Astero Maryam, Huang Zhiren, Saramäki Jari
Department of Computer Science, Aalto University, Espoo, Finland.
Helsinki Institute of Information Technology HIIT, Aalto University, Espoo, Finland.
Front Big Data. 2022 Feb 24;5:822889. doi: 10.3389/fdata.2022.822889. eCollection 2022.
Understanding the patterns of human mobility between cities has various applications from transport engineering to spatial modeling of the spreading of contagious diseases. We adopt a city-centric, data-driven perspective to quantify such patterns and introduce as a tool for understanding how a city (or a region) is embedded in the wider mobility network. We demonstrate the potential of the mobility signature approach through two applications that build on mobile-phone-based data from Finland. First, we use mobility signatures to show that the well-known radiation model is more accurate for mobility flows associated with larger Finnish cities, while the traditional gravity model appears a better fit for less populated areas. Second, we illustrate how the SARS-CoV-2 pandemic disrupted the mobility patterns in Finland in the spring of 2020. These two cases demonstrate the ability of the mobility signatures to quickly capture features of mobility flows that are harder to extract using more traditional methods.
了解城市之间的人口流动模式在从交通工程到传染病传播空间建模等诸多领域都有应用。我们采用以城市为中心、数据驱动的视角来量化此类模式,并引入一种工具,以了解一个城市(或地区)是如何融入更广泛的流动网络的。我们通过基于芬兰手机数据的两个应用案例展示了流动特征方法的潜力。首先,我们使用流动特征表明,著名的辐射模型对于与芬兰较大城市相关的流动更为准确,而传统的引力模型似乎更适合人口较少的地区。其次,我们说明了2020年春季新冠疫情如何扰乱了芬兰的流动模式。这两个案例展示了流动特征能够快速捕捉流动模式特征的能力,而这些特征使用更传统的方法则更难提取。