地理科学进展 ›› 2006, Vol. 25 ›› Issue (3): 62-69.doi: 10.11820/dlkxjz.2006.03.008

• 遥感与GIS应用 • 上一篇    下一篇

一种改进人口数据空间化的方法:农村居住地重分类

杨小唤,刘业森,江东,罗春,黄耀欢   

  1. 中国科学院地理科学与资源研究所资源环境科学数据中心, 北京100101
  • 收稿日期:2006-03-01 修回日期:2006-05-01 出版日期:2006-05-25 发布日期:2006-05-25
  • 作者简介:杨小唤( 1965- )| 男, 安徽潜山人, 研究员.主要从事遥感和GIS 应用、资源环境科学空间数据库 等研究.E- mail:yangxh@lreis.ac.cn
  • 基金资助:

    国家自然科学基金项目( 40471112) .

An Enhanced Method for Spatial Distr ibuting Census Data: Re- classifying of Rur al Residential

YANG Xiaohuan,LIU Yesen,JIANG Dong,LUO Chun,HUANG Yaohuan   

  1. Institute of Geographic Sciences and National Resources Research, CAS, Beijing 100101, China
  • Received:2006-03-01 Revised:2006-05-01 Online:2006-05-25 Published:2006-05-25

摘要:

人口( 统计) 数据空间化是解决统计数据与自然要素数据融合分析的有效途径。本文在论 述已有人口空间化方法的基础上, 认为遥感影像得到的居民地数据是表达人口分布的最好指标。 为了使居民地数据更好地应用于人口空间化的研究, 论文在分析各种与人口居住密度相关指标 的基础上, 确定了用农村居民地面积所占百分比对农村居民地进行重新分级, 然后应用于人口空 间化的计算。结果检验表明, 人口空间分布数据的误差从分级前的17.4%降到分级后的12%, 尤 其是误差高于30%的乡镇个数从8 个减少到1 个, 该方法有效地提高了人口空间数据的精度。

关键词: 居民地, 空间化, 人口

Abstract:

Spatial distributing of census data is an effective way to integrate statistical data and natural factors. Land- cover and land- use change (LUCC) is the effect of human activities, and spatial distribution of population has close relationship with LUCC pattern both at regional and global scales. Population can be redistributed onto geo- referenced square grids according to this relation. Since there exist efficient approaches for monitoring LUCC with remote sensing and GIS, geo- referenced population data can also be updated conveniently. According to existing methods, it is found that the population density is directly related to land use types and the residential area is the best index for population distribution. Residential areas could be reclassified into three sub classes: urban residential, rural residential, and commercial- industrial. The paper presented an enhanced method for spatial distributing census data: re- classification of rural residential areas. On the basis of the relationship of various kinds of indexes and inhabitation density, several indexes were selected for re- classifying rural residential areas. Using these re- classified rural residential data, the precision of census redistributing pattern was improved obviously. Methods and main algorithms used in these studies were presented in the paper. Characters and prospect of this study were also discussed.

Key words: census data, rural residential, spatial distributing

中图分类号: 

  • P283