According to the Global Investigative Journalism Network, Matt Kiefer and Julia Smith did research about the settlements and judgments of police misconduct and aimed to revealed the results to the public via a standalone App.
Their research based on a 2012 investigation, which showed Chicago police officers sued in different lawsuits. Kiefer and Smith believed that publishing defendants and settlement amount with data-driven approach would restrain the police officer’s misconduct.
At the first stage of research, Kiefer and Smith found it was hard to identify the officers because of the partial or misspelled names given in many legal complaints, along with the lack of badge numbers.
In the consideration of being responsible for the accuracy of their research, Kiefer and Smith decided to take a new approach. They loaded the complete list of police officers FOIA’d from the Chicago Police Department into a Django object-relational mapper. Based on the officers’ full names, badge numbers and dates of service, they wrote algorithms to derive a match for each named defendant with a high degree of certainty. In this approach, 85% of the matches could be automated and published on the GitHub.
To bring the data to the public, Kiefer and Smith were devoted to build a standalone news App with the help of the INN technology team. They used a Chicago Tribune’s static site generator named Tarbell to provide easy content management and a built-in publishing workflow that “pushes pre-baked files to Amazon S3.” Also, they used Backbone as their data-binding framework for the front end.
Although there were some technical problems during the development process, Kiefer and Smith thought these problems as fun puzzles. They continued to collect data on police misconduct in Chicago and believed this App would become a worldwide database for those who are interested in policing issues.