects the gathering of cultural and creative industries can be divided into market-oriented mechanism and resource-oriented mechanism.
引文
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[2]周尚意,姜苗苗,吴莉萍.北京城区文化产业空间分布特征分析[J].北京师范大学学报:社会科学版,2006,(6):127-133.
[3]Wang F F.The role of cultural and creative industries in optimizing industrial structure under low-carbon emission constraint[J].Ecological Economy,2010,06(4):329-333.
[4]雷宏振,谢卫军.文化产业集群内知识共享与产业集聚关系研究[J].情报杂志,2010,29(6):76-80.
[5]史征.文化产业集群竞争力评价实证研究-以杭州数字娱乐产业集群为例[J].生产力研究,2009,(18):141-143.
[6]戴钰.湖南省文化产业集聚及其影响因素研究[J].经济地理,2013,33(4):114-119.
[7]王家庭,张容.我国文化产业发展影响因素及提升路径的区域分析[J].统计与决策,2010,(2):79-81.
[8]蓝庆新,郑学党,韩晶.我国文化产业国际竞争力比较及提升策略-基于2011年横截面数据的分析[J].财贸经济,2012,(8):80-87.
[9]滕堂伟,翁玲玲,韦素琼,等.中国文化产业发展的区域差异[J].经济地理,2014,34(7):97-102.
[10]吴志峰,柴彦威,党安荣,等.地理学碰上“大数据”:热反应与冷思考[J].地理研究,2015,34(12):2207-2221.
[11]龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报,2012,67(10):1339-1352.
[12]Hollenstein L,Purves R.Exploring place through user-generated content:Using flickr to describe city cores[J].Journal of Spatial Information Science,2010,1:21-48.
[13]Becker R A,Caceres R,Hanson K,et al.A tale of one city:Using cellular network data for urban planning[J].IEEE Pervasive Computing,2011,10(4):18-26.
[14]Roth C,Kang S M,Batty M,et al.Structure of urban movements:Polycentric activity and entangled hierarchical flows.[J].Plos One,2011,6(1):15923.
[15]吴康敏,张虹鸥,王洋,等.广州市多类型商业中心识别与空间模式[C].2016海峡两岸经济地理学研讨会,2016.
[16]郭洁,吕永强,沈体雁.基于点模式分析的城市空间结构研究-以北京都市区为例[J].经济地理,2015,35(8):68-74.
[17]黄斌.北京文化创意产业集聚区对城市空间的影响[C].规划创新:2010中国城市规划年会论文集,2010.
[18]Wenhao Y U,Tinghua A I.The visualization and analysis of POI features under network space supported by kernel density estimation[J].Acta Geodaetica Et Cartographica Sinica,2015,44:82-90.
[19]王劲峰,廖一兰,刘鑫.空间数据分析教程[M].北京:科学出版社,2010.
[20]王远飞,何洪林.空间数据分析方法[M].北京:科学出版社,2007.
[21]Xun S.Selection of bandwidth type and adjustment side in kernel density estimation over inhomogeneous backgrounds[J].International Journal of Geographical Information Science,2010,24(5):643-660.
[22]Wenhao Y U,Tinghua A I.The visualization and analysis of POI features under network space supported by kernel density estimation[J].Acta Geodaetica Et Cartographica Sinica,2015,44:82-90.
[23]王劲峰,葛咏,李连发,等.地理学时空数据分析方法[J].地理学报,2014,69(9):1326-1345.
[24]马荣华,蒲英霞,马晓冬.GIS空间关联模式发现[M].北京:科学出版社,2007.
[25]Liu Q,Li G,Tong D,et al.The spatial pattern and influence factors of high-tech firms in Shenzhen based on ESDA[J].Economic Geography,2011,31(6):926-933.
[26]Anselin L.Local indicators of spatial association-LISA[J].Geographical Analysis,1995,27(2):93-115.