Particle Filters (PFs) hold great promise to support data assimilation for spatial temporal simulations to achieve more accurate simulation or prediction results. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. To effectively support data assimilation for large-scale spatial temporal simulations, this paper develops a spatial partition-based particle-filtering framework that breaks the system state and observation data into smaller spatial regions and then carries out localized particle filtering based on these spatial regions. The developed framework exploits the spatial locality property of system state and observation data, and employs the divide-and-conquer principle to reduce state dimension and data complexity. Within this framework, a two-level automated spatial partitioning method is presented to provide automated and balanced spatial partitions with less boundary sensors. The developed framework is applied to a case study of wildfire spread simulations and achieved improved results compared to using standard PFs-based data assimilation methods.