방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 전체 연구 지역의 공간적 자기상관을 측정하는 전역 모란 I× | 지역적 모란 I (LISA)× | |
|---|---|---|
| 분야 | 공간분석 | 공간분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1950 | 1995 |
| 창시자≠ | Patrick Alfred Pierce Moran | Luc Anselin |
| 유형≠ | Global spatial autocorrelation test / index | Local spatial autocorrelation statistic |
| 원전≠ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| 별칭 | Moran's I, global spatial autocorrelation index, Moran index, GMI | Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index |
| 관련 | 6 | 6 |
| 요약≠ | Global Moran's I is the most widely used single-number summary of spatial autocorrelation across an entire study area. It compares the attribute value at each location with values at neighbouring locations using a spatial weights matrix, and returns a statistic ranging from −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering). A significance test determines whether the observed pattern is stronger than random chance. | 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. |
| ScholarGate데이터셋 ↗ |
|
|