# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 4 Experimental Results and Comparative Performances
### Pollution Episode Clustering  
The segmented episodes implicitly correspond to spatiotemporal activities in a pollution cycle such as concentration accumulation, transportation, dispersion, and diminishing. Due to the local properties related to microclimate, land terrain, and anthropogenic activities, the pollution episodes have repeated patterns, such as emerging pollution concentrations from the downtown area, transporting pollution from the west river valley to the east, and scattering to larger areas before they diminish. It is desirable to group the episodes belonging to the same pollution pattern in a cluster for further causal analysis. However, a pollution episode is a sequence of images. It is difficult to cluster episodes directly. GEI is an effective representation scheme that compresses a short video into an image while still preserving the main scene-changing dynamics in the video. We thus transform the segmented episodes into GEIs. Figure 5 shows an illustration for constructing the GEI from a given PM\({}_{2.5}\) episode. The episode consists of a time series of eight pollution hourly maps, as shown in the first two rows, and the image in the last row is the constructed GEI. It is seen from the episode series that the orange-alert pollution emerges from the upper-right towards the lower-left until it almost covers the entire map (see the fifth hour image). At the sixth hour image, a red-alert pollution appears in the upper-center and lasts for two hours. Finally, at the eighth hour image, the pollution is reduced to orange-alert. This episode shows many dynamics of the pollution cycle, including concentration accumulation (between the first and third hours), dispersion (between the fourth and fifth hours), intensification (at the sixth hour), and reduction (between the seventh and eighth hours). By accumulating the intensity over the eight image maps, the episode is condensed to a GEI image, as shown in the last row. The darkest cells indicate the constantly lower PM\({}_{2.5}\) intensity observed during the whole duration of the episode, while on the contrary, the brighter area manifests the continuingly high PM\({}_{2.5}\) concentrations in the episode. The various intermediate gray levels correspond to the changing dynamics of PM\({}_{2.5}\) concentrations during the time span of the episode. The brightest cells in the upper center indicate there was serious pollution in this region. GEI is a condensed representation of an episode, and it reserves information about the main pollution dynamics observed in the episode. \begin{table}
\begin{tabular}{c c c c} \hline  & **Precision** & **Recall** & **F1-Score** \\ \hline RS & 0.7225909 & 0.74270123 & 0.732508063 \\ HD & 0.6956363 & 0.78076398 & 0.735745954 \\ ED & 0.55856833 & 0.702592087 & 0.622356495 \\ SB & 0.6792861 & 0.68540246 & 0.682330574 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparative performances of the proposed episode segmentation algorithms. Figure 4: _Cont._  
Figure 4: Cut-detection results obtained by various episode segmentation methods. (**a**) RS. (**b**) HD. (**c**) ED. (**d**) SB. Now we proceed to the clustering of the GEIs into groups such that the GEIs assigned to the same group manifest similar pollution dynamics, and the comparative analysis conducted on these GEIs may disclose the multi-factors (terrains, climate, anthropogenic activities, etc.) that result in the particular pollution dynamics. We evaluate the performance on GEI clustering obtained by the proposed HV, BoW, and CNN methods. The specified number of clusters, \(k\), ranges from 2 to 9. The SC, CHI, and DBI derived for each competing method are listed in Tables 2-4. The best value in terms of each performance measure for determining the optimal number of clusters is printed in boldface. It is seen that the suggested number of clusters is 5, 7, or 2 for the HV method, 3, 6, or 2 for the BoW method, and 2, 2, or 6 for the CNN method.