일자리 창출을 위한 창조계층 및 창조산업 육성의 도시계획 정책 연구 = A Study on Urban Planning Policies Fostering the Creative Industry and Creative Class to Create More Jobs
저자
발행기관
학술지명
권호사항
발행연도
2015
작성언어
-KDC
300
자료형태
학술저널
수록면
1-165(165쪽)
제공처
□ Research Background and Purposes
It has become difficult to create added value with the manufacturing-centered industry and employment. In order to turn Busan into a creative city by invigorating its creative economy, fostering the creative industry and the creative class in which people work in the industry is of the utmost necessity.
While the majority of the support policies have been administrative and financial measures to foster the creative industry and the creative class, urban planning approaches can provide creative foundations to the city to facilitate the development of the industry. For doing so, it is necessary to analyze what kind of influences certain urban planning features have on the creative industry and the creative class so as to suggest policy implementation related to urban planning. This study, therefore, used quantitative methods for the analysis to draw a scientific conclusion. In addition, it proposed urban planning policies to foster both the creative industry and the creative class in Busan, based on the conclusion.
□ Definitions of the Creative Industry and the Creative Class
The creative industry and the creative class with which the research was concerned were defined by analyzing previous research, both domestic and overseas. The definition of the creative industry first derived from the subclass criteria of the Korea Standard Industrial Classification, considering the availability of relevant statistical data. Additionally, this study reflected the creative industry that had been defined more than three times in previous research. Workers in such creative industry were defined as the creative class. In accordance with the subclassification criteria, nineteen industries in total were identified as the creative industry. When an analysis method happened to be unable to account for the industries under the subclasses, it factored in relevant, more generic industries in the three main classification criteria to which the nineteen industries belong.
□ Analysis Results
The analysis of this study is focused on three aspects: (1) spatial clustering characteristics of the creative industry and the creative class in the neighborhoods (dong) of Busan through spatial autocorrelation analysis, (2) competitiveness of the creative industry and the creative class in Busan by calculating the RRCA index and the LQ, and (3) urban planning features’ influences on the creative industry and the creative class by employing the panel model.
First, the spatial autocorrelation analysis employed the creative industry and the creative class that were identified under the main classification criteria. When it comes to publication, visual entertainment, info-communications, and info-services, it was shown that the numbers of both business entities and workers had stronger clusters in the eastern part of Busan, where Centum City had great influences, than in the rest, As to professional, scientific, and technical services, the number of their business entities were clustered around Geumjeong-gu along Jungang-daero, one of the main arterial roads in the city, while the number of their workers were clustered around Haeundae-gu. In regards to arts, sports, and leisure services, the number of their business entities had a stronger cluster in the northern part of Busan than in the rest, whereas the number of their workers did not particularly demonstrate a cluster.
Second, competitiveness of the creative industry and the creative class was analyzed by calculating the RRCA index and the LQ. The creative industry and the creative class under the main classification criteria were employed to calculate the RRCA index, while those under the subclassification criteria were used for the LQ. The result of the RRCA index analysis showed that arts, sports, leisure services and professional, scientific, and technical services appeared to have comparative advantages at the national level, and each district (gu and gun) appeared to have different industries with comparative advantages at the city level. As a result of analyzing the LQ, radio broadcasting appeared to be specialized in terms of the number of business entities and photographing services appeared to be specialized in terms of the number of workers at the national level, while each district appeared to have different specialized industries at the city level.
Third, for the panel model analysis, the numbers of both business entities and workers in the nineteen creative industries were selected as dependent variables, and some characteristics expected to have influences on the dependent variable as independent variables, including regional characteristics, architectural characteristics, urban planning facility characteristics, and zoning characteristics. To establishing the panel data, statistical data about the selected variables from 2007 to 2013 were collected. As a result of the analysis, it was shown that the panel model was more appropriate than the OLS regression model with respect to the numbers of business entities and workers in every creative industry. Influential variables and their effects were different in each industry. This study combined the result of the panel model analysis with that of the analysis of the LQ, finally proposing policy implementation to foster the creative industry and create related jobs.
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