Reposted from Stanford Journalism Program website.
The project is collecting, cleaning, releasing and analyzing over 100 million highway patrol stops across the United States.
The John S. and James L. Knight Foundation announced Tuesday that an interdisciplinary data reporting project affiliated with the Stanford Computational Journalism Lab that will collect and analyze more than 100 million highway patrol stops throughout the country is among the winners of the Knight News Challenge on Data. The project has been awarded $310,000 in support.
Traffic stops are one of the primary ways in which the public interacts with law enforcement, yet currently, there is little easily accessible information on the nature of the practice. Based at Stanford, the “Law, Order & Algorithms” project is gathering traffic stop data from as many of the 50 states that collect it, in order to understand and improve police interactions with the public through analysis and journalistic reporting. The data, which is being obtained under states’ public records laws, will eventually be made publicly accessible for journalists and researchers, with data recipes for how to best analyze the records.
The project was one of more than a thousand applications submitted to the Knight News Challenge on Data. The challenge was an open call for ideas that asked people to answer the question: How might we make data work for individuals and communities?
“By collecting and analyzing data on over 100 million traffic stops across the United States, we hope to bring greater transparency, equity and accountability to police interactions with the public,” said Sharad Goel, assistant professor in Stanford’s Department of Management Science & Engineering.
“The project reveals the power of data to unlock useful information and increase people’s understanding of everyday issues that affect their lives,” said John Bracken, Knight Foundation vice president for media innovation.
In addition to Goel, the interdisciplinary team includes both social scientists and journalists: Ravi Shroff, research scientist at New York University’s Center for Urban Science and Progress; Camelia Simoiu, Stanford Ph.D. student in management science & engineering; Sam Corbett-Davies, Stanford Ph.D. student in computer science and a Fulbright Scholar from New Zealand; and Vignesh Ramachandran, managing editor of Stanford’s Peninsula Press and an affiliate of the Stanford Computational Journalism Lab.
Goel and Shroff are currently co-teaching a Stanford winter quarter class, “Law, Order & Algorithms,” that discusses topics at the intersection of criminal justice and computer science, and that incorporates the traffic stop data into class projects. The two previously examined New York City’s controversial “stop-and-frisk” policing tactic by analyzing three million records.
Simoiu and Corbett-Davies have developed new statistical tests for discrimination, and they are now analyzing data from several states to determine the extent to which there is racial bias in decisions by officers to search motorists.
Ramachandran, a 2012 master’s graduate of the Stanford Journalism Program, is working with current Stanford senior Katie Kramon to negotiate and obtain the data from state agencies, as well as report out and write journalistic stories based on the project’s insights. Stanford Journalism Program Hearst Professional-In-Residence Cheryl Phillips — whose students started obtaining the state data last year — plans to use California’s traffic stop data in her public affairs data journalism course for analysis and reporting on statewide trends.
“This project and the Knight support will enable us to make data more accessible to journalists and others interested in the stories and reasons behind interactions with law enforcement,” said Phillips, a Pulitzer Prize-winning data journalist who is also co-founder of the Stanford Computational Journalism Lab. “One of the biggest challenges for data journalists is being able to access data that is suitable for analysis — this project takes on the burden of obtaining and cleaning the data. Our hope is that valuable public service journalism will come out of this effort.”
“The project brings together ideas from engineering, social science and journalism to understand how data can be used to analyze how public institutions are truly operating,” said Jay Hamilton, Hearst Professor of Communication, Stanford Journalism Program Director and co-founder of the Stanford Computational Journalism Lab. “I am very hopeful that the work will lower the costs to reporters of discovering many stories which currently go untold.”
Watch the full video from Tuesday’s announcement on Knight Foundation’s site: http://knightfoundation.org/live
About the John S. and James L. Knight Foundation Knight Foundation supports transformational ideas that promote quality journalism, advance media innovation, engage communities and foster the arts. The foundation believes that democracy thrives when people and communities are informed and engaged. For more, visit knightfoundation.org.
About Stanford Computational Journalism Lab The Stanford Computational Journalism Lab, launched in Fall 2015, is an initiative to support the evolution of computational approaches to public affairs journalism through research, teaching and the production of reporting. Learn more at cjlab.stanford.edu. Follow Stanford Journalism Program/Computational Journalism Lab on Twitter: @StanfordJourn
About the “Law, Order & Algorithms” team
(From left to right) Vignesh Ramachandran, Sam Corbett-Davies, Camelia Simoiu, Ravi Shroff and Sharad Goel.
Sam Corbett-Davies is a Ph.D. student at Stanford in the Department of Computer Science. He is a Fulbright Scholar from New Zealand who received his Bachelor of Engineering in Mechatronics from the University of Canterbury. Sam is interested in applying machine learning and statistics to questions of politics and policy. Follow Sam on Twitter: @
Sharad Goel is an assistant professor at Stanford in the Department of Management Science & Engineering, and, by courtesy, Computer Science and Sociology. Sharad draws on a combination of methods from machine learning and crowdsourcing to study contemporary issues in public policy, including stop-and-frisk, voter ID laws, media bias and online privacy. Follow Sharad on Twitter: @5harad
Vignesh Ramachandran is the managing editor of Stanford’s Peninsula Press and is affiliated with the Stanford Computational Journalism Lab. He previously covered technology news at Mashable, and U.S. news and travel issues for NBC News Digital. He received an M.A. in journalism from Stanford in 2012. Follow Vignesh on Twitter: @VigneshR
Ravi Shroff is a research scientist at New York University’s Center for Urban Science and Progress. He applies statistical and machine learning methods to study urban settings, particularly in the context of criminal justice. Ravi has a Ph.D. in mathematics from UC San Diego, and an M.S. in applied urban science and informatics from NYU. Follow Ravi on Twitter: @
Camelia Simoiu is a Ph.D. student at Stanford in the Management Science & Engineering Department. She is interested in developing novel methods to evaluate and engineer complex social processes such as public policies and online platforms. Camelia received an Honors B.S. in applied statistics and economics from the University of Toronto and was a Fellow at the University of Chicago’s Data Science for Social Good program. Follow Camelia on Twitter: @cameliasimoiu