基于微博文本的灾害事件信息时空过程聚合方法

doi:10.11947/j.AGCS.2023.20210496

测绘学报 ›› 2023, Vol. 52 ›› Issue (3): 490-500.doi: 10.11947/j.AGCS.2023.20210496

基于微博文本的灾害事件信息时空过程聚合方法

叶鹏^1,2,3, 张雪英^1,4, 张春菊⁵

1. 南京师范大学虚拟地理环境教育部重点实验室, 江苏南京 210023;
2. 扬州大学城市规划与发展研究院, 江苏扬州 225127;
3. 扬州大学建筑科学与工程学院, 江苏扬州 225127;
4. 江苏省地理信息资源开发与利用协同创新中心, 江苏南京 210023;
5. 合肥工业大学土木与水利工程学院, 安徽合肥 230009

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:
国家自然科学基金(41971337);国家重点研发计划(2021YFB3900903);江苏高校哲学社会科学研究一般项目(2022SJYB2128);虚拟地理环境教育部重点实验室开放课题(2021VGE01)

Spatio-temporal process based information aggregation method of disaster events in microblog text

YE Peng^1,2,3, ZHANG Xueying^1,4, ZHANG Chunju⁵

1. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;
2. Urban Planning and Development Institute, Yangzhou University, Yangzhou 225127, China;
3. College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China;
4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;
5. School of Civil Engineering, Hefei University of Technology, Hefei 230009, China

Received:2021-08-27 Revised:2022-06-15 Published:2023-04-07
Supported by:
The National Natural Science Foundation of China (No. 41971337);The National Key Research and Development Program of China (No. 2021YFB3900903);The Key Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (No. 2022SJYB2128);The Open Fundation of Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education (No. 2021VGE01)

摘要/Abstract

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。

关键词: 灾害事件, 时空过程, 多层次信息聚合, 微博文本

Abstract: The frequent occurrence of various disaster events has become a major threat to global sustainable development. In the era of big data, microblog text has been gradually applied to the prevention, preparation, response and recovery of disaster management. However, previous studies mostly focused on the acquisition of different disaster information in microblog text, while ignoring the structural integration of this fragmented information. In this paper, an information aggregation method of disaster events in microblog text is proposed. First, a multi-level disaster event information model is constructed from the spatio-temporal perspective. Then, the information elements of disaster events are extracted from the microblog text. Finally, a process-oriented information aggregation model based on “Object-State” is constructed. On this basis, it can be solved that the problems of decentralization, spatio-temporal granularity diversification and disorder of disaster event information in microblog text. Based on the case study of typhoon “Lekima” event using Sina Weibo, the results show that the method proposed in this paper can comprehensively acquire the information of different objects on every spatio-temporal node during the process of typhoon events, which is beneficial to mining disaster emergencies in small scale from microblog text.

Key words: disaster events, spatio-temporal process, multi-level information aggregation, microblog text

中图分类号:

P208
TP391.1

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.

