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Research Article

Extracting Urban Areas of Interest Using HDBSCAN Clustering Method

HDBSCAN 클러스터링 방법을 이용한 도시 관심 지역 추출에 관한 연구

김윤식1, 양병윤2†

Yunsik Kim1, Byungyun Yang2†

1동국대학교 일반대학원 지리학과 석사과정
2동국대학교 사범대학 지리교육과 부교수
1Master’s Student, Department of Geography, Dongguk University
2Associate Professor, Department of Geography Education, Dongguk University
30 April 2023. pp. 67-77
PDF Citation
Abstract
Since 2020, the COVID-19 pandemic has caused a variety of social and economic problems worldwide, leading to a rapid increase in online activities and the use of social network services (SNS). SNS data can provide a large amount of low-cost data with diverse information that can be utilized for urban planning and management. In response, it is possible to explain the change of spatial patterns in various ways using advanced artificial intelligence technology. While K-means and DBSCAN algorithms have been widely used for clustering, the applications of HDBSCAN in domestic research has been rarely performed. Therefore, this study aims to detect the city’s interest areas using HDBSCAN with Flickr data from Seoul in 2019 and 2020. In this study, HDBSCAN is used to cluster Flickr posts and extract urban interest areas, and the clustering level for each cluster is evaluated using silhouette scores. As a result, clusters are extracted along the major points in downtown Seoul and suburbs, and the cluster level was found to be statistically significant based on the silhouette score.
Keywords
Flickr
Geotagged data
Urban areas of interest
HDBSCAN
Silhouette score
2020년 이후 코로나-19로 인해 전 세계적으로 다양한 사회･경제적 문제가 발생하였고, 이로 인해 비대면 문화와 소셜 네트워크 서비스(SNS)를 통한 활동이 급격히 늘어났다. SNS 데이터는 저비용으로 많은 양의 데이터를 확보할 수 있으며, 다양한 정보를 포함하고 있어 도시 계획 및 운영에 활용될 수 있다. 이에 따라, 새로운 방법의 적용이 필요해졌고, 최근 발전한 인공지능 기술을 활용하여 공간 유형의 변화를 설명하는 것이 가능해졌다. 이를 위해 머신러닝 군집화 방법을 사용하여 관광지 분포 패턴 및 도시 관심 지역을 추출할 수 있게 되었다. 기존에는 군집화를 위해 K-means, DBSCAN을 활용해 왔으나. HDBSCAN에 대한 국내 연구 활용 사례는 부족한 상황이다. 따라서, 본 연구는 2019년과 2020년의 서울시 플리커 데이터와 HDBSCAN을 활용하여 도시 관심 지역에 대한 공간 유형의 변화를 설명하고자 하였다. 본 연구에서는 HDBSCAN 방법을 이용하여 플리커 게시물을 군집화하고 도시 관심 지역을 도출하였으며, 실루엣 점수를 통해 각 군집에 대한 군집화 정도를 점수화하였다. 연구 결과, 실제 서울 시내의 도심 및 부도심 등 주요 지점을 따라 군집이 도출되었고, 실루엣 점수를 활용한 평가 결과 군집화 정도가 통계적으로 유의미한 수준으로 계산되었다.
키워드
플리커
지오태깅된 데이터
도시 관심 지역
HDBSCAN
실루엣 점수
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Information
  • Publisher :The Korean Cartographic Association
  • Publisher(Ko) :한국지도학회
  • Journal Title :Journal of the Korean Cartographic Association
  • Journal Title(Ko) :한국지도학회지
  • Volume : 23
  • No :1
  • Pages :67-77
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