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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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Abstract  

[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: ydl@sic.gov.cn

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0322     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I7/38

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
P
模型
P
北京
线下返岗率
截距 -0.17 0.03 0.00 0.00
北京出行恢复度 1.35 0.10 0.00
重庆
线下返岗率
截距 -0.29 0.06 0.00 0.00
重庆出行恢复度 1.27 0.15 0.00
上海
线下返岗率
截距 -0.13 0.01 0.00 0.00
上海出行恢复度 0.99 0.03 0.00
天津
线下返岗率
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
相关系数
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
系数
标准误差 变量P 模型P
重庆线
下返岗率
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
上海线
下返岗率
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
[1] 冯兰芳, 周慧玲, 卢讯文. 全面职业康复服务对工伤环卫工的复工影响研究[J]. 中国康复, 2019,34(2):105-108.
[1] ( Feng Lanfang, Zhou Huiling, Lu Xunwen. Research on the Impact of Comprehensive Occupational Rehabilitation Services on the Reinstatement of Work-related Sanitation Workers[J]. Chinese Journal of Rehabilitation, 2019,34(2):105-108.)
[2] 舒甜, 白钟飞, 余丹, 等. 上海地区工伤患者复工特点及预测因素[J]. 中国康复医学杂志, 2018,33(2):206-210,214.
[2] ( Shu Tian, Bai Zhongfei, Yu Dan, et al. Characteristics and Predictive Factors of Resumption of Work-related Injuries in Shanghai[J]. Chinese Journal of Rehabilitation Medicine, 2018,33(2):206-210, 214.)
[3] 周慧玲, 黄琼, 马科科, 等. 现场工作分析评估对职业康复职工复工率的影响[J]. 中国康复医学杂志, 2017,32(11):1261-1264.
[3] ( Zhou Huiling, Huang Qiong, Ma Keke, et al. The Impact of Field Work Analysis and Evaluation on the Rate of Resumption of Vocational Rehabilitation Workers[J]. Chinese Journal of Rehabilitation Medicine, 2017,32(11):1261-1264.)
[4] 刘武忠, 王祖兵, 张雪涛, 等. 复工复产用人单位新型冠状病毒肺炎关键风险点评估[J]. 职业卫生与应急救援, 2020,38(2):138-141.
[4] ( Liu Wuzhong, Wang Zubing, Zhang Xuetao, et al. Evaluation on Key Risk Points for Prevention of COVID-2019 by Employers During Resumption of Work and Production[J]. Occupational Health and Emergency Rescue, 2020,38(2):138-141.)
[5] 胡越秋, 王军, 董泽华. 新冠肺炎疫情防控期间企业复工决策分析——基于行为经济学视角[J]. 统计与决策, 2020(5):157-160.
[5] ( Hu Yueqiu, Wang Jun, Dong Zehua. Analysis of the Decision to Resume Work During the Prevention and Control of COVID-19 Epidemic:Based on Behavioral Economics[J]. Statistics & Decision, 2020(5):157-160.)
[6] 李倩, 唐彪, Wu Jianhong, 等. 缓疫策略执行力与依从性对COVID-19后期疫情及复工影响的模型研究[J]. 陕西师范大学学报(自然科学版), 2020,48(3):1-6.
[6] ( Li Qian, Tang Biao, Wu Jianhong, et al. Mathematical Model Reveals the Influence of Execution and Adherence of Mitigation Strategies on the Later Period of COVID-19 and Resumption of Work[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2020,48(3):1-6.)
[7] 王霞, 唐三一, 陈勇, 等. 新型冠状病毒肺炎疫情下武汉及周边地区何时复工?数据驱动的网络模型分析[J]. 中国科学:数学, 2020,50.DOI: 10.1360/SSM-2020-0037.
[7] ( Wang Xia, Tang Sanyi, Chen Yong, et al. When will be the Resumption of Work in Wuhan and Its Surrounding Areas During COVID-19 Epidemic? A Data-driven Network Modeling Analysis [J]. SCIENTIA SINICA Mathematica, 2020,50.DOI: 10.1360/SSM-2020-0037.)
[8] Shen L, Stopher P R. Review of GPS Travel Survey and GPS Data-Processing Methods[J]. Transport Reviews, 2014,34(3):316-334.
[9] 周超然. 基于大规模GPS轨迹数据的活动链信息分析方法研究[D]. 长春:吉林大学, 2017.
[9] ( Zhou Chaoran. Research on Methods of Activity-chain Information Analysis Based on Large Scale GPS Tracking Data[D]. Changchun: Jilin University, 2017.)
[10] Zheng Y, Chen Y, Li Q, et al. Understanding Transportation Modes Based on GPS Data for Web Applications[J]. ACM Transactions on the Web, 2010, 4(1): Article No. 1.
