A Fast Detection Method for Geospatial Network Subset
Aiming at the problem of low detection efficiency of geospatial network subsets under big data,this paper proposes a fast detection method for geospatial network subsets by introducing two index tables of"arc to point"and"point to arc".This method innovatively realizes the search and positioning of arcs and points directly through the two index tables,avoids the inefficiency caused by excessive search calculation redundancy in traditional subset detection methods,and significantly improves the calculated efficiency of geospatial network subset detection.The Delaunay Triangulation network containing different numbers of random points is simulated and generated by MATLAB software,and the subset detection is carried out by using the proposed method in this paper and the traditional method.The results show that both methods can achieve successful subset detection,but the method proposed in this paper significantly improves the efficiency of subset detection.
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