| 摘要: |
| 文化创意产业包含多种类型，空间集
聚呈现异质性，成因受多种因素影响。本文
以北京市主城区为研究对象，通过数据和量
化分析确定了各类文化创意产业空间集聚中
心，并揭示了其分异成因，依据成因主导类型
的不同将文化创意产业重新分为三类：文化
主导类、创意主导类和既有存续类。最后依
据集聚规律和差异化发展提出了三个空间优
化建议。 |
| 关键词: 文化创意产业 空间集聚 分异成
因 分异类型 北京市主城区 |
| DOI：10.13791/j.cnki.hsfwest.20200308 |
| 分类号: |
| 基金项目:国家自然科学基金资助项目（51778403；
51778406） |
|
| Differentiation Contributing Factors and Types of Spatial Agglomeration of Cultural andCreative Industries in Downtown Beijing |
|
QIU Ning,LI Ze,HAN Xinyu
|
| Abstract: |
| In recent years, with the urban renewal, transformation and development, the role
of cultural and creative industries in urban renewal and development has received extensive
attention. Cultural and creative industries include nine types (culture and art, news and
publications, tourism, advertising and exhibitions, design services, computer services, radio
and television, art trading, other ancillary services) in China, and the location of each type
shows heterogeneity of spatial agglomeration. This paper takes downtown Beijing as the
research object, and focus on the factors that influence the location of agglomeration.
In the experimental part, the first step uses the enterprise data to identify the spatial
agglomeration centers of each cultural and creative industry through spatial statistics.
Though combining each cultural and creative industry’s location and the environmental
characteristics, its attribution can be qualitatively analyzed. Secondly, it summarizes all
the main spatial agglomeration centers, including comprehensive ones and single ones,
among which the comprehensive type is CBD area (agglomerating seven kinds of cultural
and creative industries), university agglomeration area (agglomerating four kinds of cultural
and creative industries), historical district agglomeration area (agglomerating three kinds
of cultural and creative industries) and other single types. Then it summarize all the main
attributions and obtains relevant data, including infrastructure (X 1 ), policy effect (X 2 ),
creative class (X 3 ), historical culture (X 4 ), land rent (X 5 ), universities (X 6 ), and tourism (X 7 ),
innovation atmosphere (X 8 ), population density (X 9 ), and inclusiveness (X 10 ). Finally, the
study quantitatively analyzes these attributions in each type of cultural and creative industry
based on the GeoDetector method. This method is used to detect the degree of geographical
factors which is the cause of the heterogeneity of spatial distribution of cultural and creative
industries.
The result indicates that according to the quantitative attribution analysis, the nine types of
cultural and creative industries can be re-classified into three heterogeneity categories: culture-
leading type, creativity-leading type and history-continuation type. The dominant attributions
of each category are different from each other. To be specific, 1) culture-leading type
includes four kinds of cultural and creative industries, including history and culture, tourism
and entertainment, advertising and radio, television and film. The domain attributions are
atmosphere of history and culture, tourism, infrastructure and population density. 2) Creativity-
leading type includes three kinds of cultural and creative industries: design service, software and network and publishing. The domain attributions are creative class, land rent, university, creative atmosphere, policy and inclusiveness. 3)
History-continuation type includes two kinds of cultural and creative industries: art trading and other ancillary services. This type is less affected
by the above attributions, but more affected by the existing market and historical persistence. From the analysis results, the attributions of the
differentiation of the cultural and creative industries also can be roughly divided into three levels. The first level, over-high, includes infrastructure
and land rent. It has the highest average impact on the cultural and creative industry clusters, and generally has a high impact on all cultural and
creative industries. The second level, partially-high, includes population density, creative class, tourism, policy effect and universities. The third
category is generally low-level, and includes historical culture and inclusiveness. The result help us understand the motivation and influential
factors of cultural and creative industries.
In the end, this paper puts forward three related optimization suggestions to facilitate the prospect of cultural and creative industries:
creating brand value of space through cultural identity, providing spatial support for industrial linkage, creation and cultivation of low-cost
innovation space. |
| Key words: Cultural and Creative Industry Spatial Agglomeration Differentiation Contribution Factors Differentiation Types Downtown
Beijing |
