Week 6 - AppsGIS - Hotspot Analysis

This week we looked at hotspot mapping, using crime data. We compared the results of three different methods: Kernel Density, Local Moran’s I, and Grid Method. We used different data to determine things such as burglaries per housing unit and homes for rent per census tract. We also used the graphing abilities of ArcMap in order to compare the results, such as:

Figure 1. Graph of burglary rate per housing unit compared to
number of housing units that are rented.

We also looked at Kernel Density hotspots, based off of the average, twice the average, three times the average, etc.

Figure 2. Kernel Density analysis of crimes.

Finally, we performed all three methods of analysis on the same dataset of burglaries in Albuquerque, New Mexico in 2007. We wanted to see how the results compared to one another, as well as how well it predicted crimes in the following year.

For the grid-based thematic mapping, first a Spatial Join was created in order to combine the grids with the burglaries in 2007. A SQL query was set: Join_Count = 0, then switched the selection to choose all grids with at least 1 crime. This was exported into a new shapefile of the grids where crime occurred. Then sorted the attribute table by descending crime counts for each grid. The grids with the top 20% of the number of crimes were selected and exported as a new shapefile. In this attribute table, added a new field named “Dissolve” and the field calculator was used to set the values to 1 for all of the grids. This way, the Dissolve tool could be used in order to create one polygon as the result.

Figure 3. Grid-based thematic mapping result of burglary hotspot areas
(top 20% number of crimes per grid).

For Kernel Density, the Environments were set to those of Grids for both Processing Extent and Raster Analysis Settings. For the Kernel Density tool, the Burglaries dataset was used with a search radius set at .5 miles, or 2640 feet. The symbology, was adjusted in order to determine the mean value (of crimes) when areas with 0 crimes were excluded, and then categories were adjusted to 0 – mean, mean to 2xMean, 2xMean to 3xMean, etc. Reclassified the raster so that all values below 3xMean equal to NoData, and all values above are classified as 1. This was then turned from Raster to Polygon, and then dissolved into one polygon.

Figure 4. Kernel Density result of burglary hotspot areas
(higher than 3 times the average).

In order to perform the Moran’s I analysis, I first performed a Spatial Join as before. The Match Option was set to “Contains” so that the count would be for the number of points within each polygon (census block). One large polygon was removed because it was outside of the police jurisdiction, and was impacting analysis. Within the attribute table of the shapefile, a New Field was added for Crime_Rate. Using the Field Calculator, Crime_Rate was set to = [Join_Count] / [HSE_UNITS] * 1000. This divides the number of crimes per census block by the number of housing units, and then multiplies this number by a thousand, which is a common threshold for this type of analysis. Cluster and Outlier Analysis (Anlesin Local Morans I) was then performed, with the Input Field set as the calculated crime rate. Within the attribute table of the resulting shapefile, used Select by Attributes to select the entries with High-High (HH) cluster results. This refers to the areas with a high crime rate in close proximity to other areas with high crime rates. The selection was then exported to create a new shapefile. Dissolve tool was used to create a single polygon out of the resulting hotspots.

Figure 5. Local Moran’s I result of areas with high-high crime rates.

These maps were then combined onto one map in order to show how the analyses overlap. Afterwards, additional steps were taken in order to determine if the hotspots accurately determined the area of high crime for the next year (2008). Analysis was based primarily on crimes per square kilometer within each determined hotspot, as this gives the best picture.

Figure 6. Map output showing the overlap of the three hotspot analyses.