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Пространственный анализ на основе сетевых данных в панельных исследованиях×Панельная географически взвешенная регрессия (Panel GWR)×
ОбластьПространственный анализПространственный анализ
СемействоRegression modelRegression model
Год появления2000s–2010s2000s–2010s
Автор методаDeveloped from LeSage & Pace spatial econometrics and Elhorst panel spatial frameworksFotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature
ТипPanel spatial regressionLocal spatial regression with panel structure
Основополагающий источникLeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Другие названияpanel spatial network analysis, longitudinal network spatial analysis, panel network spatial econometrics, PNBSAPanel GWR, PGWR, spatiotemporal GWR, geographically weighted panel regression
Связанные54
СводкаPanel 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.Panel Geographically Weighted Regression (Panel GWR) extends the standard GWR framework to panel data, allowing regression coefficients to vary both across geographic locations and over time. It captures spatially non-stationary relationships in longitudinal or repeated-measures spatial datasets, combining local spatial estimation with panel-data controls for unit-specific heterogeneity.
ScholarGateНабор данных
  1. v1
  2. 2 Источники
  3. PUBLISHED
  1. v1
  2. 2 Источники
  3. PUBLISHED

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ScholarGateСравнение методов: Panel Network-Based Spatial Analysis · Panel Geographically Weighted Regression. Получено 2026-06-17 из https://scholargate.app/ru/compare