## 4 Results
### Spatial Heterogeneity Analysis of EQI Impact Factors  
The different bandwidths of the variables indicate that the variables have different scales of influence. The spatial scales of the effect of the influencing factors on the EQI varied considerably in this study (Table 3). The bandwidths of average temperature and precipitation are significantly larger than those of other variables, illustrating that the scale of action of climate factors on EQI tends to be global and their spatial heterogeneity is weak. The low bandwidth of human activities and elevation showed that human activities and elevation tend to influence EQI at a local scale with high spatial heterogeneity. Factors, such as climate, topography, and human activities explain 63-83% of the spatial variation in EQI, according to the adjusted R\({}^{2}\) of the MGWR model. From the MGWR standardized coefficients, the ranking of factors on the EQI was derived as the following: elevation > human activity > slope > average temperature > precipitation (Table 4). Human activities have a negative impact on EQI, and this negative impact increases over time. The average temperature shows an overall negative effect  
Figure 11: Local spatial clustering of EQI and human activities, 2000–2018. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**MGWR Parameters**} & \multicolumn{3}{c}{**Variable Bandwidth**} \\ \cline{2-9}  & **AICc** & **Adj R\({}^{2}\)** & **H** & **T** & **P** & **E** & **S** \\ \hline
2000 & 4215 & 0.826 & 217 & 3573 & 3573 & 217 & 671 \\
2005 & 5668 & 0.738 & 250 & 314 & 3573 & 43 & 1101 \\
2010 & 6928 & 0.633 & 52 & 3573 & 3573 & 95 & 706 \\
2015 & 6376 & 0.678 & 121 & 2388 & 813 & 51 & 1664 \\
2018 & 5904 & 0.729 & 51 & 3573 & 3573 & 1120 & 325 \\ \hline \hline \end{tabular} Where H is human activities; T is average temperature; P is precipitation; E is elevation; and S is slope.  
\end{table} 
Table 3: MGWR model parameters. on the EQI, but the regression coefficient of the average temperature in 2005 shows a divergence, which is due to the fact that the average temperature in Anhui Province in 2005 was the highest in history. Precipitation had a predominantly positive effect on the EQI, with the greatest impact in 2015. Elevation and slope direction have a consistent positive effect on EQI. The distribution of the MGWR standardized regression coefficients of EQI impact factors is shown in Figure 12. The impact of human activities on EQI was the lowest in the JHH hills and the highest in the city centers. The effect of average temperature on EQI gradually increased from southwest to northeast, and its regression coefficient was negative within the province. Precipitation, with a gradual increase from south to north, had a positive effect on the EQI. The effect of elevation on EQI increases from southwest to northeast. High regression coefficient values are concentrated in Chuzhou. There is a positive spatial correlation between slope and EQI, and the areas with the greatest impact are concentrated in the central eastern part of Anhui Province. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{4}{c}{**Regression Coefficient Mean \(\pm\) Standard Deviation**} \\ \cline{2-6}  & **H** & **T** & **P** & **E** & **S** \\ \hline
2000 & \(-\)0.135 \(\pm\) 0.594 & \(-\)0.083 \(\pm\) 0.003 & 0.066 \(\pm\) 0.001 & 0.323 \(\pm\) 0.307 & 0.108 \(\pm\) 0.104 \\
2005 & \(-\)0.195 \(\pm\) 0.128 & 0.156 \(\pm\) 0.393 & \(-\)0.050 \(\pm\) 0.002 & 0.34 \(\pm\) 0.892 & 0.130 \(\pm\) 0.074 \\
2010 & \(-\)0.294 \(\pm\) 0.224 & \(-\)0.223 \(\pm\) 0.002 & \(-\)0.048 \(\pm\) 0.006 & 0.429 \(\pm\) 0.611 & 0.091 \(\pm\) 0.132 \\
2015 & \(-\)0.327 \(\pm\) 0.164 & \(-\)0.136 \(\pm\) 0.011 & 0.452 \(\pm\) 0.401 & 0.394 \(\pm\) 0.593 & 0.135 \(\pm\) 0.035 \\
2018 & \(-\)0.237 \(\pm\) 0.191 & \(-\)0.108 \(\pm\) 0.002 & 0.083 \(\pm\) 0.007 & 0.175 \(\pm\) 0.137 & 0.388 \(\pm\) 0.251 \\ \hline \hline \end{tabular}
\end{table}