Regional Population Density Function And Spatial Patterns Of Economic Growth In China

Any economic activity can not do without the participation of the people. As the labour force, people can act as the supply side of economic activities. And as the consumer, people can act as the demand side of economic activities. The trend of economic activities can be clearly reflected by population distribution and it's changes. By analyzing population distribution and its' changes, we can inspect the spatial pattern of regional economic growth condition. Regional population density model can be used to describe the regional population density and it's changes with the distance from regional center. The changes of the fitting coefficient of population density model can show the changes of population distribution, and offer a new perspective of the research on spatial pattern of regional economic growth.According to the relationship of population and economy, this research firstly discusses thirteen regional population density models in China from the perspective of functional area. Secondly, inquiring into the spatial pattern of regional economic growth based on population density model. And then put forward a set of spatial regulation policies. This article can be divided into the following four parts:First part (including chapter one, two, and three) introducing the research backgrounds, research framework, research objective, research bases, and a review of related researches.Second part (chapter four) mainly discussing the spatial characteristics of regional population density distribution. The research points include the gravity centers of regional population distribution and it's changes, regional population spatial polarization and it's evolution, spatial autocorrelation analysis of regional population density.Third part (chapter five) evaluates the regional population density. Firstly, doing fitting test and comparing the fitting goodness of the empirical models that are logarithmic models and the square root of negative exponential models. Secondly, comparing the fitting results of regional population density model based on street network distance and euclidean distance. Thirdly, discussing the fitting effects of importing dummy variables. Lastly, trying some other models to see the fitting results.Fourth part (including chapter six, and seven) discussing the regional growth patterns. Firstly, the part analyzing the changes of regional population density and the spatial pattern of regional economic growth. Secondly, discriminating and discussing the spatial pattern of the economic development in thirteen regions. Lastly, putting forward a set of spatial regulation policies.The main conclusions of the article are as follows:(1) The gravity centers of regional population all deviate from regional centers in different degrees. From1990to2010, all the regional population gravity centers moved to regional centers except Shenyang, Zhengzhou, and Wuhan. All of the regional population exists the phenomenon of spatial polarization in different degrees. Regional centers makes a great contribution to population spatial polarization. Six regions'population density show a strong positive spatial autocorrelation, such as Shenyang, Nanjing, Guangzhou, Hangzhou, Chengdu, and Xi'an. This states that there is great spatial dependence of regional population in these regions. However, the population density in Wuhan shows relatively less positive spatial autocorrelation. Other regions do not show any spatial autocorrelation.(2) Regional population density declines with the increasing distance from the region center. Comparing with the square root of negative exponential model, logarithmic model is more suitable to Chinese regional population density. When using logarithmic model to analyze the distribution of regional population density, the determination coefficient based on street network distance is higher than the determination coefficient based on euclidean distance. This means street network distance can be better suited to describe the regional population density than euclidean distance. By introducing dummy variables, such as wether prefecture level city or not, the proportion of mountainous and hilly landform, the fitting results become better. Therefore, the two dummy variables are beneficial to describe the distribution of population density. The fitting results of Logrithtic-Quadratic model are better than logarithmic model, but the applicability is limited.(3) All the economic development patterns reflected by the population density model of the thirteen regions are nearly the same. The economic develops faster in regional center, areas near the regional center grows relatively faster than other places. However, the marginal areas develops too slowly, even shows negative growth trend. This shows the spatial difference of regional growth. That is strong agglomeration and near place spatial diffusion pattern, which are greatly different in spatial scale during different periods.