Hiking is a popular outdoor activity in urban settings. Previous studies have primarily focused on the mobility paradigm and ritual paradigm, emphasizing spatial transformation and functional analysis, but they lack attention to and analysis of bodily sensations and embodied practices during hiking. This paper adopts Lefebvre's perspective of Rhythmanalysis, focuses on the changes in the perception of bodily rhythms within specific spatiotemporal contexts, and uses time, space, and the body as analytical dimensions. Based on an analysis of the hiking experiences of 25 urban youths, the findings reveal that: 1) The alienation of urban time and space generates imbalanced and chaotic rhythms, manifesting as disrupted and disordered perceptions of time, a lack of urban place identity, and physical and mental fatigue. 2) Hiking is a way to alleviate imbalanced rhythms. During hiking, through perceptual, social, and physical practices, multiple senses are activated, physical and mental vitality is restored, interpersonal relationships are established and strengthened, and imbalanced rhythms are adjusted and restored. 3) The analysis of hiking experiences suggests that the body is not only an indicator of rhythm，presenting multiple rhythms both within and outside itself, but can also act as a regulator, facilitating their restoration and adjustment. The multidimensional aspects of the body in rhythm analysis also provide insights for reflecting on urban life and city planning, urban planners should prioritize the design and development of slow-paced spaces.

In the context of a shifting focus from 'talent' to 'population' in the battle for talents, there is an urgent need to increase attention to the long-term residence intention of low-educated floating populations in cities. Based on data from the China Migrants Dynamic Survey (CMDS) in 2014 and 2018, and using junior college education as a dividing line, this paper explores the differences in characteristics and influencing factors of long-term residence intention among floating population of different educational backgrounds, and how these differences evolve over time. The findings are as follows: 1) Over time, the long-term residence intention among these two groups has significantly increased in the western and central regions. 2) In terms of influencing factors, economic characteristics exert a greater influence on the long-term residence decisions of the less educated migrants, while family characteristics have a stronger impact on those with higher education. Over the years, the economic factors' influence on both groups diminisher, whereas the significance of family characteristics grows. 3) At the city level, large cities outside of urban agglomerations hold the greatest appeal for long-term residency among both highly and less educated individuals. High-educated people weigh factors such as industry and natural environment more heavily than those with low educational attainment when making long-term residence decisions. However, the considerations of these two groups tend to converge as time goes on. The comparative study enriches the analysis of group heterogeneity in the field of migrants, and complements the insufficient research on migrants with lower educational levels.

With the process of economic development and industrial structure transformation, China's employment spatial pattern is undergoing a huge transformation. This article is based on the data from the fifth to seventh population censuses by county, revealing the spatiotemporal pattern of the employed population at the city and county levels in China since the new century. It identifies the regional types and industrial characteristics of the contraction of the employed population, and uses the ordinal logistic regression model to explore the influencing factors of the contraction of the employed population. The main conclusions are as follows: 1) Since 2000, the employed population in China has shown a trend of first increasing and then decreasing, The number of counties maintaining the employment growth has significantly decreased; 2) Among the three types of contraction areas, the episodic shrinkage area accounts for about half of the country, mostly transitioning from growth cessation to contraction, mainly distributed in the central and western regions. The continuous shrinkage area accounts for about 1/3, mainly distributed at the junction of the three major economic belts. The non-shrinking area accounts for about 15%, mainly distributed in the northwest region and the three major urban agglomerations; 3) The intensity of industrial reduction in areas with reduced employment shows a decreasing trend in the primary, secondary, and tertiary industries; 4) A high wage level, the age structure with a high proportion of working-age population, and the development of the secondary and tertiary industries can help suppress the contraction of the employed population, while an increase in the minimum wage standard can promote the occurrence of employment population shrinkage.

Escalator Region Theory builds a bridge between spatial mobility and intergenerational social mobility. However, when exploring the impact of spatial mobility on intergenerational social mobility, Escalator Region Theory only focuses on the impact of their destinations on the intergenerational social mobility of migrants. On the other hand, Childhood Exposure Effect emphasizes the neighborhood where the children live on their intergenerational social mobility. This study attempts to connect the influence of birthplace and their destinations on the intergenerational social mobility of migrants. Based on the data from Chinese General Social Survey (CGSS) in 2010, 2013, and 2015, this study finds that spatial mobility has a significant positive impact on the intergenerational social mobility of migrants in China. Cities with a high level of economic development, large-scale agglomeration economy, and central cities, play the role of escalator regions in China. Besides, being born in central cities and cities with a high level of economic development are also significantly conducive to the intergenerational social mobility of migrants. Moreover, the results show that the characteristics of the birthplace, including the level of economic development, the scale of agglomeration economy, and whether it is a central city, have a significant positive moderating effect on the relationship between the relative characteristics of the destinations and the intergenerational social mobility of migrants, indicating that the characteristics of birthplace will affect the functions of the escalator regions. The findings of this study can make up for the lack of attention of Escalator Region Theory to the space before the mobility of migrants.

The authors use Pearson correlation analysis, Dagum Gini coefficient, GTWR model and grey prediction model and other methods, based on the national population census data in 2000, 2010 and 2020, taking Heilongjiang province, which has a large population contraction and a high level of aging, reveal the spatio-temporal coupling relationship between the two, and analyze the factors influencing population aging, Predict population size and aging development trend. Research shows that:1) Over the past 20 years, the population size has undergone an evolutionary process from slow growth to mild contraction to severe and severe contraction. 2) The overall level of population aging shows a gradient upward trend from adult type I to elderly type I to elderly type III. 3) Both municipal and county-level units exhibit a highly positive correlation between population contraction and population aging growth. 4) The migration rate, elderly population base, per capita education years, mortality rate, and per capita GDP have a positive impact on the level of population aging. 5) In the next decade, the population size of the region will continue to decline, and the level of population aging will be accelerating.

