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. |
Year | DOI | Venue |
---|---|---|
2022 | 10.1080/13658816.2021.2017440 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE |
Keywords | DocType | Volume |
Multiscale, spatial dependence, Moran's I, Fourier transform, spatial weights matrix | Journal | 36 |
Issue | ISSN | Citations |
5 | 1365-8816 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Hanchen Yu | 1 | 0 | 2.03 |
A. Stewart Fotheringham | 2 | 143 | 33.77 |