# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
###### Abstract  
The visualization of geographic vector data is an important premise for spatial analysis and spatial cognition. Traditional geographic vector data visualization methods are data-driven, and their computational costs have increased rapidly with the growth of the scale of data used. Even if the distributed parallel strategy is used, it is still difficult to achieve a real-time response when dealing with big geographic vector data (BGVD). To solve this problem, this paper proposes a viewport generalization model and a visualization method for the online interactive visualization of BGVD. The method takes the viewport display pixel as the analysis unit and synthesizes the existence or quantity results of geographic vector data in the corresponding spatial range of each viewport display pixel into the display value of this display pixel; thus, it converts traditional computational complexity, dependent on the data scale, into computational complexity dependent on the number of pixels in the viewport. When the number of pixels in the viewport is much smaller than that of the geographic vector data, the visualization efficiency is greatly improved. In order to realize the above conversion, the pixel quadtree index (VPQ) structure and the real-time visualization algorithm of geographic vector data based on VPQ are proposed. Experiments show that the proposed method can achieve the near-real-time interactive visualization of BGVD, and provides more than a tenfold performance improvement over the best existing methods. e 2022  
for graphing distributions of each and every variable and displaying them in a geographic space that provides the necessary sense of location and place [5]. Interactive exploratory visual analysis is even more important for statisticians to study geospatial phenomena and laws in the domain of big geographic data [6]. If the overview map of a large-scale complex geographic vector dataset can be quickly presented to users, it can provide strong support for users to quickly grasp the coverage, density distribution, and general trend of geographical spatial phenomena reflected by the data. Through continuous interactive operations, users can probe and analyze the patterns, relationships, and characteristics hidden within the data [7]. In these application scenarios, users require the system to provide a near-real-time response, and also desire the online modification of spatial analysis constraint parameters through the real-time preview of data visualization, so as to gradually obtain satisfactory results in a progressive and interactive way. In the existing research on online visualization technology for geographic vector data, the visualization results of the dataset are mainly organized and expressed in the form of map tiles. The main technical difficulties when using map tile data are time-consuming generation, high storage cost, and the difficulty of making style changes. Under the existing visualization methods, even if the high-performance parallel technology is adopted to improve the generation speed of the tile dataset, when the scale of the geographic vector data reaches more than ten million features, the average time delay of visualization will reach the level of minutes, which struggles to meet the needs of online real-time interactive applications. In this paper, a processing framework and algorithm supporting online real-time interactive visualization are proposed to meet the requirements of big geographic vector data (BGVD) visualization. The major contributions are as follows:  
* A visualization model based on the viewport generalization strategy is proposed. The method takes the display pixels in the viewport area as processing units and analyzes the distribution of geographic vector data within the spatial range mapped by each unit; the analysis results are used to generate the display values of the unit. This model completely changes the traditional processing idea that geographical vector elements are treated as processing units.