VectorCubeWarp: Lossless, efficient warping of global, multi-temporal, gridded data cubes using a versatile, vector-based areal interpolation approach
The conversion of gridded data between two grids and/or spatial reference systems, commonly referred to as “warping”, is a fundamental step in geospatial data processing workflows. This process involves data resampling, which inherently introduces uncertainty. When dealing with stacks of statistical, gridded datasets measuring cell-level densities, consistently enumerated across multiple points in time, it is crucial to employ a volume-preserving method. Such a method should not only preserve changes in observations along the temporal dimension but also maintain the total sums of measured data per point in time, and allowing for different resampling strategies, while minimizing local distortions in the warped data. Conventional raster-based warping tools available in Geographic Information Systems and coding-based geospatial data processing environments lack explicit control over these critical properties. To address this limitation, we propose a novel vector-based method for areal interpolation based on spatial overlay operations. This approach enables lossless resampling of gridded data, which we apply to the gridded built-up surface data from the Global Human Settlement Layer (GHSL) covering the period from 1975 to 2030. As vector-based spatial data operations are computationally expensive, our method leverages a parallel-processing framework, allowing efficient warping of global gridded data cubes. Furthermore, this approach facilitates the provision of statistical data cubes across various spatial reference systems and grid definitions at planetary scale and high spatial resolution, extendible to the use of areal or spatio-temporal interpolation methods. We implemented this method in Python and call it “VectorCubeWarp”.
UHL Johannes H.;
MAFFENINI Luca;
POLITIS Panagiotis;
SCHIAVINA Marcello;
PESARESI Martino;
KEMPER Thomas;
2024-09-06
Publications Office of the European Union
JRC137864
978-92-68-19564-2 (online),
1831-9424 (online),
EUR 32009,
OP KJ-NA-32-009-EN-N (online),
https://publications.jrc.ec.europa.eu/repository/handle/JRC137864,
10.2760/944370 (online),
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