地域特有の移動行動を組み込んだ都市活動拠点の検出方法

―局所化空間自己回帰モデルによるアプローチ―

抄録

This study explores how considering spatial variation in trip behavior affects the identification of Urban Activity Centers (UAC) using Person Trip data in Tokyo. Standard UAC identification models typically apply a single distance threshold for the definition of spatial neighborhood, assuming uniform spatial relationships between centers and their service areas. In contrast, this research incorporates local trip distance statistics into the weights matrix of a spatial autoregression model, capturing contextual differences across the city. The modified model identifies fewer peripheral UAC but reveals broader edge areas of major centers. Particular attention is given to clusters of public services, where local trip behavior strongly shapes spatial structure. These findings highlight both the technical feasibility and analytical value of considering local trip behavior in UAC models.