地球信息科学学报

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (7): 969-976.doi: 10.3724/SP.J.1047.2016.00969

• 地理空间分析综合应用 • 上一篇    下一篇

基于夜间灯光和人口密度数据的京津冀GDP空间化对比

王旭(), 吴吉东*(), 王海, 李宁   

  1. 1. 北京师范大学 环境演变与自然灾害教育部重点实验室,北京 100875
    2. 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
    3. 北京师范大学 民政部教育部减灾与应急管理研究院,北京 100875
  • 收稿日期:2015-12-04 修回日期:2016-03-31 出版日期:2016-07-15 发布日期:2016-07-15
  • 通讯作者: 吴吉东 E-mail:wangxu2015@mail.bnu.edu.cn;wujidong@bnu.edu.cn
  • 作者简介:

    作者简介：王旭(1992-), 男, 硕士生, 研究方向为自然灾害经济损失评估。E-mail: wangxu2015@mail.bnu.edu.cn

  • 基金资助:
    国家重大科学研究计划项目(2012CB955402);国家自然科学基金项目(41571492);教育部-国家外国专家局高等学校创新引智计划(B08008)

Comparison of GDP Spatialization in Beijing-Tianjin-Hebei Based on Night Light and Population Density Data

WANG Xu(), WU Jidong*(), WANG Hai, LI Ning   

  1. 1. Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing 100875, China
    2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
    3. Academy of Disaster Reduction and Emergency Management, MOE & MCA, Beijing Normal University, Beijing 100875, China
  • Received:2015-12-04 Revised:2016-03-31 Online:2016-07-15 Published:2016-07-15
  • Contact: WU Jidong E-mail:wangxu2015@mail.bnu.edu.cn;wujidong@bnu.edu.cn

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摘要：

国内生产总值（GDP）是衡量地区经济发展水平的重要指标,GDP的空间化可以为灾害风险分析等多学科交叉研究提供基础数据。空间化代用数据的选择是社会经济统计数据空间化的关键,本文以京津冀地区作为研究区,将夜间灯光、全球人口密度（LandScan）和亚洲人口密度（AsiaPop）空间分布信息作为代用数据,将市级GDP统计数据空间展布到栅格单元,以绝对误差、相对误差和均方根误差为指标,利用县级统计数据对展布结果进行误差分析,并对比3种数据对GDP空间模拟的表达效果。结果表明：相对于夜间灯光和LandScan数据,AsiaPop模拟得到的综合误差最小;基于夜间灯光和LandScan的GDP空间展布误差格局比较接近,即存在经济较发达的市辖区GDP值被低估、市郊区县GDP被高估的误差“两极区”倾向,而基于AsiaPop的GDP空间展布误差格局与经济发展水平关系不密切。因此,利用单一代用数据很难合理地反映经济活动的空间分布,综合夜间灯光、人口密度、道路和建筑物等多源空间数据是提高GDP空间展布精度的发展趋势。

关键词: GDP空间化, 京津冀, 夜间灯光, 人口密度

Abstract:

As an important indicator in measuring the economic development level of a region, GDP spatialization is of great significance to study the socio-economic heterogeneity. The ancillary spatial density data selection is the key technique in controlling the GDP spatialization′s accuracy. In this paper, the prefectural GDP statistics is distributed to grid cells according to the spatial distribution information of GDP such as the population density (LandScan, AsiaPop) and night light data in Beijing-Tianjin-Hebei. Moreover, the absolute errors and relative errors of the GDP disaggregation at county-level are both calculated in order to compare the errors among the three different ancillary data as mentioned above. These results can provide a reasonable reference to ancillary spatial density data selection in GDP disaggregation. The results show that, the spatial distributions of the three types of ancillary spatial density data for GDP have revealed their own advantages and disadvantages. Comparing with both of the night light and the LandScan data, the AsiaPop simulation generally has the smallest error, especially in the suburban districts and rural areas of Beijing where the GDP tends to be overestimated, while the GDP is often underestimated in the economically developed city centers. For the LandScan simulation, six counties have presented a relative error of more than 200%, as the LandScan data are concentrated in Beijing and Tianjin, while the suburban districts and counties have also been overestimated. The AsiaPop simulation has only three counties (which locate in Tianjin) presenting a relative error being more than 200%. Because of the spatial heterogeneity of the economic activities, the GDP disaggregation error will increase with respect to the refinement of the administrative units, therefore, using the single-generation data to reasonably reflect the spatial distribution of economic activities is difficult, we need to take advantage of the distribution data such as the night light, roads, housing distribution and cell phone signals to improve the GDP disaggregation′s accuracy in future, and to reflects the GDP distribution characteristics in a more detailed manner. High-quality exposure data not only provide the basic data for the study of spatial analysis of natural disaster risk, but also provide a reference for other multidisciplinary research fields; meanwhile, the comprehensive application of using both the multi-source remote sensing data and the statistics data is the trend for socio-economic data spatialization.

Key words: GDP spatialization, Beijing-Tianjin-Hebei, night light, population density