Wednesday, June 17, 2015

Homeland Security- DC Crime Mapping

I can't believe we are on week 5! This week, we are looking at how to analyze data using the Kernel Density Tool. The subject of this week's Lab is to analyze DC Crime data by examining various categories of crime ad mapping the proximity of crimes by type and distance from various Police Stations.

I created a graph that shows all the reported crimes in the DC area:

To create this graph, my first step was to summarize the data and create a database file (dbf).
                 - Open the Attribute table, right click the column "Offense" and select Summarize
After I created the graph I experimented with different settings and selections.
                 - I decided to use the “palette” color and named the other items accordingly.
Analyzing this graph was a quick way to see how many of each type of offense was reported.
                 - I could clearly see that the top three Offenses were Theft, Theft from Auto and Burglary.

But this was only one step in the entire process to create the two maps for this week's Lab. Creating the first map, I also had the chance to use the Ring Buffer Tool. I created rings at distances of 0.5 miles, 1.0 miles and 2.0 miles. This told me how many total crimes were reported within these distances. The results were:

                                             0.5 Miles there were 668 crimes reported
                                             1.0 Miles there were 856 crimes reported
                                             2.0 Miles there were 556 crimes reported

I then used the formula:       ([Count_] / 2080) *100
To compute the percentage for each category of crime reported. 

                          At 0.5 miles the percentage of total crimes reported was: 32%
                          At 1.0 miles = 41%
                          At 2.0 miles = 27%

I looked at the data using other tools and finally created the following map:

What I learned from my analysis is that the highest crime rate is at three separate District HQs: 3D with 13% of total crime; 7D with 12% of total crime and 6D with 10%.  In fact, 6 of the top 7 crime rates are at District HQs with the only exception being the Asian Liaison unit that has 8% of the total crime. The data seems to indicate that more crime occurs in the vicinity of HQs and less crime occurs where there are Liaison units or smaller Substations.  However, more important to me is that I gained valuable experience doing this analysis using the tools I have studied in this course.

Compiling the second map was very interesting because I learned to use a knew tool: Kernel Density Tool (ArcToolbox > Spatial Analyst Tools > Density > Kernel Density). I won't try to explain the math that the tool uses; suffice it to say, this is an awesome tool and I had a blast using it. Here's the ArcGIS Help link that explains the Kernel Density Tool much better than I ever could:  
                                         The Awesome Kernel Density Tool

And here's my second map for this week's lab:

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