参考文献

[1] YAN Yingwei, ECKLE M, KUO C L, et al. Monitoring and assessing post-disaster tourism recovery using geotagged social media data[J]. ISPRS International Journal of Geo-Information, 2017, 6(5):144.
[2] YAN Yingwei, FENG C C, HUANG Wei, et al. Volunteered geographic information research in the first decade:a narrative review of selected journal articles in GIScience[J]. International Journal of Geographical Information Science, 2020, 34(9):1765-1791.
[3] WANG Zheye, YE Xinyue. Space, time, and situational awareness in natural hazards:a case study of Hurricane Sandy with social media data[J]. Cartography and Geographic Information Science, 2019, 46(4):334-346.
[4] JIA Jingyuan, WANG Bo. The development of intelligent operation method of urban public infrastruc-ture driven by accurate spatio-temporal information[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(2):27-35.
[5] NILES M T, EMERY B F, REAGAN A J, et al. Social media usage patterns during natural hazards[J]. PLoS One, 2019, 14(2):e0210484.
[6] DE BOOM C, VAN CANNEYT S, DEMEESTER T, et al. Representation learning for very short texts using weighted word embedding aggregation[J]. Pattern Recognition Letters, 2016, 80(9):150-156.
[7] WANG Yan, TAYLOR J E. Coupling sentiment and human mobility in natural disasters:a Twitter-based study of the 2014 South Napa Earthquake[J]. Natural Hazards, 2018, 92:907-925.
[8] 李静. 基于LDA的微博灾害信息聚合:以台风为例[D]. 武汉:武汉大学, 2018. LI Jing. Microblog disaster information aggregation based on LDA:a case study of typhoon[D]. Wuhan:Wuhan University, 2018.
[9] 严平勇. 基于微博的灾害信息聚合方法研究[D]. 福州:福建师范大学, 2013. YAN Pingyong. Disaster information aggregation method based on micro blog[D]. Fuzhou:Fujian Normal University, 2013.
[10] 李紫薇,邢云菲.新媒体环境下突发事件网络舆情话题演进规律研究:以新浪微博"九寨沟地震"话题为例[J].情报科学,2017,35(12):39-44. LI Ziwei, XING Yunfei. Research on the evolution of emergency public opinion topic in the new media environment:a case of "Jiuzhaigou earthquake" in sina micro-blog[J]. Information Science, 2017, 35(12):39-44.
[11] STEPHENSON J, VAGANAY M, COON D, et al. The role of Facebook and Twitter as organisational communication platforms in relation to flood events in Northern Ireland[J]. Journal of Flood Risk Management, 2018, 11(3):339-350.
[12] 仇林遥. 面向自然灾害应急任务的时空数据智能聚合方法[D]. 武汉:武汉大学, 2017. QIU Linyao. A smart aggregation method of spatial-temopral data for natural disaster emergency tasks[D]. Wuhan:Wuhan University, 2017.
[13] Cyclone Warning Markup Language (CWML)[EB/OL].[2022-04-15]. http://xml.coverpages.org/NICTA-CWML-v10-2006.pdf, 2021-1-7.
[14] 黄风华, 晏路明. 基于Jena的台风灾害领域本体模型推理[J]. 计算机应用, 2013, 33(3):771-775, 779. HUANG Fenghua, YAN Luming. Reasoning of ontology model for typhoon disasters domain based on Jena[J]. Journal of Computer Applications, 2013, 33(3):771-775, 779.
[15] HAN M, LEE J. Bayesian typhoon track prediction using wind vector data[J]. Communications for Statistical Applications and Methods, 2015, 22(3):241-253.
[16] CHEN Yu, DUAN Zhongdong. A statistical dynamics track model of tropical cyclones for assessing typhoon wind hazard in the coast of southeast China[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2018, 172:325-340.
[17] YE Peng, ZHANG Xueying, SHI Ge, et al. TKRM:a formal knowledge representation method for typhoon events[J]. Sustainability, 2020, 12(5):2030.
[18] YE Peng, ZHANG Xueying, HUAI An, et al. Information detection for the process of typhoon events in microblog text:a spatio-temporal perspective[J]. ISPRS International Journal of Geo-Information, 2021, 10(3):174.
[19] 张雪英, 张春菊, 吴明光, 等. 顾及时空特征的地理知识图谱构建方法[J]. 中国科学:信息科学, 2020, 50(7):1019-1032. ZHANG Xueying, ZHANG Chunju, WU Mingguang, et al. Spatiotemporal features based geographical knowledge graph construction[J]. Scientia Sinica (Informationis), 2020, 50(7):1019-1032.
[20] 吴宾. 基于对象的地理时空演变分析与知识发现[D]. 上海:华东师范大学, 2018. WU Bin. Object-based analysis and knowledge discovery by modeling spatio-temporal evolution of geographical phenomena[D]. Shanghai:East China Normal University, 2018.
[21] 杨腾飞, 解吉波, 李振宇, 等. 微博中蕴含台风灾害损失信息识别和分类方法[J]. 地球信息科学学报, 2018, 20(7):906-917. YANG Tengfei, XIE Jibo, LI Zhenyu, et al. A method of typhoon disaster loss identification and classification using micro-blog information[J]. Journal of Geo-Information Science, 2018, 20(7):906-917.
[22] 张春菊, 张雪英, 李明, 等. 中文文本中时间信息解析方法[J]. 地理与地理信息科学, 2014, 30(6):1-7. ZHANG Chunju, ZHANG Xueying, LI Ming, et al. Interpretation of temporal information in Chinese text[J]. Geography and Geo-Information Science, 2014, 30(6):1-7.
[23] 张雪英, 叶鹏, 王曙, 等. 基于深度信念网络的地质实体识别方法[J]. 岩石学报, 2018, 34(2):343-351. ZHANG Xueying, YE Peng, WANG Shu, et al. Geological entity recognition method based on deep belief networks[J]. Acta Petrologica Sinica, 2018, 34(2):343-351.
[24] 宋国民, 张三强, 贾奋励, 等. 中文文本中时间信息抽取及规范化方法[J]. 测绘科学技术学报, 2019, 36(5):538-544. SONG Guomin, ZHANG Sanqiang, JIA Fenli, et al. Temporal information extraction and normalization method in Chinese texts[J]. Journal of Geomatics Science and Technology, 2019, 36(5):538-544.
[25] 叶鹏, 张雪英, 杜咪. 顾及字符特征的中文地名词典查询方法[J]. 地球信息科学学报, 2018, 20(7):880-886. YE Peng, ZHANG Xueying, DU Mi. Query method of Chinese gazetteer based on the character features[J]. Journal of Geo-Information Science, 2018, 20(7):880-886.
[26] LIU Meijie, WANG Jin, ZHONG Shilei, et al. Quantitative evaluation of sea-ice disaster in Bohai Sea based on GOCI and Sentinel-1[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(1):49-55.

