Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City, China

Benyong Wei; Guiwu Su; Fenggui Liu

doi:10.1007/s13753-023-00480-3

Volume 14 Issue 4

Sep. 2023

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Article Navigation > International Journal of Disaster Risk Science > 2023 > 14(4): 649-665

Benyong Wei, Guiwu Su, Fenggui Liu. Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City, China[J]. International Journal of Disaster Risk Science, 2023, 14(4): 649-665. doi: 10.1007/s13753-023-00480-3

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Benyong Wei, Guiwu Su, Fenggui Liu. Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City, China[J]. International Journal of Disaster Risk Science, 2023, 14(4): 649-665. doi: 10.1007/s13753-023-00480-3

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Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City, China

doi: 10.1007/s13753-023-00480-3

Benyong Wei^{1,2
,
,},
Guiwu Su^1,2,
Fenggui Liu^3,4

1. Institute of Geology, China Earthquake Administration, Beijing, 100029, China;
2. Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing, 100029, China;
3. Academy of Plateau Science and Sustainability, People's Government of Qinghai Province and Beijing Normal University, Xining, 810008, China;
4. School of Geography, Qinghai Normal University, Xining, 810016, China

Funds:

This work was funded by the National Natural Science Foundation of China (42177453

41601567) and the National Key R&D Program of China (2018YFC1504403). The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions, which were all valuable and helpful for improving the manuscript.

Accepted Date: 2022-12-19
Publish Date: 2023-04-05

Abstract

Abstract

High-resolution, dynamic assessments of the spatiotemporal distributions of populations are critical for urban planning and disaster management. Mobile phone big data have real-time collection, wide coverage, and high resolution advantages and can thus be used to characterize human activities and population distributions at fine spatiotemporal scales. Based on six days of mobile phone user-location signal (MPLS) data, we assessed the dynamic spatiotemporal distribution of the population of Xining City, Qinghai Province, China. The results show that strong temporal regularity exists in the daily activities of local residents. The spatiotemporal distribution of the local population showed a significant downtown-suburban attenuation pattern. Factors such as land use types, holidays, and seasons significantly affect the spatiotemporal patterns of the local population. By combining other spatiotemporal trajectory data, high-resolution and dynamic real-time population distribution evaluations based on mobile phone location signals could be better developed and improved for use in urban management and disaster assessment research.
- China,
- High-resolution mobile phone data,
- Spatiotemporal population distribution,
- Urban management,
- Xining City

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References(72)

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