东北大学学报(自然科学版) ›› 2023, Vol. 44 ›› Issue (5): 743-751.DOI: 10.12068/j.issn.1005-3026.2023.05.018

• 管理科学 • 上一篇    下一篇

沈阳市内五区开发强度时空演变及影响因素研究

高雁鹏1, 陈文俊2   

  1. (1.东北大学 江河建筑学院, 辽宁 沈阳110169; 2.厦门大学 公共事务学院, 福建 厦门361005)
  • 发布日期:2023-05-24
  • 通讯作者: 高雁鹏
  • 作者简介:高雁鹏(1976-),男,吉林松原人,东北大学副教授.
  • 基金资助:
    国家自然科学基金资助项目(41871162).

Study on Spatio-Temporal Evolution and Influencing Factors of Development Intensity in Shenyang’s Five Districts

GAO Yan-peng1, CHEN Wen-jun2   

  1. 1. School of Jangho Architecture, Northeastern University, Shenyang 110169, China; 2. School of Public Affairs, Xiamen University, Xiamen 361005, China.
  • Published:2023-05-24
  • Contact: CHEN Wen-jun
  • About author:-
  • Supported by:
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摘要: 以沈阳市内五区作为研究区域,通过构建开发强度模型,对2005—2020年开发强度时空演变规律及影响因素进行探究.结果表明:开发强度总体呈现先快速下降、后平稳发展的变化态势,2009年后受政策调控,开发趋于合理,地区间差异逐渐减小;开发强度表现出中心高四周低的空间格局,圈层式发展特征显著;开发强度空间集聚性明显,表现出中部热点集聚和边缘冷点集聚的“核心-边缘”特征;人均城乡用地面积、城市用地弹性增长系数以及地均GDP是影响开发强度的主要因素.

关键词: 开发强度;时空演变;开发强度模型;核密度估计;地理加权回归

Abstract: A development intensity model was constructed to explore the spatio-temporal evolution of development intensity of Shenyang’s five districts from 2005 to 2020 and its influencing factors. The results showed that: the development intensity shows a trend of rapid decline at first and then steady development. After 2009, due to the influence of policy regulation, the development activities tend to be more reasonable, which have also led to a gradual reduction in regional differences. The spatial pattern of development intensity is high in the center and low in the periphery and is characterized by circular development. The spatial agglomeration of development intensity is obvious, showing the“core-edge”characteristics of hot spots agglomeration in the central regions and cold spots agglomeration in the edges. Per capita urban and rural land area, elastic growth coefficient of urban land and average land GDP are the main factors affecting the development intensity.

Key words: development intensity; spatial-temporal evolution; development intensity model; kernel density estimation; GWR (geographical weighted regression)

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