
测绘学报 ›› 2023, Vol. 52 ›› Issue (3): 490-500.doi: 10.11947/j.AGCS.2023.20210496
叶鹏1,2,3, 张雪英1,4, 张春菊5
收稿日期:2021-08-27
修回日期:2022-06-15
发布日期:2023-04-07
通讯作者:
张雪英
E-mail:zhangsnowy@163.com
作者简介:叶鹏(1991-),男,博士,讲师,研究方向为地理大数据挖掘。E-mail:007839@yzu.edu.cn
基金资助:YE Peng1,2,3, ZHANG Xueying1,4, ZHANG Chunju5
Received:2021-08-27
Revised:2022-06-15
Published:2023-04-07
Supported by:摘要: 各类灾害事件频发已成为全球可持续发展面临的重大威胁。在大数据环境下,微博文本逐渐被应用于灾害管理的预防、准备、响应和恢复工作。以往研究多关注微博文本中灾情信息的获取,却忽略对这些碎片化信息进行有序化整合。本文从时空视角构建多层次的灾害事件信息模型,在抽取出微博文本中灾害事件信息要素的基础上,提出基于“对象-状态”的过程信息聚合方法,解决微博文本中灾害事件信息分散化、时空粒度多样化和无序化的问题。基于新浪微博进行台风“利奇马”事件的案例分析,结果表明,本文方法能够全面地获取灾害事件过程中各个时空节点上的灾情信息,有利于从微博文本中挖掘小尺度下的灾害突发状况。
中图分类号:
叶鹏, 张雪英, 张春菊. 基于微博文本的灾害事件信息时空过程聚合方法[J]. 测绘学报, 2023, 52(3): 490-500.
YE Peng, ZHANG Xueying, ZHANG Chunju. Spatio-temporal process based information aggregation method of disaster events in microblog text[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(3): 490-500.
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