基于道路交叉点邻域扩张曲线的城市边界识别——以成都、西安、武汉、南京和长沙为例

doi:10.18306/dlkxjz.2018.06.005

地理科学进展 ›› 2018, Vol. 37 ›› Issue (6): 781-789.doi: 10.18306/dlkxjz.2018.06.005

基于道路交叉点邻域扩张曲线的城市边界识别——以成都、西安、武汉、南京和长沙为例

林晓娟^1,²(), 房世峰², 徐亚莉^1,³, 邹宝裕¹, 罗明良^1,^3,^*()

1. 西华师范大学国土资源学院,四川南充 637002
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. 西华师范大学地表过程与环境变化研究所,四川南充 637002

收稿日期:2017-04-09 修回日期:2017-08-14 出版日期:2018-06-28 发布日期:2018-06-28
通讯作者: 罗明良 E-mail:lxjxiaojuan@163.com;lolean586@163.com
作者简介:
作者简介：林晓娟(1996-),女,四川成都人,本科生,主要从事GIS空间分析与应用,E-mail: lxjxiaojuan@163.com。
基金资助:
国家自然科学基金项目(41101348,U1503184);中国科学院大学生创新实践训练计划项目;西华师范大学基本科研业务费专项资金资助项目(15C002);绵阳师范学院四川省生态安全与保护重点实验室资助项目(ESP1606)

Identifying urban boundaries by clustering street node based on neighborhood dilation curve:A case study of Chengdu, Xi'an, Wuhan, Nanjing and Changsha

Xiaojuan LIN^1,²(), Shifeng FANG², Yali XU^1,³, Baoyu ZOU¹, Mingliang LUO^1,^3,^*()

1. School of Land and Resources, China West Normal University, Nanchong 637002, Sichuan, China
2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
3. Institute of Land Surface Processes and Environmental Changes, China West Normal University, Nanchong 637002, Sichuan, China

Received:2017-04-09 Revised:2017-08-14 Online:2018-06-28 Published:2018-06-28
Contact: Mingliang LUO E-mail:lxjxiaojuan@163.com;lolean586@163.com
Supported by:
National Natural Science Foundation of China, No.41101348, No.U1503184;Chinese Academy of Sciences Innovation Practice Training Program for College Students;Basic Research Project Financed by the Special Fund of China West Normal University, No.15C002;Sichuan Key Laboratory of Ecological Security and Protection in Mianyang Normal College, No.ESP1606

摘要/Abstract

摘要：

城市边界识别是定性和定量研究城市的基础和前提,已有的关于城市边界提取的研究大都需要提前设定阈值或依赖人口统计数据。基于分形几何学,利用矢量建筑物分布数据识别城市边界,虽可克服这一缺陷,但国内城市边界的研究往往受阻于矢量建筑物分布数据获取困难。本文提出了一种基于道路交叉点的邻域扩张曲线作为识别城市边界的新方法。结果表明：该方法以电子地图为数据源,基于道路交叉点矢量数据进行研究时,城市集群数据随搜索半径增大而改变,城市扩张曲线中的最佳距离阈值是提取城市边界的关键;提取成都、西安、武汉、南京和长沙城市边界的最佳距离阈值分别为133、114、139、124和129 m,各城市的集群面积分别为769、350、270、317和359 km²。利用道路交叉点提取城市边界,方法简便可行,数据较易获得,本文结论有望为城市形态发展演变和城市规划等相关研究提供参考。

关键词: 城市边界, 道路交叉点, 邻域扩张曲线, 曲率变点, 电子地图

Abstract:

