类别制图及其误差建模的概念框架(英文稿)

尽管离散目标和连续场的误差建模已得到了发展, 名义场却存在实质性的和多半悬而未决的概念问题。致力于为确定信息和不确定特性整合出一个概念框架。这个概念模型是基于判别空间而构建的; 后者是由面状类别时空表象的特质或驱动过程定义的。这个模型通过加入特定类的平均结构( 其可进行基于判别变量的回归分析) 的方式, 奠定类别制图一致性的基础, 并且使基于尺度的误差建模变得更为简便易行。这种误差建模可以有效地仿效观测者在类别、边界位置、多边形个数和边界网络拓扑特性等方面的差异。通过基于模拟数据的实验, 与基于指示克里格的随机仿真结果相对比, 肯定判别空间模型在确定平均面状类别( 反映判别变量的平均响应) 以及空间不确定性( 实为空间自相关的残差在地理空间的镜像) 的复现性或可重复性。

A Conceptual Framework for Categorical Mapping and Error Modeling

Despite developments in error modeling in discrete objects and continuous fields,there exist substantial conceptual problems in the domain of nominal fields,which are largely unsolved with little consensus.This paper seeks to consolidate a conceptual framework for categorical information and uncertainty characterization.A conceptual framework is proposed on the basis of discriminant space,defined by essential properties or driving processes underlying occurrences of area-classes.Such a framework furnishes consistency in categorical mapping by imposing class-specific mean structures that can be regressed against discriminant variables,and facilitates scale-dependent error modeling that can effectively emulate the variation found among observers in terms of classes,boundary positions,numbers of polygons,and boundary network topology.Based on simulated data,comparisons between indicator Kriging-based stochastic simulation and Gaussian simulation confirmed the replicability of the discriminant space model for mapping the "mean" area-classes,reflecting mean responses of discriminant variables,and spatial uncertainty therein,mirroring spatially correlated residuals.