Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Near-Repeat Analysis× | Ripley K-funktsioon× | |
|---|---|---|
| Valdkond≠ | Criminology | Ruumianalüüs |
| Perekond≠ | Process / pipeline | Hypothesis test |
| Tekkeaasta≠ | 2003 | 1977 |
| Looja≠ | Michael Townsley, Shane Johnson & Kate Bowers | Brian Ripley |
| Tüüp≠ | Space-time clustering test for crime contagion | Spatial point pattern test |
| Algallikas≠ | Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries: A test of the near repeat hypothesis. British Journal of Criminology, 43(3), 615–633. DOI ↗ | Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B, 39(2), 172–212. DOI ↗ |
| Rööpnimetused | Near Repeat Calculator Method, Space-Time Near-Repeat Analysis, Near-Repeat Victimization, Contagion Crime Pattern Analysis | Ripley's K Function, Second-Order Intensity Function, K(d) Function, Ripley K Fonksiyonu |
| Seotud≠ | 4 | 2 |
| Kokkuvõte≠ | Near-repeat analysis tests whether crimes cluster in space and time beyond chance: after a crime occurs, are nearby locations at elevated risk for a short period? Developed in the early 2000s by Townsley, Johnson, Bowers and colleagues for burglary, it formalizes the 'contagion' or 'communicable disease' pattern of crime using a Knox space-time test against a Monte Carlo reference distribution. | The Ripley K function, introduced by Brian Ripley in 1977, is a second-order summary statistic for spatial point patterns. It measures how the number of points within a given distance d of a typical point compares to what would be expected under complete spatial randomness (CSR). Widely used in ecology, epidemiology, criminology, and geography, the K function reveals whether events cluster, disperse, or distribute randomly across a study area at multiple spatial scales simultaneously. |
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