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

doi:10.1007/s11442-022-1958-9

摘要/Abstract

Abstract:

Since the launch of China’s reform and opening up policy, the process of urbanization in China has accelerated significantly. With the development of cities, inter-city interactions have become increasingly close, forming urban agglomerations that 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 regional coordinated 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 introduced the HGLM-CA model for the simulation of the spatial expansion of an urban agglomeration, and compared 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 analyzed 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, making the cell-level factors of different cities show different driving effects. The HGLM-CA model is able to 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. When 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, urban agglomeration, middle reaches of the Yangtze River

. [J]. 地理学报(英文版), 2022, 32(3): 499-516.

WANG Haijun, WU Yue, DENG Yu, XU Shan. Model construction of urban agglomeration expansion simulation considering urban flow and hierarchical characteristics[J]. Journal of Geographical Sciences, 2022, 32(3): 499-516.

图/表 10

		HGLM-CA			Logistic-CA			BBO-CA
		OA	Kappa	FoM	OA	Kappa	FoM	OA	Kappa	FoM
Overall accuracy	Urban agglomeration	0.99436	0.79872	0.18085	0.99427	0.79574	0.17374	0.99455	0.80567	0.19779
Local accuracy	Wuhan	0.95525	0.78844	0.14126	0.93924	0.74020	0.18817	0.94952	0.77246	0.18303
	Huangshi	0.97859	0.81788	0.17746	0.98138	0.83651	0.16789	0.98146	0.83816	0.18391
	Yichang	0.99445	0.78486	0.20519	0.99461	0.78646	0.18571	0.99448	0.78702	0.21576
	Xiangyang	0.99498	0.82902	0.15516	0.99514	0.82517	0.03963	0.99475	0.82610	0.19173
	Ezhou	0.94591	0.69046	0.14923	0.95070	0.70959	0.15293	0.96023	0.75093	0.16121
	Jingmen	0.99516	0.82205	0.11262	0.99563	0.83010	0.01952	0.99485	0.81586	0.14256
	Xiaogan	0.98711	0.77240	0.17225	0.98773	0.77889	0.16251	0.98763	0.77942	0.17561
	Jingzhou	0.99185	0.78990	0.15924	0.99196	0.79076	0.14808	0.99238	0.80328	0.18866
	Huanggang	0.99170	0.76696	0.15049	0.99208	0.77326	0.14080	0.99192	0.77603	0.18343
	Xianning	0.99392	0.82252	0.12214	0.99464	0.83704	0.08533	0.99347	0.81337	0.13292
	Xiantao	0.99208	0.83366	0.00041	0.99204	0.83445	0.02894	0.99065	0.82169	0.16428
	Qianjiang	0.98856	0.81205	0.04288	0.98857	0.81206	0.04001	0.98702	0.80615	0.18890
	Tianmen	0.99343	0.79276	0.01163	0.99346	0.79249	0.00210	0.99301	0.80157	0.19527
	Changsha	0.97542	0.79792	0.19991	0.97230	0.78174	0.21561	0.97574	0.80148	0.21485
	Zhuzhou	0.98999	0.77736	0.18150	0.99006	0.77920	0.18720	0.99081	0.79106	0.18815
	Xiangtan	0.97724	0.75437	0.21572	0.97718	0.75427	0.21746	0.98161	0.78615	0.20658
	Hengyang	0.99068	0.77667	0.20602	0.99089	0.78041	0.20796	0.99141	0.78814	0.20171
	Yueyang	0.99359	0.81224	0.14242	0.99422	0.82528	0.12560	0.99355	0.81314	0.16095
	Changde	0.99417	0.78845	0.17407	0.99461	0.79653	0.14288	0.99435	0.79537	0.19085
	Yiyang	0.99511	0.78612	0.18180	0.99544	0.79554	0.17560	0.99578	0.80790	0.18663
	Loudi	0.99284	0.80869	0.19307	0.99392	0.82569	0.13254	0.99350	0.82162	0.19003
	Nanchang	0.96873	0.79236	0.25293	0.96683	0.78406	0.25614	0.97187	0.80732	0.25144
	Jingdezhen	0.98913	0.79545	0.27337	0.98991	0.80425	0.26468	0.99082	0.81581	0.25770
	Pingxiang	0.98945	0.79041	0.17084	0.99075	0.79585	0.01749	0.98967	0.79303	0.16650
	Jiujiang	0.99296	0.80933	0.21376	0.99308	0.81093	0.20696	0.99303	0.81233	0.22926
	Xinyu	0.98812	0.85138	0.27223	0.99066	0.86902	0.09938	0.98876	0.85730	0.26929
	Yingtan	0.98118	0.73188	0.23667	0.98301	0.74787	0.23628	0.98613	0.77889	0.23856
	Ji’an	0.99532	0.77351	0.20956	0.99562	0.78092	0.19306	0.99591	0.79410	0.21761
	Yichun	0.99310	0.81064	0.12226	0.99302	0.80679	0.09893	0.99240	0.80345	0.18943
	Fuzhou	0.99558	0.80872	0.17760	0.99598	0.81430	0.09066	0.99566	0.81252	0.19129
	Shangrao	0.99397	0.79382	0.16224	0.99446	0.80489	0.14914	0.99459	0.81160	0.18193

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Data name	Data specification	Data source
Land-use data	The impervious surface data of urban areas are obtained by using the reliable impervious surface mapping algorithms and GEE platform with a resolution of 30 m×30 m. The impervious surface is regarded as urban land, while the others are non-urban land, which is resampled to 90 m ×90 m	Published by Gong et al. (2019), Tsinghua University (http://data.ess.tsinghua.edu.cn/)
Road data	Shapefile data including railways, thruways and national highways	Resource and Environment Science and Data Center(http://www.resdc.cn/)
DEM	Based on the latest SRTM V4.1 data after collation and stitching, the resolution is 90 m×90 m	Resource and Environment Science and Data Center(http://www.resdc.cn/)
Urban flow data	According to statistical yearbook data and big data of spatio-temporal geography, models of economic flow, population flow, traffic flow, and information flow (Wang et al., 2018; Zhai, 2019) are constructed separately to obtain the intensity of each element flow, and the weighted average of the four element flows to obtain the urban flow intensity.	See references (Zhai, 2019)

	Coefficient	P		Coefficient	P
Intercept:			Control variables:
Level 1 intercept β0			Elevation
Intercept γ00	‒1.429	0.000	Intercept γ40	‒0.592	0.000
Urban flow γ01	0.947	0.000	Slope
Independent variables：			Intercept γ50	‒1.764	0.000
Distance to city centers β1			Distance to water
Intercept γ10	‒3.261	0.000	Intercept γ60	0.313	0.315
Urban flow γ11	‒8.176	0.000	Distance to national highways
Distance to district and county centers β2			Intercept γ70	‒0.486	0.111
Intercept γ20	‒1.899	0.001	Distance to thruways
Urban flow γ21	‒1.790	0.206	Intercept γ80	‒0.297	0.391
Distance to railways β3
Intercept γ30	‒1.338	0.048
Urban flow γ31	4.752	0.021