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1.国防科技大学电子科学学院,湖南 长沙,410073
2.自然资源部南方丘陵区自然资源监测监管重点实验室,湖南 长沙,410118
Received:16 May 2025,
Published:05 January 2026
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刘泽邦,杨岸然,马梦宇,等.可视驱动的大规模地理矢量点数据实时热力图生成方法[J].武汉大学学报(信息科学版),2026,51(1):114-125.
LIU Zebang,YANG Anran,MA Mengyu,et al.Visualization-Driven Real-Time Heat Map Generation Method for Large-Scale Geographic Vector Point Data[J].Geomatics and Information Science of Wuhan University,2026,51(1):114-125.
刘泽邦,杨岸然,马梦宇,等.可视驱动的大规模地理矢量点数据实时热力图生成方法[J].武汉大学学报(信息科学版),2026,51(1):114-125. DOI:10.13203/j.whugis20250031
LIU Zebang,YANG Anran,MA Mengyu,et al.Visualization-Driven Real-Time Heat Map Generation Method for Large-Scale Geographic Vector Point Data[J].Geomatics and Information Science of Wuhan University,2026,51(1):114-125. DOI:10.13203/j.whugis20250031
热力图作为一种流行的可视分析方法,有助于用户直观地浏览地理矢量点数据的空间分布和数据密度。针对当前热力图生成方法计算效率随点数据规模增长而大幅下降的问题,提出可视驱动的实时热力图生成方法。该方法从直接生成最终热力可视结果的角度出发,将像素点作为独立的计算单元,直接计算像素热力值来生成最终的热力可视效果。首先,基于瓦片金字塔结构对点数据进行分层组织,构建用于支持基于像素点进行计算的可视驱动型空间索引。然后,基于可视驱动型空间索引设计像素热力值生成算法,采用邻域像素叠加的方式计算像素热力值,大幅提升计算效率且保持了数据的空间分布特性。最后,设计并行热力图可视计算框架,实现了交互式热力可视化。实验结果表明,所提方法大幅提升了热力可视化效率,为千万级规模地理点数据集生成热力图的耗时仅为现有方法的13.5%,并可在0.75 s内快速完成热力可视化交互,从而支撑大规模地理矢量点数据的交互式热力分析。
Objectives
2
Heat map as a popular and mature visual analysis method at present
shows the distribution trend of geospatial data through the change of color shading. It helps users visualize the spatial distribution and data density of geographic vector point data. The rapid generation technology of geospatial vector data heat maps will significantly empower the improvement of the real-time performance and interactivity of spatial decision support.
Methods
2
Aiming at the problem that the computational efficiency of current heat map generation method decreases greatly with the increase of point data scale
a visualization-driven real-time heat map generation method is proposed
which uses pixels as computing units to directly calculate the heat value to generate the final heat map effects. First
the point data is organized hierarchically based on the tile-pyramid to build a specialized spatial index that can support pixel-based computation. Then
the pixel heat generation algorithm is designed
and the neighborhood pixel superposition strategy is adopted to greatly improve the computational efficiency and maintain the spatial distribution characteristics. Finally
a parallel computing framework is designed to realize interactive heat map visualization.
Results
2
Experimental results show that the proposed method greatly improves the efficiency of heat map visualization
and the time required to generate heat maps for tens of millions of scale geographic point data sets is only 13.5% of the existing method
and the heat map visualization interaction can be quickly completed within 0.75 s
thus supporting the interactive heat map analysis of large-scale geographic vector point data.
Conclusions
2
A large number of experimental results show that the proposed heat map generation method has the real-time performance and interactivity
and compared with the baseline method
the visualization efficiency has been significantly improved.
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