# Designing an Interactive Visual Analytics System for
## 6 Case Studies
### Case Study: Performing a Comparative Analysis on Multiple Regions
#### 6.2.2 Extreme Event Analysis  
Annual PCA analysis visualization applies PCA computation to evaluate all extracted features of annual precipitation data and project them in a 2-D scatterplot. As described in Section 5.2.1, a comprehensive set of 41 features was extracted from the precipitation data by analyzing them annually and seasonally. Especially when analyzing the data seasonally, we computed seasonal distribution metrics, including the proportion of annual precipitation occurring in each season and a seasonal variability coefficient that quantifies precipitation fluctuations across seasons. Utilizing the multi-dimensional extracted features is effective in identifying complex patterns from the precipitation data.  
Figure 16 shows a PCA projection of all six stations' annual precipitation events. In the visualization, a dense cluster visibly appeared in the center. It shows that numerous annual precipitations maintain similar patterns. Instead, several precipitations outside the  
Figure 15: Precipitation trend analysis across six weather stations’ precipitation data using multiple regression methods. (**A**) Visualization of all observed precipitation events (\(n\) = 141,973), overlaid with fitted linear regression trend lines. (**B**) Comparative trend analysis using polynomial regression (degree = 2) models, highlighting increasing patterns with hiding precipitation events for enhanced visual clarity.  
cluster can be considered possible outliers, indicating they do not follow and maintain similar precipitation trends. To understand the projected precipitation data effectively, _selection_ user interaction was applied to emphasize only the data of interest. The figure shows that the user selected possible outliers in a floating window. The selected events are highlighted in red and connected to bubble windows, representing the summary of the performed annual precipitation analysis. We found four annual precipitation events as possible outliers. They are LA 1997, Chicago 2013, Washington D.C. (DCA Airport) 2013, and Houston 2017. In 1997, LA experienced severe drought conditions in 1976-1977, followed by sudden heavy rainfall events. This dramatic shift from extreme drought to heavy precipitation was unusual and would appear as an anomaly in precipitation patterns [91; 92]. From the historical records, we found that there was significant flooding with a record-breaking rainfall amount of over 7 inches of rain in 24 h in Chicago 2013 [93]. In 2013, Washington, D.C., experienced unusual precipitation patterns, including several intense rainfall events. July 2013 was particularly notable, with nearly twice the normal monthly rainfall recorded [94]. From the PCA analysis, we found that DCA 2013 and DCA 2018 annual precipitation events appeared as possible outliers positioned next to each other. By evaluating historical records, we found that 2018 was Washington D.C.'s wettest year on record, with over 66 inches of rain recorded at DCA [95]. The normal annual precipitation is around 40 inches. We also found that several extreme rainfall events occurred, including major flooding in July and September. As we found in our previous case study, Texas experienced Hurricane Harvey in August 2017, one of the most devastating hurricanes in US history. When comparing multiple stations' precipitation data, this event was determined as an unusual precipitation event. Each of these events represents significant deviations from normal precipitation patterns that appear as outliers in our PCA analysis.