Visitor Profile-oriented Urban Park Categorization: Analyzing Home-to-park Trip Characteristics Using Mobile Phone Data

Abstract

Conventional urban park categorizations often rely on size, environmental characteristics, and historical background, ignoring visitor characteristics due to a lack of empirical data. This study uses mobile phone data and distance-based cluster analysis to classify eighty-five urban parks in Tokyo, revealing four distinct categories based on visitor behavior, particularly home-park distance and visit frequency. The new categories revealed that proportions of visitor types varied considerably across the sample parks. There also existed notable mismatches between the administrative categories and the newly derived categories. Furthermore, the findings imply that non-uniform park management strategies, not limited by conventional park categories, are needed.