The Utility was provided with the following risk factors and parameters:
Name | Feature Count | Operationalization | Spatial Influence | Analysis Increment |
---|---|---|---|---|
det_points | 66118 | Both_Proximity_and_Density | 3 Blocks | Whole |
bus_stops | 4202 | Proximity | 3 Blocks | Whole |
Type | Name | Operationalization | Spatial Influence | Coefficient | Relative Risk Value |
---|---|---|---|---|---|
Rate | bus stops | Proximity | 500 | 3.0617 | 21.3638 |
Rate | det points | Density | 500 | 2.1578 | 8.6521 |
Rate | Intercept | -- | -- | -3.3565 | -- |
Overdispersion | Intercept | -- | -- | 1.6985 | -- |
******************************************************************* Family: c("NBII", "Negative Binomial type II") Call: gamlss(formula = crime_count ~ r01d01_det_points_density_500 + r02p01_bus_stops_proximity_500, sigma.formula = ~1, family = NBII, data = raster.data, method = mixed(3, 10)) Fitting method: mixed(3, 10) ------------------------------------------------------------------- Mu link function: log Mu Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.356 0.15578 -21.55 1.717e-100 r01d01_det_points_density_500 2.158 0.04072 52.99 0.000e+00 r02p01_bus_stops_proximity_500 3.062 0.15789 19.39 3.897e-82 ------------------------------------------------------------------- Sigma link function: log Sigma Coefficients: Estimate Std. Error t value Pr(>|t|) 1.69852 0.03932 43.20191 0.00000 ------------------------------------------------------------------- No. of observations in the fit: 9199 Degrees of Freedom for the fit: 4 Residual Deg. of Freedom: 9195 at cycle: 3 Global Deviance: 18288.17 AIC: 18296.17 SBC: 18324.68 *******************************************************************
The selected risk terrain model was assigned relative risk scores to cells ranging from 1 for the lowest risk cell to 184.8 for the highest risk cell. These scores allow cells to be easily compared. For instance, a cell with a score of 184.8 has an expected rate of crime that is 184.8 times higher than a cell with a score of 1.
You can reproduce these risk scores in common GIS software by operationalizing the risk factors using the "best" model specifications displayed above. Risk factors based upon proximity should be set to 1 for areas within the distance threshold and 0 elsewhere. Risk factors based upon density should be set to 1 for areas 2 standard deviations above the mean value after applying a kernel density operation of the specified bandwidth and set to 0 in other areas.
The 2 manually produced risk map layers can then be combined through map algebra to produce a risk terrain map and to calculate relative risk scores. For example, using ArcGIS for Desktop's "Raster Calculator" function, you can copy and paste the following formula to assign relative risk scores to each cell updating the risk map layer names as needed:
Exp(-3.3565 + 3.0617 * "bus stops" + 2.1578 * "det points") / Exp(-3.3565)