Visual Analysis Of Spatiotemporal Model Based On Air Quality Data Of Urban Agglomeration

Air pollution has become a hidden danger that affects production,public health,and social order,and is no longer limited to a single city,but crosses geographical and administrative boundaries.The prevention and control of air pollution has also risen from a single city to regional collaborative governance.In this process,it is very important to divide urban agglomerations and explore their spatiotemporal patterns.However,due to the large volume,high dimension and complexity of air quality data,the extraction and analysis of urban agglomeration spatio-temporal patterns has become a huge challenge.Based on the above problems,this research proposes a visual analysis framework of spatio-temporal patterns for urban agglomeration air quality data.This framework adopts machine learning algorithms,visualization,and human-computer interaction technology,which can deeply analyze the air quality of urban agglomerations from multiple perspectives and help researchers explore their spatiotemporal patterns.The main research content of this article includes:1.In specific scenarios,the division of time series involves simply averaging data at fixed time intervals,which ignores fluctuations and anomalies in pollutant concentration changes,thereby affecting the exploration of pollutant change patterns.In response to this issue,this article proposes a multivariate spatiotemporal sequence segmentation method based on principal component analysis and greedy Gaussian.This method first performs principal component analysis to reduce the dimensionality of spatial data,and then uses greedy Gaussian method to segment the temporal data in each spatial data,in order to discover the abnormal time of pollutant changes and help analysts better understand the spatiotemporal variation characteristics of pollutant concentrations.2.In traditional clustering methods,only the distance between data points is usually considered,while ignoring the variation of variables in the time dimension.Therefore,this article proposes a density peak clustering method based on improved optimization,which introduces the distance between density peak points and adjacent peak points to determine the similarity between samples,and considers the weighted influence of variables to better explore the impact of variable value variation over time on clustering results.3.Based on the above work,this paper designs and implements a visual analysis system for urban agglomeration air quality,which solves the problems of difficult extraction of spatiotemporal patterns and difficult detection of pollutant anomalies in the visual analysis process of air quality.This system combines multi view collaborative visualization and interactive methods such as filtering,selection,overview,and details to help users explore the spatiotemporal patterns of air quality in urban agglomerations and identify periods or regions of abnormal pollutant changes.