【作者】 程林；

【导师】 支大林；

【作者基本信息】 东北师范大学 ， 区域经济学， 2014， 博士

【摘要】 现代社会的人口在空间上的分布并不是随意的,而是遵循一定规律的。区域人口密度模型用于描述区域单元人口密度随着其到区域中心距离变化的变动规律。任何经济活动都离不开人的参与,人作为劳动力时是经济活动的供给方,作为消费者时又是经济活动的需求方。人口分布及其变动在很大程度上反映了经济活动的态势。考察—定时段内区域经济增长的空间模式,可以从分析人口分布的变化入手。人口密度模型拟合系数的变化反映了人口分布的变化,为探讨区域经济增长的空间模式提供了新视角。本文从功能区域的范畴,探讨了中国13个区域的人口密度模型,然后依据人口与经济的关联性,基于人口密度模型视角探讨经济增长的空间模式,并提出空间调控对策。文章可以分为以下四部分：第一部分(第1章、第2章和第3章)：文章的研究基础,包括研究背景、框架和目标,相关研究综述和理论基础,以及数据准备、研究方法和研究区域界定。第二部分(第4章)：区域人口分布的空间特征分析,主要包括区域人口重心分布及其演变,区域人口空间极化及其演变,区域人口密度的空间自相关分析。第三部分(第5章)：区域人口密度模型估计,主要包括经验模型(对数模型和平方根负指数模型)拟合的验证和拟合优度对比,基于路网距离和欧氏距离的区域人口密度模型拟合结果对比,虚拟变量对对数模型的优化,以及其他模型的优化尝试。第四部分(第6章和第7章)：区域增长模式探讨,主要包括人口密度变动与区域经济增长空间模式的关联性分析,13个研究区域经济增长的空间模式判别与探讨,区域经济增长的空间调控对策。文章的主要结论有：(1)区域人口重心都不同程度地偏离区域中心,从1990至2010年,除沈阳、郑州和武汉区域外的其他10个区域的人口重心都向区域中心移动。全部研究区域的人口都存在不同程度的空间极化现象,区域中心对所在区域人口空间极化的贡献度均很大。沈阳、南京、杭州、广州、成都和西安等6个区域的人口密度呈较强的正向空间自相关,表明这些区域人口分布的空间依赖性较强,武汉区域人口密度的正向空间自相关则较弱。其他区域人口密度在目前的区域单元尺度上空间自相关并不明显。(2)区域单元人口密度随着其到区域中心距离的增加而降低。相比平方根负指数模型,中国区域人口密度更符合对数模型分布,并且随着区域的发展,区域人口密度分布越来越符合对数模型。对数模型对均质自然环境区域和非均质自然环境区域人口密度的拟合效果并没有显著差异。运用对数模型拟合区域人口密度分布时,基于路网距离拟合的判定系数从统计意义上要大于基于欧氏距离的判定系数,路网距离较欧氏距离能更好地刻画区域人口密度分布规律。引入区域单元“是否为地级市驻地”和“山地丘陵地貌比重”两个虚拟变量能提高多数区域人口密度的对数模型拟合优度,说明这两个虚拟变量对某些区域人口密度分布的解释是有益的。对数二次模型对某些区域人口密度的拟合结果要优于对数模型,但是其局限性也非常明显,即适用性相对有限。(3)13个研究区域的区域中心人口密度在时间序列上的增长普遍都比较大,而位于边缘的区域单元人口密度在时间序列上地变化都很小,甚至没有变化。人口密度变化所反映的区域经济增长的空间模式都较为—致。区域中心经济活动相对快速增长,临近区域中心的地区也有相对较快的增长,但边缘地区增长相对缓慢甚至停滞和负增长。这种空间上的增长差异可以概括为强向心集聚及近域扩散空间模式,但不同区域在不同时间段的向心集聚程度和扩散的空间范围存在着明显差异。更多还原

【Abstract】 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.更多还原