# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
## 4 Experiment
### Comparative Analysis of Geographic Vector Data Visualization Performance  
The proposed method was compared and analyzed with the four existing geographic vector visualization methods, namely, HadoopViz [18], GeoSparkViz [20], Mapnik, and HiVision [21]. In order to verify the advantages of the method compared with traditional data-oriented methods, the first three most-representative data-driven geographic vector data visualization methods were selected, which adopt high-performance parallel computing architecture to accelerate the realization of big geographic vector data visualization and have good performance. HadoopViz is implemented based on Hadoop, and GeosparkViz is implemented based on Spark, a distributed memory computing framework. These two methods both provide good load balancing to improve the overall computing performance. For the given geographic vector data, they can quickly generate visualization results in the form of map tiles in a distributed parallel manner. Mapnik is a high-performance mapping tool for a wide range of applications, allowing the rapid mapping output of geographic vector data with a given mapping spatial range and style. At the same time, in order to verify the performance improvement of this method compared with our previous work, we also compared the visualization algorithm proposed in our previous work, HiVision, which is a high-performance visualization algorithm of geographic vector data rasterization based on the MPI computing framework. In the experiment, the global tile pyramid division model was used to organize the visualization results, and six scenes of drawing scale were designed from small to large (Table 2). First, the experimental data were drawn on a 4096 \(\times\) 4096 canvas; then, the canvas was divided along the tile pyramid model layer by layer, with each layer being four times larger than the previous layer. We used the MPI + OpenMP hybrid parallel method to execute the proposed algorithm. According to the total number of CPU cores in the experimental configuration, 128 MPI task processes were started in the cluster environment, and 2 OpenMP threads were started in each MPI task process for the parallel computation of the preview drawing in a given area. Comparative results are shown in Figure 3.  
From the experimental results, firstly, it can be seen that among the geographic vector data visualization methods involved in the comparison, GeoSparkViz takes less time than the other three methods for drawing scenes with a larger number of pixels. HiVision takes less time than the other three methods for drawing scenes with a smaller number of pixels. Compared with the existing methods, the plotting time of the proposed VPQP algorithm is much smaller than the existing methods on all datasets and in all drawing scenes, and for the billion-scale datasets, P\({}_{2}\), L\({}_{3}\), and A\({}_{2}\), the plotting time of the algorithm is only 10.4% (=38.77 s \(\div\) 373.16 s), 15.2% (=38.11 s \(\div\) 251.43 s), and 8.8% (=37.86 s \(\div\) 432.23 s), respectively, indicating that the proposed algorithm has excellent plotting performance. Secondly, with the increase in pixel size in scenes D1 to D6, the rendering time of the existing method increases sharply. Compared with this, the increasing trend of the rendering time of the VPQP algorithm is small, indicating that the algorithm has low computational complexity. Then, we observe that as the size of the dataset grows from L\({}_{1}\) to L\({}_{3}\), the difference in \begin{table}
\begin{tabular}{l l} \hline \hline  & **Scene ID** & **Pixel Size** \\ \hline D1 & 4096 \(\times\) 4096 \\ D2 & 8192 \(\times\) 8192 \\ D3 & 16,384 \(\times\) 16,384 \\ D4 & 32,768 \(\times\) 32,768 \\ D5 & 65,536 \(\times\) 65,536 \\ D6 & 131,072 \(\times\) 131,072 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The design of drawing scenes.