The design and analysis of spatial data structures
The design and analysis of spatial data structures
Explicit generation of orthogonal grids for ocean models
Journal of Computational Physics
On R-trees with low query complexity
Computational Geometry: Theory and Applications
The Priority R-tree: a practically efficient and worst-case optimal R-tree
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A performance comparison of distance-based query algorithms using R-trees in spatial databases
Information Sciences: an International Journal
Matplotlib: A 2D Graphics Environment
Computing in Science and Engineering
Execution time analysis of a top-down R-tree construction algorithm
Information Processing Letters
Environmental Modelling & Software
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In the earth sciences, data are commonly cast on complex grids in order to model irregular domains such as coastlines, or to evenly distribute grid points over the globe. It is common for a scientist to wish to re-cast such data onto a grid that is more amenable to manipulation, visualization, or comparison with other data sources. The complexity of the grids presents a significant technical difficulty to the re-gridding process. In particular, the regridding of complex grids may suffer from severe performance issues, in the worst case scaling with the product of the sizes of the source and destination grids. We present a mechanism for the fast re-gridding of such datasets, based upon the construction of a spatial index that allows fast searching of the source grid. We discover that the most efficient spatial index under test (in terms of memory usage and query time) is a simple lookup table. A kd-tree implementation was found to be faster to build and to give similar query performance at the expense of a larger memory footprint. Using our approach, we demonstrate that regridding of complex data may proceed at speeds sufficient to permit regridding on-the-fly in an interactive visualization application, or in a Web Map Service implementation. For large datasets with complex grids the new mechanism is shown to significantly outperform algorithms used in many scientific visualization packages.