## 4 Results
### Statistical Analysis and Model Assessment  
#### 4.2.1 FVC and Major Driving Factors: OLS, GWR, and SGWR  
FVC driving factors included climate, terrain, land cover, and socio-economic factors. According to the stepwise regression method and correlation test, the major driving factors affecting vegetation  
Figure 2: Fractional Vegetation Cover (FVC) from 2000 to 2015 in the study area. coverage were selected from the independent variables by the stepwise regression method. In our study, elevation, sunshine duration, rainfall, urban land, population, GDP per capita, and gross industrial output value were used as the primary driving factors in the OLS, GWR, and SGWR models to reveal the complex relationship with the FVC. OLS, GWR, and SGWR models were established to analyse and compare the relationships between FVC and major driving factors. Table 2 represents the coefficient estimates for the global regression models. According to the R\({}^{2}\), the performance of OLS model in 2015 is better than that of other OLS models: the independent variable effectively explains approximately 58% of the fractional vegetation cover variable. Global variable estimates vary by period. The estimated values of elevation, sunshine duration, and rainfall in the OLS model are positive in the four periods from 2000-2015, implying the positive correlation of those factors with FVC. Thus, a county with a relatively high average elevation has a greater degree of FVC. The positive value of the sunshine duration coefficient decreases from 2000-2015, possibly owing to increased air pollution caused by development in the Pearl River Delta, which leads to fluctuating, decreased, and weakened sunshine in Guangdong. Table 3 displays statistics of the local GWR model estimates. A set of local estimates varying with the geographical locations of the counties were derived from the GWR for each variable. The spatial heterogeneity of the estimates of different variables shows that the relationship between FVC and major driving factors has a spatial, non-stationary relationship with changes in location. For example, the global and local estimates for rainfall in 2010 were 0.031 and \(-\)0.003-0.047, respectively. The median value of local estimates in the GWR model is different from that of the OLS model. Global rainfall estimates from 2000-2015 range from 0.027-0.033, whereas median local estimates range from 0.022-0.029. Differences in the overall impact of rainfall on FVC in different periods may largely depend on the irregular fluctuation of annual accumulated rainfall and the uneven time distribution of rainfall in the study area. Local and global model variables must be distinguished to obtain the fitting results of the SGWR model. A geographical variability test performed to determine the local and global variables shows that elevation, sunshine duration, rainfall, urban land, and population have a negative DIFF, and are assumed to be local variables owing to their significant spatial heterogeneity (Table 4). In contrast, GDP per capita and gross industrial output values are assumed to be global variables with a positive DIFF in the GVT. Table 5 shows the statistical results of global and local estimates of the SGWR model for fractional vegetation cover, which considers spatial stability and non-stationarity. The local estimates for urban land in each period are negative, indicating that the local influence of urban land expansion on FVC in the study area is negatively correlated, perhaps owing to accelerated urbanisation in the past two decades and increasing demand for land resources in urban construction. Additionally, the GVT determined that the population factor had a geographical variability in the SGWR, with local estimates ranging from \(-\)0.102 to \(-\)0.003 and \(-\)0.120 to \(-\)0.011 in 2000 and 2015, respectively.