Project Journal, Week 7

Tiffany Nguyen
Chicago Crime Anaysis
2 min readMay 10, 2019

This week we continue to look into some other interesting visualization methods.

After doing further research, we found a very intriguing article about the system of Chicago’s surveillance cameras from 2016.

We question the reason behind this set-up and also the efficiency of this hi-tech security contributing to the deduction in Crime Rate.

We got the data set containing a lot of information about the geolocation where cameras in Chicago are placed. Hence, we decided to graph these points into our Google Maps to see if it connected to our crime map these recent years. In this case, we found a library called gmaps which is the best fit for us. Some visible patterns can be seen below:

Chicago’s gun crime incidents and cameras map. Heat: incidents, blue dots: cameras

According to this heatmap, it is reasonable why security cameras are placed where they are placed now. The camera clusters seem to fit right into the gun crime frequency clusters.

We group these clusters into two main clustered areas divided by the yellow line. The most interesting part, as we notice, is that cameras are very dense in the upper cluster but not the lower cluster. We theorize the reason for this divergence is because of the uncontrolled gang activity downwards. The scene down south has gone so rough that the police doesn’t bother to install more surveillance cameras, even though there are more activities down south.

Heat map of Weapon Violation incidents vs. Robbery incidents

Weapon violation incidents are very dense on the lower east side, which is notoriously known for Chicago gang activities. It is not the case for robbery though, as the distribution of robbery incidents is less dense than that in the lower cluster. We believe that the cameras are installed for community security, and thus it is rather installed where there are more robbery cases (community security) than where there are more weapon violation cases (gang activities).

Overall, it is interesting to plot our data on a map to figure out patterns. Nevertheless, there is more to the story than what we included in our blog. If you have any suggestions, please let us know below! Thanks for reading!

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