Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Nearest Neighbour Index× | Scan Statistic Cluster Detection× | |
|---|---|---|
| Vakgebied | Human Geography | Human Geography |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1954 | 1997 |
| Grondlegger≠ | Philip J. Clark & Francis C. Evans | Martin Kulldorff |
| Type≠ | Summary statistic for the degree of clustering or dispersion in a point pattern | Hypothesis-testing method for detecting statistically significant spatial clusters |
| Oorspronkelijke bron≠ | Clark, P. J., & Evans, F. C. (1954). Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology, 35(4), 445–453. DOI ↗ | Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics – Theory and Methods, 26(6), 1481–1496. DOI ↗ |
| Aliassen | Clark-Evans Index, Nearest Neighbour Analysis, NNI | Kulldorff Scan Statistic, Spatial Scan Statistic, SaTScan Cluster Detection |
| Verwant≠ | 4 | 3 |
| Samenvatting≠ | The nearest neighbour index, introduced by Clark and Evans in 1954, is a simple summary statistic that quantifies whether a set of points is clustered, randomly scattered, or evenly dispersed across an area. It compares the average distance from each point to its closest neighbour with the average distance that would be expected if the same number of points were placed completely at random. The ratio of observed to expected distance, together with a significance test, gives a single interpretable number that has become a staple of point-pattern analysis in geography and ecology. | The spatial scan statistic, introduced by Martin Kulldorff in 1997, is a method for detecting and testing the significance of spatial clusters of events such as disease cases. It moves windows of many sizes and positions across the study region, treating each window as a candidate cluster, and scores it by a likelihood ratio comparing the rate of events inside the window to the rate outside. The window with the highest score is the most likely cluster, and its significance is assessed by Monte Carlo simulation, giving a principled answer to the recurring question of whether an apparent hotspot is real or chance. |
| ScholarGateGegevensset ↗ |
|
|