Research On Visual Analysis Method For Statistical Data With Multidimensional Spatio-temporal Characteristics

Statistical data usually includes multi-dimensional attribute features such as spatio,temporal and attribute information,which can describe the potential environment and state of the data from different angles.With the rapid development of China's economy and the standardization of the information collection process,more and more statistical data is stored and recorded,which can comprehensively and meticulously reflect the economic development of the society and the social mode of human beings.Effective analysis of multidimensional Spatio-temporal statistics helps the public to understand the national conditions intuitively,assists scholars in analyzing and researching the economic situation,and assists relevant functional departments in formulating policies,which has important theoretical significance and practical value.However,the statistical data has the characteristics of attribute diversification,temporal and spatio hierarchy,etc.The traditional statistical analysis method can't find the complex attribute association mode and the spatio-temporal dynamic evolution law among the multidimensional spatio-temporal statistics.Therefore,it is especially important to build an analytical method that helps users explore multidimensional spatio-temporal statistics data simply and efficiently.As an interdisciplinary field,visual analysis integrates more theory and methods such as data mining and model analysis,and adopts interactive means,allowing users to interactively control data extraction and screen display according to specific needs,helping people to better.For intuitive insight into the information behind the data.This paper aims to use the visual analysis method to develop related research on multidimensional Spatio-temporal attributes of statistical data.The main innovations of this paper are as follows:1.Aiming at the statistics data with multi-dimensional attributes,we proposed a visual analysis method that cluster-aware arrangement of the parallel coordinate plots.The hierarchical clustering algorithm is used to cluster the multi-dimensional attributes of statistical data,and the information entropy calculation formula is used to measure the correlation between clusters based on clustering.The correlation between attributes is visualized by MDS view and related matrix graph.The optimal path planning obtains a cluster-based parallel coordinate axis arrangement order and visualizes it in a parallel coordinate view.Through the case analysis of three actual multidimensional statistics,the effectiveness and practicability of the algorithm and the designed visual analysis tools are verified.2.Aiming at the spatial multi-dimensional statistical data,a visual analysis method for dynamic parallel coordinate axis arrangement of map is proposed.Considering spatial attributes and multi-dimensional attribute design the spatial multi-dimensional attribute clustering analysis algorithm for spatial features,and spatial features can be effectively identified.By Voronoi diagram and color mapping to characterize the spatial clustering results.And the multi-dimensional attributes are clustered by nuclear density.Mutual information is introduced as a criterion for measuring the correlation between spatial clustering and attribute clustering.The map view is dynamically embedded in parallel coordinates,and the parallel axis alignment order and spatial distribution of data lines are optimized.Through the visual analysis case and expert feedback,the effectiveness and superiority of the algorithm are proved,which has obvious advantages in enhancing the spatial distribution of multi-dimensional attributes and the visual perception of related features.3.Aiming at the spatial-temporal multi-dimensional statistical data,a spatial-temporal clustering algorithm based on NMF model is proposed.Define the spatial granularity and temporal granularity of spatial-temporal taxi OD data,and construct a spatial-temporal matrix.Combined with the NMF model,spatial regions with similar temporal-varying patterns are grouped together to construct urban functional areas.And design a large number of visual views to allow users identify the city's functions and capture the specific traffic patterns between the urban functional areas quickly.The validity and practicability of the urban functional area division method are verified by a large number of case studies and user feedback.