论文标题
识别人类流量的水槽和来源：一种表征城市结构的新方法
Identifying sinks and sources of human flows: A new approach to characterizing urban structures
论文作者
论文摘要
人类流量数据是与人们关于在哪里居住，工作，购物等的决策有关的丰富行为数据，并提供了为识别城市中心的重要信息。但是，这并不容易理解大量的关系数据，并且数据集通常仅减少到目的地的旅行计数统计数据，从而将关系信息从原点到目的地丢弃。在这项研究中，我们提出了一种基于人类流动性数据的替代中心识别方法。该方法根据组合霍奇理论提取人类旅行的标量潜在领域。它不仅检测到统计上显着的有吸引力的位置，作为人类旅行的水槽，而且作为旅行来源的重要起源。作为一个案例研究，我们确定了东京大都会地区通勤和购物旅行的水槽和来源。这种针对特定的分析会根据人类流动性的不同方面对城市中心进行组合分类。所提出的方法可以应用于具有相关属性的其他移动性数据集，并帮助我们从人类流动性的多个角度来检查当代大都市地区的复杂空间结构。
Human flow data are rich behavioral data relevant to people's decision-making regarding where to live, work, go shopping, etc., and provide vital information for identifying city centers. However, it is not as easy to understand massive relational data, and datasets have often been reduced merely to the statistics of trip counts at destinations, discarding relational information from origin to destination. In this study, we propose an alternative center identification method based on human mobility data. This method extracts the scalar potential field of human trips based on combinatorial Hodge theory. It detects not only statistically significant attractive locations as the sinks of human trips but also significant origins as the sources of trips. As a case study, we identify the sinks and sources of commuting and shopping trips in the Tokyo metropolitan area. This aim-specific analysis leads to a combinatorial classification of city centers based on the distinct aspects of human mobility. The proposed method can be applied to other mobility datasets with relevant properties and helps us examine the complex spatial structures in contemporary metropolitan areas from the multiple perspectives of human mobility.
