POI 빅데이터를 활용한 도시활동 중심지 도출과 중심지 기능 분석 : 서울 대도시권을 중심으로Identifying Urban Activity Centers and Their Functions using POI Big Data : The Case of Seoul Metropolitan Area
- Other Titles
- Identifying Urban Activity Centers and Their Functions using POI Big Data : The Case of Seoul Metropolitan Area
- Authors
- 김선재; 이수기
- Issue Date
- Aug-2021
- Publisher
- 대한국토·도시계획학회
- Keywords
- Point-of-Interest (POI); Urban Spatial Structure; Urban Activity Center; Urban Big Data; Point-of-Interest (POI); 도시공간구조; 도시활동 중심지; 도시 빅데이터
- Citation
- 국토계획, v.56, no.6, pp 36 - 52
- Pages
- 17
- Indexed
- KCI
- Journal Title
- 국토계획
- Volume
- 56
- Number
- 6
- Start Page
- 36
- End Page
- 52
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/141240
- DOI
- 10.17208/jkpa.2021.11.56.6.36
- ISSN
- 1226-7147
2383-9171
- Abstract
- 본 연구는 웹 기반 POI 빅데이터를 활용하여서울 대도시권의 활동중심지를 도출하고 중심지의 기능을 분석한다. 구체적으로 POI 밀도분석과 Contour tree 방법론으로 서울 대도시권의 공간구조를 파악하고, 입지계수(Location Quotient: LQ)로 중심지의 기능을 진단한다. 연구의 분석 결과는 기존 도시기본계획의 중심지 체계를 진단해 볼 수 있을 뿐만 아니라서울 대도시권의 체계적인 공간구조 개편 방향을 도출하는 데 유용하게 활용될 수 있다.
During the past several decades, urban scholars and regional economists have tried identifying urban activity centers in metropolitan areas using indicators, such as, population, employment, and housing prices. However, the identification of these urban activity centers has been a challenging assignment due to the limited availability of data. In this study, various activity centers and their functions in the Seoul metropolitan area were identified. Using the Point-of-Interest (POI) big data and Contour tree method, the urban spatial structure of the Seoul metropolitan area was analyzed and the existing urban and regional policies that have induced the polycentric urban form were evaluated. The results indicated that the POI big data had excellent advantages while identifying the urban activity centers and their functional relationships across the Seoul metropolitan area. Further, this study was discovered that the activity centers of the Seoul metropolitan area have been formed around the newly developed areas or important nodes of transportation networks. As a result of comparing the urban activity center and the urban master plans, this study was found that there was some difference. In particular, it has been observed that realistic urban activity centers can be identified by deriving centers beyond administrative districts. As a result of analyzing these urban activity centers with Location Quotient (LQ), city centers and sub-centers were found to have specialized financial functions. In contrast, residential and public facility functions are specialized in regional centers outside Seoul. In conclusion, the urban activity centers outside Seoul should accommodate various facilities to strengthen their regional centrality in the polycentric metropolitan spatial structure.
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