Abstract
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.
Highlights
An array is a storage form for a sequence of objects of similar type
Two-dimensional arrays can partition a two-dimensional surface in square cells with constant size, but representing a spherical surface, such as the Earth’s surface, by a two-dimensional array leads to grid cells that are no longer squares or have constant area [3]
We review array abstraction of space-time phenomena, geographic data analysis methods on arrays and several array data analytic and management systems in terms of their features in supporting geographical analysis and the array operations that are implemented
Summary
Data tables, collections of records of identical type, are one-dimensional arrays when the records form a sequence Time series data, such as daily mean temperatures for a measurement site, form a natural one-dimensional array with the time stamp unambiguously ordering the observations, but more typically arrays will have rows and columns, creating two-dimensional arrays, or be higher dimensional. Arrays typically arise when we try to represent a phenomenon that varies continuously over space and time, or a field variable [1,2], by regularly discretising space and time. Data on such phenomena can be the result of observation or computation.
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