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
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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。
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, multisource 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 multiscale 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.
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, multisource 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 multiscale 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.
引文
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陈妍,梅林.2018.东北地区资源型城市人口分布与影响因素的定量分析[J].地理科学,38(3):402-409.[Chen Y,Mei L.2018.Quantitative analysis of population distribution and influencing factors of resource-based cities in northeast China[J].Scientia Geographica Sinica,38(3):402-409.]
杜国明,于凤荣,张树文.2010.城市人口空间分布模拟与格局分析:以沈阳市为例[J].地球信息科学学报,12(1):34-39.[Du G,Yu F Y,Zhang S W.2010.Urban population distribution modeling and the pattern identifying:Taking Shenyang City as an example[J].Journal of Geo-information Science,12(1):34-39.]
杜国明,张树文,张有全.2007.城市人口分布的空间自相关分析:以沈阳市为例[J].地理研究,26(2):383-390.[Du G,Zhang S W,Zhang Y Q.2007.Analyzing spatial autocorrelation of population distribution:A case of Shenyang City[J].Geographical Research,26(2):383-390.]
胡云锋,赵冠华,张千力.2018.基于夜间灯光与LUC数据的川渝地区人口空间化研究[J].地球信息科学学报,20(1):68-78.[Hu Y F,Zhao G H,Zhang Q L.2018.Spatial distribution of population data based on nighttime light and LUC data in the Sichuan-Chongqing region[J].Journal of Geo-information Science,20(1):68-78.]
匡文慧,杜国明.2011.北京城市人口空间分布特征的GIS分析[J].地球信息科学学报,13(4):506-512.[Kuang W H,Du G M.2011.Analyzing urban population spatial distribution in beijing proper[J].Journal of Geo-information Science,13(4):506-512.]
廖顺宝,孙九林.2003.基于GIS的青藏高原人口统计数据空间化[J].地理学报,58(1):25-33.[Liao S B,Sun J L.2003.GIS based spatialization of population census data in Qinghai-Tibet Plateau[J].Acta Geographica Sinica,58(1):25-33.]
谭敏,刘凯,柳林,等.2017.基于随机森林模型的珠江三角洲30 m格网人口空间化[J].地理科学进展,36(10):1304-1312.[Tan M,Liu K,Liu L,et al.2017.Spatialization of population in the Pearl River Delta in 30 m grids using random forest model[J].Progress in Geography,36(10):1304-1312.]
王静,杨小唤,石瑞香.2012.山东省人口空间分布格局的多尺度分析[J].地理科学进展,31(2):176-182.[Wang J,Yang X H,Shi R X.2012.Spatial distribution of the population in Shandong Province at multi-scales[J].Progress in Geography,31(2):176-182.]
香港立法会.2018.粤港澳大湾区概况[EB/OL].2018-02-23[2018-10-20].https://www.legco.gov.hk/research-publications/chinese/1718fs03-overview-of-guangdong-hongkong-macao-bay-area-20180223-c.pdf[LegCo.2018.Overview of Guangdong-Hong Kong-Macao Bay Area[EB/OL].2018-02-23[2018-10-20].https://www.legco.gov.hk/research-publications/chinese/1718fs03-overview-ofguangdong-hong-kong-macao-bay-area-20180223-c.pdf.]
张国俊,黄婉玲,周春山等.2018.城市群视角下中国人口分布演变特征[J].地理学报,73(8):1513-1525.[Zhang G J,Huang W L,Zhou C S,et al.2018.Spatio-temporal characteristics of demographic distribution in China from the perspective of urban agglomeration[J].Acta Geographica Sinica,73(8):1513-1525.]
张鸿生,林殷怡,王挺,等.2018.融合光学与雷达遥感数据的城市不透水面提取方法[J].地理与地理信息科学,34(3),39-46.[Zhang H S,Lin Y Y,Wang T,et al.2018.Fusing optical and SAR remote sensing data for urban impervious surface estimation[J].Geography and Geo-Information Science,34(3):39-46.]
周成虎,欧阳,马廷.2009.地理格网模型研究进展[J].地理科学进展,28(5):657-662.[Zhou C H,Ou Y,Ma T.2009.Progresses of geographical grid systems researches[J].Progress in Geography,28(5):657-662.]
Azar D,Graesser J,Engstrom R,et al.2010.Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti[J].International Journal of Remote Sensing,31(21):5635-5655.
Deng C,Wu C.2012.BCI:A biophysical composition index for remote sensing of urban environments[J].Remote Sensing of Environment,127:247-259.
Lu D S,Weng Q H,Li G Y.2006.Residential population estimation using a remote sensing derived impervious surface approach[J].International Journal of Remote Sensing,27(16):3553-3570.
