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BiFlowLISA: Measuring spatial association for bivariate flow data

机译：BIFLOWLISA：测量双变型流量数据的空间关联

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Spatial flow data are often used to represent spatial interaction phenomena such as daily commuting trips, human or animal migrations, and the exchanges of commodities, capital, or even information between regions. With the increasingly available large volume of flow data in fine spatiotemporal resolution, exploratory spatial data analysis (ESDA) has become more important than ever to gain understanding of the data and the story behind it. A major group of flow-related ESDA methods focus on measuring spatial associations, which proves useful in improving the prediction power and interpretability of spatial interaction model (SIM), as well as in identifying local clusters and outliers of flow events. This paper introduces a new spatial statistical method called BiFlowLISA-a local indicator of spatial association of bivariate flow data. BiFlowLISA evaluates the association between two types of flows in close proximity, in other words, how the value of type-I flows associate with the value of nearby type-II flows. We develop BiFlowLISA by extending the local bivariate Moran's I to the flow context. We also put forth its global version to measure the global patterns, and another variant of BiFlowLISA to measure both spatial and in-situ correlations at the same time. Several flow-specific issues are discussed and solved, including flow neighbor definition, OD matrix sparsity, and conditional permutation. We experiment with synthetic datasets to verify its functionality and to summarize its characteristics. A case study of taxi and ride-hailing services in New York City demonstrates its usefulness in the comparative analysis of the spatial patterns of two types of travel flows. More applications of BiFlowLISA await to be explored in the future.
机译：空间流量数据通常用于代表空间交互现象，例如日常通勤旅行，人或动物迁移，以及地区之间的商品，资本甚至信息的交流。随着越来越多的流量数据在较好的时空分辨率中，探索性空间数据分析（ESDA）与以往以往以往以往以往以往任何时候的数据及其背后的故事变得更加重要。一个主要的流动相关的ESDA方法专注于测量空间关联，这证明了在提高空间交互模型（SIM）的预测权力和可解释性方面，以及识别流动事件的局部集群和异常值。本文介绍了一种新的空间统计方法，称为Biflowlisa-A局部指标的一体的双变量流量数据。 Biflowlisa评估了两种类型的流程之间的关联，换句话说，类型-i流的值如何与附近II型流的值相关联。我们通过将当地的双变量莫兰的I扩展到流程背景来发展Biflowlisa。我们还提出了其全球版本来衡量全局模式，以及Biflowlisa的另一个变体，可以同时测量空间和原位相关性。讨论和解决了几个流动特定的问题，包括流邻居定义，OD矩阵稀疏性和条件排列。我们尝试合成数据集以验证其功能并总结其特征。纽约市的出租车和乘车服务案例研究展示了对两种旅行流动空间模式的比较分析中的有用性。更多在未来探索Biflowlisa的应用。

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