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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 208-213     DOI: 10.6046/gtzyyg.2018.02.28
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Urban sprawl metrics based on night-time light data for metropolitan areas
Lu LIU1,2()
1. School of Urban Planning and Management,Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518000, China
2. Center for Assessment and Development of Real Estate, Shenzhen 518000, China
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Abstract  

In the process of urbanization, a phenomenon called “urban sprawl” usually occurs. In the aspect of measuring and comparing urban sprawls among metropolitan areas, consistent and easy measuring or calculations are lacking although they are urgently required. Therefore, this study aims to introduce a set of metrics of urban sprawl, which include intensity, coefficient of variation, poly-centers and centrality extracted from the night-time light (NTL)data. Moreover, 50 metropolitan areas in China were used to test the feasibility of these metrics in representing urban sprawl and clarify the situation in China. The results show that these metrics are independent of each other, and can represent the urban sprawl accurately from various perspectives. The methods proposed in this paper would provide the useful tools for government and urban planners to understand urban sprawl so as to make appropriate policy and plan to achieve sustainable development of the metropolitan areas.

Keywords night-time light(NTL)      urban sprawl      metropolitan area     
:  TP79  
Issue Date: 30 May 2018
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Lu LIU
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Lu LIU. Urban sprawl metrics based on night-time light data for metropolitan areas[J]. Remote Sensing for Land & Resources, 2018, 30(2): 208-213.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.28     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/208
Fig.1  Boundary of Beijing-Tianjin-Hebei metropolitan areas
指标名称 定义 计算方式
密度(I) 城市周边郊区的发展强度 郊区的平均亮度
发展分散度(CV) 描述城市发展的分散程度 变异系数
多核心度(PC) 多中心的程度 NTL三维模型的山顶点个数/区域面积
向心度(CT) 城市的发展向城市中心集中的程度 像元值大于阈值的像元比例的累加和
Tab.1  Urban sprawl metrics
Fig.2  Boundary of city center and surburban
Fig.3  City cores of metropolitans
Fig.4  Calculation of centrality
指标 Area I CV PC CT
Area 1.00 0.21 -0.168 -0.001 0.205
I 1.00 -0.780 0.040 0.530
CV 1.000 0.000 -0.420
PC 1.000 0.160
CT 1.000
Tab.2  Correlation coefficients of metrics
城市群名称 面积/km2 I CV PC CT CS
长江三角洲 >10 000 1.42 -1.15 -0.25 1.31 1.33
北京—天津—唐山 -0.54 0.86 0.20 0.03 0.55
珠江三角洲 2.41 -2.46 -0.68 2.40 1.67
中原 -0.81 0.69 0.40 -0.98 -0.71
山东半岛 -0.31 0.66 -0.30 -0.02 0.03
辽东半岛 5 000~10 000 -0.23 0.53 -0.50 0.26 0.07
济宁—枣庄 -0.87 0.83 0.63 -1.05 -0.47
太原—临汾 -0.66 0.07 0.28 -0.97 -1.29
海峡西岸 1.59 -1.25 0.24 0.44 1.02
成都 -0.22 0.60 0.88 -0.59 0.68
邯郸 -0.67 0.21 0.76 -1.11 -0.82
西安 -0.62 0.99 -0.40 -0.12 -0.15
台州 1.72 -1.58 0.50 0.24 0.88
石家庄 2 000~5 000 -1.69 1.56 0.77 -1.43 -0.79
武汉 0.67 -0.31 1.04 0.29 1.70
长治—晋城 -0.47 -0.63 0.47 -1.20 -1.83
潮汕 1.70 -1.47 0.64 0.57 1.44
青岛 0.43 -0.35 -0.83 1.30 0.54
烟台—威海 -0.01 0.13 -1.26 0.47 -0.68
长春 -0.79 1.25 0.47 0.09 1.02
重庆 0.81 -0.83 1.66 -0.07 1.58
长沙 1.46 -1.13 0.25 0.47 1.06
哈尔滨 -0.48 0.86 0.51 0.56 1.45
大庆 1.48 -1.20 -0.63 1.89 1.53
昆明 0.11 0.02 0.87 0.76 1.76
大同 -0.53 0.15 0.77 -0.63 -0.24
乌鲁木齐 0.13 0.25 -0.55 0.80 0.63
大连 1 000~2 000 0.49 -0.37 -3.31 1.22 -1.98
内蒙古 -1.13 -1.10 0.61 -1.04 -2.66
银川 -0.54 0.95 -0.59 0.00 -0.18
武汉 0.66 -0.99 -1.12 -0.21 -1.66
合肥 0.76 -0.74 0.31 1.67 2.00
连云港 -0.37 0.52 -0.29 -0.57 -0.71
柳州 -1.39 1.50 1.42 -1.34 0.19
燕南 0.92 -1.10 -2.10 -0.13 -2.41
张家口 -1.08 1.47 0.23 -0.70 -0.08
包头 -0.18 0.41 0.75 0.86 1.84
葫芦岛 -0.52 0.77 -2.54 -0.45 -2.73
淮南 -0.56 -0.42 0.06 -1.40 -2.32
南昌 1.13 -0.65 1.30 0.68 2.46
吕梁 -1.31 0.33 -0.13 -1.65 -2.76
兰州 -0.33 0.63 0.57 0.34 1.21
呼和浩特 -0.28 0.68 0.54 0.66 1.60
南宁 1.55 -1.35 1.23 1.50 2.93
贵阳 0.84 -0.63 0.39 0.02 0.61
淮安 -0.39 0.71 -2.04 0.34 -1.38
湛江 -2.12 1.99 -0.46 -1.74 -2.33
吉林 -0.03 0.51 0.32 0.67 1.46
西宁 -0.08 0.40 -0.53 -0.29 -0.51
阳泉 -0.75 0.27 -0.58 -1.17 -2.22
Tab.3  Computation results of urban sprawl metrics
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