# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 4 Experimental Results and Comparative Performances
### Pollution Episode Clustering
The best SC and CHI performance is achieved by HV, followed by CNN, while the best DBI performance is  
Figure 6 shows some members of the first two GEI clusters generated by using the HV method. It is seen that the GEIs in the same cluster are more homogeneous than those in different clusters, indicating the effectiveness of the HV method. The first cluster is composed of the GEIs, which have a horizontal pollution strip in the middle. The second cluster contains the GEIs, which have relatively clean air (darker gray) at the center surrounded by pollution concentrations. Both clusters deliver specific, meaningful pollution episodes that are very helpful in causal analysis to disclose why this particular pattern of episodes repeatedly appears at different times of the year. Figure 7 shows some members of the first two GEI clusters generated by using the BoW method. It should be noted that this is unsupervised clustering; the cluster order has nothing indicating its content. As a result, the first cluster produced by HV should not be associated with the first cluster generated by BoW or CNN. It is seen that the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**K** & **SC** & **CHI** & **DBI** \\ \hline
2 & **0.426706271** & **681.1978051** & 1.142826628 \\
3 & 0.312079422 & 566.5341183 & 0.861172038 \\
4 & 0.26829956 & 493.234105 & 0.817419345 \\
5 & 0.278307506 & 469.2196243 & 0.898008358 \\
6 & 0.271767518 & 428.1691441 & **0.758196849** \\
7 & 0.264814784 & 400.385274 & 0.77981305 \\
8 & 0.245515484 & 379.3548626 & 0.778609268 \\
9 & 0.230716353 & 359.9879666 & 0.774757245 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The clustering performance of the CNN method. Figure 6: Some members of the first two GEI clusters generated by using the HV method. (**a**) the first cluster. (**b**) the second cluster. GEIs in the first cluster have serious pollution concentrations with very high intensity at the bottom center area. This is very likely incurred by particular weather scenarios or anthropogenic activities that repeatedly appear at different times of the year, so we see several episode GEIs grouped in this cluster. For the second cluster, the included GEIs also have a particular pattern at the center of the map. This particular pattern may indicate a complex dispersion route where the pollution goes across the horizontal middle but circumvents some center cells (with very dark intensity). The reason for this phenomenon should be further investigated by examining the local terrain and artificial constructions. Figure 8 shows some members of the first two GEI clusters obtained by the CNN method. In the first cluster, the GEIs show very rich episode dynamics. There are triangular shapes near the map sides, but the intensity range of the triangles differs among different GEI members. Additionally, these GEI members may have similar transportation behaviors but carry distinct pollution-alert categories. This is inherited from the nature of VGG-19, which recognizes generic object classes without paying too much attention to the object's color. For the GEIs contained in the second cluster, a square of four brighter cells appears at the center, with darker cells surrounding the square. This isolated white square at the map center indicates a hot spot of PM\({}_{2.5}\) pollution, while the gradually decreasing intensity of its surroundings may show the dispersion routes of the center pollution source. However, this pattern is quite different from the square pattern we observed in the second cluster generated by BoW, where the center square pattern is dark with bright surroundings. Again, the reason behind this complex phenomenon can only be disclosed by further investigating the atmospheric and anthropogenic conditions that cause it. For instance, temperature  
Figure 7: Some members of the first two GEI clusters generated by using the BoW method. (**a**) the first cluster. (**b**) the second cluster. inversions, rush/off-peak hours, weekdays/weekends, and crop burning may all entail such a pattern.