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Based on statistical data and population flow data for 2016, and using entropy weight TOPSIS and the obstacle degree model, the centrality of cities in the Yangtze River Economic Belt (YREB) together with the factors influencing centrality were measured. In addition, data for the population flow were used to analyze the relationships between cities and to verify centrality. The results showed that: (1) The pattern of centrality conforms closely to the pole-axis theory and the central geography theory. Two axes, corresponding to the Yangtze River and the Shanghai-Kunming railway line, interconnect cities of different classes. On the whole, the downstream cities have higher centrality, well-defined gradients and better development of city infrastructure compared with cities in the middle and upper reaches. (2) The economic scale and size of the population play a fundamental role in the centrality of cities, and other factors reflect differences due to different city classes. For most of the coastal cities or the capital cities in the central and western regions, factors that require long-term development such as industrial facilities, consumption, research and education provide the main competitive advantages. For cities that are lagging behind in development, transportation facilities, construction of infrastructure and fixed asset investment have become the main methods to achieve development and enhance competitiveness. (3) The mobility of city populations has a significant correlation with the centrality score, the correlation coefficients for the relationships between population mobility and centrality are all greater than 0.86 (P<0.01). The population flow is mainly between high-class cities, or high-class and low-class cities, reflecting the high centrality and huge radiating effects of high-class cities. 516 Journal of Geographical SciencesFurthermore, the cities in the YREB are closely linked to Guangdong and Beijing, reflecting the dominant economic status of Guangdong with its geographical proximity to the YREB and Beijing's enormous influence as the national political and cultural center, respectively.

China's rapid socioeconomic growth in recent years and the simultaneous increase in many forms of pollution are generating contradictory pictures of residents' well-being. This paper applies multilevel analysis to the 2013 China General Social Survey data on social development and health to understand this twofold phenomenon. Multilevel models are developed to investigate the impact of socioeconomic development and environmental degradation on self-reported health (SRH) and self-reported happiness (SRHP), differentiating among lower, middle, and higher income groups. The results of the logit multilevel analysis demonstrate that income, jobs, and education increased the likelihood of rating SRH and SRHP positively for the lower and middle groups but had little or no effect on the higher income group. Having basic health insurance had an insignificant effect on health but increased the likelihood of happiness among the lower income group. Provincial-level pollutants were associated with a higher likelihood of good health for all income groups, and community-level industrial pollutants increased the likelihood of good health for the lower and middle income groups. Measures of community-level pollution were robust predictors of the likelihood of unhappiness among the lower and middle income groups. Environmental hazards had a mediating effect on the relationship between socioeconomic development and health, and socioeconomic development strengthened the association between environmental hazards and happiness. These outcomes indicate that the complex interconnections among socioeconomic development and environmental degradation have differential effects on well-being among different income groups in China.

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