Mobility signatures: a tool for characterizing cities using intercity mobility flows
Front. Big Data (2022) 5:822889 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 mo...
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Zusammenfassung: | Front. Big Data (2022) 5:822889 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. |
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DOI: | 10.48550/arxiv.2112.01789 |