基于微博数据挖掘的九寨沟7.0级地震灾情时空特征分析
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摘要
2017年8月8日21时19分九寨沟发生7.0级地震,震后数小时里,大量与地震相关的信息广泛传播,互联网社交媒体高度关注,九寨沟地震成为最热议话题。本文以新浪微博为例,获取了距震中200km范围内震前、震后24h的微博数据,通过对数据清洗、分类和挖掘,分析了此次地震微博的数量、灾情分类、词频统计、时间序列和空间分布等特征,同时与实际灾评结果进行了对比分析。研究结果表明,对震后社交媒体数据进行充分挖掘,分析提取地震灾情关键信息,有助于对灾情的宏观把握,对救灾决策部署有一定的参考意义,是解决震后灾情获取难度大、覆盖小、时效性差等问题的一种有效的辅助手段。
At 21: 19 p.m.,August 8,2017,a 7.0-magnitude earthquake struck Jiuzhaigou,Sichuan Province. In a couple of hours,a great deal of the earthquake-related information spread on internet. The earthquake drew much attention from the social media and soon became a hot topic.In this paper we searched those Sina Weibo users who are within the range of 200 km from the epicenter,and copied their Weibo data released 24 hours before and after the earthquake event.After cleaning,mining,and classifying these data,we analyzed their characteristics such as quantity,word-frequency,and classification,spatial and temporal distribution. We found that extracting data from the social media would help governments learn overall the post-earthquake information,and on this basis make decisions and arrangements for earthquake relief.
At 21: 19 p.m.,August 8,2017,a 7.0-magnitude earthquake struck Jiuzhaigou,Sichuan Province. In a couple of hours,a great deal of the earthquake-related information spread on internet. The earthquake drew much attention from the social media and soon became a hot topic.In this paper we searched those Sina Weibo users who are within the range of 200 km from the epicenter,and copied their Weibo data released 24 hours before and after the earthquake event.After cleaning,mining,and classifying these data,we analyzed their characteristics such as quantity,word-frequency,and classification,spatial and temporal distribution. We found that extracting data from the social media would help governments learn overall the post-earthquake information,and on this basis make decisions and arrangements for earthquake relief.
引文
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徐敬海、褚俊秀、聂高众等,2015,基于位置微博的地震灾情提取,自然灾害学报,24(5),12~18。
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Sakaki T,Okazaki M,Matsuo Y,2013,Tweet analysis for real-time event detection and earthquake reporting system development,Knowledge and Data Engineering,IEEE Transactions on,25(4),919~931.
(1)中央网络安全和信息化领导小组办公室等,2017,中国互联网络发展状况统计报告[EB/OL],(2017-01)[2017-10-30],http://www.cac.gov.cn/2017-01/22/c_1120352022.htm
(2)中国地震局震灾应急救援司,2017-08-12 20:56:37,中国地震局发布四川九寨沟7.0级地震烈度图[EB/OL],http://www.cea.gov.cn/publish/dizhenj/464/478/20170812211337414565961/index.html
陈梓、高涛、罗年学等,2017,反映自然灾害时空分布的社交媒体有效性探讨,测绘科学,42(8),44~48。
褚俊秀、徐敬海,2016,地震灾情位置微博抓取与展示,地理空间信息,14(5)38~40。
李德仁、姚远、邵振峰等,2014,智慧城市中的大数据,武汉大学学报:信息科学版,39(6),631~640。
刘经南、方媛、郭迟等,2014,位置大数据的分析处理研究进展,武汉大学学报:信息科学版,39(4),379~385。
苏晓慧、张群燕、张晓东等,2013,基于微博的芦山地震前后宏观异常信息筛选与分析,震灾防御技术,8(4),451~458。
王艳东、李昊、王腾等,2016,基于社交媒体的突发事件应急信息挖掘与分析,武汉大学学报:信息科学版,46(3),290~297。
徐敬海、褚俊秀、聂高众等,2015,基于位置微博的地震灾情提取,自然灾害学报,24(5),12~18。
Crooks A,Croitoru A,Stefanidis A,et al,2013,Earthquake:Twitter as a Distributed Sensor System,Transactions in GIS,17(1),124~147.
Sakaki T,Okazaki M,Matsuo Y,2013,Tweet analysis for real-time event detection and earthquake reporting system development,Knowledge and Data Engineering,IEEE Transactions on,25(4),919~931.
(1)中央网络安全和信息化领导小组办公室等,2017,中国互联网络发展状况统计报告[EB/OL],(2017-01)[2017-10-30],http://www.cac.gov.cn/2017-01/22/c_1120352022.htm
(2)中国地震局震灾应急救援司,2017-08-12 20:56:37,中国地震局发布四川九寨沟7.0级地震烈度图[EB/OL],http://www.cea.gov.cn/publish/dizhenj/464/478/20170812211337414565961/index.html