Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Paneļa lokālie telpiskās asociācijas rādītāji (Panel LISA)× | Lokālais Morana I (LISA)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1995 (LISA); panel extension 2000s–2010s | 1995 |
| Autors≠ | Anselin (1995), panel extension developed through spatial econometrics literature | Luc Anselin |
| Tips | Local spatial autocorrelation statistic | Local spatial autocorrelation statistic |
| Pirmavots≠ | Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Citi nosaukumi | Panel LISA, spatiotemporal LISA, panel local spatial autocorrelation, LISA panel extension | Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index |
| Saistītās≠ | 4 | 6 |
| Kopsavilkums≠ | Panel Local Indicators of Spatial Association extends Anselin's LISA statistics — most commonly Local Moran's I — to panel datasets, identifying spatial clusters and outliers at each location across multiple time periods. By applying local autocorrelation measures repeatedly over time, researchers can detect whether spatial concentration patterns emerge, persist, or dissolve, giving a richer spatiotemporal picture than a single cross-section allows. | 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. |
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