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Spatial crowdsourcing (SC) engages large worker pools for location-basedtasks, attracting growing research interest. However, prior SC task allocationapproaches exhibit limitations in computational efficiency, balanced matching,and participation incentives. To address these challenges, we propose agraph-based allocation framework optimized for massive heterogeneous spatialdata. The framework first clusters similar tasks and workers separately toreduce allocation scale. Next, it constructs novel non-crossing graphstructures to model balanced adjacencies between unevenly distributed tasks andworkers. Based on the graphs, a bidirectional worker-task matching scheme isdesigned to produce allocations optimized for mutual interests. Extensiveexperiments on real-world datasets analyze the performance under variousparameter settings.

Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity