基于城市流和层级性的城市群扩展模型构建

doi:10.11821/dlxb202112010

地理学报 ›› 2021, Vol. 76 ›› Issue (12): 3012-3024.doi: 10.11821/dlxb202112010

基于城市流和层级性的城市群扩展模型构建

王海军¹^,²(), 武悦¹, 邓羽³(), 徐姗⁴

1.武汉大学资源与环境科学学院,武汉 430079
2.武汉大学地理信息系统教育部重点实验室,武汉 430079
3.中国科学院地理科学与资源研究所,北京 100101
4.中国农业大学土地科学与技术学院,北京 100193

收稿日期:2021-03-15 修回日期:2021-10-18 出版日期:2021-12-25 发布日期:2022-02-25
通讯作者: 邓羽(1985-), 男, 湖北人, 博士, 副研究员, 主要从事城市发展与空间管治研究。E-mail: dengy@igsnrr.ac.cn
作者简介:王海军(1972-), 男, 陕西凤翔人, 博士, 教授, 主要从事地理模拟、国土空间规划和土地资源评价研究。E-mail: landgiswhj@163.com
基金资助:
国家自然科学基金项目(42171411);中国科学院青年创新促进会(2019055)

Model construction of urban agglomeration expansion simulation considering urban flow and hierarchical characteristics

WANG Haijun¹^,²(), WU Yue¹, DENG Yu³(), XU Shan⁴

1. School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
2. Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China
3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
4. College of Land Science and Technology, China Agricultural University, Beijing 100193, China

Received:2021-03-15 Revised:2021-10-18 Published:2021-12-25 Online:2022-02-25
Supported by:
National Natural Science Foundation of China(42171411);Youth Innovation Promotion Association, CAS(2019055)

摘要/Abstract

摘要：

城市群是具有网络关系与层级性的区域空间,一直是中国推进城镇化与区域协调发展的主体单元。本文综合考虑城市群的网络与层级性特征,运用城市流刻画城际网络交互作用,采用分层广义线性模型（HGLM）揭示城市群分层驱动机制。同时,选取长江中游城市群开展实证研究,通过与元胞自动机（CA）耦合,构建HGLM-CA模型模拟城市群空间扩展。将模拟结果与Logistic-CA模型、BBO-CA模型进行比对,据此评析HGLM-CA模型的优劣与改进方向。实证结果表明：城市群空间扩展是多层驱动因素共同作用的结果,城市流不仅会推动城市群空间扩展,而且对元胞层因素起到重要的调节作用,使之具有城际分异性;HGLM-CA模型相比Logistic-CA模型模拟精度更高,说明顾及城市流与层级性的城市群空间扩展模拟结果更为精准;与智能模型BBO-CA相比,HGLM-CA模型模拟精度较低,但其便于从层级性角度把握城市群空间扩展机制。

关键词: 城市流, 层级性, 元胞自动机, 驱动机制, 城市群空间扩展, 长江中游城市群

Abstract:

Since the launch of China's reform and opening up policy, the process of urbanization in China has been accelerated. With the development of cities, inter-city interactions have become increasingly close, and urban agglomerations tend to be integrated. Urban agglomerations are regional spaces with network relationships and hierarchies, and have always been the main units for China to promote urbanization and coordinated regional development. In this paper, we comprehensively consider the network and hierarchical characteristics of an urban agglomeration, while using urban flow to describe the interactions of the inter-city networks and the hierarchical generalized linear model (HGLM) to reveal the hierarchical driving mechanism of the urban agglomeration. By coupling the HGLM with a cellular automata (CA) model, we introduce the HGLM-CA model for the simulation of the spatial expansion of an urban agglomeration, and compare the simulation results with those of the logistic-CA model and the biogeography-based optimization CA (BBO-CA) model. According to the results, we further analyze the advantages and disadvantages of the proposed HGLM-CA model. We selected the middle reaches of the Yangtze River in China as the research area to conduct this empirical research, and simulated the spatial expansion of the urban agglomeration in 2017 on the basis of urban land-use data from 2007 and 2012. The results indicate that the spatial expansion of the urban agglomeration can be attributed to various driving factors. As a driving factor at the urban level, urban flow promotes the evolution of land use in the urban agglomeration, and also plays an important role in regulating cell-level factors, therefore, the cell-level factors of different cities show different driving effects. The HGLM-CA model can obtain a higher simulation accuracy than the logistic-CA model, which indicates that the simulation results for urban agglomeration expansion considering urban flow and hierarchical characteristics are more accurate. Compared with the intelligent algorithm model, i.e., BBO-CA, the HGLM-CA model obtains a lower simulation accuracy, but it can analyze the interaction of the various driving factors from a hierarchical perspective. It also has a strong explanatory effect for the spatial expansion mechanism of urban agglomerations.

Key words: urban flow, hierarchical characteristics, cellular automata, driving mechanism, spatial expansion of urban agglomerations, urban agglomeration in the middle reaches of the Yangtze River

王海军, 武悦, 邓羽, 徐姗. 基于城市流和层级性的城市群扩展模型构建[J]. 地理学报, 2021, 76(12): 3012-3024.

WANG Haijun, WU Yue, DENG Yu, XU Shan. Model construction of urban agglomeration expansion simulation considering urban flow and hierarchical characteristics[J]. Acta Geographica Sinica, 2021, 76(12): 3012-3024.

图/表 10

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图4

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图5

表3

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表4

2017年长江中游城市群空间扩展模型模拟精度对比

		HGLM-CA				Logistic-CA				BBO-CA
		OA	Kappa	FoM		OA	Kappa	FoM		OA	Kappa	FoM
整体精度	城市群	0.99436	0.79872	0.18085		0.99427	0.79574	0.17374		0.99455	0.80567	0.19779
分区精度	武汉市	0.95525	0.78844	0.14126		0.93924	0.74020	0.18817		0.94952	0.77246	0.18303
	黄石市	0.97859	0.81788	0.17746	0.98138		0.83651	0.16789	0.98146		0.83816	0.18391
	宜昌市	0.99445	0.78486	0.20519	0.99461		0.78646	0.18571	0.99448		0.78702	0.21576
	襄阳市	0.99498	0.82902	0.15516	0.99514		0.82517	0.03963	0.99475		0.82610	0.19173
	鄂州市	0.94591	0.69046	0.14923	0.95070		0.70959	0.15293	0.96023		0.75093	0.16121
	荆门市	0.99516	0.82205	0.11262	0.99563		0.83010	0.01952	0.99485		0.81586	0.14256
	孝感市	0.98711	0.77240	0.17225	0.98773		0.77889	0.16251	0.98763		0.77942	0.17561
	荆州市	0.99185	0.78990	0.15924	0.99196		0.79076	0.14808	0.99238		0.80328	0.18866
	黄冈市	0.99170	0.76696	0.15049	0.99208		0.77326	0.14080	0.99192		0.77603	0.18343
	咸宁市	0.99392	0.82252	0.12214	0.99464		0.83704	0.08533	0.99347		0.81337	0.13292
	仙桃市	0.99208	0.83366	0.00041	0.99204		0.83445	0.02894	0.99065		0.82169	0.16428
	潜江市	0.98856	0.81205	0.04288	0.98857		0.81206	0.04001	0.98702		0.80615	0.18890
	天门市	0.99343	0.79276	0.01163	0.99346		0.79249	0.00210	0.99301		0.80157	0.19527
	长沙市	0.97542	0.79792	0.19991	0.97230		0.78174	0.21561	0.97574		0.80148	0.21485
	株洲市	0.98999	0.77736	0.18150	0.99006		0.77920	0.18720	0.99081		0.79106	0.18815
	湘潭市	0.97724	0.75437	0.21572	0.97718		0.75427	0.21746	0.98161		0.78615	0.20658
	衡阳市	0.99068	0.77667	0.20602	0.99089		0.78041	0.20796	0.99141		0.78814	0.20171
	岳阳市	0.99359	0.81224	0.14242	0.99422		0.82528	0.12560	0.99355		0.81314	0.16095
	常德市	0.99417	0.78845	0.17407	0.99461		0.79653	0.14288	0.99435		0.79537	0.19085
	益阳市	0.99511	0.78612	0.18180	0.99544		0.79554	0.17560	0.99578		0.80790	0.18663
	娄底市	0.99284	0.80869	0.19307	0.99392		0.82569	0.13254	0.99350		0.82162	0.19003
	南昌市	0.96873	0.79236	0.25293	0.96683		0.78406	0.25614	0.97187		0.80732	0.25144
	景德镇	0.98913	0.79545	0.27337	0.98991		0.80425	0.26468	0.99082		0.81581	0.25770
	萍乡市	0.98945	0.79041	0.17084	0.99075		0.79585	0.01749	0.98967		0.79303	0.16650
	九江市	0.99296	0.80933	0.21376	0.99308		0.81093	0.20696	0.99303		0.81233	0.22926
	新余市	0.98812	0.85138	0.27223	0.99066		0.86902	0.09938	0.98876		0.85730	0.26929
	鹰潭市	0.98118	0.73188	0.23667	0.98301		0.74787	0.23628	0.98613		0.77889	0.23856
	吉安市	0.99532	0.77351	0.20956	0.99562		0.78092	0.19306	0.99591		0.79410	0.21761
	宜春市	0.99310	0.81064	0.12226	0.99302		0.80679	0.09893	0.99240		0.80345	0.18943
	抚州市	0.99558	0.80872	0.17760	0.99598		0.81430	0.09066	0.99566		0.81252	0.19129
	上饶市	0.99397	0.79382	0.16224	0.99446		0.80489	0.14914	0.99459		0.81160	0.18193

表4

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数据名称	数据说明	数据来源
土地利用数据	城市区域不透水面数据,利用不透水表面映射算法和GEE得到,分辨率为30 m×30 m,将不透水面视为城市用地,其他为非城市用地,重采样为90 m×90 m	由清华大学宫鹏等^[30]研究公布(http://data.ess.tsinghua.edu.cn/)
道路数据	包括铁路、高速公路、国道在内的shp格式数据	中国科学院资源环境科学与数据中心（ http://www.resdc.cn/）
DEM数据	基于最新的SRTM V4.1数据经整理拼接生成,分辨率为90 m×90 m	中国科学院资源环境科学与数据中心（ http://www.resdc.cn/）
城市流数据	根据统计年鉴数据与时空地理大数据,分别构建经济流、人口流、交通流、信息流模型^[31,32],得到各要素流强度,4种要素流加权平均得到城市流强度	见参考文献[31]

	系数	P值		系数	P值
截距：			控制变量：
level 1截距β₀			高程
截距γ₀₀	-1.429	0.000	截距γ₄₀	-0.592	0.000
城市流γ₀₁	0.947	0.000	坡度
自变量：			截距γ₅₀	-1.764	0.000
距市中心距离β₁			距水域距离
截距γ₁₀	-3.261	0.000	截距γ₆₀	0.313	0.315
城市流γ₁₁	-8.176	0.000	距国道距离
距区县中心距离β₂			截距γ₇₀	-0.486	0.111
截距γ₂₀	-1.899	0.001	距高速距离
城市流γ₂₁	-1.790	0.206	截距γ₈₀	-0.297	0.391
距铁路距离β₃
截距γ₃₀	-1.338	0.048
城市流γ₃₁	4.752	0.021

基于城市流和层级性的城市群扩展模型构建

Model construction of urban agglomeration expansion simulation considering urban flow and hierarchical characteristics

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