地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (3): 435-442.doi: 10.3724/SP.J.1047.2014.00435

• 地理空间分析综合应用 • 上一篇    下一篇

基于土地利用类型的村级人口空间分布模拟——以湖北鹤峰县为例

张建辰, 王艳慧   

  1. 首都师范大学资源环境与地理信息系统北京市重点实验室, 首都师范大学三维信息获取与应用教育部重点实验室, 首都师范大学城市环境过程与数字模拟国家重点实验室培育基地, 北京100048
  • 收稿日期:2013-12-11 修回日期:2014-02-19 出版日期:2014-05-10 发布日期:2014-05-10
  • 通讯作者: 王艳慧(1977- ),女,河南上蔡人,博士,副教授,研究方向为多尺度空间数据组织与应用。E-mail:huiwangyan@sohu.com E-mail:huiwangyan@sohu.com
  • 作者简介:张建辰(1988- ),男,硕士生,研究方向为地理信息系统方法与应用。E-mail:openbuildstar@126.com
  • 基金资助:

    国家自然科学基金项目(41371375);北京市自然科学基金项目(8132018);“十二五”国家科技支撑计划项目(2012BAH33B03、2012BAH33B05)。

Simulation of Village-Level Population Distribution Based on Land Use: A Case Study of Hefeng County in Hubei Province

ZHANG Jianchen, WANG Yanhui   

  1. Beijing key Laboratory of Resource Environment and Geographic Information System, Capital Normal University;Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University;State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China
  • Received:2013-12-11 Revised:2014-02-19 Online:2014-05-10 Published:2014-05-10

摘要:

在人口分布及其相关研究中,常常会遇到小尺度人口数据部分缺失的问题。本文以湖北省鹤峰县为例,在分析土地利用与人口分布关系的基础上,从全局与局部、线性回归与非线性回归考虑,基于土地利用类型,分别利用地理加权回归(GWR)方法、格网方法、BP神经网络方法对缺失数据的行政村人口数据进行模拟,并进行了多角度精度对比验证。研究结果表明:(1)各种土地利用类型中,耕地、林地、城镇村及工矿用地、交通用地是影响研究区村级人口分布的主要因素;(2)30个调查村中,3种方法模拟的人口总数误差小于3%,通过每个村的模拟值与实际值相比,BP神经网络方法能更好地模拟研究区村级人口的分布,格网方法次之,GWR方法最差;(3)研究区各村人口分布呈现较高的空间正相关性,各乡镇的人口密度在空间上并不独立,而是呈现紧密的集聚特征。

关键词: 村级人口, GWR模型, 格网方法, BP神经网络, 空间分布, 精度分析

Abstract:

The problem that population data is usually missing in small scale areas such as administrative villages which are always mentioned in population distribution studies and related researches. In this context, we took the Hefeng County in Hubei Province as the study area and analyzed the correlation between land use type index and population density. The simulation of the village-level population distribution is performed using Geographically Weighted Regression (GWR) method, grid method and BP neural network method respectively. Then, from the perspective of global-local and linear-nonlinear, the comparative precision validation was taken to verify the simulation accuracy of the population in villages with missing population data, which utilizes cross-validation method between the simulated population and the actual population. Results show that: (1) in all kinds of land use types, the main factors affecting population distribution are farmland, woodland, urban industrial land, and transportation land;(2) with regard to the three simulation methods we concerned, the errors of the simulated total population using these methods are all less than 3% for the 30 invested villages. By comparing the ratios of estimated values to the actual values of population in each village, and taking 10% as the tolerance, the reliability of GWR method is 43.33%, while grid method is 76.67% and BP neural network is 86.67 %. It shows that the BP neural network method is the optimal method among the three methods for the study area, and grid method is better than GWR method. In addition, the prediction accuracy of nonlinear regression is higher than that of linear regression;(3) population spatial distribution in the study area shows a high spatial positive correlation and a "high–high"agglomeration type which is also the main type in the study area;moreover, it shows that the population densities of the county are not spatially independent but intensively agglomerated.

Key words: grid method, BP neural network, village-level census, spatial distribution, accuracy analysis, geographically weighted regression model