Finding the story: Open policing data
Researchers at Stanford University have collected and examined the records from millions of local police stops in more than 50 cities. Using the programming language R, learn how to analyze this local policing data and find patterns for stories.
This session is good for: People who have worked with data (or R) and want to learn how to analyze police data.
Daniel Jenson is an engineer in the Stanford Computational Policy Lab. Previously, he spent five years at Facebook working on risk analytics and machine learning automation. He is currently pursuing an M.A. in Computer Science with a Specialization in Machine Learning through the Georgia Institute of Technology.
Cheryl Phillips teaches journalism at Stanford, is the founder of Big Local News and co-founder of The Stanford Open Policing Project. She worked at The Seattle Times for 12 years as a reporter and editor. She twice was part of breaking news coverage that received the Pulitzer Prize and twice served on teams that were Pulitzer finalists. She has worked at USA Today and at newspapers in Michigan, Montana and Texas. She is a former IRE board president. @cephillips
Amy is a data scientist at the Stanford Computational Policy Lab, a team using mathematical tools to understand, raise awareness about, and work to change systemic inequities. Her focus lately has been evaluating policing practices. She received her B.S. in Pure Mathematics from Pomona College and her M.S. from Stanford University’s Institute for Computational and Mathematical Engineering. She loves hiking, sunshine, rock climbing, tea, and good books.
No tipsheets have yet been uploaded for this event.