## 4 Results and Discussions
### Geographical Exploration of Spatial Differentiation of Water Conservation in Yunnan Province
#### 4.4.3 Interactive Detection of Spatial Differentiation of Water Conservation in Yunnan Province  
The preceding analysis primarily delves into the individual role of each factor in shaping the spatial differentiation of water conservation function within the study area. However, it is essential to recognize that water conservation function is often a product of the interplay between multiple factors. Consequently, it is necessary to further investigate how these factor interactions impact the spatial distribution of water conservation in Yunnan Province through interaction analysis. As detailed in Table 6, the interactions between various influencing factors are characterized by dual-factor enhancement (BE) and nonlinear enhancement (NE), with no factors operating in complete independence. Among these interaction factors, precipitation exhibits the most pronounced interactivity, as indicated by its interaction \(q\) value surpassing 0.7 in relation to other variables. Except for the nonlinear enhancement between precipitation and population density in 2018, all other factor interactions manifest as dual-factor enhancements. This underlines the importance of factor interactions, which collectively exerts a more substantial explanatory power than the isolated influence of individual factors. Prior research has highlighted the discernible shifts in water conservation function, with the combined impact of both time and space contributing to the variations in the ecological environment within the study area [42]. By conducting dynamic period interaction analysis, as outlined in Table 7, it becomes evident that the top six interaction factors across the three time periods pertain to the interplay of precipitation variations with other influencing factors. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Reciprocal Factor**} & \multicolumn{2}{c}{**2000**} & \multicolumn{2}{c}{**2010**} & \multicolumn{2}{c}{**2018**} & \multirow{2}{*}{**2018**} \\  & \(q\)**-Value** & & \multicolumn{1}{c}{**Reciprocal Factor**} & \(q\)**-Value** & & \multicolumn{1}{c}{**Type**} & \multicolumn{1}{c}{**Reciprocal Factor**} & \(q\)**-Value** & **Type** \\ \hline \(X_{1}\cap X_{6}\) & 0.811 & BE & \(X_{1}\cap X_{2}\) & 0.871 & BE & \(X_{1}\cap X_{7}\) & 0.829 & NE \\ \(X_{1}\cap X_{4}\) & 0.808 & BE & \(X_{1}\cap X_{7}\) & 0.851 & BE & \(X_{1}\cap X_{9}\) & 0.826 & BE \\ \(X_{1}\cap X_{5}\) & 0.807 & BE & \(X_{1}\cap X_{3}\) & 0.848 & BE & \(X_{1}\cap X_{4}\) & 0.826 & BE \\ \(X_{1}\cap X_{9}\) & 0.803 & BE & \(X_{1}\cap X_{5}\) & 0.845 & BE & \(X_{1}\cap X_{8}\) & 0.824 & BE \\ \(X_{1}\cap X_{2}\) & 0.798 & BE & \(X_{1}\cap X_{8}\) & 0.843 & BE & \(X_{1}\cap X_{5}\) & 0.815 & BE \\ \(X_{1}\cap X_{8}\) & 0.796 & BE & \(X_{1}\cap X_{9}\) & 0.843 & BE & \(X_{1}\cap X_{6}\) & 0.812 & BE \\ \(X_{1}\cap X_{7}\) & 0.793 & BE & \(X_{1}\cap X_{6}\) & 0.840 & BE & \(X_{1}\cap X_{2}\) & 0.808 & BE \\ \(X_{1}\cap X_{3}\) & 0.793 & BE & \(X_{1}\cap X_{4}\) & 0.836 & BE & \(X_{1}\cap X_{3}\) & 0.799 & BE \\ \(X_{4}\cap X_{5}\) & 0.718 & BE & \(X_{3}\cap X_{4}\) & 0.711 & BE & \(X_{3}\cap X_{4}\) & 0.764 & BE \\ \(X_{4}\cap X_{2}\) & 0.716 & BE & \(X_{4}\cap X_{8}\) & 0.687 & NE & \(X_{3}\cap X_{8}\) & 0.667 & NE \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Static year interactive detection of geodetectors.