地理科学进展

地理科学进展 ›› 2018, Vol. 37 ›› Issue (12): 1644-1652.doi: 10.18306/dlkxjz.2018.12.006

所属专题： 人口与城市研究； 粤港澳大湾区

• 专栏：粤港澳大湾区 • 上一篇    下一篇

基于城市不透水面—人口关联的粤港澳大湾区人口密度时空分异规律与特征

林珲1,2(), 张鸿生1,2,*(), 林殷怡1, 魏姗1, 吴志峰3   

  1. 1. 香港中文大学太空与地球信息科学研究所,香港 999077
    2. 香港中文大学深圳研究院,广东 深圳518057
    3. 广州大学地理科学学院,广州 510006
  • 收稿日期:2018-10-25 修回日期:2018-12-07 出版日期:2018-12-28 发布日期:2018-12-28
  • 通讯作者: 张鸿生
  • 作者简介:

    作者简介：林珲(1954-),男,广东省汕头市人,教授,研究方向为多云多雨遥感、虚拟地理环境,E-mail: huilin@cuhk.edu.hk。

  • 基金资助:
    香港特别行政区研究资助局项目(CUHK14635916, CUHK14605917);国家自然科学基金项目(41401370)

Spatiotemporal changes of gridded urban population in the Guangdong-Hong Kong-Macao Greater Bay Area based on impervious surface-population correlation

Hui LIN1,2(), Hongsheng ZHANG1,2,*(), Yinyi LIN1, Shan WEI1, Zhifeng WU3   

  1. 1. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong 999077, China
    2. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, Guangdong, China
    3. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
  • Received:2018-10-25 Revised:2018-12-07 Online:2018-12-28 Published:2018-12-28
  • Contact: Hongsheng ZHANG
  • Supported by:
    The Research Grant Counsil of Hong Kong, No.CUHK14635916, No.CUHK14605917;National Natural Science Foundation of China, No.41401370

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摘要：

城市人口数据是社会经济各领域的基础数据,高分辨率的空间化城市人口数据则对社会经济各领域的分析和研究具有重要意义。本文首先通过多源遥感技术提取空间分辨率为30 m的粤港澳大湾区2007-2015年间城市不透水面的变化,再利用Dasymetric映射方法得到30 m分辨率的网格化人口密度分布,从而分析大湾区2007-2015年间城市人口的时间和空间变化。通过Google Earth时间序列高分辨率影像采集的样本验证,粤港澳大湾区城市不透水面提取精度均在80%以上;通过统计年鉴中县级人口统计数据,分析大湾区网格化城市人口与统计数据之间的一致性,得到决定系数R2总体在0.7以上。研究表明,粤港澳大湾区城市人口具有特殊的时间和空间分异规律和特征：①大湾区内除了香港和澳门人口分布较稳定,其他城市人口都有不同程度和不同方向的扩张,其中广州、深圳、东莞的人口扩张最为明显;②大湾区城市人口空间分布具有明显的多尺度和多中心特征。总体上,大湾区人口集中在以珠江口为中心的城市群核心区内,离核心区较远的肇庆、江门、惠州人口较为稀疏,城市化程度相对较低,是支撑大湾区经济社会继续深入发展的重要区域。在核心区内,城市人口的分布则在城市尺度和城市群尺度上都体现了多中心分布特征,香港和广州都有多个城市中心,而香港、澳门、深圳、广州则是整个大湾区的4个中心。地理位置上4个中心分布在大湾区的不同地方,可以带动整个粤港澳大湾区的全面发展。研究结果可为粤港澳大湾区在社会经济各领域的分析与规划提供决策支持。

关键词: 粤港澳大湾区, 不透水面, 多源遥感, 人口空间化, 城市人口

Abstract:

Urban population data are the basic data in various social and economy fields, and high-resolution spatialized urban population data are of great importance for the research in such fields. In this article, multi-source remote sensing data were used to extract the urban impervious surface changes in the Guangdong-Hong Kong-Macao (GHM) Greater Bay Area at a spatial resolution of 30 meters from 2007 to 2015. The Dasymetric mapping method was used to spatialize the population at different times to a resolution of 30 meters. We finally estimated the gridded population density distribution of 30 meters resolution, and analyzed the spatiotemporal changes of the urban population in the GHM Greater Bay Area from 2007 to 2015. Validated with Google Earth time series high-resolution images, the accuracy of the derived urban impervious surfaces in GHM is generally above 80%. Using the county-level demographic data, the consistency between the estimated population and the statistical data in the GHM Greater Bay Area was analyzed, and the correlation coefficient (R2) was generally above 0.7. Finally, according to the spatial distribution of urban population and the change of population density, urban expansion and population increase patterns of different cities in the GHM Greater Bay Area were analyzed. The research shows that the urban population of the GHM has special spatiotemporal characteristics: (1) Stable population distribution is observed for Hong Kong and Macao, but other urban areas have experienced expansion of population to different extents and in different directions. The population expansion of Guangzhou, Shenzhen, and Dongguan is most obvious. (2) The spatial distribution of urban population in GHM shows multi-scale and multi-center characteristics. In general, the population of GHM is concentrated in the core area centered at the Pearl River Estuary. The Zhaoqing, Jiangmen, and Huizhou areas are sparsely populated. In the core area, the distribution of urban population shows the characteristics of multi-center distribution on both urban and metropolitan scales. Hong Kong and Guangzhou have multiple urban centers, while Hong Kong, Macao, Shenzhen, and Guangzhou are the centers of the GHM. These four centers can drive the overall development of the GHM Greater Bay Area.

Key words: Guangdong-Hong Kong-Macao Greater Bay Area, impervious surface, multi-source remote sensing, population spatialized, urban population