Community-Detection-Based Spatial Range Identification for Assessing Bilateral Jobs-Housing Balance: The Case of Beijing

• A new perspective based on mobility is provided to detect the range of evaluation. • Bilateral assessment provides a comprehensive view of jobs-housing balance. • Administrative boundaries are not consistent with mobility. • Communities can serve as management boundaries to curb COVID-19. • Bilateral assessment can clarify the source and the extent of tidal traffic. As a key path to sustainable urban development, the jobs-housing balance is crucial in addressing the issues of large commuting distances, congestion, and environmental pollution. Numerous studies have assessed jobs-housing relationships for different geographical units, leading to controversial findings that confuse planners and policymakers. The aim of this study is to develop a mobility-based framework to determine geographical units for assessing bilateral jobs-housing balance, i.e., work-based self-containment (WSC) and home-based self-containment (HSC). The commuting connection intensity index is proposed based on the Gravity model to reflect the actual commuting relationship among regions using the cell phone signaling data of China Unicom in September 2021. The community detection algorithm is then applied to determine geographic units for the assessment. Evidence from Beijing shows an inconsistency between WSC and HSC in the 39 identified geographic units that are not subjected to administrative boundaries. In addition, lower WSC and HSC values in Beijing indicate a mismatch between jobs and housing throughout the city, particularly in the urban center. The findings provide a geographical recognition method for assessing the jobs-housing balance and assist in understanding bilateral jobs-housing relationships from the perspectives of jobs and housing.