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ISPRS Int. J. Geo-Inf. 2019, 8(1), 21;

Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data

College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Author to whom correspondence should be addressed.
Received: 2 December 2018 / Revised: 1 January 2019 / Accepted: 8 January 2019 / Published: 10 January 2019
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
PDF [1859 KB, uploaded 16 January 2019]


Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided ( View Full-Text
Keywords: buffer analysis; overlay analysis; big data; real-time; parallel computing buffer analysis; overlay analysis; big data; real-time; parallel computing

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ma, M.; Wu, Y.; Chen, L.; Li, J.; Jing, N. Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data. ISPRS Int. J. Geo-Inf. 2019, 8, 21.

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