Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Risk Terrain Modeling (Criminology)× | Kernel Density Crime Mapping× | |
|---|---|---|
| Nyanja | Criminology | Criminology |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2011 | 2008 |
| Mwanzilishi≠ | Joel Caplan & Leslie Kennedy | Bernard Silverman (KDE); Spencer Chainey (crime mapping application) |
| Aina≠ | Spatial risk-factor aggregation model for crime forecasting | Nonparametric density estimation for crime surfaces |
| Chanzo asilia≠ | 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 ↗ | Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1–2), 4–28. DOI ↗ |
| Majina mbadala | RTM, Risk Terrain Analysis, Environmental Risk Factor Modeling, Spatial Risk Factor Modeling | KDE Crime Mapping, Crime Density Surface Mapping, Hot Spot Density Mapping, Kernel Smoothing of Crime Events |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | 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. | Kernel density crime mapping turns a scatter of geocoded crime points into a smooth, continuous surface that shows where incidents concentrate. Each event is spread out over a small neighborhood by a kernel function, and the overlapping contributions are summed across a fine grid so that areas with many nearby crimes glow as peaks. Chainey, Tompson, and Uhlig (2008) showed that, among common hot-spot mapping techniques, kernel density estimation is one of the most accurate at predicting where future crime will occur, which is why it became the default crime-mapping surface in policing. |
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