Qi W,Liu S H,Gao X L,et al.2015.Modeling the spatial distribution of urban population during the daytime and at night based on land use:A case study in Beijing,China[J].Journal of Geographical Sciences,25(6):756-768.
O'Connor C D.2014.Agency and reflexivity in boomtown transitions:Young people deciding on a school and work direction[J].Journal of Education and Work,27(4):372-391.
Ridd M K.1995.Exploring a V-I-S(Vegetation-Impervious Surface-Soil)model for urban ecosystem analysis through remote-sensing:Comparative anatomy for cities[J].International Journal of Remote Sensing,16(12):2165-2185.
Tomás L,Fonseca L,Almeida C,et al.2016.Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data[J].International Journal of Remote Sensing,37(1):1-28.
Tatem,Andrew J.2017.WorldPop,open data for spatial demography[J].Scientific Data.4.170004.DOI:10.1038/sdata.2017.4
Wu C,Murray A T.2003.Estimating impervious surface distribution by spectral mixture analysis[J].Remote Sensing of Environment,84(4):493-505.
Xu H.2010.Analysis of impervious surface and its impact on urban heat environment using the Normalized Difference Impervious Surface Index(NDISI)[J].Photogrammetric Engineering and Remote Sensing,76(5):557-565.
Zhang H S,Lin H,Li Y,et al.2013.Feature extraction for high-resolution imagery based on human visual perception[J].International journal of remote sensing,34(4):1146-1163.
Zhang H S,Lin H,Wang Y P.2018.A new scheme for urban impervious surface classification from SAR images[J].ISPRSJournal of Photogrammetry and Remote Sensing,139:103-118.
Zhang H S,Xu R.2018.Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta[J].International Journal of Applied Earth Observation and Geoinformation,64:87-95.
Zhang Y Z,Zhang H S,Lin H.2014.Improving the impervious surface estimation with combined use of optical and SAR remote sensing images[J].Remote Sensing of Environment,141:155-167.
Zhu H L,Li Y,Liu Z L,et al.2015.Estimating the population distribution in a county area in China based on impervious surfaces[J].Photogrammetric Engineering&Remote Sensing,81(2):155-163.
陈丽娜,吴升,陈洁,等.2018.基于手机定位数据的城市人口分布近实时预测[J].地球信息科学学报,20(4):523-531.[Chen L,Wu S,Chen J,et al.2018.The near-realtime prediction of urban population distributions based on mobile phone location data[J].Journal of Geo-information Science,20(4):523-531.]
陈妍,梅林.2018.东北地区资源型城市人口分布与影响因素的定量分析[J].地理科学,38(3):402-409.[Chen Y,Mei L.2018.Quantitative analysis of population distribution and influencing factors of resource-based cities in northeast China[J].Scientia Geographica Sinica,38(3):402-409.]
杜国明,于凤荣,张树文.2010.城市人口空间分布模拟与格局分析:以沈阳市为例[J].地球信息科学学报,12(1):34-39.[Du G,Yu F Y,Zhang S W.2010.Urban population distribution modeling and the pattern identifying:Taking Shenyang City as an example[J].Journal of Geo-information Science,12(1):34-39.]
杜国明,张树文,张有全.2007.城市人口分布的空间自相关分析:以沈阳市为例[J].地理研究,26(2):383-390.[Du G,Zhang S W,Zhang Y Q.2007.Analyzing spatial autocorrelation of population distribution:A case of Shenyang City[J].Geographical Research,26(2):383-390.]
胡云锋,赵冠华,张千力.2018.基于夜间灯光与LUC数据的川渝地区人口空间化研究[J].地球信息科学学报,20(1):68-78.[Hu Y F,Zhao G H,Zhang Q L.2018.Spatial distribution of population data based on nighttime light and LUC data in the Sichuan-Chongqing region[J].Journal of Geo-information Science,20(1):68-78.]
匡文慧,杜国明.2011.北京城市人口空间分布特征的GIS分析[J].地球信息科学学报,13(4):506-512.[Kuang W H,Du G M.2011.Analyzing urban population spatial distribution in beijing proper[J].Journal of Geo-information Science,13(4):506-512.]
廖顺宝,孙九林.2003.基于GIS的青藏高原人口统计数据空间化[J].地理学报,58(1):25-33.[Liao S B,Sun J L.2003.GIS based spatialization of population census data in Qinghai-Tibet Plateau[J].Acta Geographica Sinica,58(1):25-33.]
