hedj17/DST

This project implements two dynamic spatiotemporal interpolation (DST) methods, i.e., coarse-grained DST (CGDST) and fine-grained DST (FGDST) using both temporal and spatial interpolation results. Different from other hybrid spatiotemporal interpolation methods, they make differences in the contribution of temporal and spatial interpolation results and assign them with different weights. Both CGDST and FGDST treat each missing value differently and fill it by considering the reliability of both temporal and spatial interpolation results in terms of the lengths of its column gap and row gap. CGDST treats each missing value in a continuous missing area equally and all missing values have same lengths of column and row gaps and FGDST goes beyond CGDST and treats each missing value differently based on its temporal distance to the nearest real observed values in both forward and backward directions.

Python