A New Urban Center/Subcenters Identification Approach Based on Open Street Map in Polycentric Urban Landscapes in the U.S.

Abstract

This study concentrates on identifying urban centers and subcenters (UCSs) in the United States (U.S.). Conventional approaches to UCS estimation face challenges due to the discontinuation of crucial U.S. Census Geographies. This change has resulted in the inability to accurately define UCSs for research or policymaking. By leveraging open-source points-of-interest and performing Kernel Density Estimations (KDE), this research identifies UCSs in Virginia metropolitan areas. Validation is achieved by combining the KDE estimates with local zoning codes, showing an alternative path to overcome the UCS research challenges. Through this innovative methodology, researchers, practitioners, and planners can identify spaces of urban economic activity and facilitate a deeper understanding of urban landscapes.