University of Sao Paulo, Institute of Mathematics and Computer Sciences, Sao Carlos, SP, 13566-590, Brazil.
Northwestern University, Department of Chemical and Biological Engineering, Evanston, IL, 60208-3112, USA.
Sci Rep. 2019 Aug 13;9(1):11801. doi: 10.1038/s41598-019-48295-x.
Human mobility has a significant impact on several layers of society, from infrastructural planning and economics to the spread of diseases and crime. Representing the system as a complex network, in which nodes are assigned to regions (e.g., a city) and links indicate the flow of people between two of them, physics-inspired models have been proposed to quantify the number of people migrating from one city to the other. Despite the advances made by these models, our ability to predict the number of commuters and reconstruct mobility networks remains limited. Here, we propose an alternative approach using machine learning and 22 urban indicators to predict the flow of people and reconstruct the intercity commuters network. Our results reveal that predictions based on machine learning algorithms and urban indicators can reconstruct the commuters network with 90.4% of accuracy and describe 77.6% of the variance observed in the flow of people between cities. We also identify essential features to recover the network structure and the urban indicators mostly related to commuting patterns. As previously reported, distance plays a significant role in commuting, but other indicators, such as Gross Domestic Product (GDP) and unemployment rate, are also driven-forces for people to commute. We believe that our results shed new lights on the modeling of migration and reinforce the role of urban indicators on commuting patterns. Also, because link-prediction and network reconstruction are still open challenges in network science, our results have implications in other areas, like economics, social sciences, and biology, where node attributes can give us information about the existence of links connecting entities in the network.
人口流动对社会的多个层面都有重大影响，包括基础设施规划和经济发展、疾病传播以及犯罪活动等。我们可以将该系统表示为一个复杂网络，其中节点分配给各个区域（例如城市），而连接两个节点的边表示人口流动。受物理启发的模型已经被提出，用于量化从一个城市到另一个城市的迁移人数。尽管这些模型取得了进步，但我们预测通勤者人数和重建流动网络的能力仍然有限。在这里，我们提出了一种使用机器学习和 22 个城市指标来预测人口流动并重建城市间通勤者网络的替代方法。我们的结果表明，基于机器学习算法和城市指标的预测可以以 90.4%的准确率重建通勤者网络，并描述城市间人口流动中观察到的 77.6%的方差。我们还确定了恢复网络结构的基本特征和与通勤模式最相关的城市指标。如前所述，距离在通勤中起着重要作用，但 GDP 和失业率等其他指标也是人们通勤的驱动力。我们相信，我们的研究结果为迁移建模提供了新的思路，并强调了城市指标对通勤模式的作用。此外，由于链路预测和网络重建仍然是网络科学中的开放性挑战，我们的结果对其他领域也具有重要意义，例如经济学、社会科学和生物学，其中节点属性可以为我们提供有关网络中连接实体的链路存在的信息。