基于微博文本的灾害事件信息时空过程聚合方法

Spatio-temporal process based information aggregation method of disaster events in microblog text

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	吴华意, 胡秋实, 李锐, 刘朝辉. 城市人口时空分布估计研究进展[J]. 测绘学报, 2022, 51(9): 1827-1847.
[2]	刘经南, 罗亚荣, 郭迟, 高柯夫. PNT智能与智能PNT[J]. 测绘学报, 2022, 51(6): 811-828.
[3]	韩李涛, 周丽娟, 龚城, 张爱国. 顾及步行习惯的室内导航网络及其生成算法[J]. 测绘学报, 2022, 51(5): 729-738.
[4]	方金凤, 孟祥福. 基于LBSN和多图融合的兴趣点推荐[J]. 测绘学报, 2022, 51(5): 739-749.
[5]	张睿卓. 基于多源数据的林区电力走廊安全风险评估方法[J]. 测绘学报, 2022, 51(5): 784-784.
[6]	夏吉喆, 周颖, 李珍, 李帆, 乐阳, 程涛, 李清泉. 城市时空大数据驱动的新型冠状病毒传播风险评估——以粤港澳大湾区为例[J]. 测绘学报, 2020, 49(6): 671-680.
[7]	朱庆, 冯斌, 李茂粟, 陈媚特, 徐肇文, 谢潇, 张叶廷, 刘铭崴, 黄志勤, 冯义从. 面向动态关联数据的高效稀疏图索引方法[J]. 测绘学报, 2020, 49(6): 681-691.
[8]	陆川伟, 孙群, 陈冰, 温伯威, 赵云鹏, 徐立. 车辆轨迹数据的道路学习提取法[J]. 测绘学报, 2020, 49(6): 692-702.
[9]	尹烁, 闫小明, 晏雄锋. 基于特征边重构的建筑物化简方法[J]. 测绘学报, 2020, 49(6): 703-710.
[10]	吴华意, 黄蕊, 游兰, 向隆刚. 出租车轨迹数据挖掘进展[J]. 测绘学报, 2019, 48(11): 1341-1356.
[11]	郭庆胜, 刘洋, 李萌, 程晓茜, 何捷, 王慧慧, 魏智威. 基于网格模型的导航道路图渐进式化简方法[J]. 测绘学报, 2019, 48(11): 1357-1368.
[12]	吴政, 武鹏达, 李成名. 对等网络下自适应层级的矢量数据时空索引构建方法[J]. 测绘学报, 2019, 48(11): 1369-1379.
[13]	王培晓, 张恒才, 王海波, 吴升. ST-CFSFDP:快速搜索密度峰值的时空聚类算法[J]. 测绘学报, 2019, 48(11): 1380-1390.
[14]	万子健, 李连营, 杨敏, 周校东. 车辆轨迹数据提取道路交叉口特征的决策树模型[J]. 测绘学报, 2019, 48(11): 1391-1403.
[15]	胡光辉, 熊礼阳, 汤国安. DEM地表坡向变率的向量几何计算法[J]. 测绘学报, 2019, 48(11): 1404-1414.