生态与农村环境学报 ›› 2021, Vol. 37 ›› Issue (6): 689-697.doi: 10.19741/j.issn.1673-4831.2020.0579

• 区域环境与发展 • 上一篇    下一篇

基于珞珈一号的珠三角地区GDP空间化研究

徐嘉源1, 陈美招1, 郑荣宝2, 马小宁2   

  1. 1. 广东外语外贸大学政治与公共管理学院, 广东 广州 510420;
    2. 广东工业大学管理学院, 广东 广州 510520
  • 收稿日期:2020-07-20 出版日期:2021-06-25 发布日期:2021-06-24
  • 通讯作者: 陈美招 E-mail:chenmz068@163.com
  • 作者简介:徐嘉源(1993-),男,广东广州人,主要研究方向为土地政策与社会保障。E-mail:846263666@qq.com
  • 基金资助:
    国家自然科学基金(41001054);教育部人文社会科学项目(18YJAZH063,19YJA630009,19YJAZH116)

A Spatialization Study of GDP Based on LJ1-01 Data in the Pearl River Delta

XU Jia-yuan1, CHEN Mei-zhao1, ZHENG Rong-bao2, MA Xiao-ning2   

  1. 1. College of Social and Public Management, Guangdong University of Foreign Studies, Guangzhou 510420, China;
    2. College of Management, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2020-07-20 Online:2021-06-25 Published:2021-06-24

摘要: 灯光数据与人类社会经济活动有很强的相互关系,因此基于灯光数据构建GDP预测模型与GDP空间化具有重要的研究价值。以珠三角地区为研究区,基于珞珈一号(LJ1-01)与索米国家极轨道伙伴关系卫星(NPP-VIIRS)这2种夜间灯光数据,提取区县级夜间灯光总强度(TNL)、综合灯光指数(CNLI)、平均相对灯光强度(I)以及灯光面积比(S)这4种灯光指数,分别构建其与GDP的线性、对数、幂指数和指数模型,并据此进行GDP空间化。结果表明,LJ1-01夜间灯光在数据空间分辨率、数据细节特征及灯光强度识别精度方面优于NPP-VIIRS夜间灯光数据,更能够体现城市结构与城市肌理;对比2018年珠三角地区GDP与LJ1-01、NPP-VIIRS灯光指数间的相关性,发现LJ1-01数据较NPP-VIIRS更适合进行珠三角地区的区县级GDP空间化,其中根据LJ1-01夜间灯光数据的TNL灯光指数所构建GDP的线性模型拟合效果较好;通过构建密度图发现,GDP较高值像元高度集中分布于广州和深圳这2个副省级城市的市中心,呈典型的"双核"布局。

关键词: 珞珈一号, 夜间灯光数据, GDP空间化, 珠三角地区

Abstract: There is a strong correlation between light data and human social and economic activities. Therefore, building a GDP prediction model based on light data and GDP spatialization has important research value. Taking the Pearl River Delta region as an example, the night light data of Luojia No.1 were preprocessed, including mosaic registration, intra-year stability correction, mask denoising, and radiation correction, etc.,which aims to obtain the 2018 Pearl River Delta LJ1-01 night light data set; Based on the night light data, the spatial distribution characteristics were analyzed, and the total light radiation value of each administrative area in the Pearl River Delta region was extracted for hot spot analysis; In addition, based on the night light data and GDP statistics,four lighting indices (TNL, I, S, CNLI) for the districts and counties of the administrative area were extracted, and the linear, logarithmic, power index, and exponential models with GDP were constructed. The best lighting index and the best regression model were determined through the determination coefficients; Based on the optimal lighting indicators and the best regression model, error correction was performed to obtain a spatial density map of GDP for the Pearl River Delta in 2018. Research results show that the small-scale grid format data obtained based on the GDP spatialization of the LJ1-01 data can reflect the region's spatial differences and spatial accuracy in a more detailed way than NPP-VIIRS data.

Key words: LJ1-01, night light data, spatialization of GDP, Pearl River Delta

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