【摘要】 研究采用地理空间技术,明确人口和经济要素的空间载体,细划人口的要素分类,将人口划分为资源型人口(城市人口、乡村人口和就业人口)和创新型人口(35岁以下人口、高学历人口和人口利用效率)。同时将经济要素也划分为资源型经济(一产业产值、二三产业产值和建设用地强度)和创新型经济(高校、科研院所和混合用地率)。研究建立了统计数据离散化方法和点核密度方法,求取不同要素的空间化结果。从成都市的要素空间分布看,中心城区是人口与经济所有要素的极值区,而二三圈层与中心城区的值差别较大,有局部地区如邛崃,在部分指标,如人口集约分布上呈现与中心城区相连的特点。研究结果表明创新人口的增长,促进了高新企业的发展和新技术的使用,可以很好地提高土地利用效率和潜力,使得建设用地的需求下降。研究方法较好地展现了人口与经济要素的空间差异化分布,可为实现区域内生活、生产、生态的"三生空间"的合理布局提供数据支撑。更多还原

【Abstract】 In the traditional economic growth mode,the population is considered to be a kind of resources,and the size and spatial distribution of the population have been paid much attention. With the promoting of the new development concept,persons as an important element of innovation driven,the innovative of the persons which is defined and expressed has been widely discussed in the academic circles. With the GIS, identify the spatial objects that carry the population and economic. Population factors are divided into resource population include urban population,rural population and employment population,and innovative population include population under 35, highly educated population and population utilization efficiency. Economic factors are divided into resource economic include primary industrial output value,secondary and tertiary industrial output value and Intensity of construction land,and innovative economic include university distribution,research institutes distribution and mixed land use rate. Using the statistical data discretization method and point kernel density method,gained the spatial results of each factors. Based the spatial distribution of population and economic factors in Chengdu,the results showed the urban center is the extreme value area of almost all elements,and the values are far difference between the urban circles. There are some values show that some area such as Qionglai has connected with the urban center. The results show that the methods identify the spatial heterogeneity of population and economic factors. The spatial data provide the support for dividing the "Sansheng space".更多还原