Big data analysis is very important to support rescue activities when natural disaster happens, through understanding various situations, such as power/water outage regions. The traditional way to process big data is based on high-performance computation/storage resources in a cloud center. However, this is hard to be guaranteed in a disaster scenario due to destruction of communication infrastructure. Meanwhile, high latency between local sensing devices and cloud center sets a big obstacle enabling a near real-time big data analysis. On the other hand, movable base station, such as vehicle-based movable & deployable ICT resource unit (MDRU) developed by NTT, is a possible solution to reconstruct an emergency communication network and process data at the edge sites with reduced data transmission time. In this paper, we study the optimal overall delay in a fog/edge-computing platform constructed by vehicle-based MDRUs with guaranteed data resolution. We formalize the problem as a mixed-integer nonlinear program, which is a well-known NP-hard problem, and then relax the original problem to an mixed integer linear programing (MILP). Finally, we propose a two-stage heuristic algorithm to solve it in a time-efficient manner. Through evaluation, the effectiveness of the proposed heuristic approach has been validated in terms of minimizing overall delay with sufficient given data resolutions.