Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.

Root turnover is a key process of terrestrial ecosystem carbon cycle, which is of great significance to the study of soil carbon pool changes and global climate change. However, because there are many measurement and calculation methods of root turnover, the results obtained by different methods are quite different, and the current research on root turnover of forest ecosystem on the global regional scale is not sufficient, so the change law of root turnover of global forest ecosystem is still unclear. By collecting literature data and unifying the calculation method of turnover rate, this study integrates the spatial pattern of fine root turnover of five forest types in the world, and obtains the factors affecting fine root turnover of forest ecosystem in combination with soil physical and chemical properties and climate data. The results showed that there were significant differences in fine root turnover rate among different forest types, and it gradually decreased with the increase of latitude; the turnover rate of fine roots in forest ecosystem is positively correlated with annual average temperature and annual average precipitation; fine root turnover rate of forest ecosystem is positively correlated with soil organic carbon content, but negatively correlated with soil pH value. This study provides a scientific basis for revealing the law and mechanism of fine root turnover in forest ecosystem.

Based on the research on 31 provincial-level administrative regions at the end of 2022, we used the geographic concentration index, geographic imbalance index, SPSS and ARCGIS spatial analysis techniques to study the spatial distribution, distribution factor correlation, and accessibility of national 5A-level scenic spots. The research results show that the overall distribution of my country's 5A-level scenic spots is unbalanced, with a low degree of concentration, showing a pattern of denseness in the east and sparseness in the west, with large inter-provincial differences. The density of traffic highways is positively correlated with the distribution density of 5A-level scenic spots. The traffic lines in the central and eastern regions are dense, and there are a large number of 5A-level scenic spots, especially the Beijing-Tianjin-Hebei region, the Yangtze River Delta region, and the middle and lower reaches of the Yangtze River and Yellow River. Therefore, the spatial distribution of China's 5A-level tourist attractions is mainly affected by the interaction of economic, transportation and social factors, among which GDP, transportation network and attraction of scenic spots are the most critical factors. These research results can provide a reference for optimizing the spatial layout of China's scenic resources and promoting regional socio-economic development.

The Primary and secondary shadow education refers to a kind of unofficial education that exists outside the traditional mainstream primary and secondary education system in China, with both commercial and educational attributes. As the primary and secondary school stage is an important key stage for further education, existing research mainly focuses on the spatial distribution of primary and secondary school basic education facilities and non-subject training, with fewer studies targeting primary and secondary school subject tutoring shadow education. With the changes in China’s education industry and the introduction of the Double Reduction Policy, there is an urgent need to conduct in-depth research on the spatial aggregation characteristics and influencing factors of Shadow Education Enterprises for primary and secondary school students. This paper takes the main urban area of Zhengzhou City as the study area, and takes primary and secondary school Shadow Education Enterprises as the research object, and applies spatial analysis methods such as kernel density, nearest-neighbor index, and geographic detector to quantitatively analyze the spatial distribution characteristics of primary and secondary school shadow education tutoring enterprises in Zhengzhou City and the factors affecting them The results show that: 1) The overall spatial pattern of primary and secondary school tutoring Shadow Education Enterprises in the main urban area of Zhengzhou City has largely formed a core-edge structural feature that spreads from the urban center to the periphery, and presents the spatial agglomeration feature of “double nuclei many times” distributed along both sides of the Beijing-Guangzhou Line. 2) The distribution of mentoring Shadow Education Enterprises in the main urban area of Zhengzhou City in relation to provincial model primary and secondary schools is significant and there is a significant difference between the distribution around secondary schools and primary schools. 3) The spatial distribution of Shadow Education Enterprises in the main urban area of Zhengzhou City is mainly influenced by factors such as the size of the school-age population, the level of commercial development, the location of school buildings and the accessibility of transport.