Intermunicipal travel networks of Mexico during the COVID-19 pandemic

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作者
Oscar Fontanelli
Plinio Guzmán
Amilcar Meneses-Viveros
Alfredo Hernández-Alvarez
Marisol Flores-Garrido
Gabriela Olmedo-Alvarez
Maribel Hernández-Rosales
Guillermo de Anda-Jáuregui
机构
[1] CINVESTAV-IPN,Department of Computer Science
[2] Astronomer LTD,Computational Genomics Division
[3] CINVESTAV-IPN,Investigadores e Investigadoras por México
[4] Centro de Ciencias Genómicas,Centro de Ciencias de la Complejidad (C3)
[5] UNAM,undefined
[6] ENES Morelia,undefined
[7] UNAM,undefined
[8] National Institute of Genomics Medicine,undefined
[9] National Council of Humanities,undefined
[10] Sciences and Technologies,undefined
[11] Universidad Nacional Autónoma de México,undefined
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摘要
Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society’s local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020–2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.
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