Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Multiskala rumlig autokorrelation× | Lokale indikatorer for rumlig association (LISA)× | |
|---|---|---|
| Fagområde | Rumlig analyse | Rumlig analyse |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2002 | 1995 |
| Ophavsperson≠ | Borcard & Legendre; Csillag & Kabos | Luc Anselin |
| Type≠ | Spatial autocorrelation decomposition | Local spatial statistic |
| 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 ↗ | Anselin, L. (1995). Local Indicators of Spatial Association — LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Aliasser | multi-scale spatial autocorrelation, scale-decomposed spatial autocorrelation, multiscale Moran analysis, MSA | LISA, local spatial autocorrelation statistics, local Moran's I, Anselin LISA |
| Relaterede | 6 | 6 |
| 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. | LISA, introduced by Luc Anselin in 1995, decomposes a global spatial autocorrelation index into a location-specific statistic for every observation. It identifies where statistically significant spatial clusters and outliers occur on a map, enabling researchers to move beyond a single global summary and pinpoint the geographic sources of spatial dependence. |
| ScholarGateDatasæt ↗ |
|
|