城市疫情态势发展与动态调控可视分析

doi:10.11959/j.issn.2096-0271.2023027

大数据 ›› 2023, Vol. 9 ›› Issue (6): 110-123.doi: 10.11959/j.issn.2096-0271.2023027

城市疫情态势发展与动态调控可视分析

王松¹, 陈仕杰¹, 李杭霖², 李孝慧³, 冯琼芳¹, 王慧杰¹

¹ 西南科技大学计算机科学与技术学院，四川绵阳 621010
² 电子科技大学计算机科学与工程学院，四川成都 611731
³ 西南交通大学计算机与人工智能学院，四川成都 611756

出版日期:2023-11-15 发布日期:2023-11-01
作者简介:王松（1989- ），男，博士，西南科技大学计算机科学与技术学院讲师，主要研究方向为可视化与可视分析、智能交互、混合现实。
陈仕杰（1998- ），男，西南科技大学计算机科学与技术学院硕士生，主要研究方向为可视化与可视分析。
李杭霖（2000- ），女，电子科技大学计算机科学与工程学院硕士生，主要研究方向为可视化与可视分析。
李孝慧（1998- ），女，西南交通大学计算机与人工智能学院硕士生，主要研究方向为可视化与可视分析。
冯琼芳（2001- ），女，西南科技大学计算机科学与技术学院在读，主要研究为可视化与可视分析。
王慧杰（2000- ），男，西南科技大学计算机科学与技术学院在读，主要研究为可视化与可视分析。
基金资助:
国家自然科学基金资助项目(61872304);四川省自然科学基金资助项目(2022NSFSC0961);西南科技大学博士点基金资助项目(19ZX7144);西南科技大学一般教育教学改革与研究项目(22xn0048)

Visual analytics of urban epidemic situation development and dynamic regulation

Song WANG¹, Shijie CHEN¹, Hanglin LI², Xiaohui LI³, Qiongfang FENG¹, Huijie WANG¹

¹ School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China
² School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu 611731, China
³ School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China

Online:2023-11-15 Published:2023-11-01
Supported by:
The National Natural Science Foundation of China(61872304);Sichuan Natural Science Foundation of China(2022NSFSC0961);Southwest University of Science and Technology PhD Fund Grant(19ZX7144);Southwest University of Science and Technology General Education Reform Fund Grant(22xn0048)

摘要/Abstract

摘要：

为了解决新冠肺炎疫情医疗资源的合理调配问题，以武汉方舱新冠肺炎疫情数据为基础，融合舆情、时空轨迹等多源数据，针对疫情防控搭建了方舱医院动态调控平台。引入水滴图表征方式动态监测方舱医院，采用主题模型融合情感词典提取群众情感特征，并借助WordStream呈现城市舆情发展，提出基于医院负载量的路径规划算法实现合理路线推荐，提供面向群众的科普信息和城市复苏板块以提升抗疫信心。该系统有利于实现人力、物力的合理配置，及时引导群众的情绪动向，关注新政策/决定发布后的舆情变化，减缓患者聚集现象。最后，结合多组案例分析验证了系统的功能性和有效性。

关键词: 动态调控, 城市舆情发展, 路径规划

Abstract:

In order to solve the reasonable allocation of limited medical resources for patients, a visual platform for dynamic control of cabin hospital was built based on the dataset of the COVID-19 epidemic in Wuhan cabin hospital, which integrates multi-source data such as public opinion, space-time trajectory and science popularization.The droppingwater diagram was introduced to dynamically monitor the overall situation of the cabin hospital.The theme model and sentiment dictionary were used to extract the emotional features of the masses, the WordStream were used to visually present the trend of urban public opinion.The path planning algorithm based on hospital load capacity were built for treatment route, the population-oriented science information of access to cabin hospital and the urban recovery model can enhance anti-epidemic confidence.The system is helpful to realize the reasonable allocation of human and material resources, timely guide the emotional trend of the masses, pay attention to the change of public opinion after the release of new polices/decisions, and slow down the phenomenon of patients gathering.The function and effectiveness of the system was verified by the analysis of several cases.

Key words: dynamic regulation, city public opinion development, route planning

中图分类号:

TP391

王松, 陈仕杰, 李杭霖, 李孝慧, 冯琼芳, 王慧杰. 城市疫情态势发展与动态调控可视分析[J]. 大数据, 2023, 9(6): 110-123.

Song WANG, Shijie CHEN, Hanglin LI, Xiaohui LI, Qiongfang FENG, Huijie WANG. Visual analytics of urban epidemic situation development and dynamic regulation[J]. Big Data Research, 2023, 9(6): 110-123.

