## 4 Experimental Validation and Result Analysis
### Comparative Analysis of Heat Map Rendering Speed  
The present experiment employs the optimal clustering parameters with an Eps value of 0.0029 and a Minputs value 69. Utilizing a density-based partitioning clustering algorithm that incorporates road coefficients to circumvent architectural obstacles, clustering is performed on the taxi trajectory data from Xiamen City. Subsequently, a heatmap is generated based on the outcomes of this clustering analysis. We employ the refined data storage model to extract trajectory point data before and after clustering for visualization. After engaging in a sequence of 10 trials, the rendering efficiency of heatmaps is assessed across consistent zoom levels. The outcomes of these evaluations are illustrated in Figure 8. The comparative results show that the visualization rendering time for post-clustered trajectory data noticeably decreases. For non-clustered trajectory point data, there is an enhancement of approximately 40% in loading speed. This improved loading speed eradicates conspicuous instances of map stuttering when panning and zooming. Consequently, it is discernible that the density-based partitioning clustering algorithm, which incorporates road coefficients to circumvent architectural obstacles, as proposed in this study, efficiently enhances the heatmap rendering rate of trajectory data by reducing data transmission volume. This innovation successfully addresses the previous challenges associated with prolonged map rendering times and diminished interactivity when visualizing extensive datasets.  
Figure 6: Comparison of retrieval speed between the original storage model and the improved storage model.  
The heatmaps generated from taxi trajectory point data before and after clustering are illustrated in Figures 9 and 10, respectively. As is discernible from the figures, it is apparent that the heatmap generated prior to data clustering exhibits a rather disordered visual presentation, making it arduous to extract meaningful insights from the visualization results; the heatmap of Figure 10, as improved by the present paper's clustering algorithm, achieves a heightened visual refinement. This enhancement effectively circumvents the impact of architectural obstructions on the veracity of the clustered results derived from the visualization results.  
Figure 8: Comparison of heat map rendering rates before and after clustering.  
Figure 7: Comparison of retrieval speed between the space–time-coded storage model and the improved storage model.  
taxi trajectory data. Extracting the heat points from architectural impediments achieves a more precise representation of the authentic data. The visual outcomes after clustering not only preserve the positional attributes of data points but also effectively addresses the heat kernel phenomenon caused by the abundance of local data points within the original data during visualization, resulting in a substantial enhancement of the heatmap's visualization effectiveness, rendering a clear visual effect that is conducive to fully unearthing the latent geographical spatial information embedded within the extensive reservoir of trajectory data.