방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 네트워크 기반 공간 분석× | 공간 자기상관× | |
|---|---|---|
| 분야 | 공간분석 | 공간분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1990s–2000s | 1950 |
| 창시자≠ | Atsuyuki Okabe and colleagues | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| 유형≠ | Spatial network model | Spatial statistic / exploratory spatial data analysis |
| 원전≠ | Okabe, A., Satoh, T., Furuta, T., Sugihara, K., & Okano, K. (2006). Generalized network Voronoi diagrams: Concepts, computational methods, and applications. International Journal of Geographical Information Science, 22(9), 965–994. DOI ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| 별칭 | network spatial analysis, network-constrained spatial analysis, spatial network analysis, NBSA | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| 관련≠ | 3 | 5 |
| 요약≠ | Network-based spatial analysis (NBSA) analyzes the distribution and interaction of spatial phenomena constrained to a network structure — such as roads, railways, or rivers — using network distance rather than straight-line (Euclidean) distance. It is the appropriate framework whenever movement, proximity, or risk is governed by the underlying network topology rather than open space. | 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. |
| ScholarGate데이터셋 ↗ |
|
|