Spatial data dimension reduction using quadtree: A case study on satellite-derived solar radiation

Satellite data is discrete in both space and time; it can be considered as temporal snapshots (time series) of lattice processes. As the raw datasets are often too large to host publicly, processed datasets with a coarse spatial resolution are often hosted as an alternative. Nevertheless, with a regular grid, the inhomogeneous variability in the lattice processes cannot be captured effectively. In this paper, a quadtree-based spatial data dimension reduction algorithm is demonstrated. Based on the stratum variance, this algorithm iteratively divides lattice data into strata of fours. In this way, the number of strata in an area can be correlated to the variability of that area. A satellite-derived surface solar radiation (SSR) dataset is used for the case study. Using parallel computing, the quadtree algorithm is applied on each temporal snapshot of SSR in the dataset. The processed data is then saved in a list structure. Finally, a solar resource assessment application, namely, optimizing the orientation of a photovoltaic array, is considered to demonstrate the effectiveness and efficiency of the dimension-reduced dataset.