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参考文献 16

[1]	金思辰, 陶煜波, 严宇宇 ,等. 基于多维时空数据可视化的传染病模式分析[J]. 计算机辅助设计与图形学学报, 2019,31(2): 241-255.
	JIN S C , TAO Y B , YAN Y Y ,et al. Infectious disease patterns analysis based on visualization of multidimensional space-time data[J]. Journal of Computer-Aided Design ＆ Computer Graphics, 2019,31(2): 241-255.
[2]	胡雪芸, 何宗宜, 苗静 . 疾病数据的时空聚集分析及可视化[J]. 测绘通报, 2015(11): 106-111.
	HU X Y , HE Z Y , MIAO J . Space-time clustering analysis and visualization based on the data of tuberculosis[J]. Bulletin of Surveying and Mapping, 2015(11): 106-111.
[3]	RIND A , AIGNER W , MIKSCH S ,et al. Visual exploration of time-oriented patient data for chronic diseases:design study and evaluation[C]// Symposium of the Austrian HCI and Usability Engineering Group. Heidelberg:Springer, 2011: 301-320.
[4]	曲玉冰, 李仁东, 庄大方 ,等. 空间信息技术和大数据技术在登革热风险评估系统中的应用[J]. 中华疾病控制杂志, 2017,21(11): 1165-1169,1174.
	QU Y B , LI R D , ZHUANG D F ,et al. Study and application of dengue fever risk assessment system based on spatial information technology and big data technology[J]. Chinese Journal of Disease Control ＆ Prevention, 2017,21(11): 1165-1169,1174.
[5]	彭宗超, 黄昊, 吴洪涛 ,等. 新冠肺炎疫情前期应急防控的“五情”大数据分析[J]. 治理研究, 2020(2): 5-20.
	PENG Z C , HUANG H , WU H T ,et al. “Five situations” big data analysis in the early stage of emergency prevention and control of novel coronavirus pneumonia[J]. Governance Studies, 2020(2): 5-20.
[6]	AFZAL S , GHANI S , JENKINSSMITH H C ,et al. A visual analytics based decision making environment for COVID-19 modeling and visualization[C]// Proceedings of 2020 IEEE Visualization Conference (VIS). Piscataway:IEEE Press, 2021: 86-90.
[7]	邓少存, 张健钦, 张学东 ,等. 基于WebGIS的新冠肺炎疫情可视化系统研发[J]. 北京建筑大学学报, 2020,36(4): 93-99.
	DENG S C , ZHANG J Q , ZHANG X D ,et al. Research and development of a COVID-19 epidemic visualization system based on WebGIS[J]. Journal of Beijing University of Civil Engineering and Architecture, 2020,36(4): 93-99.
[8]	刘建湘, 刘海砚, 陈晓慧 ,等. 新冠肺炎疫情数据多维度可视分析方法[J]. 计算机辅助设计与图形学学报, 2020,32(10): 1617-1627.
	LIU J X , LIU H Y , CHEN X H ,et al. Visual analysis method for COVID-19 epidemic situation[J]. Journal of Computer-Aided Design ＆ Computer Graphics, 2020,32(10): 1617-1627.
[9]	LEITE R A , SCHETINGER V , CENEDA D ,et al. COVIs:supporting temporal visual analysis of covid-19 events usable in datadriven journalism[C]// Proceedings of 2020 IEEE Visualization Conference (VIS). Piscataway:IEEE Press, 2021: 56-60.
[10]	ZHENG T L , ZHANG C L , SHI Y T ,et al. Influencing factors and clustering characteristics of COVID-19:a global analysis[J]. Big Data Mining and Analytics, 2022,5(4): 318-338.
[11]	徐恒, 张梦璐, 孙德厂 . 基于LDA模型的国内评论挖掘与情感分析领域主题分析与演化趋势[J]. 河南工业大学学报(社会科学版), 2021,37(2): 38-46.
	XU H , ZHANG M L , SUN D C . The research topic mining and evolutional trends of opinion mining and sentiment analysis in China based on LDA[J]. Journal of Henan University of Technology (Social Science Edition), 2021,37(2): 38-46.
[12]	DANG T , NGUYEN H N , PHAM V ,et al. WordStream:interactive visualization for topic evolution[C]// EuroVis (short papers).[S.l.:s.n.], 2019: 103-107.
[13]	JIA L P , LI Z H . Emotional analysis on the public sentiment of students returning to university under COVID-19[C]// Proceedings of 2020 16th International Conference on Computational Intelligence and Security (CIS). Piscataway:IEEE Press, 2021: 238-242.
[14]	CHEN C X , CHEN J , SHI C . Research on credit evaluation model of online store based on SnowNLP[J]. E3S Web of Conferences, 2018,53:03039.
[15]	SOWMIYA J S , CHANDRAKALA S . Joint sentiment/topic extraction from text[C]// Proceedings of 2014 IEEE International Conference on Advanced Communications,Control and Computing Technologies. Piscataway:IEEE Press, 2015: 611-615.
[16]	刘云翔, 陈荦, 李军 ,等. 基于城市道路网的最短路径分析解决方案[J]. 小型微型计算机系统, 2003,24(7): 1390-1393.
	LIU Y X , CHEN L , LI J ,et al. An implementation of the shortest path analysis based on city road network[J]. Mini-Micro Systems, 2003,24(7): 1390-1393.

原评价语料	无比激动！最后一家方舱医院休舱！洪山体育
	馆，第一家也是最后一家！武汉加油，希望所
	有方舱医院都能关门大吉
情感抽取结果	无比激动休舱加油关门大吉

城市疫情态势发展与动态调控可视分析

Visual analytics of urban epidemic situation development and dynamic regulation

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