Detecting cross-boundary regional collaboration in China by network community scanning with human mobility data

Accurately identifying and analyzing cross-boundary regional cooperation remains challenging due to administrative constraints and data limitations. This study utilizes high-resolution human mobility data, network community scanning (NCS), and association rule mining to examine trans-provincial cooperation across 369 Chinese cities, leveraging Location-Based Services (LBS) data from 1.3 billion users. The findings indicate that border-adjacent cooperation dominates trans-provincial interactions in China, while non-adjacent cooperation is embedded within broader cooperative networks formed through adjacent ties reinforcing the interwoven nature of cross-administrative collaborations. Additionally, emerging cooperative clusters extend beyond officially designated urban agglomerations, revealing unrecognized regional synergies not yet captured in planning frameworks. These results highlight discrepancies between actual collaboration linkages and government-defined regional plans, underscoring the need for adaptive, data-driven policymaking and offering practical insights for regional planning and governance.