## 4 Results and Analysis
### Correlation Analysis
#### 4.2.2 Spatial Correlation Analysis  
NDVI, LAI, GPP, LST, Wet, and CEI were obtained through trend degree analysis and MK testing. The trend degree results of the six indicators are shown in Figure 5. It can be seen that the trend degree for vegetation growth in the study area is basically rising; LAI and GPP decreased in the middle of the study area and decreased or increased slightly in the southeast and northwest of the study area; Wet has an obvious upward trend in the northwest of the study area; the trend for LST in the study area is almost zero. In general, the CEI in the study area shows an upward trend. seen from the figure that the LAI values of each city are mostly similar each year and are arranged in vertical lines. The results of the Moran scatter diagrams in Figure 6 show the spatial crosscorrelation results of the annual LE data and various ecological indexes. The Moran's I of the CEI in 2010 is about 0.228, which is the highest, indicating that the spatial crosscorrelation between the CEI and LE is the highest. This is consistent with the correlation coefficient results, where the Pearson coefficient of CEI is about 0.769, which is the highest; the Moran's I for GPP in 2015 is about 0.224, which is second only to the Moran's I of Wet, i.e., 0.281, indicating that the spatial crosscorrelation between GPP and LE is high. It is consistent with the correlation coefficient results, where the Pearson coefficient for GPP is about 0.653, which is the highest. The Moran's I for GPP in 2020 is about 0.173, which is the highest, indicating that the spatial crosscorrelation between GPP and LE is the highest. This is consistent with the correlation coefficient results, where the Pearson coefficient for GPP is  
Figure 5: Result chart of the trend degree for each index dataset. about 0.580, which is the highest. In conclusion, the spatial crosscorrelation between GPP and LE is higher than other ecological indicators. Figure 6: _Cont_. Figure 6: _Cont._  
vegetation index (NDVI) in 2010; (**e**) the Moran's I results for life expectancy and Wet in 2010; (**f**) the Moran's I results for life expectancy and comprehensive ecological index (P) in 2010. (**2**) Results of spatial correlation analysis between the indexes and LE in 2015, (**a**) the Moran's I results for life expectancy and GPP in 2015; (**b**) the Moran's I results for life expectancy and LAI in 2015; (**c**) the Moran's I results for life expectancy and LST in 2015; (**d**) the Moran's I results for life expectancy and NDVI in 2015; (**e**) the Moran's I results for life expectancy and Wet in 2015; (**f**) the Moran's I results for life expectancy and comprehensive ecological index in 2015. (**3**) Results of spatial correlation analysis between the indexes and LE in 2020, (**a**) The Moran's I results for life expectancy and GPP in 2020; (**b**) the Moran's I results for life expectancy and LAI in 2020; (**c**) the Moran's I results for life expectancy and LST in 2020; (**d**) the Moran's I results for life expectancy and NDVI in 2020; (**e**) the Moran's I results for life expectancy and Wet in 2020; (**f**) the Moran's I results for life expectancy and comprehensive ecological index in 2020. GeoDa software was used to analyze the spatiotemporal lag spatial crosscorrelation between the LE data and the various index data. The Moran's I results are shown in Figure 7. The data of each indicator in 2010 and 2015 are spatially correlated with the data of LE five years later (Figure 7 (1)a,b), and the data for each indicator in 2010 are spatially correlated with the data of LE after 2020 (Figure 7 (1)c). It can be seen from the results that the lag in the indicators in 2010 is positively correlated with the results for LE in 2015, and the correlation between NDVI and CEI is larger. As seen in Figure 7 (2), the lag of various indicator data in 2015 and the results obtained by the LE data in 2020 show that, except for the fact that LST is negatively correlated with LE, the rest show a positive correlation, and the correlation is small.