国家城市规模分布的时空演化特征及影响因素——基于LandScan的全球实证研究

doi:10.18306/dlkxjz.2021.11.002

Abstract

Abstract:

Understanding the patterns of city size distribution around the world is important for the formation of reasonable city size distribution and high-quality national economic development. Based on a panel two-way fixed effects model and multiple databases such as LandScan from 2000 to 2018, this study identified the spatial and temporal change characteristics of city size distribution at the national level and analyzed its influencing factors as well as their heterogeneity based on global cities with a uniform definition. It was found that city size distribution across countries showed a fluctuating equalizing trend. In terms of spatial change, African and South American countries showed gradually equalized city size distribution, while Asian and European countries showed a trend of polarization. In a comparison of the present situation, African countries lead in the polarization of city size distribution, while European countries are the most equalized. Higher river densities, city population densities, and industrialization levels promote a equalized city size distribution, while larger topographic relief and national unrest reinforce their polarization. Compared to other factors, the socioeconomic factors (especially urban population density) have the most significant impact on the heterogeneity of city size distribution across countries with different income levels, industrial structures, and levels of government intervention. Although increased city population density contributes to a more equalized city size distribution overall, it has a significant polarizing effect in service-dominated and weak government intervention countries and a less pronounced effect in high-income countries. The balancing effect of industrial development on city size distribution and the polarizing effect of increased railway density are more significant in low-income countries, manufacturing-dominated countries, and countries with weak government intervention. Increased economic development significantly contributes to the polarization of city size distribution in service-dominated and weak government intervention countries and the expansion of openness contributes to the equalization of city size distribution in high-income countries. The increase in city population and internet penetration contributes to polarization and equalization of the city size distribution in countries with strong government intervention, and the increase in government spending only has a balancing effect on the city size distribution in countries with strong government intervention. This study deepens the knowledge of the patterns of city size distribution and its formation mechanism, and provides strong empirical support for the formation of a reasonable city size distribution. In the future, China should reshape the national economic geography by constructing an appropriately equalized city size distribution, expanding the two-way internal and external openness, and applying new generation information technologies.

Key words: city size distribution, high-quality development, influencing factors, heterogeneity, globe

WANG Yu, SUN Bindong, ZHANG Tinglin. Spatio-temporal change and influencing factors of city size distribution: Global empirical research based on LandScan data[J].PROGRESS IN GEOGRAPHY, 2021, 40(11): 1812-1823.

Figures/Tables 6

Fig.1

Tab.1

Fig.2

Fig.3

Tab.2

Influencing factors of city size distribution at the national level and their heterogeneity across countries

变量	Pareto1(ln)
变量	所有国家	高收入国家	低收入国家	制造业为主国家	服务业为主国家	强政府干预国家	弱政府干预国家
河流密度(ln)	0.3790^***	0.3860^***	0.4830^***	0.4670^***	0.3900^***	0.5130^***	-0.0038
	(0.1120)	(0.1050)	(0.1570)	(0.1650)	(0.1210)	(0.0860)	(0.1830)
地表粗糙度(ln)	-0.1050^*	-0.0435^***	-0.2580^***	-0.0317^**	-0.0574	-0.0282^**	-0.2850^***
	(0.0571)	(0.0119)	(0.0426)	(0.0158)	(0.1660)	(0.0139)	(0.0452)
人均GDP(ln)	0.0358	0.0372	-0.0069	7.43E-05	-0.1250^*	-0.0617	-0.1740^**
	(0.0553)	(0.1030)	(0.0776)	(0.0798)	(0.0657)	(0.0777)	(0.0763)
城市人口(ln)	-0.0727	0.1230	-0.0710	0.0221	-0.2090	-0.3260^**	0.1830
	(0.1030)	(0.2570)	(0.1120)	(0.1420)	(0.1990)	(0.1510)	(0.2010)
城市人口密度(ln)	0.1880^**	-0.0349	0.1820^**	0.2150^***	-0.1190^*	0.1380^*	-0.1470^*
	(0.0918)	(0.0659)	(0.0812)	(0.0665)	(0.0688)	(0.0731)	(0.0868)
进出口贸易总额占	0.0257	0.1440^*	-0.0518	-0.0767	-0.1000	-0.0132	-0.0916
GDP比重(ln)	(0.0531)	(0.0759)	(0.0499)	(0.0523)	(0.0805)	(0.0598)	(0.0600)
工业增加值占	0.1780^**	-0.1980	0.2240^***	0.1760^*	0.1640	0.0368	0.3350^***
GDP比重(ln)	(0.0714)	(0.1420)	(0.0796)	(0.0918)	(0.1350)	(0.1090)	(0.0975)
制造业与服务业	-0.0803	0.1030	0.00924	0.0413	0.0371	0.0792	-0.0467
增加值之比(ln)	(0.0704)	(0.0871)	(0.0521)	(0.0499)	(0.0909)	(0.0562)	(0.0493)
铁路密度(ln)	-0.0145	-0.0051	-0.1040^*	-0.1270^**	-0.0074	0.0016	-0.1510^**
	(0.0089)	(0.0092)	(0.0571)	(0.0523)	(0.0076)	(0.0050)	(0.0671)
互联网普及率(ln)	-0.0078	0.0234	-0.0005	0.0008	0.0158	0.0215^**	-0.0236
	(0.0110)	(0.0282)	(0.0141)	(0.0139)	(0.0182)	(0.0104)	(0.0153)
政府支出占	0.0347	-0.0353	0.0786	0.1290	0.0890	0.2800^**	0.0227
GDP比重(ln)	(0.0643)	(0.1040)	(0.0646)	(0.0779)	(0.0874)	(0.1170)	(0.0786)
公民政治权利半自由	-0.0371	0.0571^**	-0.0922	-0.0655	-0.3440^***	-0.1930^**	-0.1690^*
	(0.0801)	(0.0223)	(0.0775)	(0.0542)	(0.0683)	(0.0918)	(0.0857)
公民政治权利自由	-0.0941	—	-0.1580^*	-0.1150^**	—	-0.1910^*	—
	(0.0656)	—	(0.0840)	(0.0562)	—	(0.0980)	—
中等脆弱	-0.0410	0.0054	-0.0422	-0.0640^*	-0.0331	-0.0578^**	-0.0701^*
	(0.0317)	(0.0362)	(0.0272)	(0.0362)	(0.0519)	(0.0288)	(0.0410)
高等脆弱	-0.1370^*	—	-0.1380^**	-0.1410	0.0648	-0.0017	-0.1670
	(0.0721)	—	(0.0621)	(0.0915)	(0.1050)	(0.0707)	(0.1180)
时间	控制	控制	控制	控制	控制	控制	控制
国家	控制	控制	控制	控制	控制	控制	控制
常数	-0.6120	-1.8450	0.5860	-2.9570	6.9020	5.9960	-1.1140
	(2.3500)	(5.7770)	(2.8920)	(3.4140)	(4.1670)	(3.9190)	(4.7470)
观测值	303	131	172	187	116	194	109
样本国家数	89	30	59	67	44	55	45
R²	0.413	0.441	0.639	0.588	0.484	0.575	0.712

