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基于随机森林模型的东北地区收缩城市分布格局及影响因素研究
引用本文:闫广华,陈曦,张云. 基于随机森林模型的东北地区收缩城市分布格局及影响因素研究[J]. 地理科学, 2021, 41(5): 880-889. DOI: 10.13249/j.cnki.sgs.2021.05.016
作者姓名:闫广华  陈曦  张云
作者单位:吉林大学行政学院,吉林长春130061;长春师范大学地理科学学院,吉林长春130032;长春师范大学地理科学学院,吉林长春130032;中国科学院南京地理与湖泊研究所/中国科学院流域地理学重点实验室,江苏南京210008;国家海洋环境监测中心,辽宁大连116023
基金项目:吉林省科技厅重大科技攻关项目(20190303017SF);吉林省教育厅科学研究项目资助(JJKH20210878KJ)
摘    要:基于2005-2009年、2010-2014年、2015-2019年和2005-2019年人口变化数据,判定东北地区收缩城市,分析其空间分布格局,并对比运用多元线性回归方法和随机森林回归方法探索东北地区收缩城市形成的影响因素及其影响作用.结果 表明:①空间上,东北地区的收缩城市主要分布在以长白山、三江平原、小兴安岭和大...

关 键 词:收缩城市  东北地区  人口变化  线性回归  随机森林
收稿时间:2020-05-27
修稿时间:2020-11-12

Shrinking Cities Distribution Pattern and Influencing Factors in Northeast China Based on Random Forest Model
Yan Guanghua,Chen Xi,Zhang Yun. Shrinking Cities Distribution Pattern and Influencing Factors in Northeast China Based on Random Forest Model[J]. Scientia Geographica Sinica, 2021, 41(5): 880-889. DOI: 10.13249/j.cnki.sgs.2021.05.016
Authors:Yan Guanghua  Chen Xi  Zhang Yun
Affiliation:1. School of Public Administration, Jilin University, Changchun 130012, Jilin, China
2. School of Geographical Sciences, Changchun Normal University, Changchun 130032, Jilin, China
3. Key Laboratory of Watershed Geographic Science/Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, Jiangsu, China
4. National Marine Environmental Monitoring Center, Dalian 116023, Liaoning, China
Abstract:Based on the demographic data of four periods (2005-2009, 2010-2014, 2015-2019 and 2005-2019), shrinking cities were identified in Northeast China, and their spatial distribution patterns were analyzed. We further compared the multiple linear regression (MLR) and the random forest regression (RFR) to explore the influencing factors and mechanisms of shrinking cities in Northeast China. The results showed that: 1) Spatially, the shrinking cities in Northeast China were mainly distributed in the "land-edge" regions represented by the Changbai Mountains, the Sanjiang Plain, Lesser and the Da Hinggan Mountains. Temporally, the shrinkage center showed an obvious northward trend, while the expansion center showed a southward trend. In addition, the shrinking cities were further clustered. 2) The results of both MLR and RFR indicated that socio-economic factors play a major role in the formation of shrinking cities. 3) The accuracy of RFR was higher than that of MLR. The results of RFR showed that GDP per capita has the greatest influence on the shrinkage intensity, followed by unemployment rate, expenses of science and education, and average wage of employed workers. There four influencing factors, except unemployment rate, the remaining three influencing factors restrict the formation of shrinking cities to varying degrees.
Keywords:shrinking cities  Northeast China  population change  linear regression  random forest  
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