IV. a. Identify the high crash density locations” layer
Estimated time:
2 minutes
As a traffic engineer safety is one of the main concerns. The identification of high crash density locations is therefore necessary
This exercise will help you:
- a1. Add a new layer
- a2. Change the properties of the new layer
- a3. Identify high crash density locations
Text Version:
- Uncheck all layers except “All_Crashes_04-06” layers. Add “ADMIN_COUNTIES_AHTD_2005_poly” layer from Cities 2005 folder
- a1: Add “Highcrash” layer from the “Cities2005” folder
- Drag and drop the new layer (Highcrash) to the second top of the list
- a2 & a3: Right click on the “All_Crashes_04-06” layer and click on “Properties” click on “Display” and in the “Transparency” enter “80” click “Apply”, the “All_Crashes_04-06” layer should be on the top of all layers list
- a2 & a3: Right click on the “ADMIN_COUNTIES_AHTD_2005_poly” layer and click on “Properties” click on “Display” and in the “Transparency” enter “80” click “Apply”
- a2 & a3: Right click on the new layer (Highcrash) and click on “Properties” on “Display” and in the “Transparency” enter “40” click “Apply”
- a2 & a3: Right click on the new layer (Highcrash) and then click on “Properties”, select “Symbology” in the “Show” region select “Classified”, on the right side of the “Classified” in the “Color Ramp” drag down and select an appropriate color scheme
- a2 & a3: [Left] Click “Apply”
- a3: Select the “Label” field and by click on each value in the “Label” delete the decimal places
- a3: [Left] Click “Apply”
- a3: [Left] Click “Ok”
(Note: Density type is Simple, and if the input is points, density calculates the density of point features around each output raster cell. Conceptually, a neighborhood is defined around each raster cell center, and the number of points that fall within the neighborhood is totaled and divided by the area of the neighborhood. If a population field setting other than none is used, the population field’s value determines the number of times to count the point.
Density type is Kernel; conceptually a smooth curved surface is fitted over each point feature. The surface value is highest at the location of the point, and diminishes with increasing distance from the point, reaching zero at the search radius distance from the point. The volume under the surface equals the population field value for the point, or one if none is specified. The density at each output raster cell is calculated by adding the values of all Kernel surfaces when they overlay the raster cell center)