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 the mobility signature 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 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.
翻译:理解城市之间的人员流动模式,从交通工程到传染性疾病传播的空间建模等各种应用。我们采用以城市为中心的、数据驱动的视角来量化这种模式,并采用流动特征作为工具,了解城市(或一个区域)如何嵌入更广泛的流动网络。我们通过基于芬兰移动电话数据的两个应用,展示了流动特征方法的潜力。首先,我们使用流动特征来表明众所周知的辐射模型对于与大城市相关的流动流动更为准确,而传统的重力模型似乎更适合人口较少的地区。第二,我们说明2020年春季SARS-COV-2大流行是如何扰乱芬兰流动模式的。这两个案例表明流动特征能够迅速捕捉流动的特征,这些特征更难用传统方法提取。