Module 1: Crime Analysis

 Module 1: Crime Analysis

This weeks module focused on how to use ArcGIS Pro to make crime analysis maps. There are three different types of maps that are used to represent crime, which are grid-based thematic, kernel density, and Local Moran's I. Grid-based thematic mapping overlays a regular grid of polygons on top of the crime events to produce a count of crime events per grid cell. Kernel density mapping is when points lying near the center of a search area are weighted more heavily than those lying near the edge. Local Moran's I is asking the question of if there are similar features nearby.

Grid-based Thematic

When making the Grid-based Thematic Hotspot Mapping you once again need to use the Spatial Join tool that shows the information from the 2017 homicides within the grid cells. The Select By Attributes tool you need to select the correct column so you can get the information you want which was any grid that had a homicide count greater than zero. The way I exported this feature was under the Data tab where I selected “Layer From Selection” where it creates the feature off what I selected. Next, we needed to get the top 20% of the features that were selected previously, and this is done by opening the attribute table sorting the Join_Count field and dividing the total number of records by 5 and making sure to rounding down. Since there is no selection tool for this you need to do it manually by clicking the first box and then hold the shift button to where you want to end to get all the numbers in between. After all of that we need to dissolve the features together. This is done by making a new tab that says Dissolve and using the field calculator to give the entire column the same value. Then find the Dissolve tool to create the multipart features. You can see it in the image below.

Kernel Density

First, we want to search for the Kernel Density Tool. As stated earlier make sure there are no spaces in your folders and your features. This was done similar to the maps we did for Washington DC kernel map. We need to make sure the output cell size and the search radius are in same which is in feet. After creating the map, we needed to only make two breaks based off the mean and the maximum value. This is done by right clicking the feature and hitting the symbology tab. Once you are in the tab you need to click the more button and select “Show Statistics” which will show all the information we need to make your break values. Next, we need to make the raster to a polygon but first need to use the Reclassify tool to reclassify the feature. After that we need to use the Raster to Polygon tool to same the feature to a feature class within the project geodatabase. Lastly, we needed to do the Select by Attributes and find the value of 2 which is three times the mean of the original kernel density. The image below that shows the kernel density from the 2017 hotspots.

Local Moran''s I

The last map we produced was the Local Moran’s I Hotspot which is supposed to use crime counts or rates aggregated with meaningful boundaries. Once again we used the Spatial Join tool between census tracts and 2017 homicides. Then we needed to create a new field for crime rate and create a field calculator where homicides per 1000 housing units. Next, we used the Cluster and Outlier Analysis (Anselin Local Moran’s I) tool and leave the parameters as their default settings. After that we needed to get the high-high clusters because it shows the areas that had a high among of crime per 1000 households. Lastly, we dissolve those features so it looks good on the map which you can see below.

Based on all the information above it appears that the kernel density map would be the best because it shows a smoother and more continuous map instead of breaking it into blocks or zones. Some lessons I learned when doing this module was when setting up your environment setting make sure the extent and the mask are the same. If it is not, then the feature class will show up empty. Also, in the Kernel Density (Spatial Analyst) tool you need to make sure there are no spaces in either the folder the information is saved in and the feature class.