基于POI与NPP/VIIRS灯光数据的城市群边界定量识别

doi:10.18306/dlkxjz.2019.06.005

Abstract

Abstract:

Identification of urban agglomeration boundary is the key to the smart and compact development of urban agglomerations. It also contributes to the development of national spatial governance system and the ability of spatial governance. Taking the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations as an example and by combining the Suomi National Polar-orbiting Partnership / Visible Infrared Imaging Radiometer Suite (NPP /VIIRS) night light images and points of interest (POI) data, this study aimed to accurately identify the actual physical boundaries of the three urban agglomerations and to analyze the spatial features of the agglomeration space by density-based curve threshold method and object-oriented fractal network evolution algorithm. The results show that: 1) The curve threshold method based on POI density and the NPP/VIIRS fractal network evolution algorithm both recognize that the boundaries of urban agglomeration are smaller than the administrative boundaries of the national urban agglomeration planning, and the identified ranges are 20.90%-24.40% of the planned ranges. 2) The areas of urban agglomeration extracted by POI and night light data are very close and can be compared and verified with each other. The areas of urban agglomerations extracted by POI data are larger, which to a great extent reflect the overall boundary of urban agglomeration instead of internal details. The urban agglomeration areas extracted by NPP/VIIRS images are more fragmented, which can identify the core areas of urban agglomerations. 3) Overlay of urban agglomerations extracted from the POI and the light data shows that in addition to the core zones, there are many isolated point areas in the three urban agglomerations, indicating that the three major urban agglomerations in China are still at a rapid development stage. The intensity of inter-city contact needs to be further strengthened.

Key words: multi-source data, spatial planning, urban scale, urban structure, urban agglomeration

Liang ZHOU, Qi ZHAO, Fan YANG. Identification of urban agglomeration boundary based on POI and NPP/VIIRS night light data[J].PROGRESS IN GEOGRAPHY, 2019, 38(6): 840-850.

Figures/Tables 9

Fig.1

Tab.1

Fractal net evolution approach (FNEA) calculation formula"

名称		公式	说明
光谱异质性		$h c = ∑ w n ? σ n$	$w n$ 是不同光谱波段的权重; $σ n$ 是每个光谱值的标准差;n为波段数量
形状异质性	紧凑度数	$h cmpact = L S$	L为对象的实际周长;b为对象的最小外接矩形周长;S为对象面积; $h cmpact$ 为紧凑度异质性; $h smooth$ 为平滑度异质性; $ω c$ 为紧凑度权重
	平滑度数	$h smooth = L b$
	形状异质性	$h s = ω c × h cmpact + (1 - ω c) × h smooth$
合并前后异质度变化差值		$h diff = ∑ ω [n 1 h m - h 1 + n 2 h m - h 2]$	$ω$ 为每个波段值;设两相邻对象内像元个数分别为 $n 1$ 和 $n 2$ , $h 1 和 h 2$ 为合并前两个相邻对象异质度, $h m$ 为与合并后新对象的异度
总异质性		$h = ω color × h color + ω shape × h shape$	$ω color$ 是光谱异质性的权重; $ω shape$ 为形状异质性的权重; $h color$ 为光谱异质性; $h shape$ 为形状异质性。 $ω color$ 与 $ω shape$ 权重之和为1

Tab.1

Tab.2

Fig.2

Fig.3

Fig.4

Tab.3

Fig.5

Fig.6

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研究方法	京津冀城市群	长三角城市群	珠三角城市群
Silverman经验法	6.25	8.31	8.63
最小外接矩形法	17.91	26.13	20.12
平方根的乘积法	27.49	28.89	23.39

研究区域	总面积	城市群识别(POI)		城市群识别 (NPP/VIIRS)		综合识别
研究区域	总面积	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
京津冀城市群	216716.08	46848.54	21.63	45364.08	20.92	51766.00	23.89
长三角城市群	358579.95	82177.72	22.95	91137.80	25.44	118903.59	33.16
珠三角城市群	182620.41	44598.09	24.42	41631.08	22.87	41260.51	22.59

Identification of urban agglomeration boundary based on POI and NPP/VIIRS night light data

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