השוואת שיטות
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| אוטוקורלציה מרחבית-זמנית× | Local Moran's I (LISA)× | |
|---|---|---|
| תחום | ניתוח מרחבי | ניתוח מרחבי |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1981–1992 | 1995 |
| הוגה השיטה≠ | Cliff & Ord; extended by Anselin and others | Luc Anselin |
| סוג≠ | Spatial autocorrelation statistic | Local spatial autocorrelation statistic |
| מקור מכונן≠ | Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134. DOI ↗ | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| כינויים | STSA, spatiotemporal autocorrelation, space-time Moran's I, temporal spatial dependence | Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index |
| קשורות≠ | 5 | 6 |
| תקציר≠ | Space-Time Spatial Autocorrelation extends classic spatial autocorrelation measures — most notably Moran's I — to data that vary across both geographic units and time periods. It detects whether nearby locations that are also temporally close tend to share similar attribute values, revealing clusters, trends, or anomalies that purely spatial or purely temporal analyses would miss. | 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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