Forecast of urban and rural construction land based on the multiple regression analysis and gray model in Kangle Country | 基于多元回归分析和灰色模型的康乐县城乡建设用地预测
2012
Wang Rong, Gansu Agricultural University, Lanzhou (China), College of Resources and Environmental Science | Zhang Renzhi, Gansu Agricultural University, Lanzhou (China), College of Resources and Environmental Science | Chen Ying, Gansu Agricultural University, Lanzhou (China), College of Resources and Environmental Science
chinois. 结合多元回归分析和GM（1，1）灰色预测模型，以康乐县为研究对象，通过2000年～2008年的城乡建设用地面积数据预测了2015年和2020年的康乐县城乡用地面积。结果表明：通过多元回归分析，灰色预测模型的2015年和2020年康乐县城乡建设用地预测结果分别为4671.16、4698.28hm2和4783.04、4827.81hm2，与城乡规划指标4898.25、4916.63hm2较为相近，预测可行。
Afficher plus [+] Moins [-]anglais. By analyzing the national economic indicators and the changing data of urban and rural construction land area in Kangle County from 2000 to 2008, multiple regression analysis was used to select out seven social-economic indicators as predict factors, and GM (1, 1) grey forecasting model was also used to predict these factors as the comparison, then the urban and rural construction land area of Kangle County in 2015 and 2020 was predicted. The results showed that the urban and rural construction land area of Kangle County predicted by multiple regression analysis and GM (1, 1) grey forecasting model in 2015 and 2020 were 4671.16 hm2 and 4698.28 hm2 as well as 4783.04 hm2 and 4857.81 hm2 respectively. The result was close to the planning indexes, 4898.25 hm2 and 4916.63 hm2, this showed that the prediction was feasible.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par Institute of Agricultural Information, Chinese Academy of Agricultural Sciences
Découvrez la collection de ce fournisseur de données dans AGRIS