This study constructs a theoretical framework of hedonic rent of urban housing and establishes the elements of housing characteristics suitable for rental housing. The elements include building, convenience, environment, and location characteristics. Based on the monthly data of 23126 houses available for rent in Guangzhou's urban district in March 2020, the spatial difference pattern and spatial correlation of housing rents in the district are analyzed through hierarchical spatial statistics and spatial autocorrelation. We established an index system of housing rent including 12 influencing factors. Through the comparison and selection of three models, the spatial error model is used to measure the influencing factors of housing rent, and the core influencing factors are identified. The results reveal that: (1) The theoretical framework and model of hedonic rent can be used to examine the influencing factors of urban housing rent. (2) Low- and middle-rent houses account for the largest proportion of rental housing in Guangzhou's urban district. Housing rent presents a spatial differentiation pattern that is higher in the core area and lower in the peripheral urban area and old city, with significant spatial agglomeration and correlation characteristics. (3) Four characteristics -- building (building area, orientation & floors, building age, elevator & property management), convenience (metro, office, education convenience), environment (park accessibility, industrial pollution), and location characteristics (distance from central business district (CBD)) -- including 10 factors, have a significant impact on the housing rent difference in Guangzhou's urban district. (4) Building area, building age, and distance from CBD are the three most important core influencing factors for housing rent, followed by elevator and property management and office convenience.

It is disputable that global large-scale urbanization and climate change has become an outstanding issue, which requires the common concern of mankind. However, it is not yet clear what is the complex relationship between urbanization and climate change and how to scientifically deal with climate change in the process of urbanization. Further exploration from science and management to practice are needed in order to achieve global and regional sustainable development. This paper first displayed the basic facts of mass urbanization and climate change and summarized interactions and possible mechanisms of urbanization and climate change. Urbanization leads to heat island effect, uneven precipitation distribution and extreme weather, together with local-regional-global multi-scale superposition effect, which aggravates global climate change. The impact of climate change on urbanization is mainly manifested in the aspects such as changes of energy consumption, mortality and the spread of infectious diseases, sea level rise, extreme weather damage to infrastructure and water shortage. This paper also briefly reviewed relevant international research and joint actions, and put forward an analysis framework of multidimensional sustainable urbanization adapting to and mitigating climate change, from the perspective of key dimensions of urbanization, namely, population, land use, economy and society. We call on to strengthen the interdisciplinary research of science and humanities, take urbanization and other human activities into consideration of the land - atmosphere system, and explore the human-land-atmosphere coupling process. The adaptation and mitigation from the perspective of human activities represented by urbanization might be the most critical and realistic way to deal with climate change.

The lack of governmental and systematic urban population data limit the study progress in urban systems in China's historical period, which has great negative effect on quantitative analysis of urban system rank-size distribution. This paper digitized the "China Postal Atlas" in 1936, based on its 13240 postal points and more than 40000 km postal routes, and examined the spatial distribution of the kernel density of postal points and routes, using the Thiessen Polygon, Zonal Statistics and other GIS tools to give value to the postal capacity T of modern Chinese cities. T-value is used as a proxy for city size in modern China to analyze the rank-size rule. The conclusions are as follows. (1) The distribution of rank-size of cities in modern China based on the T-value was in accordance with Zipf's law. (2) In 1936, there were five postal aggregation areas in China: Beijing-Tianjin-Hebei region, the Yangtze River Delta, the Pearl River Delta, Wuhan and Chengdu-Baxian region, which is highly consistent with the current distribution of China's urban agglomerations. (3) In 1936, the spatial distribution of postal points and routes was very different on both sides of the "Hu Huangyong Line". The northwestern half to the line had a slightly smaller proportion of points than that of population, but a much higher proportion of high-grade points and routes than that of population. (4) In this paper, the sample size of urban system in modern China increased from about 200 (urban population) to 2360 (T-value). For the first time, the "three-stage" distribution caused by the unique "depression in the middle section" of the city size curve was found, which was quite different from that of the United States and China today. (5) The postal capacity T-value can be considered as a proxy for city size in urban geography studies in modern China.

The novel coronavirus epidemic has led to an unprecedented concern regarding public security. Communities, as the basic units of social governance, play a prominent role during this period. However, little is known about the relationship between residents' sense of public security and their community environment in the existing geography and planning literature. To fill this gap in the research, this paper builds a theoretical framework to identify personal and community-level factors that influence the sense of public security. Based on a large-scale survey conducted in Beijing in 2019, we use multilevel linear models to analyze how and to what extent personal and community features impact this sense. The results show that (1) most of the personal attributes have significant effects on the sense of public security. Those who are older or less healthy are more likely to report a lower sense of security, and residents with lower incomes or education levels are also liable to suffer from insecurity. Stable employment has a positive effect on people's sense of security, and unemployed people report the lowest sense of security compared to others. Migrants feel safer than local residents, the main reason is that they compared their current city with their hometown and found Beijing to be much safer. (2) There is significant difference in residents' sense of public security across communities. The model results suggest that a built environment with a denser population and a bus route has a negative effect on the sense of security, while open space with more road crossings can improve residents' safety perception. Additionally, residents will feel more secure if their community gets closer to type I emergency shelters and hospitals. However, the influences of small- and medium-sized facilities are not significant, such as type II/III emergency shelters and community healthcare centers. (3) The social environment of the community plays a more important role in promoting residents' sense of public security than the built environment. However, it is found that renters can hardly benefit from the improvement of property management. Based on these findings, the paper provides some suggestions for improving the community's safety and resilience.

