Abstract
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.
Highlights
A buffer in a geographic information system (GIS) is defined as the zone around a spatial object, measured by units of time or distance [1]
To avoid the sawtooth distortion of these buffers, we propose the Tile-Pyramid-Based Stepless Zooming (TPBSZ) method
In HiBuffer, we treat the rendering of one buffer tile as an independent task, and each task is processed with multiple Open Multiprocessing (OpenMP) threads in one Message Passing Interface (MPI) process
Summary
A buffer in a geographic information system (GIS) is defined as the zone around a spatial object, measured by units of time or distance [1]. Buffers of spatial objects are generated separately first, and the buffers are merged to get the final results Such an approach is data-oriented and straightforward. The computational scales expand rapidly with the volume of spatial objects; as a result, it is difficult for the traditional data-oriented methods to provide real-time buffer analysis of large-scale spatial data. We present a visualization-oriented parallel buffer analysis model, HiBuffer, to provide an interactive and online buffer analysis of large-scale spatial data. Different from traditional data-oriented methods, the core problem of HiBuffer is to determine whether the pixels for display are in the buffer of a spatial object or not. A fully optimized hybrid parallel processing architecture is proposed in HiBuffer to achieve a real-time buffer for large-scale spatial data.
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