地理科学进展 ›› 2013, Vol. 32 ›› Issue (11): 1681-1691.doi: 10.11820/dlkxjz.2013.11.011

• GIS应用 • 上一篇    下一篇

农牧交错区退耕前/后人口空间分布模拟及其演化特征——以太仆寺旗为例

兰玉芳1,2, 徐霞1, 蒋力1,2, 金东艳1,2   

  1. 1. 北京师范大学地表过程与资源生态国家重点实验室, 北京 100875;
    2. 北京师范大学减灾与应急管理研究院, 北京 100875
  • 收稿日期:2013-06-01 修回日期:2013-10-01 出版日期:2013-11-25 发布日期:2013-11-25
  • 通讯作者: 徐霞(1977- ),女,副教授,主要从事土地利用与生态系统过程研究。E-mail:xuxia@bnu.edu.cn E-mail:xuxia@bnu.edu.cn
  • 作者简介:兰玉芳(1987- ),女,硕士研究生,主要研究方向为GIS技术及土地利用。E-mail:lsyf@mail.bnu.edu.cn
  • 基金资助:
    973计划项目(2011CB952001);国家自然科学基金项目(41030535,30900197)。

Simulation of spatial distribution of population and its evolution before/after the Grain for Green Project in agro-pastoral zone: A case study in Taips County

LAN Yufang1,2, XU Xia1, JIANG Li1,2, JIN Dongyan1,2   

  1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;
    2. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
  • Received:2013-06-01 Revised:2013-10-01 Online:2013-11-25 Published:2013-11-25

摘要: 人口空间分布信息在地理学和相关学科研究中占有重要地位。位于中国北方农牧交错区的太仆寺旗深受国家退耕政策影响,在退耕前后人口空间分布发生了剧烈变化。根据2000 年和2008 年研究区175 个行政村不同特点进行空间分析尺度的划分,并分析了各个空间尺度上行政村人口密度与土地利用指数、平均高程、平均坡度、距主要道路和水域距离的相关性。结合GIS和SPSS软件,运用多元回归分析方法对研究区2 期人口统计数据进行空间化,并分别用各行政村实际人口密度检验空间化结果。研究中,采取逐级增加自变量个数的方法来寻求精度较高、较合适的人口空间化模型。结果表明,区域人口密度与土地利用类型的关系最为密切;2000 年和2008年由空间化模型得到的各村人口密度与实际人口密度的相关性判定系数R分别为0.961 和0.881,且两者线性拟合斜率都接近1,精度较高;8 年间,研究区人口空间格局发生了很大变化,城镇及其周围人口密度不断增加,其他区域则急剧下降,人口呈现出向城镇及其周围集聚的趋势,而退耕政策是导致区域人口格局变化的主要驱动力之一。

关键词: GIS, 多元回归方法, 农牧交错区, 人口空间化, 太仆寺旗, 退耕政策

Abstract: The information of spatial distribution of population plays a significant role in the studies on resource environment, social economics, evaluation of the loss caused by a natural disaster, land use change, and other topics in geography and related disciplines. Traditional method hypothesizes that a population is distributed uniformly in a region, but the actual situation is not like that. Spatialization of census data for a population becomes rather important for a comprehensive analysis which combines social economics with natural environment. Therefore, the research on spatialization of census data has become a hot spot in geographic science and other social sciences. In this paper, Taips County, a typical region in the agro-pastoral zone of North China, was taken as a study case. This region has been heavily affected by the Grain-for-Green Project in China, and the population in the region has changed dramatically since the implementation of the project. Based on the characteristics of each of the 175 administrative villages in the region in 2000 and 2008, different scales were applied in the analysis. Through multi-variables regression analysis of the population's census data and various impacting factors, including land use indexes, topographical indices (mean elevations and mean slopes), and distance to main roads and rivers at the village's level in Taips County, using GIS software and SPSS statistical software as the tools, a model for the spatial distribution of population was established. In the meantime, the actual population density of each administrative village was used to validate the precision of the model. In this study, the number of independent variables was gradually increased to explore the model of the population's spatialization to achieve higher precision and make the model more suitable to the study area. It was found that there was a significant correlation between population density and land use type of each administrative village. The correlation ratio between actual administrative village's population density and the density calculated by spatialization model reached to 0.961 and 0.881 in 2000 and 2008, respectively, and the linear fitting slopes of both simulation results were close to 1. These results indicated that the spatialization model worked very well for simulation, and the accuracy satisfied the application of the model to the research on population's spatial distribution in small scales. And also, dividing spatial scales can improve the precision. In addition, the population in the region has changed dramatically during the 8 years. The population grew rapidly near the town center and in the suburban areas but dropped sharply in the other areas, indicating the trend that the population became concentrated near the town center and the surrounding areas. In conclusion, Grain-for-Green Project is one of the most important driving factors of the change of the spatial pattern of population in the regional scale.

Key words: agro-pastoral zone, GIS, Grain-for-Green Project, multi-variables regression, population spatialization, Taips County