HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2018-11, Vol.7 (12), p.467
Main Authors: Ma, Mengyu, Wu, Ye, Luo, Wenze, Chen, Luo, Li, Jun, Jing, Ning
Format: Article
Language:English
Subjects:
Adaptability
Algorithms
Analysis
buffer analysis
Buffers
Computation
Computer applications
Geographic information systems
Geographical information systems
Information systems
Methods
parallel computing
Parallel processing
Performance evaluation
Performance indices
Real time
Remote sensing
Spatial analysis
Spatial data
Stability
tile-pyramid
Visualization
visualization-oriented
Zooming
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Summary:Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi7120467