Category Archives: Chicago Data

Chicago Public High Schools SY1617 Maps and Charts

A look into School Profile Information for the school year of 2016/2017.
Data set from Chicago Data Portal
School profile information for all schools in the Chicago Public School district for the school year 2016-2017.
Filter conditions used:

ACT vs Total Students By School Type

ACT vs Total Students By Majority Dmeographic

Maps (Click for Full Size)

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Chicago Police Gun Related Incident Visualizations 2008 – 2016

A look into Chicago Police Department’s gun related incident reports from January 2008 through February 2016. The data is from data.cityofchicago.org.

Stacked bar chart of monthly incidents by primary type.
Click chart image to expand
Chicago Police Department Monthly Gun Related Incident Reports
Line chart of monthly incidents by primary type.
Click chart image to expand
Chicago Police Department Monthly Gun Related Incident Reports Line Chart

Static Map
Chicago Police Department Gun Related Incident Reports Jan 2008 - Feb 2016 Heat Map

chicago-homicideschicago-race-map

Animated Map

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Visualizing Chicago’s affordable rental housing developments (Multifamily)

Multifamily public housing development visualizations by unit count/neighborhood for multifamily property types, and a word cloud of the most popular management companies. The data is from data.cityofchicago.org

chicagopublichousing

The affordable rental housing developments listed below are supported by the City of Chicago to maintain affordability standards.

ChicagoMultifamily Affordable Rental Housing Developments Common Community Areas ColorChicagoMultiFamilyUnitCountCountBubble

ChicagoMultiFamilyUnitCountCountColorAffordable Rental Housing Developments management Company WordCloud

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.

Comcast_DPolitical_Donations_2000-2015-6_2

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:

donations-2-commit-2-candids

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.

top_20_illinois_Candidates_Recieve_Donation_from_Comcast

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Cook County Employee Healthcare Costs by Department – June 2012

I came across an interesting data set while browsing datacatalog.cookcountyil.gov.  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:

datascreenshot

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

AverageEmployeeCostPerDepartment_GreaterThan40Employees

 

Greater than 40 Employees and less than 1,000

AverageEmployeeCostPerDepartment_GreaterThan40EmployeesLessThan1000

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

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