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| Near-Repeat Analysis× | Hot Spot Analysis× | |
|---|---|---|
| Field≠ | Criminology | Spatial analysis |
| Family≠ | Process / pipeline | Regression model |
| Year of origin≠ | 2003 | 1992 |
| Originator≠ | Michael Townsley, Shane Johnson & Kate Bowers | Arthur Getis and J. Keith Ord |
| Type≠ | Space-time clustering test for crime contagion | Local spatial statistic |
| Seminal source≠ | 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 ↗ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ |
| Aliases | Near Repeat Calculator Method, Space-Time Near-Repeat Analysis, Near-Repeat Victimization, Contagion Crime Pattern Analysis | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA |
| Related≠ | 4 | 5 |
| Summary≠ | 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. | Hot Spot Analysis uses the Getis-Ord Gi* local spatial statistic to identify geographic locations where high or low attribute values cluster together to a degree that is statistically significant. Each feature is evaluated in relation to its neighbours, producing a z-score that flags genuine spatial hot spots and cold spots against a background of random variation. |
| ScholarGateDataset ↗ |
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