JSK, Big Local News, Stanford Computational Policy Lab sponsor free data journalism workshop

BLD Police Stop

Journalists from around the country will soon have the opportunity to learn how to analyze millions of traffic stops made by local police departments through training organized by Stanford groups that are collaborating to encourage locally focused data journalism.

The workshop led by Stanford’s Big Local News program and the Stanford Computational Policy Lab is part of a new initiative by the John S. Knight Journalism Fellowships to accelerate efforts to improve the quality of news and information reaching the public. Through its Impact Partnerships, JSK offers a range of support to projects that advance its four call-to-action themes: challenging misinformation and disinformation; holding the powerful accountable; fighting bias, intolerance and injustice; and strengthening local news.

The March 6 workshop in Newport Beach, California, is a precursor to the NICAR conference, the signature annual gathering of data journalists in the United States, which will be held March 7-10 at the Newport Beach Marriott Hotel.

The JSK Fellowships has provided funding for 15 journalists selected by Big Local News to attend both the workshop and NICAR. Seats are available for an additional 15 journalists to participate in the workshop. Registration for the free workshop is available on a first-come, first-served basis.

It will focus on analyzing traffic stop data collected from nearly 50 local police departments as part of the Stanford Open Policing Project. This is an expansion of the Open Policing Project, which initially released traffic stop data collected from state law enforcement agencies. To download state-level data or to read studies and articles that have been written about it, visit the Stanford Open Policing Project website.

The new data, which includes an update to state police traffic stops as well as the new city-level data sets, will be released to the public around the same time as the conference. The local policing data contains detailed information on individual stops including time, date, location and driver race, ethnicity, gender and age. It covers the following cities:

  • Little Rock, Arkansas
  • Gilbert, Arizona
  • Mesa, Arizona
  • Bakersfield, California
  • Long Beach, California
  • San Bernardino, California
  • San Diego, California
  • San Francisco, California
  • San Jose, California
  • Santa Ana, California
  • Stockton, California
  • Aurora, Colorado
  • Denver, Colorado
  • Hartford, Connecticut
  • Tampa, Florida
  • Idaho Falls, Idaho
  • Chicago
  • Wichita, Kansas
  • Owensboro, Kentucky
  • New Orleans
  • St. Paul, Minnesota
  • Charlotte, North Carolina
  • Durham, North Carolina
  • Fayetteville, North Carolina
  • Greensboro, North Carolina
  • Grand Forks, North Dakota
  • Camden, New Jersey
  • Albany, New York
  • Cincinnati, Ohio
  • Columbus, Ohio
  • Oklahoma City, Oklahoma
  • Tulsa, Oklahoma
  • Philadelphia
  • Nashville, Tennessee
  • Arlington, Texas
  • Austin, Texas
  • Houston
  • San Antonio, Texas
  • Burlington, Vermont
  • Seattle
  • Tacoma, Washington
  • Madison, Wisconsin

Workshop participants will be trained to look for patterns related to race and policing using the statistical programming language R and R Studio. They will be able to take the data and any findings they uncover back to their newsrooms to develop into their own stories. Big Local News will continue to assist local reporters as they move forward with their work.

Participants are expected to provide their own laptops with R and R Studio already installed. Knowledge of R is not required for this training; a guide on how to install R and R Studio will be provided if needed. This workshop is hosted by Big Local News, the JSK Fellowships and the Stanford Computational Policy Lab. It will take place Wednesday, March 6, from 1 to 5 p.m. at the Newport Beach Marriott.

Eric Sagara is a senior data reporter with Reveal from the Center for Investigative Reporting. He is working with Big Local News with support from the JSK Journalism Fellowships.