Framework for Integrated Identification of Functionally Balanced Urban Activity Centers

Context and challenges in identifying urban activity centers for practical applicationsThe importance of understanding the location and characteristics of urban centers and subcenters is widely recognized in urban studies and urban planning.Depending on the context, the creation of new centers and the management of existing ones are considered effective ways to address numerous challenges.In large metropolitan areas, integrating subcenters into urban policies has become essential for decentralizing traffic flows and activities fostering more sustainable urban growth, while in declining regional cities, this approach serves as a tool to improve service accessibility, support economic and social activities, and create a lively urban environment 1)2)3)4)5) .In Japan, the necessity of urban development around compact functional centers has been recently promoted through the spread of the multi-polar network-like compact city () concept across national and local urban planning policies 6) .A crucial step in analyzing urban centers and subcenters is the process of their identification.Over the past decades, this process has evolved significantly, shifting from subjectively defined CBD 7) to multifunctional urban activity centers (UAC) identified using spatialstatistical modeling and machine learning 8)9) .An important component of this transformation has been the type of data primarily used for center identification.In earlier works, employment statistics, such as job density, were the main indicators 1)10)11)12) .Other studies adopted a mixed perspective or maintained a clear focus on commercial activities 13)14) .More recent works have approached the task from a multifunctional perspective by using indicators that represent a wider range of activities.Examples of such data include building and social media data 9) , nighttime imagery 8) , and points of interest 15) .Unlike earlier research, these studies emphasized the importance of cultural, recreational, and public activities alongside commercial and employment-related ones.While models that integrate various activities provide a high-level understanding of urban spatial structure, they often fail to account for differences in the spatial distribution and characteristics of these activities.This inevitably leads to an implicit bias toward employment and commercial activities due to their quantitative dominance 16) .Overfitting models to biased data can result in the omission of places where people gather for diverse activities, leading to incomplete insights and potentially misinformed urban planning.Additionally, several policyoriented studies demonstrated the need for a more customized approach that would reflect variations in activity concentrations across different subcenters in Japanese cities 4)17) .Considering this, we believe that a more accurate, function-and context-sensitive approach is required for applications in urban planning.The objective of this study is to develop a comprehensive framework for identifying UAC that accounts for differences in activity types and allows for flexibility depending on priorities.Such a framework can provide urban planners with more accurate and place-specific insights, facilitating the design of targeted policies that enhance the functionality and livability of urban centers. Differences between activities and groupingAs we mentioned in the previous section, the use of indicators exhibiting explicit or implicit bias in previous studies often led to overlooking certain types of activities and UAC with distinct functional structures.One possible reason for this unequal representation is Framework for Integrated Identification of Functionally Balanced Urban Activity Centers Practical Leverage of Person Trip Data to Reflect Diverse Activity Patterns for Urban Planning