Research On Visual Analysis Method Of Environmental Monitoring Data Based On Data Cube

With the rapid development of our country's economy,the environment of information technology has been rapid development,the environmental protection department has carried out a variety of environmental quality monitoring,the investigation of the ecological environment and pollution source management,accumulated a large amount of data,and with the development of the physical network technology,a growing number of environmental monitoring sites of data can be transferred over the network,the current environment also is experiencing big data era.Environmental monitoring data has significant spatio-temporal characteristics,and has multidimensional and multi-level characteristics.However,when faced with such massive data,analysts in the field of environment are often unable to carry out effective analysis,and most of them use a single chart and statistical analysis method,which is difficult to meet the actual demand for analysis.For the characteristics of environmental monitoring data,a data model that can process such characteristics quickly is needed.Data cube technology is an efficient multidimensional data model that performs cross-dimensional aggregation of every possible dimension set of a relational table to support rapid data exploration.In this paper,spatial quadtree and flat trees structure are used to express the location distribution and classification dimensions of spatial point objects,and the link of Shared nodes among different dimensions is used to realize the aggregation design across dimensions.The Nanocubes structure of cross-dimensional query of environmental monitoring data is constructed by time sequence mapping,which is used to efficiently store and query high-dimensional and multi-granularity spatial and temporal data,laying a foundation for real-time exploratory visualization analysis of multi-dimensional spatial and temporal data sets.A visual analysis algorithm considering thematic attribute similarity and spatial constraints of monitoring data is studied.This paper discusses a concavity construction method which accurately represents the location of attributes in each cluster.The hierarchical clustering results are calculated in advance to support clustering maps and to store statistical information based on geographic semantic clustering,thus effectively supporting visualization and user interaction.In this study,based on the geographical semantics of the real world and the rapid and efficient data cube structure,several kinds of visual components were designed for different analysis tasks by using density clustering and boundary clustering technology,so as to help us study environmental monitoring data.Eventually built a big environment data visual analysis platform,according to the environmental monitoring data,the characteristics of space and time,an interactive visual analysis can show intuitive and efficient environmental monitoring data in time and space distribution characteristics and hierarchy,and combined with interactive design to provide users with personalized display program,effective regulation and improve the ecological environment monitoring and effective analysis.