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A procedure is described for adjusting sampling locations in one spatially discretized dataset to those in another when the value differences between these sets are mainly caused by the sampling intervals that locally lengthen and shorten. This adjustment is formulated into an optimization form that can be solved by dynamic programming. Unknown parameters involved in the form can be identified using the maximum likelihood procedure that employs non-linear filtering for a generalized state-space model. This procedure is based on the fact that the optimal solution in dynamic programming is equivalent to the "maximum a posteriori (MAP) estimation" in a Bayesian framework.