한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제26권3호
  • /
  • Pages.65-84
  • /
  • 2023
  • /
  • 1226-9719(pISSN)
  • /
  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
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머신러닝과 공간분석을 활용한 부산시 중심지 체계 및 영향권 분석

Assessment of Busan City Central Area System and Service Area Using Machine Learning and Spatial Analysis

  • 최지윤 (부산대학교 도시공학과) ;
  • 박민영 (부산대학교 도시공학과) ;
  • 강정은 (부산대학교 도시공학과)
  • Ji Yoon CHOI (Dept. of Urban Planning and Engineering, Pusan National University) ;
  • Minyeong PARK (Dept. of Urban Planning and Engineering, Pusan National University) ;
  • Jung Eun KANG (Dept. of Urban Planning and Engineering, Pusan National University)
  • 투고 : 2023.08.31
  • 심사 : 2023.09.13
  • 발행 : 2023.09.30
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초록

지자체 차원의 균형발전 계획을 수립하기 위해서는 현 상태의 도시공간구조를 파악해야 한다. 특히 중심지는 균형발전의 핵심 요소이므로 그 위치와 규모를 정확히 판단하는 것이 필요하다. 따라서 본 연구는 부산시를 대상으로 중심지 체계를 식별하고 중심지의 기능을 누릴 수 있는 중심지의 영향권에서 소외되는 지역을 도출하고자 하였다. 중심지 체계 식별을 위해 부산시 전역에 대해 4개 지표(지가, 생활인구, 카드 소비, 상업 용도 건축물)를 활용하여 중심지 면적 지수를 산출하고 Getis-Ord Gi*와 DBSCAN 분석을 수행하였다. 식별된 중심지에 대해서는 위계를 구분하고 위계별 네트워크 분석을 통해 영향권을 도출하였다. 분석 결과 중심지는 서면, 중앙, 연산, 장산, 해운대, 덕천, 동래, 대연, 사상, 부산대, 부산역, 사직 총 12곳으로 나타났다. 중심지 영향권 소외지역은 대부분 동부산권역과 서부산권역에서 나타났으며 노후 공업지역과 주거지역, 신도시 일부에서도 도출되었다. 본 연구는 기존 연구에서 다루지 않았던 머신러닝 방법론을 적용하여 기존 계획상 중심지와 실제 데이터 간 차이를 밝히고 중심지 위치와 소외지역을 식별하였다는 점에서 의의가 있다.

In order to establish a balanced development plan at the local government level, it is necessary to understand the current urban spatial structure. In particular, since the central area is a key element of balanced development, it is necessary to accurately identify its location and size. Therefore, the purpose of this study was to identify the central area system for Busan and to derive underprivileged areas that were alienated from the service areas where the functions of the central area could be used. To identify the central area system, four indicators(De facto Population, Land Price, Commercial Buildings, Credit Card Consumption) were used to calculate the central area index, and Getis-Ord Gi* and DBSCAN analysis were performed. Next, the hierarchy of the central areas were classified and the service areas were derived through network analysis by using it. As a result of the analysis, a total of 12 central areas were found in Seomyeon, Jungang, Yeonsan, Jangsan, Haeundae, Deokcheon, Dongnae, Daeyeon, Sasang, Pusan National University, Busan Station, and Sajik. Most of the underprivileged areas affected by the central area appeared in the Eastern area of Busan and the Western area of Busan, and were derived from old industrial areas, residential areas, and some new cities. Based on the results of the study, we can find three meanings. First, we have made a new attempt to apply a machine learning methodology that has not been covered in previous studies. Second, our data show the difference between the actual data and the existing planned central areas. Third, we not only found the location of the central areas, but also identified the underprivileged areas.

키워드

과제정보

본 연구는 환경부 「기후변화특성화대학원사업」의 지원과 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2021R1A2C1011977)

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