Research On Multi-source Earthquake Disaster Data Processing And Visualization

China is a country with frequent earthquakes.A large earthquake is very destructive and causes serious consequences.Therefore,rapid disaster assessment after a destructive earthquake is of great significance to the rescue.Traditional disaster assessments are based on geological information monitored by seismic equipment,and the data source is single.However,earthquake relief often requires as much information as possible about the disaster in a short period of time.In this case,it is necessary to quickly evaluate the disaster situation using multi-source data such as communication networks and the Internet based on group intelligence perception and big data ideas.Multi-source data,such as the situation of damaged mobile phone base stations and social networks information posted by the public,can be used to assess the earthquake disaster.Based on this,this paper firstly analyzes the current situation of earthquake disaster assessment at home and abroad,and summarizes the research of multi-source disaster data analysis worldwide.After that,data mining and visualization technologies are introduced briefly,including data analysis methods,spatial cluster analysis methods,and visualization technologies.In terms of communication base stations data mining,this paper considers the information of damaged base stations and normal base stations at the same time,and the kernel density method is used to solve the problem of uneven distribution of base stations in some areas.Then,we expand the amount of information by increasing the number of sampling points reasonably.Finally,an algorithm for evaluating the impact field direction and seismic intensity area based on the "damage ratio" is proposed.In terms of social network data mining,this paper crawls the keywords microblog data for a total of 24 hours during the Jiuzhaigou Ms7.0 earthquake and Jinghe Ms6.6 earthquake on Sina Weibo.Then we perform spatio-temporal analysis of these data,and summarize the characteristics of the keyword microblog data in different periods.After that,based on the spatial clustering characteristics of microblogs,we propose a method for quickly detecting earthquake events and evaluating sentimental areas for microblogs with different location types.All the processes and results above are visualized by ArcGIS.In terms of data visualization sharing,this paper designs a seismic data visualization platform based on Baidu maps,which can realize the position display and content display of microblog data on the webpage by time,It facilitates the release of disaster data and information sharing.