基于空间聚类方法的京津冀城市群多层级空间结构研究

doi:10.18306/dlkxjz.2017.11.005

Abstract

Abstract:

With the main focus of regional competition shifting from cities to urban agglomerations, it is important to analyze the spatial structure and direction of coordinated development in urban agglomerations. This is especially significant in the Beijing-Tianjin-Hebei urban agglomeration, which is a core urban agglomeration in China. Machine learning algorithms are relatively new methods for addressing geographical problems. Clustering method, as unsupervised learning, is useful for classifying geographical units without the need for priori knowledge. Using data from 156 counties in the Beijing-Tianjin-Hebei urban agglomeration, this study applied four clustering algorithms: the K-means, density-based spatial clustering of applications with noise (DBSCAN), Chameleon, and self-organizing map (SOM) methods, for classifying counties and districts in the Beijing-Tianjin-Hebei urban agglomeration from the perspectives of economic centrality, traffic centrality, information centrality, and population centrality. GDP of the counties in 2014 was used to represent economic centrality; density of road networks in counties and attraction factor, calculated by the unsold train tickets in different time periods of the year, represent traffic centrality; Sina Weibo check-in data were used to represent information centrality; and county/district population represents population centrality. The result classifies the urban agglomeration into several levels. Respectively, K-means algorithm classifies counties into five levels; DBSCAN algorithm classifies counties into six levels; Chameleon algorithm classifies counties into six levels; and SOM algorithm classifies counties into five levels. SOM is the most applicable algorithm for the division of the urban agglomeration because the structure of counties is stable. This study further analyzed the spatial structure of the urban agglomeration with the central place theory, which points out that an agglomeration should contain certain number of counties in every level. The result of the SOM algorithm matches the central place theory. This research shows that there were remarkable gaps between different levels of the urban agglomeration. The central area of Beijing, as the core of the region, has strong radiation effect on the surrounding areas, but its functions are shared by the nearby counties. Moreover, the second and third level central cities distribute evenly and play an important role in regional development.

Key words: spatial clustering, spatial structure, Beijing-Tianjin-Hebei Region Agglomeration

Xun ZHANG, Jianzhang CHEN, Jinchuan HUANG, Chongchong YU, Xiuxin CHEN. Multi-level spatial structure analysis of urban agglomeration in the Beijing-Tianjin-Hebei region based on spatial clustering algorithms[J].PROGRESS IN GEOGRAPHY, 2017, 36(11): 1359-1367.

Figures/Tables 9

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References 31

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城区名称	包含区
北京城区	东城区、西城区、朝阳区、丰台区、海淀区、石景山区
天津城区	和平区、河东区、河西区、南开区、河北区、红桥区
保定城区	莲池区、竞秀区(含高新区)、徐水区、清苑区、满城区
沧州城区	新华区、运河区
承德城区	双桥区、双滦区、高新区、营子区
邢台城区	桥东区、桥西区
张家口城区	桥东区、桥西区、宣化区、下花园区、万全区、崇礼区
唐山城区	路北区、路南区、古冶区、开平区、丰润区、丰南区、曹妃甸区
石家庄城区	桥西区、新华区、长安区、裕华区、井陉矿区、藁城区、鹿泉区、栾城区
廊坊城区	安次区、广阳区
邯郸城区	丛台区、复兴区、邯山区、峰峰矿区
秦皇岛城区	海港区、山海关区、北戴河区、抚宁区

算法	算法效率	适合的数据类型	发现的聚类类型	对噪声数据或异常数据的敏感性	对输入顺序的敏感性	其他
K-means	高	数值	凸形或球形	不敏感	一般	易于理解和实现,时间复杂度低
Chameleon	一般	任意	任意形状	不敏感	不敏感	算法灵活但执行时间长
DBSCAN	一般	数值	任意形状	敏感	敏感	处理高纬度数据效果差
SOM	高	任意	任意形状	不敏感	敏感	结果有特征映射,能可视化

	第一类	第二类	第三类	第四类	第五类	第六类
K-means	北京市城区等3个区县	保定市城区等8个区县	北辰区等9个区县	霸州市等40个区县	安国县等96个区县
Chameleon	成安县等10个区县	霸州市等19个区县	安平县等25个区县	北京市城区等22个区县	安新县等35个区县	安国市等40个区县
DBSCAN	固安县等6个区县	隆化县等6个区县	广宗县等7个区县	滦平县等7个区县	大名县等15个区县	噪声数据(115个区县)
SOM	北京市城区	石家庄、天津市城区	保定市城区等21个区县	霸州市等42个区县	安国市等90个区县

	人口	GDP	路网密度	吸引因子	微博签到数
第一级	1.000000	1.000000	0.566022	0.925568	1.000000
第二级	0.187632	0.323590	0.712439	0.824197	0.089826
第三级	0.079778	0.066928	0.263885	0.202920	0.016356
第四级	0.015132	0.036220	0.096532	0.052529	0.003622
第五级	0.008050	0.025172	0.048087	0.013577	0.000620

Multi-level spatial structure analysis of urban agglomeration in the Beijing-Tianjin-Hebei region based on spatial clustering algorithms

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聚类算法	参数设置
K-means	质心个数k=5;迭代次数n=15
Chameleon	k最邻近值设定为k=3;度量函数距离阈值MinMetric=0.015
DBSCAN	中心点P的扫描邻域半径Eps=3;以P为中心的邻域内最小包含点数阈值min=3
SOM	输入层输入神经元k=5;迭代次数n=300

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