Abstract
Overlay analysis is a common task in geographic computing that is widely used in geographic information systems, computer graphics, and computer science. With the breakthroughs in Earth observation technologies, particularly the emergence of high-resolution satellite remote-sensing technology, geographic data have demonstrated explosive growth. The overlay analysis of massive and complex geographic data has become a computationally intensive task. Distributed parallel processing in a cloud environment provides an efficient solution to this problem. The cloud computing paradigm represented by Spark has become the standard for massive data processing in the industry and academia due to its large-scale and low-latency characteristics. The cloud computing paradigm has attracted further attention for the purpose of solving the overlay analysis of massive data. These studies mainly focus on how to implement parallel overlay analysis in a cloud computing paradigm but pay less attention to the impact of spatial data graphics complexity on parallel computing efficiency, especially the data skew caused by the difference in the graphic complexity. Geographic polygons often have complex graphical structures, such as many vertices, composite structures including holes and islands. When the Spark paradigm is used to solve the overlay analysis of massive geographic polygons, its calculation efficiency is closely related to factors such as data organization and algorithm design. Considering the influence of the shape complexity of polygons on the performance of overlay analysis, we design and implement a parallel processing algorithm based on the Spark paradigm in this paper. Based on the analysis of the shape complexity of polygons, the overlay analysis speed is improved via reasonable data partition, distributed spatial index, a minimum boundary rectangular filter and other optimization processes, and the high speed and parallel efficiency are maintained.
Highlights
Overlay analysis is a common geographic computing operation and an important spatial analysis function of geographic information systems (GIS)
According to the complexity and location of graphics, the polygons are divided reasonably, and an index based on spatial location is established to realize fast data access and load balancing of parallel computing nodes
In high-performance parallel overlay analysis, the differences in shape complexity of a polygon can lead to serious data skew
Summary
Overlay analysis is a common geographic computing operation and an important spatial analysis function of geographic information systems (GIS). It is widely used in applications related to spatial computing [1,2]. This operation involves the spatial overlay analysis of different data layers and their attributes in the target area. With the development of the social economy and the progress of data acquisition technologies, the number of land use classification patches will continue to increase. Calculating land use change using traditional single-computer calculation models is difficult
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