Lab 6- Hotspot Analysis

I think this is the first week in GIS Applications that I finished the lab and felt less confused than when I started. Normally I work through lab but get completely stumped and frustrated at some point but that did not happen this week. Maybe that's just because I found the topic interesting but I'll find out once my lab is graded. The topic for the week was crime hotspot analysis and we created three different types of maps to show crime hotspots using ArcMap, Spatial Analyst Tools, and Spatial Statistics Tools.

The first type of analysis was grid-based thematic mapping. To analyze the data a grid was laid over the crime data point file and areas with high occurrences of crime were highlighted. The majority of the work for the first section was completed through a spatial join and selections with SQL queries. The spatial join counted the number of crime occurrences for each grid square and the SQL queries enabled the quick selection of grid cells with high numbers of crimes to export as a file to display on a map. The final product from this section can be seen as the pink colored block shapes in the graphic below.

The second map was a kernel density map. After using an a spatial join between census block areas and point crime data I used the Kernel Denisty Tool in ArcMap. Using a cell size of 100ft and a radius of 2640ft the computer analyzed the crime data and came up with the dark blobs in the graphic below to denote areas that had crimes rates the were greater than three times the average for the area.

The third map uses Local Moran's I (Cluster and Outlier Analysis) to identify hotspots. Again, this process started with a spatial join and SQL selection query. Then I used the Cluster and Outlier Analysis (Anselin Local Morans I) Tool in ArcMap. After the tool was ran I used another SQL query to select the results with a high-high outcome. This produced a result similar in area size to the grid-based method but with an irregular shape the covered different regions.

My final graphic shows the all three hotspot results from the lab.

The maps were based off of crime data from 2007. The final section of our lab was to look the the hotspots from the 2007 data and see how they compared to the actual crime spots for 2008. The grid-based map from 2007 encompassed 40% of the crimes reported in 2008 and a crime density of 43. The kernel density map accounted for 1/3 of the area that the grid-based map did but encompassed 18% of the crimes reported in 2008 and had a crime density of 66. I think based of of these statistics the kernel density map is a good predictor of potential high crime areas.