Abstract
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 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.
Highlights
Collective human mobility patterns describe population movements between regions
The results reveal that the mobility signatures of large municipalities are more compatible with the estimated mobility flows from the radiation model, while the signatures associated with the gravity model are a good estimation for mobility patterns for regions with medium or small populations
We introduce mobility signatures as a tool for quantifying the patterns of travel of individual cities in the overall mobility network
Summary
Collective human mobility patterns describe population movements between regions. To predict or quantify the flow volumes between regions, several classical theoretical models have been proposed by considering the impact of distances (Zipf, 1946; Wilson, 1971) or intervening opportunities (Stouffer, 1940; Simini et al, 2012; Yan et al, 2017). From the network science perspective, the collective human mobility pattern is usually represented as a weighted mobility network (Barbosa et al, 2018). Such networks have proven to be useful for transport engineering (Wang et al, 2012; Ren et al, 2014; Guirao et al, 2018) and they have provided crucial information for emergency management (Lu et al, 2012; Huang et al, 2018). The mobility network plays an important role in predicting the spreading of epidemics (Brockmann and Helbing, 2013; Oliver et al, 2020), and evaluating the effects of interventions (Arenas et al, 2020; Kraemer et al, 2020)
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