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".<b>Title </b>A kriging interpolation model for geographical flows<b>Abstract</b>The 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.<br>本数据集包含论文《A kriging interpolation model for geographical flows》（《地理流克里金插值模型》）中提及的案例研究所使用的代码与数据。 <b>Title </b>地理流克里金(Kriging)插值模型 <b>Abstract</b>克里金(Kriging)模型可适配多种空间支撑(spatial supports)条件，已在水文学、气象学、土壤学等领域得到广泛应用。随着应用场景的不断拓展，针对高维数据的新型空间支撑场景拓展克里金模型已成为迫切需求。地理流(geographical flows)可刻画地理对象的移动过程，并隐含地理现象背后的潜在流动模式。然而，受现实世界中流数据存在偏差、稀疏性与质量不均等问题的制约，相关流研究仍因缺乏完整的流数据与有效的流插值方法而进展受阻。本研究基于若干流相关概念与流的自相关性(autocorrelation)，设计了一款面向流数据的克里金插值模型。同时，本研究还对流空间随机场(spatial random field)中的二阶平稳性(second-order stationarity)与各向异性(anisotropy)展开了系统性分析。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：其一基于北京移动信令数据开展流密度插值的多组对比实验，阐明了该模型的适用场景范围；其二将模型拓展至出行时间不确定性等其他流属性，采用北京出租车起讫点(origin-destination, OD)流数据完成验证。上述案例的实验结果证实了本模型的有效性与高精度特性。