Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пространственный анализ на основе сетевых данных в панельных исследованиях× | Пространственная автокорреляция× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2000s–2010s | 1950 |
| Автор метода≠ | Developed from LeSage & Pace spatial econometrics and Elhorst panel spatial frameworks | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Тип≠ | Panel spatial regression | Spatial statistic / exploratory spatial data analysis |
| Основополагающий источник≠ | LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Другие названия | panel spatial network analysis, longitudinal network spatial analysis, panel network spatial econometrics, PNBSA | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Связанные | 5 | 5 |
| Сводка≠ | Panel Network-Based Spatial Analysis extends standard spatial econometric models to repeated-measures (panel) data by representing spatial dependence through network connectivity rather than simple geographic proximity. It captures how units connected in a network influence each other's outcomes over time, while controlling for unit-level and time-level fixed effects. | 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. |
| ScholarGateНабор данных ↗ |
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