空间数据自相关结构和回归关系非平稳性的多尺度分析方法与可视化技术的研究

项目名称： 空间数据自相关结构和回归关系非平稳性的多尺度分析方法与可视化技术的研究

项目编号： No.11271296

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 数理科学和化学

项目作者： 梅长林

作者单位： 西安交通大学

项目金额： 60万元

中文摘要： 空间数据的结构和关系一般不仅是空间非平稳的，而且是空间尺度相关的。基于局部统计量和局部光滑技术的探索性分析方法已被普遍认为是分析空间数据结构和关系空间非平稳性的有效方法并得到广泛的研究和应用，但空间数据的结构和关系对于空间尺度的敏感性是人们长期关注但仍缺乏有效分析方法的困难问题。本项目从尺度空间理论的观点出发，在局部建模的框架下，结合统计假设检验方法，研究空间数据自相关结构和回归关系空间非平稳性的多尺度分析方法以及分析结果的可视化问题，建立既有探索性数据分析自适应特点、又有统计推断理论支撑的挖掘空间数据自相关结构和回归关系空间非平稳性特征及其随空间尺度变化规律的理论与方法。

中文关键词： 空间尺度；局部时空关联性；空间非平稳性；SiZer推断；Bootstrap

英文摘要： The structures and relationships of spatial data are in general not only spatially non-stationary but also spatially scale-dependent. The exploratory analysis methods based on local statistics and local smoothing techniques have been recognized as effective tools for analyzing spatial non-stationarity of structures and relationships of spatial data and has been extensively studied and widely applied to many real-world fields. However, The sensitivity to spatial scales of structures and relationships remains to be such a stubborn problem that much attention has been paid to but few effective approaches have been developed to deal with. This project focuses on developing some multi-scale methods with visualization techniques for analyzing spatial non-stationarity of autocrrelation structures and regression relationships from the viewpoint of scale space theory and by combining some local modeling approaches and statistical hypothsis testing. It is expected that the developed methods with the theoretical framework are of both the adaptability of the exploratory data analysis and the solid basis of statistical inference in mining informative patterns of autocorrelation structures and regression relationships under different spatial scales and, furthermore, uncovering the variation rules of autocorrelation structures

英文关键词： Spatial scale；Local spatio-temporal association；Spatial non-stationarity；SiZer inference；Bootstrap