Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Moran's I Bayesian× | Moran's I Tempatan (LISA)× | |
|---|---|---|
| Bidang | Analisis Reruang | Analisis Reruang |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1950 / 2000s | 1995 |
| Pengasas≠ | Moran (1950), Bayesian extension developed in spatial statistics literature (late 1990s–2000s) | Luc Anselin |
| Jenis≠ | Bayesian spatial autocorrelation test | Local spatial autocorrelation statistic |
| Sumber perintis≠ | Haining, R. (2003). Spatial Data Analysis: Theory and Practice. Cambridge University Press. ISBN: 9780521774611 | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Alias | Bayesian spatial autocorrelation test, Bayesian Moran statistic, Moran's I under Bayesian inference, Bayesian global spatial association | Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index |
| Berkaitan | 6 | 6 |
| Ringkasan≠ | Bayesian Moran's I embeds the classical Moran's I spatial autocorrelation test within a Bayesian probabilistic framework. Rather than producing a single p-value, it yields a posterior distribution over the spatial autocorrelation parameter, enabling uncertainty quantification, incorporation of prior knowledge, and more principled inference in small or irregular spatial datasets. | Local Moran's I, introduced by Luc Anselin in 1995, is a Local Indicator of Spatial Association (LISA) that decomposes global spatial autocorrelation into location-specific contributions. For every observation it produces a signed statistic and a significance value, enabling researchers to identify spatial clusters (high-high, low-low) and spatial outliers (high-low, low-high) on a map. |
| ScholarGateSet data ↗ |
|
|