Spatio-temporal characteristics of population and economy in transitional geographic space at the southern end of"Hu Huan-yong Line"

"Hu Huan-yong Line(Hu Line)"depicts a geographical pattern of China’s population distribution.Its essence is the regionality of humanland relationship and reflects basic characteristics and laws of human beings’adaptation to the natural environment.With the development of the times and the progress of modern science and technology,especially the rapid urbanization and construction of transportation network system in China,the connection between economic and geographical space has been continuously strengthened.The geographical transition zones from mountain areas to plains,i.e.,transitional geographical space,have promoted the changes in human-land relationships through population migration and agglomeration.Taking Sichuan-Yunnan provinces at the southern end of Hu Line as study area,this study analyzed the spatial correlation between population distribution and economy in this region,explored the pattern of geographical agglomeration and deagglomeration,and explained the changing characteristics of humanland relationship in transitional geographic space using global Moran’s I index,global regression model(GRM)and geographically weighted regression(GWR).The results show that population and Gross Domestic Product(GDP)have significant spatial dependence to this region,with obvious aggregation in geographical distribution and positive autocorrelation;comparing with the general least square model,the GWR model incorporating spatial effect was more suitable for revealing the distribution characteristics of geographical elements,with fine results and better fitting;the spatial model of population and GDP as well as the spatio-temporal evolution model of their changes,all of them strongly indicated that Normalized Difference Vegetation Index(NDVI)and road density were important factors governing the spatial differentiation of population and economy;under the rapid development of regional economy and continuous evolution of urban-rural relations,rural transformation and spatial reconstruction promoted the change of population migration and agglomeration.

Optimal design of building openings to reduce the risk of indoor respiratory epidemic infections

The design of indoor airflow environments can significantly reduce the risk of respiratory epidemic infections indoors.Some studies have successfully developed theoretical models for calculating the effect of airflow fields on infection rates.However,up until now,studies have primarily focused on simulating and calculating the distribution of viral infection rates in current building scenarios.Due to the lack of a direct influence model for the design parameters and infection rate calculation,the present studies lack a quantitative analysis of the design parameters.This paper investigates the building openings design approach in a medium-sized kindergarten in Germany,intending to explore passive-based design solutions to improve the building's ability to prevent the virus'spread.We calculate the infection rate distribution in space by CFD combined with the Wells-Riley model.And then,use the Grasshopper platform to build an optimization model with the design parameters of building openings and infection rate values to discuss the relationship between geometric parameters and infection rate variation.The results show that the building openings'design parameters in transition spaces significantly affect the indoor infection rate under the condition that the input wind speed at the building openings is stable.We can see that optimizing building openings significantly reduces the average infection rate in space.The infection rate in the area with the largest decrease can be reduced by 18.41%.The distribution of infection rate in space is much more uniform,and the excess area is significantly reduced.This study has implications for future research and practice in designing public buildings under the influence of long-standing and cyclical outbreaks of epidemics.