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2025 , Vol. 48 >Issue 1: 168 - 178

DOI: https://doi.org/10.12118/j.issn.1000-6060.2024.099

区域发展

2000—2020年兰州市人口时空格局演变及驱动因素分析

  • 马晓敏 ,
  • 张志斌 ,
  • 郭倩倩 ,
  • 赵学伟 ,
  • 张宁
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  • 西北师范大学地理与环境科学学院，甘肃 兰州 730070
马晓敏（1997-），女，博士研究生，主要从事城市与区域规划研究. E-mail: mxm202009@163.com
张志斌（1965-），男，博士，教授，主要从事城市与区域规划研究. E-mail: zbzhang@nwnu.edu.cn

收稿日期: 2024-02-20

  修回日期: 2024-04-02

  网络出版日期: 2025-01-21

基金资助

国家自然科学基金项目(41961029)

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

  • MA Xiaomin ,
  • ZHANG Zhibin ,
  • GUO Qianqian ,
  • ZHAO Xuewei ,
  • ZHANG Ning
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  • College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, Gansu, China

Received date: 2024-02-20

  Revised date: 2024-04-02

  Online published: 2025-01-21

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摘要

基于2000、2010年和2020年人口普查数据，运用偏移-分享法、随机森林模型等方法，揭示2000—2020年兰州市人口时空演变过程及其驱动因素。结果表明：（1） 兰州市不同时段、不同地域的人口增长差异显著，人口郊区化特征明显，且表现为从中心城区向远郊区的“跳跃式”扩散。其中，中心城区始终是吸纳人口最多地区，不过增速放缓；近郊区人口增速不断加快；远郊区人口呈先减少后快速增长态势。（2） 从人口偏移增长时空特征来看，以2010年为分界点，2010年前人口正偏移增长的街区主要分布于中心城区，而2010年后则转变为远郊区，特别是国家级新区、开发区人口增长更为明显，呈现“飞地型”人口集聚特征。（3） 从驱动因素来看，自然因素、经济因素、社会发展水平、历史沿革是人口空间演变的主要驱动力，政策因素与环境舒适度因素的影响逐步提升，且各重要驱动因子对人口分布的影响作用呈现非线性特征。研究结果对西北内陆城市人口布局优化调控政策具有参考意义。

关键词： 人口分布; 时空演变; 郊区化; 驱动因素; 随机森林模型; 兰州市

本文引用格式

马晓敏 , 张志斌 , 郭倩倩 , 赵学伟 , 张宁 . 2000—2020年兰州市人口时空格局演变及驱动因素分析[J]. 干旱区地理, 2025 , 48(1) : 168 -178 . DOI: 10.12118/j.issn.1000-6060.2024.099

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

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