地理科学进展 ›› 2018, Vol. 37 ›› Issue (6): 781-789.doi: 10.18306/dlkxjz.2018.06.005
林晓娟1,2(), 房世峰2, 徐亚莉1,3, 邹宝裕1, 罗明良1,3,*(
)
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:
基金资助:
Xiaojuan LIN1,2(), Shifeng FANG2, Yali XU1,3, Baoyu ZOU1, Mingliang LUO1,3,*(
)
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:
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。
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.
表1
成都市城市边界识别过程"
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 |
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