# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 2 Literature Review
### Air-Pollution Spatiotemporal Analysis
[11] further applied the entropy weight method (EWM) to calculate the weighted mean PM\({}_{2.5}\) of the four clusters for each monitoring site in the city, where the cluster weight is given by the reciprocal entropy. The weighted mean PM\({}_{2.5}\) can be used to identify the PM\({}_{2.5}\) hotspots in the city. [PERSON] et al. [10] used a spatially adjacent matrix and the PM\({}_{2.5}\) concentrations tallied by all the monitoring sites in 13 cities in the Beijing-Tianjin-Hebei (BTH) region to conduct a spatial clustering analysis. Their study showed that the PM\({}_{2.5}\) spatial homogeneity is highest in winter and lowest in summer, and for the same time period, the PM\({}_{2.5}\) spatial variations increase from southeast to northwest of the BTH region. [PERSON] et al. [16] systematically examined the spatiotemporal variations of air pollutants in the BTH region and then applied the random forest model and the decision tree regression for predicting spatiotemporal variations of pollution concentrations. It is found that the importance of factors influencing prediction performance depends on the spatial trend of variations. In addition to the four categories of spatiotemporal analysis approaches, it is worth noting that hybrid methods exist in order to synergize the benefits gained by different approaches. For example, [PERSON] et al. [17] proposed a hybrid method that combines different spatiotemporal analysis approaches to make the interpretation of pollution dynamics more complete. The method analyzes the spatiotemporal distribution, trend, forecast, and factors about the air pollutants in Nagasaki Prefecture, Japan, by using correlation,auto-regressive, and machine learning techniques to offer a holistic perspective about the pollution dynamics. There are several drawbacks to the existing PM\({}_{2.5}\) spatiotemporal methods. (1) Due to the high complexity of computation, traditional methods conduct spatial and temporal analysis separately. However, the higher-level correlation existing in the intertwined spatiotemporal domain is neglected in such separate analyses. (2) Existing methods design PM\({}_{2.5}\) spatiotemporal analysis on a piece-by-piece basis, lacking a comprehensive view to elucidate the linkage between different spatiotemporal analysis functions. (3) Most existing methods employ statistical approaches; only a small number of works have attempted machine learning techniques to group PM\({}_{2.5}\) time series into typical variation patterns, overlooking the benefits that may be contributed by abundant machine learning techniques. In this paper, we propose a novel design for comprehensive PM\({}_{2.5}\) spatiotemporal analytics that automatically cuts the PM\({}_{2.5}\) spatiotemporal time series maps into pollution episodes and then clusters those episodes into groups based on pollution spatiotemporal patterns. The analyzed result discloses that similar pollution episode patterns appear in different time periods of the year. They can be used further to explain the relationship between air pollution patterns and local meteorological and anthropogenic factors. Our design is based on video analysis techniques described as follows.