福建省土地利用多尺度空间自相关分析
Spatial Autocorrelation Analysis of Multi-scale Land Use in Fujian Province
查看参考文献20篇
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文摘
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常规统计方法是分析制约土地利用空间分布的影响因素常用方法，其理论假设前提是数据本身在统计上是独立的，呈正态分布。而土地利用空间数据往往具有一定的空间自相关性，同时空间自相关性中蕴含一些有用的信息，必须采用合适的方法予以解决。论文以福建省作为研究区域，采用Moran’s Ⅰ系数的自相关图来表示土地利用及其影响因子的空间自相关性特征，并且在此基础上建立了土地利用与影响因子的空间自回归方程。研究结果表明，研究区域内土地利用与影响因子普遍存在空间自相关性，并且空间自相关性与研究尺度密切相关，空间自相关性随尺度增大而增强。空间自回归模型的解释能力比经典统计回归模型略强，并且空间自回归模型中的残差较小、空间模式不明显，而经典回归模型的残差比较大，并且具有显著的空间分布模式。 |
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其他语种文摘
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Land use drivers that best describe land use patterns quantitatively are often selected through regression analysis.A problem using conventional statistical methods in spatial land use analysis is that these methods assume the data to be statistically independent while spatial land use data have the tendency to be dependent,known as spatial autocorrelation.Although several techniques are available to deal with spatial autocorrelation,only a few studies of land use modeling were seen using them.As a result,spatial statistical method was applied in this study to derive the spatial distribution of land use.Fujian Province,in Southeast China,was selected as the study area.In this paper,Moran's I are used to describe spatial autocorrelation of dependent and independent variables and spatial autoregressive models which incorporate both regression and spatial autocorrelation were constructed.Six land use types and their 27 candidate driving force variables representing bio-geophysical and socio-economic conditions were selected.The smallest spatial units of investigation were 10000 by 10000 meter cells.Land use types and their candidate driving force data were collected and attributed to these cells.All attribute data for the cells of he base resolution were aggregated to higher artificial aggregation levels through averaging the data.The spatial autocorrelation land use types and spatial regression models that incorporate spatial autocorrelation were analyzed at multiple resolutions. Results show that five main land use types except unused land and all candidate land use drivers show positive spatial autocorrelation which decreases gradually with distance.Variables of slope and aspect factors and distance to town or city show weak spatial autocorrelation,while other candidate diving force variables show strong spatial autocorrelation which extend to over 60-220km.The spatial pattern of land use type is similar to its corresponding driving force variables which show that land use is the direct or indirect consequence of its driving forces.It is also shown that the occurrence of spatial autocorrelation is highly dependent on the aggregation level,higher aggregation level shows higher Moran's I. The residuals of the standard linear regression are less auto-correlated than the original data which show the driving factors used in the regression equation capture part of the pattern.While the residuals of the ordinary regression model also show positive autocorrelation,which indicates that the standard multiple linear regression model cannot capture all spatial dependency in the land use data.The visual presentation of the residuals of the models provides a clear insight in the difference between the two models.Clear spatial pattern was seen in the residuals of the standard linear regression while the residuals of the spatial model were randomly distributed and show no spatial autocorrelation. Spatial autoregressive models yield residuals without spatial autocorrelation and have a better goodness-of-fit.The spatial autoregressive model is statistically sound in the presence of spatially dependent data in contrast with the standard linear model.By using spatial models a part of the variance probably 25%-68% is explained by neighboring values.As a result,the estimated regression coefficients of the variables become smaller and the significance of the parameters also decreases in the spatial autoregressive model.But this is a way to incorporate spatial interactions that cannot be captured by the independent variables.
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来源
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自然资源学报
,2007,22(2):311-320 【核心库】
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关键词
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土地利用
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空间自相关
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空间自回归模型
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多尺度
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福建省
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地址
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福州大学空间信息工程研究中心, 数据挖掘与信息共享教育部重点实验室, 福州, 350002
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中国科学院遥感应用研究所, 北京, 100101
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-3037 |
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学科
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社会科学总论 |
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基金
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福建省科技厅重点项目
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国家863计划
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文献收藏号
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CSCD:2859470
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