China's massive high-speed rail construction in a short time has exerted a huge impact on accessibility and connectivity of cities. As the rise of-Flow space‖ theory, the impact of high-speed rail construction on urban network is more noteworthy. According to 2007-2014 HSR time schedule data, this paper analyses China's the development of urban network structure influenced by HSR networks. By calculating DIT, RSL, ODIc, ODIl indexes, it tries to explore the urban networks structure and changes in the comprehensive Chinese HSR networks. The conclusions are: (1) with the spatial extension of HSR networks, the connectivity of cities was strengthen (2) China's urban system structure connected by HSR networks became more polycentric. (3) Compare to other regions, HSR networks are more completely developed in the Yangzi River Delta region.

A city could be defined either monocentric or polycentric, in the passage; a systematic review of the urban systems is developed. After defined the elements of disappearance of monocentric system, four types of polycentric system is classified. In conclusion, a city could be locally important or regionally important or even global important in its relevant spatial scale. Thus, the spatial scale of urban systems is crucial of analyzing the structure of urban system, be it in the local, regional, or global scale.

The evaluation of housing conditions is a crucial aspect of determining the well-being of residents and the sustainable development of settlements. Assessing housing conditions at a macro-level is imperative to understand the differences in well-being and livability among residents in various regions within a country. Unfortunately, the spatial variation characteristics of housing conditions in China have not been extensively studied at the county scale. Thus, this study examines the housing conditions in China by using 2846 counties as the basic research unit. A housing condition evaluation index system, comprising seven indicators, is constructed based on three aspects: housing spaciousness, internal facilities, and elevator configuration. The entropy value method is used to determine the weights of the indicators, and the spatial difference patterns and spatial autocorrelation characteristics of the housing conditions and types of housing conditions in China are analyzed. The correlation analysis method is used to analyze the correlation between the subtypes of housing conditions and county fundamentals (population density, urbanization, foreign population, and rental housing). The results show that: (1) the configuration of elevators is the most important indicator of the differences in housing conditions in China; (2) the better housing conditions in China are distributed on the southeast side of the “Hu Line”, while the worse areas are distributed on the northwest side of the “Hu Line”, showing significant spatial clustering characteristics, while the distribution of the different subtypes of housing conditions and their distribution in the H–H and L–L zones also have significant variability; (3) housing conditions in China’s urban areas are generally better than those in non-urban areas, and the internal infrastructure conditions of urban housing and the degree of elevator configuration are better than those in non-urban areas; and (4) the correlation between housing conditions and county fundamentals varies depending on the regional level. At the national and urban levels, a negative correlation exists between county fundamentals and housing spaciousness, although a positive correlation exists with internal infrastructure and elevator configuration. Urbanization has the greatest impact on housing conditions in these areas. In non-urban areas, there is significant variability in the correlation between county fundamentals and housing conditions.

In this paper, Linear Footloose Capital Model (LFC Model) is used to explain the spatial agglomeration principle of real estate investment, and regional return on capital determines the direction of real estate investment, while the rate of return on capital is influenced by combined action of both market scale effect and market crowding-out effect. In this paper, Spatial Dubin Model (SDM) is built by considering such four factors as urban size, related element (manufacturing scale), per capita income and market potential and based on the data of 259 cities at and above prefecture level in China from 2003 to 2014. The results show that each factor has significant positive effect on real estate investment, while the spatial lag items of urban size and market potential have significant negative impact on real estate investment, indicating that when urban size and market potential grow to a certain extent, the intensity of market competition increases and the crowding-out effect on real estate investment is dominant, causing real estate investment to flow out from big cities. Keywords-real estate development investment; footloose capital model; rate of return on capital; spatial dubin model I.

Abstract-The establishment and improvement of Yunnan regional economic multilevel medical security system, should base on the problems existed in the current Yunnan regional economics. The suggestions should be not only for the multiple needs to the people ,but also for an increase aging population.After all, establish and improve Yunnan multilevel medical insurance system has the necessity and feasibility in Yunnan.