멀티모달 대규모 언어모델과 기계학습을 활용한 도시 가로 경관 쇠퇴 영향요인 분석

초록

Cities are constantly evolving, with growth, vitality, decline, and shrinkage occurring as interrelated phenomena. While urban vitality and revitalization have been extensively studied, research on urban decline and shrinkage remains comparatively limited. Existing studies on urban decline have primarily focused on diagnostic indicators, such as physical aging, population decline, and reduction in the number of businesses, to assess patterns of decline. However, studies on the subjective perception of urban decline, particularly in relation to urban landscapes, remain limited. Given this gap, it is crucial to identify the causes of urban decline by analyzing the factors that influence the public perception of declining landscapes through subjective evaluations of urban scenery. This study quantitatively analyzes how people perceive declining urban landscapes and identifies the key factors that influence these perceptions, using street view images of Seoul. A survey was first conducted to assess urban landscape decline based on streetscape images. The Trueskill algorithm was applied to quantify perceived level of decline. Subsequently, machine learning was used to analyze the primary factors influencing these perceptions. The results of the analysis are as follows. First, perception of decline decreased as the proportion of physical environmental elements such as roads, green spaces, sidewalks, and cars increased. In contrast, an increased presence of elements such as buildings, bicycles, walls, and fences was associated with a heightened perception of urban decline. Second, an analysis of the importance of contributing factors indicated that roads, sidewalks, green spaces, and cars were the most influential in shaping perception, in that order. Third, the relationship between the proportion of physical environmental elements in urban landscape images and perceptions of decline was found to be non-linear. This study presents a methodology for evaluating urban landscape decline based on people's subjective perceptions and provides policy implications by identifying the streetscape features that substantially influence perceptions of urban decline.

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