## 4 Results and Analysis
### An Analysis of the Drivers of Conflict in the Sangsang Space
#### 4.3.2 Factor Detection Analysis  
The conflict index was designated as the dependent variable (\(\gamma\)), while the driving factors were assigned as the independent variables (\(\chi\)). The PLES conflict index and 13 driving factors were rasterized, reclassified using ArcGIS 10.2, and subsequently analyzed in RStudio 4.3.0. The results (Tables 7-9) revealed that 12 out of the 13 factors significantly  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **Code** & **Impact Factor** & **Description** \\ \hline \multirow{6}{*}{Socioeconomic Factors} & \(\chi\)1 & Distance to Government & Reflects spatial distance to the district government seat \\  & \(\chi\)2 & Nighttime Light & Reflects human activity intensity and urbanization level \\  & \(\chi\)3 & GDP & Reflects economic output and development level \\  & \(\chi\)4 & Population Density & Measures population density \\  & \(\chi\)5 & Road Density & Reflects road network density \\ \hline \multirow{6}{*}{Natural Resource Factors} & \(\chi\)6 & River Network Density & Reflects river network density \\  & \(\chi\)7 & NDVI & Reflects vegetation coverage and health \\ \cline{1-1}  & \(\chi\)8 & Annual Temperature & Reflects annual average temperature \\ \cline{1-1}  & \(\chi\)9 & Annual Precipitation & Reflects annual precipitation \\ \cline{1-1}  & \(\chi\)10 & Terrain Ruggedness & Reflects surface elevation variation \\ \cline{1-1}  & \(\chi\)11 & Slope & Reflects land surface slope \\ \cline{1-1}  & \(\chi\)12 & Elevation & Reflects elevation characteristics \\ \hline Land-Use Factors & \(\chi\)13 & Patch Density & Reflects land or tectonic plate density \\ \hline \hline \end{tabular}
\end{table}
Table 6: Indicator system for drivers of PLES conflicts in Zengcheng district.  
Figure 7: Temporal variation in the PLES Conflict Index in Zengcheng District (2010–2020).  
influenced PLES conflicts, with the exception of river network density (X6), which exhibited a low q-value and no statistically significant effect.  
From 2010 to 2020, the Normalized Difference Vegetation Index (NDVI) (X7) and nighttime lighting intensity (X2) emerged as the most significant factors, highlighting the pivotal influence of economic development and infrastructure expansion in shaping spatial conflicts. The NDVI emerged as the strongest influencing factor, particularly notable in 2020 (q = 0.5691) and 2010 (q = 0.5522). Nighttime lighting also exhibited a strong correlation, especially in 2020 (q = 0.5154) and 2015, further highlighting the impact of urbanization and human activity.  
Natural factors, such as elevation (X12) and annual temperature (X8), also played a significant role, particularly in 2020. The multi-factor q-values in 2020 were substantially higher than those in 2010, indicating that spatial conflicts have become more complex over time. This shift reflects a transition in driving forces: from the predominance of economic development and urbanization in the earlier period to a more intricate interplay of multiple factors by 2020. The exacerbation of spatial conflicts in recent years underscores the compounded effects of natural and socio-economic influences.