Category Archives: Chicago Data

Comcast’s Monetary Influence Over Illinois Political Candidates

I found an interesting dataset on political contributions in the state of Illinois.. The downloadable .zip contains multiple tab delimited database files which contain the relationships between Donations, Committees, and Candidates.

Out of curiosity in seeing Comcast’s political influence in Illinois over time, I parsed the 650,775mb file called Receipts.txt. Below is a bar chart of yearly recorded donation totals from 2000 through 2015-08.


Not being very politically oriented, I wanted to somehow relate the donations to candidates. But in the form of the available data, it appears Donors make contributions to Committees, and Committees support a Candidate. But I do not know if Candidates and Committees are a One to One relationship at the time of typing this.

Parsing the text file called CmteCandidateLinks.txt, I related the Committee Id with the candidate Id. Parsing the text file called Candidates.txt I relate the Candidate Id to the Candidate name.

Lots of candidate duplicates per donation entry. Majority of Committees represent the same candidate under different ids, while some committees represent multiple candidates. Example here:


So I decided to distribute each donation amount between a potential multitude of candidates. I did this by dividing each donation by the number of candidates which belong to the committee recipient. From that, I got this list of Comcast’s Top Illinois Candidates.


Cook County Employee Healthcare Costs by Department – June 2012

I came across an interesting data set while browsing  It can be found here.  Judging by the title, it appears to be healthcare costs for the month of June in the year of 2012.  There is no description, or any additional information I could find about it.  The data contains a department code, a number of employees, and a total cost.  It looks like this:


I was curious, so I took the average cost of employee count (Cost / Emp num), per department.  I correlated the department numbers to labels from an alternative dataset also found on cook county’s data catalog.

My experiment resulted in two bubble charts.

Greater than 40 Employees



Greater than 40 Employees and less than 1,000


And now here’s the second chart processed with Google’s Deep Dream