Identifying urban boundaries is the basis of qualitative and quantitative study of cities. Most of the existing studies on the identification of urban boundaries rely on predefined distance thresholds or incorporate census data. Although fractal geometry method using building vector maps to identify urban boundaries can overcome this problem, the research of urban boundary identification in China is often hindered by the difficulty of obtaining vector building distribution data. This study draws upon existing research results and puts forward a new method to identify urban boundaries by clustering street nodes based on neighborhood dilation curves. The results show that the key to this method that uses street nodes from electronic map as data source lies in finding the optimal distance threshold corresponding to the maximum curvature. The distance threshold for extracting urban boundaries of Chengdu, Xian, Wuhan, Nanjing, and Changsha are 133, 114, 139, 124, and 129 m; and the area of city clusters are 769, 350, 270, 317, and 359 km², respectively. The method of using street nodes vector data to identify urban boundaries is simple and feasible, and the data are easy to obtain. So the results of this study may provide some reference for the study of urban morphology, urban evolution, and urban planning.

Key words: urban boundaries, street nodes, neighborhood dilation curve, curvature inflection point, electronic map

林晓娟, 房世峰, 徐亚莉, 邹宝裕, 罗明良. 基于道路交叉点邻域扩张曲线的城市边界识别——以成都、西安、武汉、南京和长沙为例[J]. 地理科学进展, 2018, 37(6): 781-789.

Xiaojuan LIN, Shifeng FANG, Yali XU, Baoyu ZOU, Mingliang LUO. Identifying urban boundaries by clustering street node based on neighborhood dilation curve:A case study of Chengdu, Xi'an, Wuhan, Nanjing and Changsha[J]. PROGRESS IN GEOGRAPHY, 2018, 37(6): 781-789.

