서울시 생활업종 밀집지역의 시공간적 변화 패턴 분석 : 시공간 큐브 모형을 활용하여

Abstract

본 연구는 서울시 내 생활업종 밀집지역의 시공간적 변화 패턴을 도출하고자 한다. 구체적으로 시계열생활업종 자료와 시공간 핫스팟 분석 모형을 활용하여 생활업종별 밀집지역의 시공간적 변화 패턴을 도출한다. 이를 통해 생활업종별 시계열 변화 패턴과 주요 밀집지역의 분포 특성을 도출하여, 향후 생활권을 조성하고 평가하기 위한 참고사항을 제시하고자 한다.

As the demand for enhancing the quality of urban life and establishing livable neighborhood units increases, the spatial distribution and temporal dynamics of urban living facilities have emerged as critical issues in urban planning. Previous studies have often relied on administrative boundaries or single-point-in-time data, which limits their ability to capture citizens’ actual usage patterns and the long-term changes in facility clusters. This study analyzes the spatio-temporal change patterns of dense areas of urban living facilities in Seoul through a fine-scale approach. Point of Interest (POI) data for eight major facility types (dining, retail, living services, lodging, leisure, education, medical, and public facilities) from the Statistical Geographic Information Service (SGIS) were used for the analysis. A 500m grid system was employed as the spatial unit, representing an intermediate scale between administrative neighborhoods and regional living zones to enable micro-level pattern detection. Space-Time Cubes were constructed with 2,431 grids across 18 annual time steps from 2006 to 2023, and Emerging Hotspot Analysis was applied to identify spatio-temporal clustering patterns. This method compares each grid’s facility density with its spatio-temporal neighbors and classifies patterns into eight types including persistent, intensifying, new, and diminishing hotspots. Results demonstrate that dining, living services, education, and medical facilities formed consistent and intensifying hotspots in central and sub-center areas with strong co-location patterns, while retail, lodging, leisure, and public facilities showed more localized or diminishing patterns. Core districts such as Gangnam, City Hall, and Yeouido exhibited overlapping multi-facility hotspots, while peripheral areas displayed cold spots, indicating spatial inequality. Education facilities formed independent spatial structures with new hotspots emerging in residential areas. These findings offer empirical evidence for neighborhood planning: identifying functional facility clusters beyond administrative boundaries, determining complementary service provision priorities based on co-location patterns, addressing service gaps in education-centered neighborhoods, and incorporating temporal dynamics to prioritize facility supply and predict spatial expansion in urban regeneration strategies.