Visualization Research Of Big Bus Data Based On WebGIS

Urban bus is an important mode of public travel,consolidation analysis of big bus data is conducive to the construction of intelligent transport.How to effectively analyze the big data of urban bus is still a research problem.Urban transit big data is heterogeneous data that contains spatial information.Combining big data theory with GIS(Geographic Information Systems)is also a hot topic of current research.Geographic visualization effectively explains big bus data through visual interaction,and online virtual geographic space constructed by modern WebGIS is the most popular geographic visualization container.This paper designs and implements an big bus data processing analysis method.Taking Beijing for example,I integrates space data and credit card data of bus,builds a bus data model,and uses a variety of geographic visualization methods to analyze the big data.The main research contents of this article are:(1)Integrate Hadoop big data framework into WebGIS.Use Hadoop's Distributed File System(HDFS)to store and manage bus data as a data warehouse for WebGIS.The data is statistically analyzed through the MapReduce programming model to implement WebGIS background data driver.(2)Bus big data preprocessing.First,the data was cleaned,and the invalid data in the bus swiping data was detected through anomaly detection and consistency analysis.The validity of the invalid data was verified through research and other methods and removed.Bus big data has a heterogeneous nature,and data links are used to determine the links between bus swiping data,bus line site data,and administrative division data.(3)Bus model construction.The bus passenger travel mode was studied and refined,and the bus passenger Home-Work travel mode was used for analysis.Based on the time and space orderliness of passengers' swipe cards,the passengers' card swipe records were transferred and analyzed from the time dimension and space dimension to construct passengers' Bus trip chain model.The bus passenger flow model was constructed based on the analysis of public transport buses.The public transport bus card records were merged into each bus stop site,and the changes in the number of bus passengers were calculated.The bus arrival time was determined based on the time information in the credit card records.Outbound time,passenger volume and time information constitute the data of time and place passenger flow.(4)This paper proposes a method of fitting a spatial surface based on the space scatter.With reference to the arrangement of pixels on the computer screen,the screen pixels are backprojected to the virtual geographic space,and the grid vertices are related to the analysis of the data points.After the smoothing,the geospatial mesh surfaces are obtained.This method simplifies the process of interpolation calculation and surface refinement in spatial surface fitting,and can efficiently construct spatial surfaces.(5)Geographic visualization analysis of transit big data.Visualize public transit big data in geographic space.Geographic visualization includes plane visualization,three-dimensional visualization,dynamic visualization,and interactive visual expression.Multi-angle analysis of public transportation big data using different visualization methods to construct bus passenger portraits to analyze the spatial distribution and activity areas of public passengers in Beijing;regional OD maps reflect travel conditions of public transport passengers;bus passenger flow visualization shows the temporal and spatial characteristics of public transport passengers.