Over the recent decades, China has become an ageing society and how to best take care of the elderly has been in heated debate. Nursing homes have been considered as an effective way to solve the problems associated with the care of the elderly in China. To address these problems, it is of great significance to better understand the spatial distribution of nursing homes in Chinese cities and investigate why their distribution differs in space. This study used crawler technology to obtain the number of nursing homes in 285 Chinese cities by September, 2019, and applied a geo-visualization technique to map their spatial distributions. A novel spatially stratified heterogeneity method (named geographical detector) was employed to uncover the socio-economic driving factors of these nursing homes. The following findings were obtained: (1) The spatial distribution of the number of nursing homes is similar to that of the elderly population in the investigated cities, indicating that there is a close relationship between them. (2) The results of the factor detector test showed that the urban elderly population, urban economic development level, fiscal expenditure, the number of employees joining urban basic pension insurance, and the area of green land is closely related to the number of nursing homes in Chinese cities. Of these five socio-economic driving factors, fiscal expenditure and the level of economic development are the main drivers. (3) The results of the interaction detector test showed that the interaction effects of pairwise factors on nursing homes are stronger than the effect of individual factor. This indicates that the spatial heterogeneity of the number of nursing homes is affected by multiple factors. Moreover, the interactions between the elderly population factor and four other driving factors are the strongest determinants for the development of the number of nursing homes of Chinese cities. Finally, several relevant policies are proposed to promote the increase of nursing homes in Chinese cities based on the main findings.

Urban natural environment especially greenery environment has been consistently linked to better health outcome. Previous studies have shown that the greenery environment of neighborhood is related to residents' health, but scant attention has been paid to biopsychosocial pathways through which neighborhood greenery affects resident' wellbeing. In addition, a limited number of studies have evaluated the healthy pathways between neighborhood greenery and residents' wellbeing among Chinese vulnerable groups in urban setting. Based on neighborhood effect, three potential mediating pathways (namely, increasing physical activity duration, reducing psychological stress and facilitating social interaction) linking neighborhood greenery to residents' self-rated health were explored from 757 participants of Guangzhou, China. In view of questionnaire data involving 30 communities and greenery data obtained from remote sensing image and streetscape data and etc., this research employed multilevel linear regression model and mediating effect model and propensity score matching method to examine biopsychosocial pathways and mechanism linking neighborhood greenery to residents' self-rated health, as well as the difference among different social strata (e.g. different income and gender groups). According to this empirical analysis, the following conclusions can be achieved. Firstly, residential neighborhood greenery is significantly associated with residents' self-rated health outcome. Secondly, reduction in psychological stress is a kind of pathway linking residential neighborhood greenery to residents' self-rated health. Thirdly, the impact of neighborhood greenery on residents' self-rated health differs significantly among different social strata, and neighborhood greenery has greater impact on the self-rated health of low-middle income residents and female residents group, which means the beneficial effect of reduction in psychological stress on self-rated health is strengthened by neighborhood greenery. This research findings enrich the empirical research on greenery environment and public health from the perspective of health geography, and suggest the necessity of improving active spatial intervention and greenspace construction of the "healthy community" at the neighborhood scale in promoting residents' wellbeing in Chinese urban settings.

Epidemics are one of the most severe events in the human disaster network. On the basis of establishing the epidemic sequence by using historical epidemic data, and using the methods such as historical section analysis, factor correlation analysis, and time sequence analysis, this article examines the spatiotemporal characteristics and causes of the epidemic disasters for the past 2720 years (770 BC-AD 1949) in China. The results show that: (1) The frequency and intensity of epidemic disasters in China have a long-term upward trend, with troughs in the warm periods and peaks in the cold periods. (2) The epidemic prevalence in China generally occurred mainly in summer and autumn, but varied with time, so that since the 1450s, the seasonal difference tended to be less significant due to the increase of the kinds and frequency of epidemics. (3) China's epidemic fluctuation cycles are mainly 620-610 a, 320-310 a, 230-220 a, 170 a, 90 a, etc., which confirms the existence of "Twelve Earthly Branches" cycle and the significant impact of sunspot activity on the epidemic cycles. (4) For the past 3000 years, within the China's territory, the Epidemic Widespread is 93.51%, which indicates the percentage of the number of affected counties to the total number of counties, and the Epidemic Thickness is 16.86 layers, which represents the ratio of cumulative affected counties to the total number of administrative counties. The epidemics in the southeast half of China occurred much more frequently and severely than those in the northwest half. (5) The expansion of the epidemic prevalent areas in China is synchronized with the areal land exploration. The change of the epidemic centers of gravity is obviously affected by the spatial changes of the economic centers of gravity. The centers shifted from north to south before the Southern Song Dynasty, and moved from east to west after that. The import of foreign infectious diseases has a great impact on the distribution pattern of epidemics in China. (6) The prevalence of epidemics is not only a natural phenomenon, but also a sociocultural phenomenon. The change in the spatiotemporal distribution of epidemics reflects the change of human-environmental relationship. The densely populated areas, the areas along the traffic line, the surrounding areas of the capital city, the natural foci areas and the disaster-prone areas are all epidemic-prone areas. (7) The geographical environment has a basic impact on the spatial distribution of epidemics, that is, low altitude, warm and humid areas are prone to the prevalence of epidemics. Natural disasters can induce epidemics, so the disaster-prone area is also the epidemic-prone area, and the disaster-prone period is also the epidemic-prone period. Climate change has impacts on epidemic fluctuations, so epidemics are more frequent in the cold periods than in the warm periods. The land excessive development and the conflict between human and land resulting from population growth intensified the epidemic prevalence. Moreover, epidemics always follow wars, that is, the frequent periods of war are also the frequent periods of epidemic.