## 4 Results  
### Assessment Components  
Figure 2 denotes the spatial distribution of the primary assessment components such as exposure \(E\), sensitivity \(S\), and lack of adaptability \(A\), along with the intermediate assessment components such as the potential impact \(PI\), livelihood vulnerability \(LV\), and internal factor \(IF\) over the study site. Figure 3 denotes the box and whisker plots to present variation and skewness of data distribution for each assessment component in the study site. Table 2 shows relationships between primary components \(E\), \(S\), and \(A\), and relationships of each primary component to intermediate components \(PI\), \(IF\), and \(LV\), which are measured by values for the coefficient of correlation. Figure 2: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & \(E\) & \(S\) & \(A\) \\ \hline \(E\) & - & 0.519 & \(-\)0.396 \\ \(S\) & 0.519 & - & \(-\)0.697 \\ \(A\) & \(-\)0.396 & \(-\)0.697 & - \\ \(PI\) & 0.847 & 0.894 & \(-\)0.641 \\ \(LV\) & 0.596 & \(-\)0.120 & 0.501 \\ \(IF\) & 0.289 & 0.628 & 0.120 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of relationships between primary components (exposure \(E\), sensitivity \(S\), and lack of adaptability \(A\)), and relationships of each primary component to intermediate components (potential impact \(PI\), livelihood vulnerability \(LV\), and internal factor \(IF\)), measured by the coefficient of correlation. Figure 3: Box and whisker diagrams for data distribution characteristics of primary components (exposure \(E\), sensitivity \(S\), and lack of adaptability \(A\)), and intermediate components (potential impact \(PI\), livelihood vulnerability \(LV\), and internal factor \(IF\)) for the study site. Figure 2: Spatial distribution of the primary and intermediate assessment components for the study site: (**a**) Exposure \(E\); (**b**) sensitivity \(S\); (**c**) lack of adaptability \(A\); (**d**) potential impact \(PI\); (**e**) livelihood vulnerability \(LV\); and (**f**) internal factor \(IF\). As shown in Figure 2a, the values of exposure \(E\) were higher in the northwestern part around the Seoul metropolitan area where flooding damage from heavy rainfalls frequently occurred due to the recent abnormal weather conditions. Exposure \(E\) also showed higher values in the southern part near the coastline located on the main path of intense typhoons and severe storms due to regional climatic and topographic features. The values of sensitivity \(S\) were much higher in metropolitan cities such as Seoul, Busan, Incheon, Daegu, Daejeon, Ulsan, and Gwangju with the high population and infrastructure densities, whereas most of the other areas (75% of total districts) had lower values of sensitivity \(S\) less than 30 as shown in Figures 2b and 3. On the contrary, the values of the lack of adaptability \(A\) were low in most metropolitan area, whereas most of the other areas (75% of total districts) had higher values greater than 80 as shown in Figures 2c and 3. In terms of data distribution characteristics of assessment components, Figure 3 denotes that the variation of exposure \(E\) was spread out over the administrative districts with a relatively smooth gradation of values.