Spatial Heterogeneity That Matters: An Application-Oriented Benchmark for Mobile Traffic Modeling

Mobile traffic modeling is important for understanding spatial urban functions and corresponding human activity patterns, particularly the spatial heterogeneity. But it can be ignored by evaluations focusing on citywide error-based metrics aggregated across spatial and temporal dimensions, and capturing aggregate scale alone does not establish usefulness for region-level network management and planning within cities. To support these practical applications, we introduce NETSPACE-BENCH, an application-oriented benchmark that identifies modeling weaknesses of existing methods and guides the further direction by jointly evaluating modeling quality, especially the spatial context-related heterogeneity and decision utility. We achieve these through spatial reference and dispersion checks, five complementary evaluation perspectives, and five operational decision tasks. Specifically, our evaluation across 12 models and three cities reveals that 18 of 24 eligible main generation runs retain less than 5% of observed region-mean dispersion and 19 yield energy policies approximately no better than inaction. These findings motivate explicit evaluation of spatial heterogeneity for practical applications, and the operational value of controllability through spatial context remains an open question.