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| Crime Concentration Index× | Karstā punkta analīze (Getis-Ord Gi*)× | |
|---|---|---|
| Nozare≠ | Criminology | Telpiskā analīze |
| Saime≠ | Process / pipeline | Regression model |
| Izcelsmes gads≠ | 1989 | 1992 |
| Autors≠ | Lawrence Sherman, Patrick Gartin & Michael Buerger; David Weisburd | Arthur Getis and J. Keith Ord |
| Tips≠ | Descriptive concentration measure for crime across micro-places | Local spatial statistic |
| Pirmavots≠ | Sherman, L. W., Gartin, P. R., & Buerger, M. E. (1989). Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27(1), 27–56. DOI ↗ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ |
| Citi nosaukumi | Crime Concentration at Place, Hot-Spot Concentration Measure, Cumulative Crime Concentration, Law of Crime Concentration | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The crime concentration index quantifies how unevenly crime is distributed across micro-geographic places such as street segments or addresses. Building on Sherman, Gartin, and Buerger's 1989 discovery that a small fraction of addresses produces most calls for police service, and formalized in Weisburd's 2015 'law of crime concentration', it expresses the share of all crime accounted for by the most crime-prone places. | 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. |
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