Zhou Zhiguang, Meng Linhao, Tang Cheng, Zhao Ying, Guo Zhiyong, Hu Miaoxin, Chen Wei
IEEE Trans Vis Comput Graph. 2018 Aug 20. doi: 10.1109/TVCG.2018.2864503.
A variety of human movement datasets are represented in an Origin-Destination(OD) form, such as taxi trips, mobile phone locations, etc. As a commonly-used method to visualize OD data, flow map always fails to discover patterns of human mobility, due to massive intersections and occlusions of lines on a 2D geographical map. A large number of techniques have been proposed to reduce visual clutter of flow maps, such as filtering, clustering and edge bundling, but the correlations of OD flows are often neglected, which makes the simplified OD flow map present little semantic information. In this paper, a characterization of OD flows is established based on an analogy between OD flows and natural language processing (NPL) terms. Then, an iterative multi-objective sampling scheme is designed to select OD flows in a vectorized representation space. To enhance the readability of sampled OD flows, a set of meaningful visual encodings are designed to present the interactions of OD flows. We design and implement a visual exploration system that supports visual inspection and quantitative evaluation from a variety of perspectives. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in reducing the visual clutter and enhancing correlations of OD flows.
各种人类移动数据集都以起讫点（OD）形式呈现，例如出租车行程、手机位置等。作为一种常用的可视化OD数据的方法，流量图由于二维地理地图上线路的大量交叉和遮挡，总是无法发现人类移动的模式。已经提出了大量技术来减少流量图的视觉混乱，例如过滤、聚类和边捆绑，但OD流的相关性常常被忽视，这使得简化后的OD流图几乎没有语义信息。在本文中，基于OD流与自然语言处理（NPL）术语之间的类比，建立了OD流的特征描述。然后，设计了一种迭代多目标采样方案，以在矢量化表示空间中选择OD流。为了提高采样OD流的可读性，设计了一组有意义的视觉编码来呈现OD流的相互作用。我们设计并实现了一个视觉探索系统，该系统支持从各种角度进行视觉检查和定量评估。基于真实世界数据集的案例研究以及与领域专家的访谈证明了我们的系统在减少视觉混乱和增强OD流相关性方面的有效性。
