JR, AA, and YM acknowledge support from the Government of Aragon (FONDO–COVID19-UZ-164255). JR is supported by Govern de les Illes Balears through the Vicenç Mut program and the María de Maeztu Excellence Unit 2023-2027 Ref. CEX2021-001164-M, funded by MCIN/AEI/ 10.13039/501100011033. AA acknowledges support through the grant RYC2021-033226-I funded by MCIN/ AEI/ 10.13039/501100011033 and the European Union NextGenerationEU/PRTR. YM was partially supported by the Government of Aragon, Spain, and ERDF "A way of making Europe" through grant E36-20R (FENOL), and by Ministerio de Ciencia e Innovación, Agencia Española de Investigación (MCIN/AEI/ 10.13039/501100011033) Grant No. PID2020-115800GB-I00. The authors acknowledge the use of the computational resources of COSNET Lab at Institute BIFI, funded by Banco Santander through grant Santander-UZ 2020/0274, and by the Government of Aragon (FONDO–COVID19-UZ-164255). JR and AA acknowledge funding from la Caixa Foundation under the project code SR20-00386 (COVID-SHINE).

We use a multilayer network approach to build digital twins of 5 Spanish cities (Barcelona, Valencia, Sevilla, Zaragoza and Murcia), describing them at the level of individuals, who have specific ages, sexes and home districts. We inferred interactions among individuals using publicly available data and statistics linked to home, school, university, work, community and nursing homes contexts (layers). This dataset includes the source data and codes needed for building the digital twins, together with individual metadata and the link lists associated with each city and each layer.

[Description of methods used for collection/generation of data] Download from publicly available sources, with analysis, statistical inference and assembly with Python.

With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001164-M).

[Methods for processing the data] Python executed in Jupyter notebooks.

citydigitwin.zip

Peer reviewed