基于地理加权回归模型的长江中游地区人均耕地面积变化影响因素分析

目的]利用GWR模型揭示长江中游地区人均耕地面积变化影响因素的空间异质性,为今后管理该区域耕地资源提供科学依据。方法]在总结该地区人均耕地面积现状的基础上,分析人均耕地的Moran’s I指数,利用相关年份数据分析了最小二乘法(OLS)和地理加权回归方法(GWR)的差异,采用GWR模型对该区域各市人均耕地面积的影响因素进行分析。结果](1)城镇化率对人均耕地的影响由正相关向负相关变化,影响程度增强,系数值空间差异较大;(2)人口增长率与人均耕地大部分地区呈负相关,局部地区呈正相关,影响程度减小,空间差异较大;(3)第一产业总产值比重与人均耕地大部分地区呈正相关,局部呈负相关,影响程度下降,空间差异较大;(4)粮食单产与人均耕地由负相关向正相关变化,影响程度增强。结论]GWR比OLS更能反映影响因素的空间异质性,成功揭示了各因素对人均耕地的影响程度和区域差异。

Analysis on Driving Forces of Cultivated Land Area Change Per Capita in Middle Reaches of Yangtze River Based on Geographically Weighted Regression Model

Objective] The objective of this article is to reveal the spatial heterogeneity of the driving forces of per capita cultivated land area change in middle reaches of the Yangtze River based on geographically weighted regression(GWR) model in order to provide basis for the management of cultivated land resources in this area. Methods] By investigating the current situation of cultivated land area per capita in this area, the Moran''s I index of cultivated land area per capita was analyzed. A comparison was made between ordinary least squares(OLS) and GWR by using the relevant data. Based on GWR, a regression analysis on the influencing factors of cultivated land area per capita was analyzed in each city. Results] (1) The influence of urbanization ratio on cultivated land area per capita varied from positive to negative correlations, with an enhancing influence degree and obvious spatial differences. (2) The growth rate of population and the per capita cultivated land area showed negative correlations in most areas, while they showed positive correlations in local areas, with a decreasing influence degree and large spatial difference. (3) In most areas, the proportion of the gross output of the first industry and cultivated land area per capita were positively correlated, while they were negative correlated in local areas, with a decreasing influence degree and large spatial difference. (4) The influence of grain yield per unit area on cultivated land area per capita changed from negative to positive correlations, with an enhancing influence degree. Conclusion] The research shows that GWR is better than OLS in reflecting the spatial heterogeneity of the driving factors, and the results of GWR clearly reveal that different factors bring different degree of effect on cultivated land area per capita in different areas.