본 연구는 서울지역 COVID-19 확진자의 증가와 지하철 통행의 감소 변화에서 나타나는 특징을 파악하고, 이들 간의 상관관계에 근거하여 추출한 주성분을 바탕으로 지하철역을 군집 분류하고 군집별 특성을 분석하였 다. 특히 COVID-19 확산 이전인 2019년부터 사회적 거리두기가 지속된 2020년까지 서울 지하철역별 시간대별, 주별, 월별 지하철 통행의 변화 패턴을 살펴보고, 통행 증가형과 감소형 역으로 구분한 후 감소형 중 상위 10분 위에 해당하는 역, 하위 10분위에 해당하는 역의 인구 및 토지이용 특성을 파악하였다. 분석 결과, COVID-19 확 진과 지하철 통행은 서로 부(-)의 상관관계를 나타냈고, 지하철 통행과 역 배후지 토지이용 특성에 따라 서울 지 하철체계를 구성하는 역들은 6개 유형으로 군집화되었다. 통행 감소 상위 10분위에 속하는 역은 낮 시간대 선택 통행의 분포가 높고, 역 배후지는 상업용 및 문화, 교육과 사회활동 관련 토지이용이 상대적으로 높아 향후 전염 병 종료에 따른 지역 회복성도 매우 탄력적으로 반응할 것으로 전망된다.
This study identifies the characteristics of changes in the subway passengers and the number of COVID-19 confirmed cases in Seoul. We classify subway stations by applying the principal components extracted based on the correlation between them, and analyzed the passenger traffics and geographical characteristics of each cluster. We extract the “top decile” and “lowest decile” stations among the subway stations that the number of subway passengers have decreased in 2020 compared to 2019, and then examine the population and land use characteristics of station areas both station groups. As the result of cluster analysis according to principal component scores, we obtain 6 cluster types. Subway stations belong to the top deciles which have shown the larger decrease in the passenger traffics tend to more sensitive to COVID-19 shocks. Stations belonging to the top tenth percentile of reduced traffic have a high distribution of daytime traffic, and the hinterland of the station has relatively high land use related to commercial and cultural, religious, and social activities. Therefore, it is expected that the regional resilience around subway stations will also show differences in degree by type.
This study identifies the characteristics of changes in the subway passengers and the number of COVID-19 confirmed cases in Seoul. We classify subway stations by applying the principal components extracted based on the correlation between them, and analyzed the passenger traffics and geographical characteristics of each cluster. We extract the “top decile” and “lowest decile” stations among the subway stations that the number of subway passengers have decreased in 2020 compared to 2019, and then examine the population and land use characteristics of station areas both station groups. As the result of cluster analysis according to principal component scores, we obtain 6 cluster types. Subway stations belong to the top deciles which have shown the larger decrease in the passenger traffics tend to more sensitive to COVID-19 shocks. Stations belonging to the top tenth percentile of reduced traffic have a high distribution of daytime traffic, and the hinterland of the station has relatively high land use related to commercial and cultural, religious, and social activities. Therefore, it is expected that the regional resilience around subway stations will also show differences in degree by type.
