地理科学进展 ›› 2013, Vol. 32 ›› Issue (11): 1692-1702.doi: 10.11820/dlkxjz.2013.11.012

• GIS应用 • 上一篇    下一篇

人口数据空间化研究综述

柏中强1,2, 王卷乐1, 杨飞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
  • 收稿日期:2013-06-01 修回日期:2013-09-01 出版日期:2013-11-25 发布日期:2013-11-25
  • 通讯作者: 王卷乐(1976- ),男,博士,副研究员,主要从事格网化资源环境综合科学调查研究。E-mail:wangjl@igsnrr.ac.cn
  • 作者简介:柏中强(1988- ),男,博士研究生,主要研究方向为基于格网的区域人口时空模拟。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(2011FY110400);国家科技基础性工作专项课题项目(2012FY111800-05)。

Research progress in spatialization of population data

BAI Zhongqiang1,2, WANG Juanle1, YANG Fei1   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and NaturalResources Research, CAS, Beijing 100101, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2013-06-01 Revised:2013-09-01 Online:2013-11-25 Published:2013-11-25

摘要: 人口数据空间化研究旨在发掘和展现人口统计数据中隐含的空间信息,并以地理格网或其他区域划分的形式再现客观世界的人口分布,具有重要的科学意义。人口空间分布数据有助于从不同地理尺度和地理维度对人口统计数据形成有益补充,其应用广泛,相关研究方兴未艾。主要从以下3 个方面对人口数据空间化研究进行综述:① 主要空间化方法的原理及其适用性;② 空间化中用到的建模参考因素,并结合具体应用案例分析其作用机理;③ 典型人口空间化数据集。在此基础上,分析了现阶段人口数据空间化所运用的输入数据的质量和详细程度、尺度效应及时空分辨率、长时间序列数据集和精度检验等方面存在的问题;并探讨了人口数据空间化未来的研究方向。

关键词: 建模要素, 空间化方法, 人口数据, 数据集

Abstract: Readily available and accurate data on spatial population distribution is essential for understanding, and responding to, many social, political, economical and environmental issues, such as humanitarian relief, disaster response planning, environment impact assessment, and development assistance. Research on spatialization of demographic data plays an important role in grid transformation of social-economical data. Furthermore, as gridded population data can be effectively interoperate with geospatial data and remote sensing images, it is a useful supplement to census data. This paper reviewed spatialization methodologies, predictive modeling factors and typical datasets in the literature of population data spatialization research. Shortcomings of demographic data and advantages of spatial population distribution data are compared and summarized firstly. The spatialization methodologies are grouped into three categories, i.e., population distribution models from urban geography, areal interpolation methods and spatialization methods based on remote sensing and GIS. Population models from urban geography include the Clark's model and allometric growth model. The areal interpolation methods had been distinguished by point based method and area based method. Spatialization methods based on remote sensing and GIS are most widely used in nowadays, which can be further grouped into three categories for two reasons: one is the relationship between population and land use, urban area, traffic network, settlement density, image pixel characteristics, or other physical or socioeconomic characteristics, and the other is the calculation strategy. Various methods mentioned above have their own application environment and limitations. We reviewed the principles and applicability of every method in detail. After that, we generalized the frequently used factors in the spatialization process, involving land use/land cover, traffic network, topography, settlements density, night light, texture variable, and spectral reflectance. In the meantime, some typical research cases about the factors also were exemplified and analyzed. In addition, we introduced a few widely used spatial population distribution datasets or influential population spatialization projects. They consisted of China km grid population datasets, UNEP/GRID, GPW/GRUMP, LandScan, AfriPop & AsiaPop & AmriPop. The producers, resolution, characterization year and generation method of each one were presented exhaustively. Based on the above review, we discussed the current research problems and outlined research priorities in the future. The problems include the temporal inconsistency of input data, coarse resolution of demographic data, lack of in-depth study on scale effect, the scarcity of time series products and few validation works. To deal with these issues, more studies should be conducted to the following aspects: comprehension of population distribution mechanism, calculation of consistency and validation of existing datasets, application of multi-sources remote sensing data and volunteered geographic information, continuous space-time simulation of population distribution in the typical areas, sub-block-level population estimation, self-adaptive spatialization method which integrates multiple elements and multiple models. In summary, the research on spatialization of demographic data has made breakthroughs in the past two decades. Meanwhile, there are a few problems that need to be solved immediately. Since these two aspects had been reviewed as comprehensively as possible, we hope issues discussed in this paper could enlighten and promote the future study in this field.

Key words: datasets, modeling factors, population data, spatialization methodology