השוואת שיטות
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| Risk Terrain Modeling (Criminology)× | ניתוח נקודות חמות (Getis-Ord Gi*)× | |
|---|---|---|
| תחום≠ | Criminology | ניתוח מרחבי |
| משפחה≠ | Process / pipeline | Regression model |
| שנת המקור≠ | 2011 | 1992 |
| הוגה השיטה≠ | Joel Caplan & Leslie Kennedy | Arthur Getis and J. Keith Ord |
| סוג≠ | Spatial risk-factor aggregation model for crime forecasting | Local spatial statistic |
| מקור מכונן≠ | Caplan, J. M., Kennedy, L. W., & Miller, J. (2011). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28(2), 360–381. DOI ↗ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ |
| כינויים | RTM, Risk Terrain Analysis, Environmental Risk Factor Modeling, Spatial Risk Factor Modeling | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA |
| קשורות≠ | 4 | 5 |
| תקציר≠ | Risk Terrain Modeling (RTM) represents crime risk as a function of the environment: it identifies the features of a landscape — bars, bus stops, vacant lots, pawn shops, schools — that attract or generate crime, maps each one's spatial influence as a separate risk layer, and combines those layers onto a raster of place to produce a relative risk surface. Introduced by Joel Caplan and Leslie Kennedy around 2011, RTM 'brokers' environmental criminology theory and GIS methods so that crime forecasting rests on the qualities of places rather than on the history of crime alone. | 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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