모바일폰 위치기반 생활이동 빅데이터를 활용한 통행목적별 도시활력 영향요인 분석 : PageRank 알고리즘과 SHAP 기계학습을 활용하여

초록

Urban vitality is an important indicator for evaluating a city’s sustainability. Urban vitality increases when a social space emerges where people can interact with each other in a city. Although many studies have tried to measure urban vitality and its determining factors, few studies have measured it using moile phone location-based, origin-destination (OD) big data. The aim of this study is to analyze the determining factors of urban vitality with mobile phone big data using the PageRank algorithm and interpretable machine learning techniques. The focus is the nonlinear relationships between urban vitality and its determining factors. The main results of the analysis are as follows. First, urban vitality according to moile phone location-based, OD big data by travel purpose has different determining factors. For instance, while the perception of street scenery had a considerable influence on the urban vitality of non-commuting travel, it had no impact on the urban vitality of commuting travel. Second, restaurant Point of Interest (POI) density and subway station exit density had positive associations with urban vitality for both leisure and utility travel purposes. Third, street safety was a significant variable for urban vitality, regardless of travel purposes of the population. This finding indicates that the safety of the street environment encourages urban vitality. Finally, the interpretable machine learning analysis indicated that the relationships between urban vitality and its determining factors were nonlinear. Overall, the study findings demonstrate the useful application of mobile phone, location-based, OD big data to examine urban vitality and provide specific policy implications for promoting it.

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