地理科学进展 ›› 2011, Vol. 30 ›› Issue (5): 635-640.doi: 10.11820/dlkxjz.2011.05.017

• 区域经济 • 上一篇    

人口密度的空间降尺度分析与模拟——以贵州猫跳河流域为例

王磊, 蔡运龙   

  1. 北京大学城市与环境学院土地科学中心/地表过程分析与模拟教育部重点实验室, 北京 100871
  • 收稿日期:2011-01-01 修回日期:2011-04-01 出版日期:2011-05-25 发布日期:2011-05-25
  • 通讯作者: 蔡运龙(1948-), 男, 教授,主要从事自然地理学、自然资源学、土地科学、地理学理论与方法的教学和研究工作。E-mail: caiyl@urban.pku.edu.cn E-mail:caiyl@urban.pku.edu.cn
  • 作者简介:王磊(1979-) , 男, 博士研究生,主要从事LUCC 及其生态效应研究。E-mail: wlei@pku.edu.cn
  • 基金资助:

    国家自然科学基金项目(40871047)。

Spatial Down-scaling Analysis and Simulation of Population Density in Maotiaohe Basin, Guizhou Province

WANG Lei, CAI Yunlong   

  1. College of Urban and Environmental Sciences, the Center for Land Study, Peking University; Laboratory for Earth Surface Processes, the Ministry of Education, Beijing 100871, China
  • Received:2011-01-01 Revised:2011-04-01 Online:2011-05-25 Published:2011-05-25

摘要: 人口调查统计以行政区划为基本单元,数据精度不能满足较高分辨率的空间结构分析,也难以在地理综合研究中与自然地理要素数据相匹配。因此,人口密度空间化成为地理学的重要研究方向之一。本文基于贵州省猫跳河流域的乡镇人口数据,采用GIS空间分析技术与统计学方法,分析了人口密度与空间因子的关系;并采用多元回归的方法建立了人口密度数据空间化模型,在GIS平台中实现了人口密度的降尺度空间化模拟。建立的多元回归模型拟合精度达到0.577,且模拟结果与实际人口数据比较线性拟合斜率接近1,效果比较理想。研究结果表明:影响该地区人口密度的主导空间因子为建设用地指数、耕地指数与到道路的平均距离。

关键词: 贵州, 空间降尺度分析, 空间模拟, 猫跳河流域, 人口密度

Abstract: Population census is based on administrative unit, therefore, the data accuracy can not meet the higher resolution analysis of the spatial structure and is difficult to match the data of physical elements in a comprehensive analysis of geographic features. Thus, spatialization of population density becomes an important research direction in geography. Based on the statistical population data of villages in the Maotiaohe basin, this research first analyzed the relationship between population density and spatial factors, and then established a spatial down-scaling model of population density by means of multiple variables regression analysis and GIS technique. The correlation coefficient between population density and spatial factors is 0.577. For testing and verifying the model, we calculated the population of every village with modeling results and compared it with statistical data. The correlation coefficient almost reached 1. The result shows that the main spatial factors of population density are the built-up area, farmland index and the distance to road.

Key words: down-scaling spatial analysis, Guizhou, Maotiaohe basin, population density, spatial simulation