## 4 Results
### Influencing Factors of the Change in Life Expectancy
#### 4.4.2 Changes in Demographic Structure  
Significant positive correlation exists between education improvement and life expectancy extension in Chinese cities [7; 11; 14; 25]. The regression coefficients increase gradually from east to west, with most cities in eastern Qinghai and eastern Tibet reaching the maximum value. This result indicates that improving educational situation especially basic education of such poorer areas in western regions is helpful to reduce the infant fatality and promote the health conditions (Figure 4). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **Min** & **Lower Quartile** & **Median** & **Upper Quartile** & **Max** & **Average** \\ \hline Change rate of per capita GDP (base year 2000) & 0 & 0.016 & 0.021 & 0.027 & 0.090 & 0.025 \\ Change in health care & 0.019 & 0.054 & 0.112 & 0.147 & 0.285 & 0.113 \\ Change rate of road density & \(-\)0.031 & 0.031 & 0.047 & 0.057 & 0.120 & 0.041 \\ Change in average years of education & 0.139 & 0.152 & 0.172 & 0.242 & 0.455 & 0.206 \\ Change in aging rate & 0.051 & 0.101 & 0.154 & 0.162 & 0.248 & 0.140 \\ Change in birth rate & \(-\)0.355 & \(-\)0.156 & \(-\)0.123 & \(-\)0.108 & \(-\)0.093 & \(-\)0.144 \\ Change in gender ratio & \(-\)0.008 & 0.014 & 0.029 & 0.052 & 0.087 & 0.033 \\ Change in family size & \(-\)0.043 & 0.025 & 0.080 & 0.103 & 0.139 & 0.063 \\ Average level of air quality for many years & \(-\)0.210 & \(-\)0.131 & \(-\)0.084 & \(-\)0.014 & 0.190 & \(-\)0.062 \\ Average altitude (In) & 0.078 & 0.264 & 0.310 & 0.350 & 0.405 & 0.297 \\ Average slope & \(-\)0.152 & 0.025 & 0.038 & 0.117 & 0.229 & 0.043 \\ Perennial mean temperature & \(-\)0.213 & \(-\)0.061 & 0.103 & 0.241 & 0.306 & 0.089 \\ Intercept & \(-\)0.038 & 0.075 & 0.143 & 0.204 & 0.270 & 0.137 \\ \hline Adj-R\({}^{2}\) & & & & 0.285 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Descriptive analysis of the GWR model regression coefficient. Figure 3: Regression results of the GWR model: (**a**) change in health technical personnel per thousand people; (**b**) Change rate of road density. Figure 4: _Cont_. It is also demonstrated that a significant positive correlation exists between the change of aging and that of life expectancy of cities in China, with the regression coefficient reaching the maximum value in Tibet, Xinjiang, Qinghai, and other western regions, and reaching the minimum value in some cities in the Inner Mongolia Autonomous Region and Northeast China. In Western China, the aging rate is generally low and the birth rate is high. An increase in the aging rate means achievements in family planning, changes in beliefs, and progress in economic development, which is conducive to the improvement of health conditions. However, the change in the aging rate has little influence on the change in life expectancy of cities in Northeast China. A possible explanation is that the aging rate is higher than the aging rate. Figure 4: Regression results of the GWR model: (**a**) Change in average years of education; (**b**) Change in aging rate; (**c**) Change in birth rate; (**d**) Change in family size. rapid increase in the aging rate in the region is greatly affected by the large population loss. The higher aging level leads to the rapid growth of the economic burden of disease and demand for medical and health services, continuously damaging the already weak regional economy and health conditions. Meanwhile, aging itself indicates an overall decline in health. These negative effects are offset by the positive ones between change in aging rate and change in life expectancy, which ultimately resulted in insignificant results. Comparatively, a significant negative correlation is found between birth rate change and life expectancy change [7; 11; 14; 25]. The regression coefficient of the change in birth rate decrease gradually from east to west, indicating that the decrease in birth rate in Western China has a greater influence on life expectancy.