## 4 Results and Analysis
### An Analysis of the Drivers of Conflict in the Sangsang Space
#### 4.3.3 Interaction Detection Analysis  
The interaction detection results from the Optimal Parameter Geodetector (Figure 8) reveal that interactions between any two factors exhibit either nonlinear enhancement or two-factor enhancement, with nonlinear enhancement being predominant. This indicates that the explanatory power of spatial conflicts in the PLES is significantly amplified when two factors are combined compared to when each factor acts independently. These findings indicate that PLES conflicts in Zengcheng District are influenced by the complex interplay of various natural and socio-economic factors, rather than being attributed to a single driving force.  
The dominant influencing factors of PLES conflicts in Zengcheng District remained consistent across 2010, 2015, and 2020. The interaction between nighttime lighting intensity and river network density (X2\(\cap\)X6) demonstrated the highest explanatory power, with q-values of 0.7066, 0.6040, and 0.6913 for the respective years, underscoring the enduring and substantial influence of this interaction in driving spatial differentiation. Furthermore, interactions such as the NDVI and slope (X7\(\cap\)X12) and nighttime lighting and topographic  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Item** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** & **X8** & **X9** & **X10** & **X11** & **X12** & **X13** \\ \hline q & 0.0468 & 0.5154 & 0.3358 & 0.3391 & 0.1323 & 0.0132 & 0.5691 & 0.4540 & 0.1628 & 0.3680 & 0.3490 & 0.4639 & 0.0650 \\ \(p\) & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 \\ \hline \end{tabular}
\end{table}
Table 7: Factor detection results for 2020.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Item** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** & **X8** & **X9** & **X10** & **X11** & **X12** & **X13** \\ \hline q & 0.0451 & 0.4648 & 0.4223 & 0.4239 & 0.1203 & 0.0075 & 0.4281 & 0.3915 & 0.0915 & 0.3212 & 0.3102 & 0.3895 & 0.0822 \\ \(p\) & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 \\ \hline \end{tabular}
\end{table}
Table 8: Factor detection results for 2015.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Item** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** & **X8** & **X9** & **X10** & **X11** & **X12** & **X13** \\ \hline q & 0.0668 & 0.4549 & 0.5431 & 0.5464 & 0.1362 & 0.0158 & 0.5522 & 0.4500 & 0.2110 & 0.4001 & 0.3892 & 0.4773 & 0.1266 \\ \(p\) & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 \\ \hline \end{tabular}
\end{table}
Table 9: Factor detection results for 2010.  
relief (X2\(\cap\)X11) exhibited q-values exceeding 0.5, underlining the critical role of combined natural and socio-economic factors in driving spatial conflicts.  
Several other interactions, with q-values between 0.3 and 0.5, also contributed to the spatial differentiation patterns, albeit to a lesser degree.  
Overall, the interactions of factors such as nighttime lighting, river network density, slope, and topographic relief are identified as key determinants of PLES conflicts in Zengcheng District from 2010 to 2020. These findings offer a solid foundation for informing future regional land-use planning and spatial conflict management, with a focus on fostering the coordinated development of production, living, and ecological spaces.