Investigating urbanization and its spatial determinants in the central districts of Guangzhou, China

被引:31
作者
Chen, Yingbiao [1 ]
Chang, Kang-tsung [2 ]
Han, Fuzhuang [1 ]
Karacsonyi, David [3 ]
Qian, Qinglan [1 ]
机构
[1] Guangzhou Univ, Sch Geog Sci, Guangzhou 510006, Peoples R China
[2] Natl Taiwan Univ, Dept Geog, Taipei 10764, Taiwan
[3] Hungarian Acad Sci, Inst Geog, Budapest, Hungary
基金
中国国家自然科学基金;
关键词
Urban land; Spatial regression; Model comparison; Spatial determinants; Guangzhou; LAND-USE CHANGE; URBAN EXPANSION; CITY; PATTERNS; CITIES; GIS; AGGLOMERATION; PREFERENCES; PROTECTION; SHENZHEN;
D O I
10.1016/j.habitatint.2015.10.013
中图分类号
F0 [经济学]; F1 [世界各国经济概况、经济史、经济地理]; C [社会科学总论];
学科分类号
0201 ; 020105 ; 03 ; 0303 ;
摘要
Like many cities on the east coast of China, Guangzhou has experienced dramatic urban growth since the late 1970s and become the largest metropolis in southern China. This study examined urbanization in the central districts of Guangzhou in two parts: (1) analyzing the spatiotemporal changes of urban land expansion from 1980 to 2010, and (2) analyzing the spatial relationship between urban land ratio and its determinants in not fully built-up areas in 2010. Results from the first part showed that the study area's urban land doubled during the three decades but the rate of expansion had slowed down considerably after 1990. The expansion was primarily in the eastward direction away from the old city center, in a process combining outlying growth, infill, and edge expansion. Results from the second part revealed that, due to the spatial autocorrelation of urban land ratios, spatial regression performed better than ordinary least squares regression in explaining their spatial distribution. Among the determinants in the spatial regression models, road density and distances to transportation services, bank, and hotel were most important, followed by distances to company office and major road, suggesting the importance of convenient daily transport to urban residents. The two spatial regression models were consistent in both the performance statistics and the selection of significant explanatory variables, thus confirming the usefulness and reliability of spatial regression in modeling urban land patterns. These results are relevant to studies of the urban condition and urban planning in Guangzhou as well as rapidly urbanizing China. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:59 / 69
页数:11
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