Point-polygon topological relationship query using hierarchical indices

  • Authors:

  • Tianyu Zhou;Hong Wei;Heng Zhang;Yin Wang;Yanmin Zhu;Haibing Guan;Haibo Chen

  • Affiliations:

  • Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;Facebook, Menlo Park, CA;Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
  • Year:
  • 2013

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Abstract

This paper describes a point-polygon query program we submitted to the ACM SIGSPATIAL Cup 2013. Point-polygon topological relationship query is one of the core functions for commercial spatial databases, and also an active research topic in academia. Spatial indices are the key to achieve top performance. However, different datasets or query patterns require different indices for optimal performance. Based on the patterns of the training dataset, we build a hierarchy of indices, including polygon index, edge index, and interval index, which help find polygons near a point, calculate the distance from a point to a polygon, and determine whether a point is inside a polygon, respectively. Using the provided training dataset, these three indices reduce the computation time of "WITHIN n" query by 90%, 10%, and 50%, respectively. We build a large dataset with more than 1 million samples and 520 polygons by cloning and offsetting the training dataset 15 and 13 times, respectively. Our program finishes the "WITHIN 1000" query in only one second on a 4-core 3.3GHz Xeon Processor.