Koebe, Till
del Villar, Zinnya
Nutakki, Brahmani
Sagimbayeva, Nursulu
Weber, Ingmar http://orcid.org/0000-0003-4169-2579
Article History
Received: 6 March 2024
Accepted: 13 June 2024
First Online: 21 June 2024
Competing interests
: The authors declare no competing interests.
: As stated in the original dataset description (Martínez-Durive et al. ): “The mobile network traffic dataset we use to generate the dataset was collected, processed and aggregated as described in Section “Methodology” in full compliance with Article 89 of the General Data Protection Regulation (GDPR), under the supervision of the Data Protection Officer (DPO) at Orange. In particular, all data management was performed on a secure platform at the operator’s premises and the raw data was deleted immediately afterward. The resulting service-level time series represent traffic aggregated over all UEs both in space, at eNodeB level, and time, over 15-min intervals. Moreover, the traffic associated to different base stations is further aggregated via the spatial mapping described earlier. The final representation does not allow re-identifying or tracking individual users. Therefore, this article does not contain any studies with human participants performed by any of the authors.
: In order to comply with the General Data Protection Regulation in the European Union, the mobile phone operator is required to use data from only those subscribers that opted-in for having their network data analyzed for research purposes. As stated in the original dataset description (Martínez-Durive et al. ): “The mobile network traffic dataset we use to generate the dataset was collected, processed and aggregated as described in Section “Methodology” in full compliance with Article 89 of the General Data Protection Regulation (GDPR), under the supervision of the Data Protection Officer (DPO) at Orange. In particular, all data management was performed on a secure platform at the operator’s premises and the raw data was deleted immediately afterward. The resulting service-level time series represent traffic aggregated over all UEs both in space, at eNodeB level, and time, over 15-minute intervals. Moreover, the traffic associ- ated to different base stations is further aggregated via the spatial mapping described earlier. The final representation does not allow re-identifying or tracking individual users."