A vector map overlay algorithm based on distributed queue

Xiao, Zhuojian; Qiu, Qiang; Fang, Jinyun; Cui, Shaolong

doi:10.1109/igarss.2017.8128400

Cited by 1 publication

(1 citation statement)

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“…Zheng's experiments show that a grid index algorithm achieves good results when polygons are uniformly distributed; otherwise, the efficiency of the algorithm is low. Xiao [28] proves that parallel task partitioning based on polygons' spatial location achieves better load balancing than random task partitioning. In addition, SpatialHadoop [29][30][31][32] and GeoSpark [32][33][34][35] extend Hadoop and Spark to support massive spatial data computing better.…”

Section: Overlay Analysismentioning

confidence: 98%

High-Performance Overlay Analysis of Massive Geographic Polygons That Considers Shape Complexity in a Cloud Environment

Zhao

Jin

Fan

et al. 2019

IJGI

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: 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.

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