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| Multiskala rumlig autokorrelation× | Rumlig Autokorrelation× | |
|---|---|---|
| Fagområde | Rumlig analyse | Rumlig analyse |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2002 | 1950 |
| Ophavsperson≠ | Borcard & Legendre; Csillag & Kabos | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Type≠ | Spatial autocorrelation decomposition | Spatial statistic / exploratory spatial data analysis |
| Oprindelig kilde≠ | Borcard, D., & Legendre, P. (2002). All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153(1-2), 51-68. DOI ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Aliasser | multi-scale spatial autocorrelation, scale-decomposed spatial autocorrelation, multiscale Moran analysis, MSA | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | Multiscale spatial autocorrelation extends classical spatial autocorrelation analysis by computing and comparing autocorrelation statistics (such as Moran's I) across a range of spatial scales simultaneously. This reveals at which geographic distances or resolutions spatial clustering or dispersion is strongest, providing a richer picture than a single global measure. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
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