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Multidimensional Arrays for Analysing Geoscientific Data

by Meng Lu 1,2,*,†, Marius Appel 1 and Edzer Pebesma 1
Institute for Geoinformatics, University of Muenster, 48149 Muenster, Germany
Department of Physical Geography, Faculty of Geoscience, Utrecht University, 3584 CB Utrecht, The Netherlands
Author to whom correspondence should be addressed.
Current address: Vening Meinesz Building A, Princetonlaan 8A, 3584 CB Utrecht, The Netherlands.
ISPRS Int. J. Geo-Inf. 2018, 7(8), 313;
Received: 27 June 2018 / Revised: 20 July 2018 / Accepted: 30 July 2018 / Published: 3 August 2018
Geographic data is growing in size and variety, which calls for big data management tools and analysis methods. To efficiently integrate information from high dimensional data, this paper explicitly proposes array-based modeling. A large portion of Earth observations and model simulations are naturally arrays once digitalized. This paper discusses the challenges in using arrays such as the discretization of continuous spatiotemporal phenomena, irregular dimensions, regridding, high-dimensional data analysis, and large-scale data management. We define categories and applications of typical array operations, compare their implementation in open-source software, and demonstrate dimension reduction and array regridding in study cases using Landsat and MODIS imagery. It turns out that arrays are a convenient data structure for representing and analysing many spatiotemporal phenomena. Although the array model simplifies data organization, array properties like the meaning of grid cell values are rarely being made explicit in practice. View Full-Text
Keywords: multidimensional arrays; geoscientific data; data analysis; spatiotemporal modeling multidimensional arrays; geoscientific data; data analysis; spatiotemporal modeling
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Lu, M.; Appel, M.; Pebesma, E. Multidimensional Arrays for Analysing Geoscientific Data. ISPRS Int. J. Geo-Inf. 2018, 7, 313.

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