谭敏,刘凯,柳林,等.2017.基于随机森林模型的珠江三角洲30 m格网人口空间化[J].地理科学进展,36(10):1304-1312.[Tan M,Liu K,Liu L,et al.2017.Spatialization of population in the Pearl River Delta in 30 m grids using random forest model[J].Progress in Geography,36(10):1304-1312.]
王静,杨小唤,石瑞香.2012.山东省人口空间分布格局的多尺度分析[J].地理科学进展,31(2):176-182.[Wang J,Yang X H,Shi R X.2012.Spatial distribution of the population in Shandong Province at multi-scales[J].Progress in Geography,31(2):176-182.]
香港立法会.2018.粤港澳大湾区概况[EB/OL].2018-02-23[2018-10-20].https://www.legco.gov.hk/research-publications/chinese/1718fs03-overview-of-guangdong-hongkong-macao-bay-area-20180223-c.pdf[LegCo.2018.Overview of Guangdong-Hong Kong-Macao Bay Area[EB/OL].2018-02-23[2018-10-20].https://www.legco.gov.hk/research-publications/chinese/1718fs03-overview-ofguangdong-hong-kong-macao-bay-area-20180223-c.pdf.]
张国俊,黄婉玲,周春山等.2018.城市群视角下中国人口分布演变特征[J].地理学报,73(8):1513-1525.[Zhang G J,Huang W L,Zhou C S,et al.2018.Spatio-temporal characteristics of demographic distribution in China from the perspective of urban agglomeration[J].Acta Geographica Sinica,73(8):1513-1525.]
张鸿生,林殷怡,王挺,等.2018.融合光学与雷达遥感数据的城市不透水面提取方法[J].地理与地理信息科学,34(3),39-46.[Zhang H S,Lin Y Y,Wang T,et al.2018.Fusing optical and SAR remote sensing data for urban impervious surface estimation[J].Geography and Geo-Information Science,34(3):39-46.]
周成虎,欧阳,马廷.2009.地理格网模型研究进展[J].地理科学进展,28(5):657-662.[Zhou C H,Ou Y,Ma T.2009.Progresses of geographical grid systems researches[J].Progress in Geography,28(5):657-662.]
Azar D,Graesser J,Engstrom R,et al.2010.Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti[J].International Journal of Remote Sensing,31(21):5635-5655.
Deng C,Wu C.2012.BCI:A biophysical composition index for remote sensing of urban environments[J].Remote Sensing of Environment,127:247-259.
Lu D S,Weng Q H,Li G Y.2006.Residential population estimation using a remote sensing derived impervious surface approach[J].International Journal of Remote Sensing,27(16):3553-3570.
Qi W,Liu S H,Gao X L,et al.2015.Modeling the spatial distribution of urban population during the daytime and at night based on land use:A case study in Beijing,China[J].Journal of Geographical Sciences,25(6):756-768.
O'Connor C D.2014.Agency and reflexivity in boomtown transitions:Young people deciding on a school and work direction[J].Journal of Education and Work,27(4):372-391.
Ridd M K.1995.Exploring a V-I-S(Vegetation-Impervious Surface-Soil)model for urban ecosystem analysis through remote-sensing:Comparative anatomy for cities[J].International Journal of Remote Sensing,16(12):2165-2185.
Tomás L,Fonseca L,Almeida C,et al.2016.Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data[J].International Journal of Remote Sensing,37(1):1-28.
Tatem,Andrew J.2017.WorldPop,open data for spatial demography[J].Scientific Data.4.170004.DOI:10.1038/sdata.2017.4
Wu C,Murray A T.2003.Estimating impervious surface distribution by spectral mixture analysis[J].Remote Sensing of Environment,84(4):493-505.
Xu H.2010.Analysis of impervious surface and its impact on urban heat environment using the Normalized Difference Impervious Surface Index(NDISI)[J].Photogrammetric Engineering and Remote Sensing,76(5):557-565.
Zhang H S,Lin H,Li Y,et al.2013.Feature extraction for high-resolution imagery based on human visual perception[J].International journal of remote sensing,34(4):1146-1163.
Zhang H S,Lin H,Wang Y P.2018.A new scheme for urban impervious surface classification from SAR images[J].ISPRSJournal of Photogrammetry and Remote Sensing,139:103-118.
Zhang H S,Xu R.2018.Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta[J].International Journal of Applied Earth Observation and Geoinformation,64:87-95.
Zhang Y Z,Zhang H S,Lin H.2014.Improving the impervious surface estimation with combined use of optical and SAR remote sensing images[J].Remote Sensing of Environment,141:155-167.
Zhu H L,Li Y,Liu Z L,et al.2015.Estimating the population distribution in a county area in China based on impervious surfaces[J].Photogrammetric Engineering&Remote Sensing,81(2):155-163.
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