Implementing a weighted measure of multivariate spatial autocorrelation
Bibliographic Data
| ID | 16976993 |
|---|---|
| Authors | Roger Bivand (0000-0003-2392-6140, corresponding author) |
| Year | 2025 |
| Issue | 69 |
| Pages | 19-35 |
| Publication date | 2025-06-27 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cahiers du Centre de Linguistique et des Sciences du Langage (JOURNAL) |
| Journal identifiers | ISSN: 2674-1415 • E-ISSN: 2813-0103 |
| Publisher | Cantonal and University Library Fribourg (PUBLISHER) |
| DOI | 10.26034/la.cdclsl.2025.8343 |
| OpenAlex | W4413791321 |
| Language | EN |
Bavaud (2024) builds on and significantly broadens earlier work on measuring spatial autocorrelation, extending to multivariate settings and in particular regional weights. While measurement of spatial autocorrelation in multivariate data has been approached previously, the addition of regional weights is a major advance, as regions often differ in their contributions to global measures. Thus far, the proposed implementation described here involves dense matrices, as does much multivariate analysis. It is shown that the implementation largely reproduces the results presented in Bavaud (2024)
Autocorrelation · Data mining · Multivariate statistics · Spatial analysis · Statistics · Computer Science · Land Use and Ecosystem Services · Mathematics · Spatial and Panel Data Analysis
| Highly cited | No |
|---|