2000—2020年兰州市人口时空格局演变及驱动因素分析

doi:10.12118/j.issn.1000-6060.2024.099

Abstract

Abstract:

Utilizing data from population censuses conducted in 2000, 2010, and 2020, this study employs the offset-sharing analysis, the random forest model and other methods to examine the spatio-temporal evolution and driving factors of population distribution in Lanzhou City, Gansu Province, China, from 2000 to 2020. The findings reveal that: (1) Population growth exhibits significant differences across periods and regions in Lanzhou City, with clear suburbanization trends characterized by a “jumping” diffusion from the central urban area to the far suburbs. The central urban area remains the most populous, although its growth rate has slowed, while suburban growth is accelerating. Population in the far suburbs initially declined but later increased rapidly. (2) The population offset growth pattern in Lanzhou City is uneven. Taking 2010 as a pivotal year, blocks with positive population deviation growth were primarily located in the central urban area before 2010 but shifted to the far suburbs afterward, particularly in national new districts and development zones, which demonstrate “enclave” population agglomeration. (3) Natural factors, economic conditions, social development levels, and historical evolution are the main drivers of population spatial changes. Meanwhile, the influence of policy interventions and environmental comfort is increasingly significant. The impact of these driving factors on population distribution is nonlinear. These findings provide valuable insights for optimizing population distribution policies in inland cities of northwest China.

Key words: population distribution, spatial-temporal evolution, suburbanization, driving factors, random forest model, Lanzhou City

MA Xiaomin, ZHANG Zhibin, GUO Qianqian, ZHAO Xuewei, ZHANG Ning. Spatial and temporal evolution and driving factors of population in Lanzhou City from 2000 to 2020[J].Arid Land Geography, 2025, 48(1): 168-178.

Figures/Tables 7

Fig. 1

Tab. 1

Tab. 2

Description of variables"

维度	解释变量	指标说明	数据来源
经济因素	城镇化水平（X₁）	各街区城镇人口占总人口比重/%	2000、2010年和2020年兰州市人口普查数据
	就业人口（X₂）	各街区就业人口数量/个	2000、2010年和2020年兰州市人口普查数据
	经济发展水平（X₃）	街区内平均夜间灯光指数	2000、2010年和2020年的夜间灯光指数源于国家青藏高原科学数据中心（http://data.tpdc.ac.cn）
	生活成本（X₄）	各街区住宅平均销售价格/元·m^-2	2000、2010年和2020年的住宅销售价格源于安居客官网（https//:anjuke.com）
	企业数量（X₅）	各街区第二、三产业企业数量/个	2000、2010年和2020年兰州市工商管理局注册企业数据库
社会发展水平	交通设施（X₆）	各街区公交、地铁站点数量/个	2005年中国电信兰州黄页名录数据以及2010年和2020年高德POI
	医疗设施（X₇）	各街区诊所、综合医院数量/个	同上
	教育设施（X₈）	各街区幼儿园、中小学、高等院校数量/个	同上
	购物设施（X₉）	各街区商场、大型超市、便利店数量/个	同上
	娱乐设施（X₁₀）	各街区影剧院、KTV、公园游乐园数量/个	同上
	金融设施（X₁₁）	各街区银行数量/个	同上
政策因素	国家级新区（X₁₂）	街区内国家级新区面积比率/%	国家级新区规划文件
	国家级开发区（X₁₃）	街区内国家级开发区面积比率/%	国家级开发区规划文件
	省级开发区（X₁₄）	街区内省级开发区面积比率/%	省级开发区规划文件
历史沿革	老城区（X₁₅）	街区内老城区面积比率/%	参考既有文献划定（清末时期格局）
历史沿革	老工业区（X₁₆）	街区内老工业区面积比率/%	参考历版城市总体规划及政府文件划定
环境舒适度	城市绿地（X₁₇）	街区内绿地面积比率/%	通过NDVI提取得到，2000、2010年和2020年的NDVI来源于中国科学院生态学科数据中心（http://www.nesdc.org.cn）
环境舒适度	PM_2.5（X₁₈）	各街区PM_2.5浓度/μg·m^-3	2000、2010年和2020年的PM_2.5浓度来源于Zenodo欧洲科学数据开放平台（https://zenodo.org）
自然因素	地形起伏（X₁₉）	各街区平均地形起伏度/m	通过DEM提取得到，2000、2010年和2020年DEM数据源于地理空间数据云平台（http://www.gscloud.cn/sources）
自然因素	降水量（X₂₀）	各街区年均降水量/mm	兰州市气象局提供的兰州地区国家观测站点获得2000、2010年和2020年的年均降水数据

Tab. 2

Fig. 2

Fig. 3

Fig. 4

Fig. 5

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地域	人口数量/10⁴人			人口密度/人·km^-2			人口密度年均增长率/%
地域	2000年	2010年	2020年	2000年	2010年	2020年	2000—2010年	2010—2020年
中心城区	174.48	228.87	282.27	3341.45	4383.15	5405.74	2.75	2.12
近郊区	40.19	40.88	47.62	146.67	149.67	173.77	0.17	1.54
远郊区	99.58	91.87	106.05	100.29	92.52	106.81	-0.81	1.45

Spatial and temporal evolution and driving factors of population in Lanzhou City from 2000 to 2020

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