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参考文献 24

[1]	陈彦光. 2003. 中国的城市化水平有多高?城市地理研究为什么要借助分形几何学[J]. 城市规划, 27(7): 12-17. doi: 10.3321/j.issn:1002-1329.2003.07.003
	[Chen Y G.2003. What is the urbanization level of China[J]. City Planning Review, 27(7): 12-17.] doi: 10.3321/j.issn:1002-1329.2003.07.003
[2]	陈彦光. 2017. 城市形态的分维估算与分形判定[J]. 地理科学进展, 36(5): 529-539. doi: 10.18306/dlkxjz.2017.05.001
	[Chen Y G.2017. Approaches to estimating fractal dimension and identifying fractals of urban form[J]. Progress in Geography, 36(5): 529-539.] doi: 10.18306/dlkxjz.2017.05.001
[3]	李雪铭, 丛雪萍, 同丽嘎, 等. 2017. 城市边界的划分方法及其应用[J]. 城市问题, (2): 46-51.
	[Li X M, Cong X P, Tong L G, et al.2017. The defining method and application of urban boundary[J]. Urban Problems, (2): 46-51.]
[4]	林海伦, 王元卓, 贾岩涛, 等. 2017. 面向网络大数据的知识融合方法综述[J]. 计算机学报, 40(1): 1-27.
	[Lin H L, Wang Y Z, Jia Y T, et al.2017. Network big data oriented knowledge fusion methods: A survey[J]. Chinese Journal of Computers, 40(1): 1-27.]
[5]	吕添贵, 吴次芳, 李洪义, 等. 2016. 人口城镇化与土地城镇化协调性测度及优化: 以南昌市为例[J]. 地理科学, 36(2): 239-246. doi: 10.13249/j.cnki.sgs.2016.02.010
	[Lyu T G, Wu C F, Li H Y, et al.2016. The coordination and its optimization about population and land of urbanization: A case study of Nanchang City[J]. Scientia Geographica Sinica, 36(2): 239-246.] doi: 10.13249/j.cnki.sgs.2016.02.010
[6]	潘爱民, 刘友金. 2014. 湘江流域人口城镇化与土地城镇化失调程度及特征研究[J]. 经济地理, 34(5): 63-68.
	[Pan A M, Liu Y J.2014. The degree of imbalance between population urbanization and land urbanization of Xiangjiang River Basin[J]. Economic Geography, 34(5): 63-68.]
[7]	宋金超, 李新虎, 吝涛, 等. 2015. 基于夜晚灯光数据和Google Earth的城市建成区提取分析[J]. 地球信息科学学报, 17(6): 750-756. doi: 10.3724/SP.J.1047.2015.00750
	[Song J C, Li X H, Lin T, et al.2015. A method of extracting urban built-up area based on DMSP/OLS nighttime data and Google Earth[J]. Journal of Geo-Information Science, 17(6): 750-756.] doi: 10.3724/SP.J.1047.2015.00750
[8]	谭兴业, 陈彦光. 2015. 基于邻域扩展量化法的城市边界识别[J]. 地理科学进展, 34(10): 1259-1265. doi: 10.18306/dlkxjz.2015.10.006
	[Tan X Y, Chen Y G.2015. Urban boundary identification based on neighborhood dilation[J]. Progress in Geography, 34(10): 1259-1265.] doi: 10.18306/dlkxjz.2015.10.006
[9]	王法辉, 金凤君, 曾光. 2004. 区域人口密度函数与增长模式: 兼论城市吸引范围划分的GIS方法[J]. 地理研究, 23(1): 97-103. doi: 10.3321/j.issn:1000-0585.2004.01.012
	[Wang F H, Jing F J, Zeng G.2004. Analyzing regional density functions and growth patterns in China with a GIS-based method delineating influential regions of cities[J]. Geographical Research, 23(1): 97-103.] doi: 10.3321/j.issn:1000-0585.2004.01.012
[10]	王元卓, 靳小龙, 程学旗. 2013. 网络大数据: 现状与展望[J]. 计算机学报, 36(6): 1125-1138.
	[Wang Y Z, Jin X L, Cheng X Q.2013. Network big data: Present and future[J]. Chinese Journal of Computers, 36(6): 1125-1138.]
[11]	王媛, 姜博, 初楠臣, 等. 2016. 基于建设用地变化的城市边缘区提取方法与扩张模式研究: 以哈尔滨市为例[J]. 经济地理, 36(5): 26-32. doi: 10.15957/j.cnki.jjdl.2016.05.004
	[Wang Y, Jiang B, Chu N C, et al.2016. Extraction method and expansion patterns of the urban fringe based on construction land change: A case in Harbin City[J]. Economic Geography, 36(5): 26-32.] doi: 10.15957/j.cnki.jjdl.2016.05.004
[12]	吴志强, 叶锺楠. 2016. 基于百度地图热力图的城市空间结构研究: 以上海中心城区为例[J]. 城市规划, 40(4): 33-40.
	[Wu Z Q, Ye Z N.2016. Research on urban spatial structure based on baidu heat map: A case study on the central city of Shanghai[J]. City Planning Review, 40(4): 33-40.]
[13]	许泽宁, 高晓路. 2016. 基于电子地图兴趣点的城市建成区边界识别方法[J]. 地理学报, 71(6): 928-939. doi: 10.11821/dlxb201606003
	[Xu Z N, Gao X L.2016. A novel method for identifying the boundary of urban built-up areas with POI data[J]. Acta Geographica Sinica, 71(6): 928-939.] doi: 10.11821/dlxb201606003
[14]	姚君兰, 王红, 胡斌斌. 2016. 基于矢量数据的城市建成区范围提取方法[J]. 测绘通报, (5): 84-87.
	[Yao J L, Wang H, Hu B B.2016. Urban built-up area extraction based on vector data[J]. Bulletin of Surveying and Mapping, (5): 84-87.]
[15]	朱延娟, 倪周松. 2015. 基于离散点曲率的细胞图像形状特征表述[J]. 计算机应用, 35(S2): 267-270, 304.
	[Zhu Y J, Ni Z S.2015. Description of shape feature for cell image based curvature of discrete points[J]. Journal of Computer Applications, 35(S2): 267-270, 304.]
[16]	邹进贵, 陈艳华, 丁鸽, 等. 2016. 利用DMSP/OLS灯光影像提取城镇建成区的聚类阈值法[J]. 武汉大学学报: 信息科学版, 41(2): 196-201.
	[Zou J G, Chen Y H, Ding G, et al.2016. A clustered threshold method for extracting urban built-up area using the DMSP/OLS nighttime light images[J]. Geomatics and Information Science of Wuhan University, 41(2): 196-201.]
[17]	Eeckhout J.2004. Gibrat's law for (all) cities[J]. The American Economic Review, 94(5): 1429-1451. doi: 10.1257/0002828043052303
[18]	Jiang B, Jia T.2011. Zipf's law for all the natural cities in the United States: A geospatial perspective[J]. International Journal of Geographical Information Science, 25(8): 1269-1281. doi: 10.1080/13658816.2010.510801
[19]	Lowe D G.1989. Organization of smooth image curves at multiple scales[J]. International Journal of Computer Vision, 3(2): 119-130. doi: 10.1007/BF00126428
[20]	Minkowski H.1903. Volumen und oberfläche[J]. Mathematische Annalen, 57(4): 447-495. doi: 10.1007/BF01445180
[21]	Rozenfeld H D, Rybski D, Andrade Jr J S, et al.2008. Laws of population growth[J]. Proceedings of the National Academy of Sciences of the United States of America, 105(48): 18702-18707. doi: 10.1073/pnas.0807435105
[22]	Schwarz G.1978. Estimating the dimension of a model[J]. The Annals of Statistics, 6(2): 461-464. doi: 10.1214/aos/1176344136
[23]	Tannier C, Thomas I.2013. Defining and characterizing urban boundaries: A fractal analysis of theoretical cities and Belgian cities[J]. Computers, Environment and Urban Systems, 41: 234-248. doi: 10.1016/j.compenvurbsys.2013.07.003
[24]	Tannier C, Thomas I, Vuidel G, et al.2011. A fractal approach to identifying urban boundaries[J]. Geographical Analysis, 43(2): 211-227. doi: 10.1111/j.1538-4632.2011.00814.x

