방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 공간적 인과 영향 분석× | 지리 가중 회귀 분석 (Geographically Weighted Regression, GWR)× | |
|---|---|---|
| 분야≠ | 인과추론 | 공간분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2010s (codified) | 2002 |
| 창시자≠ | Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988) | Fotheringham, Brunsdon & Charlton |
| 유형≠ | Quasi-experimental causal inference with spatial data | Local spatial regression |
| 원전≠ | Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123-126. DOI ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| 별칭 | spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| 관련≠ | 4 | 5 |
| 요약≠ | Spatial causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-differences or regression discontinuity with spatial econometric models, it separates the direct local effect of a treatment from indirect effects that diffuse to neighbouring areas. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
| ScholarGate데이터셋 ↗ |
|
|