# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
## 2 Related Work  
In the era of big data, massive geospatial data are widely used in various spatiotemporal information applications, especially to solve complex geospatial analysis problems, among which the visualization of spatial data has become an important means of spatial analysis [8]. Geospatial data visualization helps users to summarize and analyze geospatial data more intuitively. Visual analysis has become the most promising direction, integrating human knowledge and experience into the whole process of data analysis and decision reasoning through a visual interactive interface [9]. In the field of cartography, many mature commercial map services, such as Google Maps, MapBox, Mapnik, GeoServer, MapServer, etc., allow users to visualize small-scale spatial data. These tools have rich data visualization styles for users to choose from, and are used to generate a wide variety of customized map products. Some stand-alone solutions allow users to visualize geographic data online by reducing the size of spatial data [10]. However, when users need to understand the details of multilevel maps of large-scale geospatial datasets from the whole to the local scale, these solutions often require a great deal of processing time to ensure the quality of map visualization. Facing the growing and complex structure of geographic vector data, traditional methods such as vector mapping generalization and serial raster slicing techniques are increasingly struggling to meet visualization needs such as seamless data browsing and efficient real-time rendering [11; 12], which are reflected in the following points: (1) When the size of geographic vector data increases, the time required to generate tiles increases, and in extreme cases, the drawing cannot even be completed. (2) As the tile pyramid hierarchy of geographic data deepens in online applications, the number of tiles required to navigate high-definition data grows exponentially, posing a huge challenge to storage organization. (3) The tile pyramid technology directly converts geographic vector data into static images that cannot be changed, and when the geographic vector data change, the tile dataset must be regenerated, which causes additional latency that further affects the user experience when it comes to the exploratory visualization of large-scale spatial data. In order to improve the performance of tile generation, some studies have introduced high-performance parallel computing techniques, which are designed on the grounds that each map tile generation task is independent from the others. How to decompose the overall task of tile generation into multiple subtasks is one of the key issues for the parallel visualization of vector data [13]. [PERSON] et al. proposed an octree-based multiresolution data structure and visualization strategy to visualize 3D geospatial data with time series [14]. [PERSON] et al. implemented a parallel contour map classification algorithm based on the spatial analysis library PySAL [15]. [PERSON] et al. used a graphics processing unit to accelerate the division of the visualization task into a large number of fine-grained subtasks and achieved a more significant acceleration performance in their experiments [16]. [PERSON] et al. proposed an adaptive decomposition method for vector line and polygon features, which makes the computational intensity more balanced and facilitates parallel visualization by constructing computational intensity transformation functions and computational intensity grids, and improves the computational speed nearly linearly, especially in the case of an extremely uneven data spatial distribution [13]. [PERSON] et al. proposed SHAHED, a MapReduce-based system for querying and visualizing large-scale spatiotemporal data [17], and experimentally showed that SHAHED could generate a global heat map of 450 million geographic vector points in three minutes. [PERSON] et al.