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Mapping Large Spatial Flow Data with Hierarchical Clustering

机译：使用分层聚类映射大型空间流数据

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摘要

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high-level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi-resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.
机译：由于存在闭塞和显示混乱的问题，因此绘制大量的空间流量数据是一项挑战，因为成千上万的流量相互重叠并相交。现有的流量映射方法通常使用预定的高级地理单位（例如，州）来聚合流量，或者捆绑空间上接近的部分流量线，这两者都会导致信息的重大损失或失真，并且可能会错过主要模式。在这项研究中，我们开发了一种流聚类方法，该方法可提取相似流的聚类，以避免混乱的问题，揭示抽象的流模式，同时尽可能保留数据分辨率。具体来说，我们的方法扩展了传统的层次聚类方法，以聚合和映射大流量数据。新方法在确定两个流的相似性时同时考虑了起点和终点，这确保了流簇代表了从相似起点到相似终点的流，从而最大程度地减少了聚合过程中的信息丢失。借助空间索引和搜索算法，该新方法可扩展到大流量数据集。作为分层方法，它可以将流概括到不同的分层级别，并且有可能支持多分辨率流映射。可以合并不同的距离定义，以适应流量的不均匀空间分布并检测不同密度的流量簇。为了评估流量聚类和流量图的质量和逼真度，我们进行了案例研究，以分析市区内243,850次出租车行程的数据集。

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