专题:防灾减灾

基于微博数据的台风灾害舆情分析与灾害损失估计

  • 李绍攀 ,
  • 赵飞 ,
  • 周义棋 ,
  • 田向亮 ,
  • 黄弘
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  • 1. 清华大学 工程物理系, 公共安全研究院, 北京 100084;
    2. 应急管理部国家减灾中心, 北京 100124;
    3. 中国安全生产科学研究院 矿山采空区灾害防治应急管理部重点实验室, 北京 100012

收稿日期: 2021-03-09

  网络出版日期: 2022-01-14

基金资助

国家重点研发计划项目(2018YFC1508900);国家自然科学基金资助项目(72091512,71774093)

Analysis of public opinion and disaster loss estimates from typhoons based on Microblog data

  • LI Shaopan ,
  • ZHAO Fei ,
  • ZHOU Yiqi ,
  • TIAN Xiangliang ,
  • HUANG Hong
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  • 1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
    2. National Disaster Reduction Center of China, Beijing 100124, China;
    3. Key Laboratory of Mining Goaf Disaster Prevention and Control of Ministry of Emergency Management, China Academy of Safety Science & Technology, Beijing 100012, China

Received date: 2021-03-09

  Online published: 2022-01-14

摘要

该文以2018年台风“山竹”和2019年台风“利奇马”为研究案例,通过网络爬虫爬取微博数据,将Bayes情感分析模型运用到台风自然灾害的舆情分析,讨论了2种不同台风的灾害舆情演化规律。在舆情演化规律的基础上,利用城市热度分布和情感指标,结合城市地理位置、经济、人口数据、台风灾害破坏能力,对临海城市和非临海城市进行了城市灾损数据估计。灾损评估结果对于各地级市的灾情评估有较好的一致性,具有一定的参考意义。研究结果及方法可为灾情研判及救灾需求分析提供参考,帮助解决台风灾害发生初期应急救援过程中各地级市物资调配问题。

本文引用格式

李绍攀 , 赵飞 , 周义棋 , 田向亮 , 黄弘 . 基于微博数据的台风灾害舆情分析与灾害损失估计[J]. 清华大学学报(自然科学版), 2022 , 62(1) : 43 -51 . DOI: 10.16511/j.cnki.qhdxxb.2021.26.031

Abstract

This study searched Microblog data related to Typhoon "Mangkhut" in 2018 and "Lekima" in 2019 and then used the Bayesian sentiment analysis model to analyze the public opinions related to these typhoons. The results show two different typhoon disaster public opinion temporal and spatial evolution laws and an emotional evolution law. Then, the urban typhoon disaster loss was estimated for coastal and inland cities based on the temporal and spatial evolution and emotional evolution laws. The data includes the city's geographic location, economy, population and typhoon disaster damage as well as sentiments. The disaster damage assessment model is consistent with the disaster assessment. The research results and methods provide references for disaster research and disaster relief demand analyses and guidelines for supply allocation in cities during initial emergency responses during typhoons.

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