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人口数据空间化研究综述
Research progress in spatialization of population data

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文摘 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。
其他语种文摘 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.
来源 地理科学进展 ,2013,32(11):1692-1702 【核心库】
关键词 人口数据 ; 空间化方法 ; 建模要素 ; 数据集
地址

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

语种 中文
文献类型 综述型
ISSN 1007-6301
基金 国家科技基础性工作专项重点项目 ;  国家科技基础性工作专项课题项目
文献收藏号 CSCD:4986138

参考文献 共 66 共4页

1.  Azar D. Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data. Remote Sensing of Environment,2013,130:219-232 CSCD被引 19 次    
2.  Balk D L. Determining global population distribution: Methods, applications and data. Advances in Parasitology,2006,62(4):119-156 CSCD被引 21 次    
3.  Bengtsson M. A SRES-based gridded global population dataset for 1990-2100. Population and Environment,2006,28(2):113-131 CSCD被引 4 次    
4.  Briggs D J. Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote Sensing of Environment,2007,108(4):451-466 CSCD被引 30 次    
5.  陈彦光. 城市人口空间分布密度衰减的一个理论证明. 信阳师范学院学报：自然科学版,2000,13(2):185-188 CSCD被引 7 次    
6.  陈振拓. 服务于地震应急的人口数据格网化方法研究,2012 CSCD被引 4 次    
7.  Clark C. Urban population densities. Journal of the Royal Statistical Society,1951,114(7):490-496 CSCD被引 108 次    
8.  Dobson J E. LandScan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing,2000,66(7):849-857 CSCD被引 54 次    
9.  杜国明. 人口数据空间化方法与实践,2008 CSCD被引 13 次    
10.  杜国明. 城市人口密度的尺度效应分析——以沈阳市为例. 中国科学院研究生院院报,2007,24(2):186-192 CSCD被引 9 次    
11.  董春. 地理因子与空间人口分布的相关性研究. 遥感信息,2002(4):61-64 CSCD被引 29 次    
12.  范一大. 行政单元数据向网格单元转化的技术方法. 地理科学,2004,24(1):105-108 CSCD被引 52 次    
13.  冯甜甜. 基于建筑物提取的精细尺度人口估算研究,2010 CSCD被引 2 次    
14.  封志明. 20世纪人口地理学研究进展. 地理科学进展,2011,30(2):131-140 CSCD被引 16 次    
15.  封志明. 中国地形起伏度及其与人口分布的相关性. 地理学报,2007,62(10):1073-1082 CSCD被引 175 次    
16.  Fisher P F. Modeling the errors in areal interpolation between zonal system by montokaro simulation. Environment and Planning A,1995,27(2):211-224 CSCD被引 8 次    
17.  符海月. 人口数据格网化模型研究进展综述. 人文地理,2006,21(3):115-119 CSCD被引 26 次    
18.  Harvey J T. Population estimation models based on individual TM pixels. Photogrammetric Engineering and Remote Sensing,2002,68(11):1181-1192 CSCD被引 13 次    
19.  胡云锋. 国家尺度社会经济数据格网化原理和方法. 地球信息科学学报,2011,13(5):573-578 CSCD被引 24 次    
20.  Jacqueline G. "Socializing the Pixel" and "Pixelizing the Social" in land-use and land-cover change. People and pixels: Linking remote sensing and social science,1998 CSCD被引 2 次    
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