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

地理科学进展 ›› 2019, Vol. 38 ›› Issue (6): 840-850.doi: 10.18306/dlkxjz.2019.06.005

基于POI与NPP/VIIRS灯光数据的城市群边界定量识别

周亮^1,²(), 赵琪^1,³, 杨帆⁴

1. 兰州交通大学测绘与地理信息学院,兰州 730070
2. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
3. 南京师范大学地理科学学院,南京 210093
4. 腾讯科技(北京)有限公司,北京 100836

收稿日期:2018-07-18 修回日期:2019-02-16 出版日期:2019-06-28 发布日期:2019-06-27
作者简介:
第一作者简介：周亮(1983— ),男,甘肃天水人,博士/博士后,副教授,硕士生导师,研究方向为城市遥感、城市与区域可持续发展。E-mail: zhougeo@126.com
基金资助:
国家自然科学基金项目(41701173);教育部人文社会科学研究青年基金项目(17YJCZH268);甘肃省飞天学者特聘计划

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

Liang ZHOU^1,²(), Qi ZHAO^1,³, Fan YANG⁴

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
3. School of Geographical Sciences, Nanjing Normal University, Nanjing 210023, China
4. Tencent Beijing Technology Co. Ltd, Beijing 100836, China

Received:2018-07-18 Revised:2019-02-16 Online:2019-06-28 Published:2019-06-27
Supported by:
National Natural Science Foundation of China, No. 41701173;Science Foundation for the Excellent Youth Scholars of Ministry of Education of China, No. 17YJCZH268;Gansu Feitian Scholar Youth Expert Support Program.

摘要/Abstract

摘要：

科学识别城市群边界是城市群精明紧凑发展的关键,也是国家空间治理体系与空间治理能力的重要标志。论文以京津冀、长三角和珠三角3大城市群为研究区域,采用NPP/VIIRS(Suomi National Polar-orbiting Partnership / Visible Infrared Imaging Radiometer Suite)夜间灯光影像与POI(Point of Interest)数据,基于密度的曲线阈值法与分形网络演化算法,对3个城市群的实际物理边界和集聚空间范围进行精准识别与空间特征解析。研究结果表明：① 基于POI密度的曲线阈值法与NPP/VIIRS分形网络演化算法识别出城市群边界均小于国家城市群规划的行政边界,识别范围约占规划范围的20.90%~24.40%,识别结果显示3大城市群中长三角城市群发育最好,识别的城市群面积是京津冀和珠三角城市群的2倍左右;② 从POI与NPP/VIIRS灯光数据提取的城市群边界面积非常接近,其中POI数据提取的城市群面积偏大,更大程度上反映的是城市群整体边界轮廓而非内部细节;NPP/VIIRS影像提取的城市群更细碎,能更好地识别城市群聚集中心与关键核心区域,2种方法可以相互比较和验证;③ POI与灯光数据识别的城市群边界叠置分析发现,3大城市群中除了关键核心地带(集聚区)以外,外围还有众多孤立的点状区域(中小城镇),外围的点状区域与城市群集聚中心区空间割裂,一定程度上很难快速有效地接受来自城市群核心区域的辐射带动(涓滴效应)。

关键词: 多源数据, 空间规划, 城市规模, 城市结构, 城市群

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

周亮, 赵琪, 杨帆. 基于POI与NPP/VIIRS灯光数据的城市群边界定量识别[J]. 地理科学进展, 2019, 38(6): 840-850.

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.

图/表 9

图1

表1

FNEA计算公式"

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

表1

表2

图2

图3

图4

表3

图5

图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

基于POI与NPP/VIIRS灯光数据的城市群边界定量识别

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

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