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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree
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Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree

机译：基于快速逆距离加权的时空插值：基于Web的应用并行编程和k-d树插值连续美国细颗粒物PM2.5的应用

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Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 concentration is a critical step for understanding the pollution problem and embarking on the necessary remedy. This research designs, implements and compares two inverse distance weighting (IDW)-based spatiotemporal interpolation methods, in order to assess the trend of daily PM2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously, using the so-called extension approach. Time values are calculated with the help of a factor under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. Various IDW-based spatiotemporal interpolation methods with different parameter configurations are evaluated by cross-validation. In addition, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure, named k-d tree, are adapted in this paper to address the computational challenges. Significant computational improvement has been achieved. Finally, a web-based spatiotemporal IDW-based interpolation application is designed and implemented where users can visualize and animate spatiotemporal interpolation results.
机译：流行病学研究确定了死亡率与颗粒物浓度变化之间的关联。这些研究突出了公众对微粒空气污染对健康的影响的关注。对细颗粒物PM2.5暴露风险进行建模并监控PM2.5浓度的每日变化是了解污染问题并着手采取必要补救措施的关键步骤。这项研究设计，实施和比较了两种基于距离距离加权（IDW）的时空插值方法，以便在两个人口普查区组水平上评估美国连续2009年的每日PM2.5浓度趋势。和县一级。传统上，在处理时空插值时，研究人员倾向于将时空插值分开对待，并将时空插值问题简化为一系列空间插值的快照。在本文中，PM2.5数据插值是通过使用所谓的扩展方法，通过同时集成空间和时间在连续的时空域中进行的。在假设时空维度在插入时空域中的连续变化现象时同等重要的假设下，借助一个因素来计算时间值。通过交叉验证评估各种具有不同参数配置的基于IDW的时空插值方法。此外，本研究还探讨了在实现时空插值的大型数据集过程中面临的计算问题（计算机处理速度）。本文采用并行编程技术和称为k-d树的高级数据结构来应对计算难题。在计算上已取得显着改进。最后，设计并实现了一个基于Web的时空基于IDW的插值应用程序，用户可以在其中可视化和动画化时空插值结果。

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