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

doi:10.18306/dlkxjz.2018.12.006

Abstract

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

Hui LIN, Hongsheng ZHANG, Yinyi LIN, Shan WEI, Zhifeng WU. Spatiotemporal changes of gridded urban population in the Guangdong-Hong Kong-Macao Greater Bay Area based on impervious surface-population correlation[J].PROGRESS IN GEOGRAPHY, 2018, 37(12): 1644-1652.

Figures/Tables 6

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References 29

[12]	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.]
[13]	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.]
[14]	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.]
[15]	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.
[16]	Deng C, Wu C.2012. BCI: A biophysical composition index for remote sensing of urban environments[J]. Remote Sensing of Environment, 127: 247-259.
[17]	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.
[18]	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.
[19]	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.
[20]	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.
[21]	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.
[22]	Tatem, Andrew J.2017. WorldPop, open data for spatial demography[J]. Scientific Data. 4. 170004. DOI: 10.1038/sdata.2017.4
[23]	Wu C, Murray A T.2003. Estimating impervious surface distribution by spectral mixture analysis[J]. Remote Sensing of Environment, 84(4): 493-505.
[24]	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.
[25]	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.
[26]	Zhang H S, Lin H, Wang Y P.2018. A new scheme for urban impervious surface classification from SAR images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 103-118.
[27]	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.
[28]	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.
[1]	柏中强, 王卷乐, 杨飞. 2013. 人口数据空间化研究综述[J]. 地理科学进展, 32(11): 1692-1702.
	[Bai Z Q, Wang J L, Yang F.2013. Research progress in spatialization of population data[J]. Progress in Geography, 32(11): 1692-1702.]
[2]	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-real-time prediction of urban population distributions based on mobile phone location data[J]. Journal of Geo-information Science, 20(4): 523-531.]
[3]	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.]
[4]	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.]
[5]	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 auto-correlation of population distribution: A case of Shenyang City[J]. Geographical Research, 26(2): 383-390.]
[6]	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.]
[7]	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.]
[8]	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.]
[9]	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.]
[10]	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.]
[11]	香港立法会. 2018. 粤港澳大湾区概况[EB/OL]. 2018-02-23[2018-10-20]. .
	[LegCo. 2018. Overview of Guangdong-Hong Kong-Macao Bay Area[EB/OL]. 2018-02-23[2018-10-20]. .]
[29]	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.

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

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