[11] Bohte W, Maat K. Deriving and Validating Trip Purposes and Travel Modes for Multi-day GPS-based Travel Surveys: A Large-scale Application in the Netherlands[J]. Transportation Research Part C: Emerging Technologies, 2009,17(3):285-297.
[12] 李浩, 王旭智, 万旺根. 基于位置数据的居民出行时空特征研究——以上海市为例[J]. 电子测量技术, 2019,42(19):25-30.
[12] ( Li Hao, Wang Xuzhi, Wan Wanggen. Research on Temporal and Spatial Characteristics of Residents’ Travel Based on Location Data-A Case of Shanghai[J]. Electronic Measurement Technology, 2019,42(19):25-30.)
[13] 杨彬彬. 基于手机信令数据的城市轨道交通客流特征研究[D]. 南京:东南大学, 2015.
[13] ( Yang Binbin. Research of Urban Rail Transit Passenger Flow Characteristics Based on Phone Signaling Data[D]. Nanjing: Southeast University, 2015.)
[14] Yuan J, Zheng Y, Xie X. Discovering Regions of Different Functions in a City Using Human Mobility and POIs[C] //Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012: 186-194.
[15] Pan G, Qi G, Wu Z, et al. Land-Use Classification Using Taxi GPS Traces[J]. IEEE Transactions on Intelligent Transportation Systems, 2013,14(1):113-123.
[16] 申悦, 柴彦威. 基于GPS数据的北京市郊区巨型社区居民日常活动空间[J]. 地理学报, 2013,68(4):506-516.
[16] ( Shen Yue, Chai Yanwei. Daily Activity Space of Suburban Mega-community Residents in Beijing Based on GPS Data[J]. Acta Geographica Sinica, 2013,68(4):506-516.)
[17] 王贤文, 王虹茵, 李清纯. 基于地理位置大数据的京津冀城市群短期人口流动研究[J]. 大连理工大学学报(社会科学版), 2017,38(2):105-113.
[17] ( Wang Xianwen, Wang Hongyin, Li Qingchun. Location Based Big Data Analysis of the Short-term Population Flow of Beijing, Tianjin and Hebei Urban Agglomeration[J]. Journal of Dalian University of Technology (Social Sciences) , 2017,38(2):105-113.)
[18] 邬群勇, 裘钰娇. 微博数据位置信息反映台风灾情的有效性分析[J]. 测绘科学技术学报, 2019,36(4):406-411.
[18] ( Wu Qunyong, Qiu Yujiao. Effectiveness Analysis of Typhoon Disaster Reflected by Microblog Data Location Information[J]. Journal of Geomatics Science and Technology, 2019,36(4):406-411.)
[19] 史新颖. 面向灾害应急的手机信令数据快速聚类及受灾人口计算方法[D]. 南昌:东华理工大学, 2019.
[19] ( Shi Xinying. Rapid Clustering of Mobile Phone Signaling Data for Disaster Emergency and Calculation Method of Disaster Population[D]. Nanchang: East China University of Technology, 2019.)
[20] 丁亮, 钮心毅, 宋小冬. 上海中心城就业中心体系测度——基于手机信令数据的研究[J]. 地理学报, 2016,71(3):484-499.
[20] ( Ding Liang, Niu Xinyi, Song Xiaodong. Measuring the Employment Center System in Shanghai Central City: A Study Using Mobile Phone Signaling Data[J]. Acta Geographica Sinica, 2016,71(3):484-499.)
[21] 王德, 朱查松, 谢栋灿. 上海市居民就业地迁移研究——基于手机信令数据的分析[J]. 中国人口科学, 2016,36(1):80-89.
[21] ( Wang De, Zhu Chasong, Xie Dongcan. Research on Intra-city Employment Mobility in Shanghai:Based on Cell Phone Data[J]. Chinese Journal of Population Science, 2016,36(1):80-89.)
[22] 张天然. 基于手机信令数据的上海市域职住空间分析[J]. 城市交通, 2016,14(1):15-23.
[22] ( Zhang Tianran. Job-Housing Spatial Distribution Analysis in Shanghai Metropolitan Area Based on Cellular Signaling Data[J]. Urban Transport of China, 2016,14(1):15-23.)
[23] 李鹏飞. 基于手机信令数据的城市就业空间特征研究——以沈阳市为例[J]. 地理信息世界, 2019,26(1):25-30.
[23] ( Li Pengfei. Detecting the Spatial Characteristics of Urban Employment Using Mobile Phone Signaling Data:A Case Study of Shenyang City[J]. Geomatics World, 2019,26(1):25-30.)
[24] 金安, 程承旗, 宋树华, 等. 基于Geohash的面数据区域查询[J]. 地理与地理信息科学, 2013,29(5):31-35.
