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Spatial big data analysis is very important in disaster scenarios to understand distribution patterns of situations, e.g., people's movements, people's requirements, resource shortage situations, and so on. In a general case, spatial big data is generated from distributed sensing devices and analyzed in a centralized way, e.g., a cloud center with high-performance computing resources. However, data transmission from sensing devices to cloud centers always takes a long time, especially in disaster scenarios with an unstable network. Fog computing is a promising technique to solve the above problem by offloading data processing tasks from the cloud to nearby computation devices. But data resolution also decreases after local processing in the fog nodes. It is necessary to investigate the optimal task distribution solutions to efficiently use computation resources in the fog layer. In this paper, we take the above …