# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
## 5 Conclusions and Future Work  
Aiming to solve the problem of the real-time visualization of BGVD, this paper explores a new method that takes the viewport pixel as the visual computing unit, analyzes the spatial relationship between the spatial range corresponding to the viewport pixel and geographic vector data, and then judges the pixel value according to the results in terms of the spatial relationship. The proposed method shifts the dependence of the visualization calculation cost on the data scale to the viewport image pixel scale. Based on the model, the viewport pixel quadtree index and the VPQ-based real-time visualization algorithm are proposed. Experiments on billion-scale data show that the proposed method has a more than ten times greater improvement in performance compared with the existing optimal methods, and achieves a real-time computation and visualization preview for BGVD. Furthermore, the visualization results of the proposed method are all temporarily stored in memory and do not require external memory space, which is more advantageous in terms of resource consumption cost than the existing visualization methods that require a large amount of external memory space to temporarily store tile data.  
In the follow-up study, combined with our previous exploration and experimental attempts, the adaptability of the proposed method to different viewport resolutions needs to be further studied. The VPQ-based visualization method of BGVD has obvious performance advantages when the viewport resolution is relatively small, especially when the viewport covers all features of the geographic vector dataset, and the pixel scale of the viewport  
Figure 4: Real-time performance evaluation of the proposed method.  
is much smaller than that of the geographic vector data. However, when the viewport resolution is relatively large and the size of the viewport pixels is larger than the size of the geographic vector data, the performance of the existing methods is inferior. Therefore, it is necessary to further study the adaptability of this method to different viewports.  
Conceptualization, L.C. and Z.L.; methodology, L.C. and Z.L.; software, Z.L. and M.M.; formal analysis, L.C. and Z.L.; resources, M.M.; writing--original draft preparation, L.C. and Z.L.; writing--review and editing, Z.L., M.M. and L.C.; visualization, Z.L. and M.M.; project administration, L.C.; funding acquisition, L.C. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant Nos. 41971362 and 42101432 and the Natural Science Foundation of Hunan Province under Grant Nos. 2019]50718 and 2022]40546.  
All data used in experiment are from openstreetmap and can be accessed with the following link: [[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) (accessed on 25 June 2022).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript  
\begin{tabular}{l l} GIS & geographic information system \\ VPQ & viewport pixel quadtree \\ VGVM & viewport generalization visualization model \\ \end{tabular}