An adaptive inverse-distance weighting spatial interpolation technique
An adaptive inverse-distance weighting spatial interpolation technique
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DOI：
10.1016/j.cageo.2007.07.010
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发表时间：
2008-09-01
影响因子：
4.4
通讯作者：
Wong, David W.
中科院分区：
文献类型：
--
作者：
Lu, George Y.;Wong, David W.
空间插值中最常用的确定性模型之一是反距离加权（IDW）方法。它是相对快速和容易计算，并直接解释。其基本思想是基于未采样点的属性值是邻域内已知值的加权平均值的假设，并且权重与预测位置和采样位置之间的距离成反比。通过恒定功率或距离衰减参数来修改逆距离权重，以调整与增加的距离相关的减小的强度。认识到潜在的不同的距离衰减关系的研究领域，我们建议的加权参数的值可以根据不同的空间模式的采样点在附近。这种自适应的方法表明，距离衰减参数可以是一个函数的点模式的邻域。我们开发了一种算法来搜索“最佳”自适应距离衰减参数。使用交叉验证来评估结果，我们得出结论，自适应IDW在大多数情况下比常数参数方法表现得更好，并且在我们的一项实证研究中，当数据中的空间结构不能由典型的变差函数有效地建模时，比普通克里格更好。(c)2008爱思唯尔有限公司保留所有权利。
One of the most frequently used deterministic models in spatial interpolation is the inverse-distance weighting (IDW) method. It is relatively fast and easy to compute, and straightforward to interpret. Its general idea is based on the assumption that the attribute value of an unsampled point is the weighted average of known values within the neighborhood, and the weights are inversely related to the distances between the prediction location and the sampled locations. The inverse-distance weight is modified by a constant power or a distance-decay parameter to adjust the diminishing strength in relationship with increasing distance. Recognizing the potential of varying distance-decay relationships over the study area, we suggest that the value of the weighting parameter be allowed to vary according to the spatial pattern of the sampled points in the neighborhood. This adaptive approach suggests that the distance-decay parameter can be a function of the point pattern of the neighborhood. We developed an algorithm to search for "optimal" adaptive distance-decay parameters. Using cross validation to evaluate the results, we conclude that adaptive IDW performs better than the constant parameter method in most cases, and better than ordinary kriging in one of our empirical studies when the spatial structure in the data could not be modeled effectively by typical variogram functions. (c) 2008 Elsevier Ltd. All rights reserved.