Journal article
Detection of anomalous spatio-temporal patterns of app traffic in response to catastrophic events
- Abstract:
- In this work, we uncover patterns of usage mobile phone applications and information spread in response to perturbations caused by unprecedented events. We focus on categorizing patterns of response in both space and time, tracking their relaxation over time. To this end, we use the NetMob2023 Data Challenge dataset, which provides mobile phone applications traffic volume data for several cities in France at a spatial resolution of 100 m2 and a time resolution of 15 minutes for a time period ranging from March to May 2019. We analyze the spread of information before, during, and after the catastrophic Notre-Dame fire on April 15th and a bombing that took place in the city centre of Lyon on May 24th using volume of data uploaded and downloaded to different mobile applications as a proxy of information transfer dynamics. We identify different clusters of information transfer dynamics in response to the Notre-Dame fire within the city of Paris as well as in other major French cities. We find a clear pattern of significantly above-baseline usage of the application Twitter (currently known as X) in Paris that radially spreads from the area surrounding the Notre-Dame cathedral to the rest of the city. We detect a similar pattern in the city of Lyon in response to the bombing. Further, we present a null model of radial information spread and develop methods of tracking radial patterns over time. Overall, we illustrate novel analytical methods we devise, showing how they enable a new perspective on mobile phone user response to unplanned catastrophic events and giving insight into how information spreads during a catastrophe in both time and space.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Corrected version of record, pdf, 3.1MB, Terms of use)
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- Publisher copy:
- 10.1140/epjds/s13688-025-00546-w
Authors
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/W523781/1
- EP/Y028872/1
- EP/V013068/1
- EP/V03474X/1
- Publisher:
- Springer Nature
- Journal:
- EPJ Data Science More from this journal
- Volume:
- 14
- Issue:
- 1
- Article number:
- 35
- Place of publication:
- Germany
- Publication date:
- 2025-05-06
- Acceptance date:
- 2025-03-27
- DOI:
- EISSN:
-
2193-1127
- Pmid:
-
40342613
- Language:
-
English
- Keywords:
- Pubs id:
-
2124581
- UUID:
-
uuid_42220d99-eda5-4ff2-ab10-b4ddedbfe5a6
- Local pid:
-
pubs:2124581
- Source identifiers:
-
W4410123465
- Deposit date:
-
2025-06-10
- ARK identifier:
Terms of use
- Copyright holder:
- Medina et al
- Copyright date:
- 2025
- Rights statement:
- ©2025 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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