地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (6): 653-660.doi: 10.3724/SP.J.1047.2015.00653

• 地球信息科学理论与方法 • 上一篇    下一篇

基于多源信息的人口分布格网化方法研究

柏中强1,2, 王卷乐1,3,*(), 姜浩1,2, 高孟绪1   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
  • 收稿日期:2014-07-22 修回日期:2014-10-17 出版日期:2015-06-10 发布日期:2015-06-10
  • 通讯作者: 王卷乐 E-mail:wangjl@igsnrr.ac.cn
  • 作者简介:

    作者简介:柏中强(1988-),男,博士生,研究方向为格网化区域人口时空模拟。E-mail: baizq@lreis.ac.cn

  • 基金资助:
    国家科技基础性工作专项重点项目“格网化资源环境综合科学调查规范”(2011FY110400);中国科学院信息化专项项目“资源学科领域基础科学数据整合与集成应用”(XXH12504-1-01);国家科技基础条件平台——地球系统科学数据共享平台(2005DKA32300)

The Gridding Approach to Redistribute Population Based on Multi-source Data

BAI Zhongqiang1,2, WANG Juanle1,3,*(), JIANG Hao   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
  • Received:2014-07-22 Revised:2014-10-17 Online:2015-06-10 Published:2015-06-10
  • Contact: WANG Juanle E-mail:wangjl@igsnrr.ac.cn
  • About author:

    *The author: SHEN Jingwei, E-mail:jingweigis@163.com

摘要:

格网化人口分布数据比行政单元人口密度数据更易直观表达人口的真实分布状况。本文面向人口格网化管理的区域发展需求,以延安市为研究对象,基于增强居民地空间分布及其内部结构信息的理念,利用乡镇界线和乡镇级人口统计数据为输入控制单元,以土地利用数据、居民点信息、DEM、夜晚灯光数据等多源信息为指示因子,采用多元回归建模方法获得了延安市2010年100 m格网人口分布数据。结果表明,本文采用的人口格网化建模方法最终模型选用变量数少,决定系数(R2)达到0.872。最终模型在用于验证的24个乡镇中,有18个乡镇的估计人口数与统计值误差绝对值小于10%。分析认为,该建模策略结果可信,多源的人口分布指示信息在人口格网化方法上明显优于单独的土地利用数据方法。本文获得的100 m格网延安市人口数据格网化结果,显著增强了人口空间分布的细节信息,对于县市一级的人口数据格网化具有借鉴意义。

关键词: 人口分布, 格网化, 多源数据, 居民地信息增强, 延安市

Abstract:

Gridded population distribution data are increasingly expanding their uses in a wide range of fields, such as resource utilization, disaster response and relief, environment protection, and economic research. The enhancement of resolution with detailed precision is a perpetual topic in population gridding research. In this paper, a 100m gridded population dataset was established for Yan’an city by developing a method that distributed the population based on the land use data with improved settlement information. Data used here include township boundary data, township-level demographic data, land use data, TM image, town-village settlement point data, and DMSP/OLS nighttime light images. Two approaches were used to improve the detailed information of settlement distribution. First, we identify the rural settlement information from land use data and enhance the information by the village point data obtained from Google Earth. Second, we downscale the DMSP/OLS data by spatial interpolation. The sum of light emission, lit area and unlit area under different land use types in each town were counted to be used as the independent variables, and the statistical population of each town were used as the dependent variable. A stepwise regression method was adopted to simulate their relationship. Finally, the sum of night light emission, the unlit area of build-up area, the unlit area of grassland, and the lit area of farmland were put into constructing the ultimate equation. All variables were significant under the level of 0.01 and the coefficient of multiple correlation is 0.872. We estimated the population at township level for selected towns as a validation. Through using the equation, we found that the mean error between the estimation and the statistical population is lower than 5%. The above analysis suggests that the proposed modeling strategy is highly efficient. As a result, we calculate the weight and distribute the population through the equation in the formation of 100m grid, by taking township as the unit. In summary, the gridding method used in this study can obviously improve the output resolution and the distribution details. Also, the expression of the final equation is relative simple. As a conclusion, this paper has its significance in guiding the population gridding research in the county level areas like Yan’an city.

Key words: population distribution, multi-source data, spatialization, enhancement of settlement distribution information, Yan'an city