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Analyzing Spatial-Temporal Distribution of Natural Hazards in China by Mining News Sources

机译:挖掘新闻来源分析中国自然灾害的时空分布

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摘要

Natural hazards cause severe consequences to society, the economy, and the environment. However, it is difficult to analyze natural hazards occurrences in China because there is no complete natural hazard database in China, and it is difficult to gather all conventional natural hazard data because they are kept by many different departments. To resolve this problem, this paper proposes a social media data mining methodology. Because social media is a real-time data source, it is an effective channel for up-to-date information about the characteristics of disasters/hazards. News about natural hazards from 2008 to 2017 is mined from a news organization in China as the key data. Text mining, descriptive statistics, association rule mining, and other methods are used to extract the natural hazard events and hazard characteristics type, time, and location for the analysis. First, from an analysis of the news headlines, each hazard-focused news event is identified and the time and location information are extracted. Second, the spatial-temporal distributions of the natural hazards are analyzed using statistical analysis and network visualization, from which it is found that rainstorms, floods, wind and hail, and other meteorological hazards are the main natural hazard types in China. The high co-occurrence of meteorological hazards and geological hazards indicates that the government needs to pay more attention to geological hazards if there is also a meteorological hazard, especially in mountainous areas. Most hazards are found to have an obvious time distribution, with the high-frequency period being from April to September. Yunnan, Sichuan, and Guizhou Provinces are found to suffer the most frequently from a range of different hazards. An analysis of the associations between hazard regions finds that the southern Chinese regions are strongly related, especially Guizhou, Sichuan, Hubei, and Hunan. The results of this study offer insights into the identification of hazard risks and assists in the development of effective hazard prevention and mitigation programs.
机译:自然灾害会对社会,经济和环境造成严重后果。但是,由于中国没有完整的自然灾害数据库,因此很难分析中国的自然灾害发生,并且由于所有常规自然灾害数据由许多不同部门保存,因此很难收集所有常规自然灾害数据。为了解决这个问题,本文提出了一种社交媒体数据挖掘方法。由于社交媒体是实时数据源,因此它是获取有关灾难/灾害特征的最新信息的有效渠道。有关中国2008年至2017年自然灾害的新闻,是从中国一家新闻机构中获取的,作为主要数据。文本挖掘,描述性统计,关联规则挖掘和其他方法用于提取自然灾害事件以及灾害特征类型,时间和位置以进行分析。首先,通过对新闻头条的分析,确定每个以灾害为重点的新闻事件,并提取时间和位置信息。其次,利用统计分析和网络可视化方法对自然灾害的时空分布进行了分析,发现暴雨,洪水,风,冰雹等气象灾害是我国主要的自然灾害类型。气象灾害和地质灾害的高度同时发生表明,如果还有气象灾害,尤其是在山区,政府需要更加重视地质灾害。发现大多数危害具有明显的时间分布,高频时期为4月至9月。发现云南,四川和贵州省受不同危害的危害最为频繁。对危险区域之间的关联性进行分析后发现,中国南部地区密切相关,尤其是贵州,四川,湖北和湖南。这项研究的结果为识别危害风险提供了见识,并有助于制定有效的危害预防和缓解计划。

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