A kriging interpolation model for geographical flows

This dataset contains the code and data used in the case study mentioned in the paper "A kriging interpolation model for geographical flows".Title A kriging interpolation model for geographical flowsAbstractThe kriging model can accommodate various spatial supports and has been extensively applied in hydrology, meteorology, soil science, and other domains. With the expansion of applications, it is essential to extend the kriging model for new spatial support of high-dimensional data. Geographical flows can depict the movements of geographical objects and imply the underlying mobility patterns in geographical phenomena. However, due to the bias, sparsity, and uneven quality of flow data in the real world, research about flows remains hindered by the lack of complete flow data and effective flow interpolation methods. In this study, we design a kriging interpolation model for flows based on several flow-related concepts and the autocorrelation of flows. We also analyze the second-order stationarity and anisotropy in the flow spatial random field. To illustrate the effectiveness and applicability of our method, we conduct two case studies. The former case study compares several experiments of flow density interpolation using Beijing mobile signaling data and illustrates the conditions of applicable areas. The latter case study extends our model to other flow attributes, such as travel time uncertainty, using Beijing taxi origin-destination flow data. The results of these cases demonstrate the effectiveness and high accuracy of our model.本数据集包含论文《面向地理流的克里金插值模型》（A kriging interpolation model for geographical flows）中案例研究所使用的代码与配套数据。 论文标题：《面向地理流的克里金插值模型》（A kriging interpolation model for geographical flows） 摘要：克里金（kriging）模型能够适配多种空间支撑单元，已在水文学、气象学、土壤学等领域得到广泛应用。随着应用场景的拓展，针对高维数据的新型空间支撑单元拓展克里金模型已成为必然需求。地理流能够刻画地理实体的移动过程，并揭示地理现象背后潜藏的流动模式。然而，现实世界中的流数据普遍存在偏差、稀疏性与质量不均等问题，完整流数据的缺失与高效流插值方法的匮乏，仍掣肘着地理流相关研究的开展。本研究基于若干流相关概念与流的自相关性，构建了面向地理流的克里金插值模型，并对地理流空间随机场的二阶平稳性与各向异性展开了分析。为验证所提方法的有效性与适用性，本研究开展了两项案例研究：第一项案例基于北京市移动信令数据，开展多项流密度插值实验，阐明了模型的适用场景条件；第二项案例则基于北京市出租车起讫点（origin-destination，OD）流数据，将所提模型拓展至出行时间不确定性等其他流属性场景。上述两项案例的实验结果证明，本模型具备良好的有效性与较高的预测精度。