Based on WorldPop dataset of 2000 and 2020 and monocentric models including negative exponential model, logarithmic model, and power model, this research reveals the evolution trends and driven factors of residential population density distributions of 263 Chinese cities at prefecture level or above. Firstly, we found that the fitting degrees of three monocentric models for population density distribution generally decreased in the decentralized cities, while remained stable in most centralized cities. The application of exponential function was more suitable for large cities, and power function was more suitable for small or medium-sized cities. Secondly, 70% and 30% cities have experienced the process of centralization and decentralization over the past 20 years, respectively. The population density gradients of eastern and high-grade cities were higher and their trends of centralization were stronger, while the majority of southwest mountainous cities were more likely to decentralize. Thirdly, both market mechanism and public policy shape the spatial pattern of population density. With the expansion of population scale, improvement of living condition, and shrinkage of family size, the urban population density gradient would drop significantly, or the probability and changing extent of decentralization would rise significantly. The construction of road network and the public transportation priority policy promote decentralization.

With the accumulation of negative population growth momentum and emergence of diversified migration, it has become a new challenge to the stability of population in the border areas. In this context, this article analyses the evolution of population and migration in China's border area, with a special attention on the influence of migration on the population security in border counties. Using the data of 2000, 2010, and 2020 National Population Census of China and the 2005, 2015 China 1% National Population Survey, this paper employs visual analysis to capture population growth and migration patterns in China's 136 border counties from 2000 to 2020. Results show that the population of border counties is still stable but declining. Migration has gradually become the major factor of border population change, and the impact of out-migration is continuously increasing especially since 2010. Migration in different regions is affected by different factors: The growth rate of per capital GDP has the most significant effect on weakening the out-migration rate and increasing the in-migration rate in the out-migration risk areas; the policy preference index has a significant positive impact on the in-migration rate in areas with stable population growth; the years of education have a positive significant impact on out-migration rate in both relative stable and closed growth area, but it is not significant in the out-migration risk areas.

International organization institutions have irreplaceable strategic significance in building national soft power, shaping global governance mechanism, and expanding national interests. Based on the data of UIA, this paper uses vector data notation and negative binomial regression model to conduct an in- depth study on the spatial-temporal evolution, agglomeration model and their influencing factors of the geographical location of 26,457 international organization institutions established from 1918 to 2017. The results are as follows.First, in the past 100 years, the cumulative number of international organizations has increased by 1200 times in geometric order. Second, the spatial distribution of international organization institutions is extremely uneven. Thirdly, international organization institutions have permanent neutral country agglomeration model, strong country agglomeration model and regional central country agglomeration model. Finally, the regression results show that economic influence, cultural influence, scientific and technological influence, and historical organizational cultural heritage of a country have a significant positive correlation with the geographical agglomeration of international organization institutions at the level of 0.01.

The research on population flow in urban agglomerations has important theoretical and practical guiding significance for promoting the orderly flow of regional population and coordinated development of urban agglomerations. In recent years, with the rapid development of information and communication technology, the data acquisition environment and collection methods in urban scale and regional scale have been greatly improved. New data sources related to geographic location are constantly emerging. This geographical behavior big data with the characteristics of real-time, quantitative objectivity, and strong spatio-temporal heterogeneity provides a new support and approach for the study of population flow. This article takes the urban agglomeration of the Yangtze River Delta in China as an example, based on Tencent migration big data, using GIS spatial statistics, spatial analysis and K-shell decomposition methods, to systematically analyze the spatio-temporal characteristics of the population flow of the Yangtze River Delta urban agglomeration during the Spring Festival travel rush in 2018, from the aspects of total population, order, time series and network level, etc. The results of the study showed that in the Yangtze River Delta urban agglomeration, Shanghai, Suzhou, Hangzhou, Ningbo, and Nanjing, which have relatively high levels of economic development, are labor-importing cities. While Yancheng, Anqing, and Chuzhou, which have relatively low economic development levels, are labor-exporting cities.

Driven by the new urbanization strategy, some new trends of village population transformation emerge, such as obvious localization of villagers' transformation towards non-agricultural employment, which is typical in the central area experiencing great population outflow. This paper attempts to explore the new patterns and micro causes through a case study of Yueyang City, Hunan Province, which is a typical three/four-level cities in central China. The general patterns of rural population transformation of Yueyang and central China are elaborated first. One village is selected from the suburban area, remote mountainous area and traditional agricultural area, respectively, to explore the overall patterns of village population transformation and the impacts of three causes including natural endowment, regional economic development and social service accessibility. We find that population mobility in central China is dominated by inter-province migration, but inner-province migration increases gradually, especially to the local towns. For the suburban village, few population outflows, and most of them work in the non-agricultural sector in villages or the nearby urban and towns. The remote mountainous village witnesses large out-migration. The traditional agricultural village witnesses the coexistence of migration to nearby urban district and employment of most villagers in agriculture. Village endowments and economic development, and regional economic development and social service provision imposes different impacts on village's migration degree, villagers' migration distance, their choice of residence and employment, household structure and even the rise and decline of a village. Policy-making should be specific to village types to promote the new urbanization strategy.