[24] ( Jin An, Cheng Chengqi, Song Shuhua, et al. Regional Query of Area Data Based on Geohash[J]. Geography and Geo-Information Science, 2013,29(5):31-35.)
[25] 曹浩泽. Geohash编码的周边查找算法优化[J]. 测绘地理信息, 2019,44(6):89-92.
[25] ( Cao Haoze. Optimization of Peripheral Search Algorithm Based on Geohash[J]. Journal of Geomatics, 2019,44(6):89-92.)
[26] 沈兵林. 基于Geohash的空间文本查询的研究[D]. 昆明:昆明理工大学, 2019.
[26] ( Shen Binglin. Research on Spatial Text Query Based on Geohash[D]. Kunming: Kunming University of Science and Technology, 2019.)
[27] 中国社会科学院语言研究所词典编辑室. 现代汉语词典 [M].第7版. 北京: 商务印书馆, 2016.
[27] ( Chinese Academy of Social Sciences. Modern Chinese Dictionary [M]. The 7th Edition. Beijing: Commercial Press, 2016.)
[28] 袁方. 社会研究方法教程[M]. 北京: 北京大学出版社, 2004.
[28] ( Yuan Fang. Social Research Methods Course[M]. Beijing: Peking University Press, 2004.)
[29] 陆娅楠. 政策密集出台对冲疫情影响[N]. 人民日报, 2020-02-25(6).
[29] ( Lu Yanan. Policy Intensive Introduction of the Impact of Hedge Epidemic [N]. People’s Daily, 2020-02-25(6).)
[30] 上海市政府办公厅. 上海市人民政府关于延迟本市企业复工和学校开学的通知[EB/OL]. ( 2020- 01- 17) [2020-03-06]. http://www.shanghai.gov.cn/nw2/nw2314/nw2315/nw43978/u21aw142 3601.html.
[30] ( General Office of Shanghai Municipal People’s Government. Notice of Shanghai Municipal People’s Government on Delaying the Resumption of Business and the Opening of Schools in this Municipality[EB/OL]. ( 2020- 01- 17) [2020-03-06] . http://www.shanghai.gov.cn/nw2/nw2314/nw2315/nw43978/u21aw1423601.html.)
[31] 浙江省人民政府办公厅. 浙江省人民政府办公厅关于延迟企业复工和学校开学的通知[EB/OL]. ( 2020- 01- 27)[2020-03-06]. http://www.zj.gov.cn/art/2020/1/27/art_1554467_41858317.html.
[31] ( General Office of The People’s Government of Zhejiang Provincial. Notice of General Office of Zhejiang Provincial People’s Government on Delaying the Resumption of Business and the Opening of Schools in this Municipality[EB/OL]. ( 2020- 01- 27)[2020-03-06]. http://www.zj.gov.cn/art/2020/1/27/art_1554467_41858317.html.)
[32] 北京市人民政府. 北京市人民政府关于在新型冠状病毒感染的肺炎疫情防控期间本市企业灵活安排工作的通知[EB/OL].( 2020- 01- 31)[2020-03-06]. http://www.beijing.gov.cn/zhengce/zhengcefagui/202001/t20200131_1622070.html.
[32] ( The People’s Government of Beijing Municipality. Notice of the Beijing Municipal People’s Government on Flexible Arrangements for Enterprises in This Municipality During the Prevention and Control of Pneumonia Epidemic Infected by New Coronavirus[EB/OL]. ( 2020- 01- 31)[2020-03-06]. http://www.beijing.gov.cn/zhengce/zhengcefagui/202001/t20200131_1622070.html.)
[33] 马玉姝. 因违规复工,淄博28家企业被责令停工!投诉举报方式在这里[EB/OL]. ( 2020-02-05) [2020-03-06]. https://baijiahao.baidu.com/s?id=1657669901646167084&wfr=spider&for=pc.
[33] ( Ma Yushu. 28 companies in Zibo were ordered to suspend work due to illegal resumption of work! Complaint reporting method is here[EB/OL]. ( 2020-02-05) [2020-03-06]. https://baijiahao.baidu.com/s?id=1657669901646167084&wfr=spider&for=pc.)
[34] 百度地图慧眼.城内出行强度[DB/OL].( 2020- 03- 04) [2020-03-05]. http://qianxi.baidu.com/.
[34] ( Baidu Map Smart Eyes. Travel Intensity in the City [DB/OL].( 2020- 03- 04) [2020-03-05]. http://qianxi.baidu.com/
[35] 滴滴. 城市交通活力恢复指数[DB/OL]. ( 2020-03-04) [2020-03-05]. https://sts.didichuxing.com/t-activity-index/.
[35] ( Didi. Urban Transport Vitality Recovery Index [DB/OL].( 2020-03-04) [2020-03-05]. https://sts.didichuxing.com/t-activity-index/.)
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