Tab.2

Tab.3

Comparison between this study and recent studies on influencing factors of city size distribution at the global scale

比较内容	本文	张亮靓等^[9]	盛科荣等^[15]	Soo^[28]
研究样本	全球89个国家	全球100多个国家	57个国家和地区	全球73个国家
人口数据来源	Landscan人口数据、欧空局土地利用类型数据	2014年《世界城市化展望》	City Population数据库	City Population数据库
计量方法	面板固定效应模型	截面回归、面板固定效应模型	截面回归	混合横截面回归
研究内容	城市规模分布时空演化城市规模分布影响因素不同类型国家影响因素异质性分析	城市规模分布演变城市规模分布影响因素	城市规模分布类型划分城市规模分布影响因素	验证Zipf定律的适用性城市规模分布影响因素
主要结论	(1) 2000—2018年,国家城市规模分布总体均衡发展,而亚洲、欧洲部分国家城市规模分布有所极化 (2) 众多影响因素中,河流密度、城市人口密度、工业增加值占GDP比重的增加会促进城市规模分布向均衡发展;地表粗糙度、国家脆弱指数上升导致城市规模分布极化;城市人口和密度、工业化水平、经济发展水平、开放水平、交通基础设施水平以及政府干预程度对国家城市规模分布的影响均存在异质性,会因国家收入水平、产业结构和政府干预程度的不同存在差异	(1) 1950—2010年,国家城市规模分布逐渐分散 (2) 从影响要素来看,土地面积、人口密度、二三产业产值占GDP比重促进城市规模分布的均衡;进出口贸易额占GDP比重、国家脆弱指数促进城市规模分布的极化;人均GDP先促进城市规模分布极化后促进城市规模分布均衡;城市化率、每百人固定电话订阅数、每百人互联网用户以及Freedom自由度对城市规模分布的影响不明显	(1) 全球各国城市规模分布分为5种类型 (2) 影响因素层面,进出口贸易额占GDP比重、每千人机动车拥有量以及民主化或降低首位城市的集聚度;区域人口数量、人均GDP会促使人口向高位序城市集聚;而城市化率对城市规模分布的影响则不显著	(1) 全球73个国家中,大部分国家城市规模分布都不符合Zipf定律 (2) 影响因素中,人口规模,人均GDP、规模经济、政府支出占GDP比重、战争促进了城市规模分布的分散;二三产业产值占GDP比重、交通成本促进了城市规模分布的集聚;进出口贸易额占GDP比重、土地面积对城市规模分布的影响不明显

Tab.3

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变量	单位	平均值	标准差	最小值	最大值
Pareto1(ln)	—	-0.218	0.241	-1.231	0.867
城市河流密度(ln)	km/km²	-1.324	0.531	-3.771	2.295
地表粗糙度(ln)	—	5.232	1.138	-0.698	8.213
人均GDP(ln)	美元/人	8.485	1.512	5.285	11.431
城市总人口(ln)	人	20.396	1.458	16.938	25.135
城市人口密度(ln)	人/km²	9.407	0.734	7.508	13.098
进出口贸易总额占GDP比重(ln)	%	4.257	0.546	-1.295	5.728
工业增加值占GDP比重(ln)	%	3.278	0.381	1.516	4.440
制造业与服务业增加值之比(ln)	—	-1.443	0.538	-3.678	0.725
铁路密度(ln)	km/100 km²	-4.341	1.462	-11.216	-2.095
互联网普及率(ln)	%	2.540	1.974	-8.794	4.602
政府支出占GDP比重(ln)	%	2.667	0.377	0.722	3.401
公民政治权利指数^①	—	3.489	2.157	1	7
国家脆弱指数^②	—	8.463	6.442	0	24

Spatio-temporal change and influencing factors of city size distribution: Global empirical research based on LandScan data

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