搜索半径/m	集群数目/个	最大集群面积/km²	lg(r)	log(N)
30	58609	1.8814	1.4771	4.7680
60	26068	130.0276	1.7782	4.4161
90	16471	373.5820	1.9542	4.2167
120	11202	650.0751	2.0792	4.0493
150	7827	888.4098	2.1761	3.8936
180	5378	1203.3913	2.2553	3.7306
210	3592	1725.4802	2.3222	3.5553
240	2295	2268.6984	2.3802	3.3608
270	1425	2676.9900	2.4314	3.1538
300	832	3394.7753	2.4771	2.9201
330	505	4484.4668	2.5185	2.7033
360	317	4937.5135	2.5563	2.5011
390	199	5289.0576	2.5911	2.2989
420	129	5612.1670	2.6232	2.1106
450	87	5836.9844	2.6532	1.9395

城市	多项式拟合次数	拟合优度R²
成都	7	0.99989
西安	8	0.99981
武汉	6	0.99991
南京	4	0.99984
长沙	5	0.99952

城市	lg(r)	最佳距离阈值r/m	最大曲率k	集群面积/km²	建成区面积/km²	面积损失/%
成都	2.1229	132.6965	0.7634	769.3466	604	27.38
西安	2.0583	114.3630	0.4296	349.7785	440	-20.50
武汉	2.1415	138.5055	0.3442	537.3624	553	-2.83
南京	2.0936	124.0469	0.3297	450.2214	734	-38.66
长沙	2.1120	129.4084	0.6481	358.7668	336	6.78

基于道路交叉点邻域扩张曲线的城市边界识别——以成都、西安、武汉、南京和长沙为例

Identifying urban boundaries by clustering street node based on neighborhood dilation curve:A case study of Chengdu, Xi'an, Wuhan, Nanjing and Changsha

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