A multiscale measure of spatial dependence based on a discrete Fourier transform
A multiscale measure of spatial dependence based on a discrete Fourier transform
复制标题
DOI：
10.1080/13658816.2021.2017440
复制
发表时间：
2021-12
影响因子：
5.7
通讯作者：
Hanchen Yu;S. Fotheringham
中科院分区：
文献类型：
--
作者：
Hanchen Yu;S. Fotheringham
摘要在一组观测值或回归中的残差中测量空间依赖性是空间分析中最常见的操作之一。但是，似乎缺乏对这些测量值通常基于空间重量矩阵的先验定义的事实，因此仅限于以单个空间尺度检测空间依赖性。本文通过当前的空间依赖度量介绍了规模依赖性问题，并定义了一种新的多尺度方法，可以根据离散的傅立叶变换来定义空间重量矩阵。这种方法被证明能够检测统计上显着的空间依赖性，哪些其他多尺度方法用于测量空间依赖性不能。因此，本文是警告不要依靠传统的空间依赖度量，并为衡量这种依赖性提供了更全面，更无限的方法。
ABSTRACT The measurement of spatial dependence within a set of observations or the residuals from a regression is one of the most common operations within spatial analysis. However, there appears to be a lack of appreciation for the fact that these measurements are generally based on an a priori definition of a spatial weights matrix and hence are limited to detecting spatial dependence at a single spatial scale. This paper highlights the scale-dependence problem with current measures of spatial dependence and defines a new, multi-scale approach to defining a spatial weights matrix based on a discrete Fourier transform. This approach is shown to be able to detect statistically significant spatial dependence which other multi-scale approaches to measuring spatial dependence cannot. The paper thus serves as a warning not to rely on traditional measures of spatial dependence and offers a more comprehensive, and scale-free, approach to measuring such dependence.