Tax Increment Finance Illumination Project
CivicLab Tax Increment Finance (TIF) Illumination Project
To investigate, illuminate and educate around the total impacts of TIFs in Chicago on a ward-by-ward basis. There are 163 TIF districts in Chicago, covering 30% of the city's footprint. In 2011 they extracted $454 million in property taxes from property owners in those districts and placed those funds in essentially what is an "off-the-books" second city budget - one that has very few checks and balances. The TIF Program is scandal ridden and highly controversial. See stories written in our local weekly, The Chicago Reader at .
Presentation parked here: 
We have begun investigation of one ward - the 27th, on the city's Near North side, which has 12 TIFs in it.
Data were compiled from multiple sources:
City of Chicago:
* TIF Projection Reports:  * Boundaries - Wards:  * Boundaries - Tax Increment Finance Districts:  * Boundaries - Building Footprints:  * District Annual Reports: 
* Property Info Portal: 
Additional data were acquired through various Freedom of Information Act (FOIA) requests, notably the taxcode data that enumerates the percentage a taxing body takes from each property based on the taxing bodies' overalpping jurisdictions.
The geospatial data (shapefiles) were loaded into a PostgreSQL () database with the geospatial PostGIS (  ) extension (see OpenGeo's excellent tutorial here:  ). From there, buildings were matched to addresses so that any new data with an address could be matched to a building (which could then be matched to a TIF, which could then be matched to a ward, etc...).
Financial data from the District Annual Reports had to be manually entered, as they are all in PDF form and the necessary fields could not be easily extracted programmatically.
Through a FOIA request, we obtained every Property Index Number (PIN) in Chicago, with the total property tax amount billed to each of those PINs. Using h[ttp://cookcountypropertyinfo.com] , PINs were matched to addresses, which were then geocoded by matching them to the buildings in the buildings footprint database (98.7% of non-vacant PINs were successfully geocoded). A spatial query could then be written to sum the property tax bills for every PIN for each TIF in the 27th ward.