# Rapid determination of seismic influence field based on mobile communication big data—A case study of the Luding Ms 6.8 earthquake in Sichuan, China

**Authors:** Dongping Li, Qingquan Tan, Zhiyi Tong, Jingfei Yin, Min Li, Huanyu Li, Haiqing Sun, Rahul Priyadarshi, Rahul Priyadarshi, Rahul Priyadarshi, Rahul Priyadarshi

PMC · DOI: 10.1371/journal.pone.0298236 · PLOS ONE · 2024-05-10

## TL;DR

This paper uses smartphone location data to quickly assess earthquake impacts, using the Luding earthquake as a case study.

## Contribution

A new method for estimating disaster distribution using mobile communication big data and base station out-of-service rates.

## Key findings

- Smartphone location data can effectively map disaster distribution with high resolution.
- A mathematical relationship between seismic intensity and base station out-of-service rates was established.
- The method enables rapid post-earthquake disaster assessment using communication big data.

## Abstract

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal–spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

## Full-text entities

- **Genes:** APP (amyloid beta precursor protein) [NCBI Gene 351] {aka AAA, ABETA, ABPP, AD1, APPI, CTFgamma}
- **Diseases:** natural disasters (MESH:D012893), seismic damages (MESH:D020263), iD (MESH:C535742), deaths (MESH:D003643)
- **Chemicals:** S (MESH:D013455)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC11086885/full.md

## Figures

12 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11086885/full.md

## References

33 references — full list in the complete paper: https://tomesphere.com/paper/PMC11086885/full.md

---
Source: https://tomesphere.com/paper/PMC11086885