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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (7): 38-49    DOI: 10.11925/infotech.2096-3467.2020.0322
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Measuring Enterprise’s Offline Resumption with Mobile Device Positioning Data
Nie Lei1,Fu Juan2,Yi Chengqi2,Yang Daoling2()
1Department of Information Management, Peking University, Beijing 100871, China
2Big Data Development Department, State Information Center, Beijing 100045, China
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[Objective] This paper quantifies the offline resumption level after public emergencies, aiming to provide data support for making and implementing policies.[Methods] First, we used manual and automated POI fence delineation strategies to obtain the number of mobile devices in 931 areas. Then, we measured the offline resumption levels based on the number of mobile devices within each company’s physical settings. Finally, we evaluated the measurements with facts and related data.[Results] We found that for days immediately following the Spring Festival 2020, the average level of offline resumption in the sampled companies was about 30% of that of the same period in 2019. At the end of February 2020, about half of the employees from the sampled companies returned to work offline.[Limitations] The sample size needs to be expanded.[Conclusions] The proposed method could dynamically monitoring offline work resumption after public emergencies.

Key wordsPublic Emergencies      Mobile Devices Data      Resumption Level     
Received: 16 April 2020      Published: 25 July 2020
ZTFLH:  G350  
Corresponding Authors: Yang Daoling     E-mail:

Cite this article:

Nie Lei,Fu Juan,Yi Chengqi,Yang Daoling. Measuring Enterprise’s Offline Resumption with Mobile Device Positioning Data. Data Analysis and Knowledge Discovery, 2020, 4(7): 38-49.

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Research Roadmap
The Process of Acquiring the Number of Mobile Terminals within Specific POI Areas
Schematic Diagram of Manually Determining the Fence
Converting the Center Point Coordinates and Radiation Radius into Monitoring Grids
Scaled Results of the Number of Mobile Devices on Working Days Before the Spring Festival 2020
The Offline Return Rate after the Spring Festival 2019
The Results of the Offline Resumption Level of all Samples in February 2020
线下返岗率 省份数量 省份
[60%,80%) 8 甘肃、福建、吉林、陕西、山东、辽宁、云南、海南
[50%,60%) 9 江苏、青海、内蒙古、宁夏、安徽、贵州、浙江、湖南、江西
[40%,50%) 11 四川、山西、广东、河北、上海、广西、河南、天津、西藏、重庆、黑龙江
[20%,40%) 2 北京、新疆
[0%,20%) 1 湖北
The Province Distribution of the Offline Return Rate on February 28, 2020
The Offline Return Rate of all Samples and Samples from Hubei in Feburary 2020
城市 指标 均值 全距 方差 Pearson相关系数
北京 线下返岗率 0.29 0.26 0.01 0.96
出行恢复度 0.34 0.21 0.00
重庆 线下返岗率 0.23 0.30 0.01 0.89
出行恢复度 0.41 0.21 0.01
上海 线下返岗率 0.27 0.34 0.01 0.99
出行恢复度 0.40 0.34 0.01
天津 线下返岗率 0.27 0.29 0.01 0.95
出行恢复度 0.36 0.22 0.01
The Offline Return Rate and the Travel Recovery Indicator
因变量 自变量 回归系数 标准误差 变量
截距 -0.17 0.03 0.00 0.00
北京出行恢复度 1.35 0.10 0.00
截距 -0.29 0.06 0.00 0.00
重庆出行恢复度 1.27 0.15 0.00
截距 -0.13 0.01 0.00 0.00
上海出行恢复度 0.99 0.03 0.00
截距 -0.25 0.04 0.00 0.00
天津出行恢复度 1.46 0.11 0.00
Regression Results of the Offline Return Rate and the Travel Recovery Indicator
省份 指标 均值 全距 方差 Pearson
重庆 线下返岗率 0.23 0.30 0.01 0.93
城市交通活力恢复指数 0.09 0.15 0.00
上海 线下返岗率 0.27 0.34 0.01 0.96
城市交通活力恢复指数 0.18 0.26 0.01
The Offline Return Rate and the Urban Traffic Vitality Recovery Index
因变量 自变量 回归
标准误差 变量P 模型P
截距 0.02 0.02 0.38 0.00
重庆城市交通活力恢复指数 2.32 0.22 0.00
截距 0.04 0.02 0.04 0.00
重庆城市交通活力恢复指数 1.30 0.09 0.00
Regression Results of the Offline Return Rate and the Urban Traffic Vitality Recovery Index
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