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Field theory for recurrent mobility
DOI:10.1038/s41467-019-11841-2.png)
摘要
En 中文
Understanding human mobility is crucial for applications such as forecasting epidemic spreading, planning transport infrastructure and urbanism in general. While, traditionally, mobility information has been collected via surveys, the pervasive adoption of mobile technologies has brought a wealth of (real time) data. The easy access to this information opens the door to study theoretical questions so far unexplored. In this work, we show for a series of worldwide cities that commuting daily flows can be mapped into a well behaved vector field, fulfilling the divergence theorem and which is, besides, irrotational. This property allows us to define a potential for the field that can become a major instrument to determine separate mobility basins and discern contiguous urban areas. We also show that empirical fluxes and potentials can be well reproduced and analytically characterized using the so-called gravity model, while other models based on intervening opportunities have serious difficulties.
Keyword:
INTERVENING OPPORTUNITIES
DISTANCE-DECAY
MODEL
MIGRATION
NETWORKS
AI总结
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期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
机构
引用论文
Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost
PLOS ONE
IF0
Limits of Predictability in Commuting Flows in the Absence of Data for Calibration
SCIENTIFIC REPORTS
IF3.9

