Mobility Signatures: A Tool for Characterizing Cities Using Intercity Mobility Flows

Frontiers in Big Data

Big Data

Maryam Astero¹  Zhiren Huang¹  Jari Saramäki² 

[1] Department of Computer Science, Aalto University, Espoo, Finland;Department of Computer Science, Aalto University, Espoo, Finland;Helsinki Institute of Information Technology HIIT, Aalto University, Espoo, Finland;

关键词: collective human mobility;    mobility signature;    mobile phones;    OD matrix;    COVID-19;    travel patterns;   

DOI  :  10.3389/fdata.2022.822889

 received in 2021-11-26, accepted in 2022-01-31,  发布年份 2022

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2022 Astero, Huang and Saramäki.