‘Known to police“, a Toronto Star investigation into race, policing and crime is nominated for a Data Journalism Award. An interview with journalist Jim Rankin, who contributed to the making of ‘Known to police’.
How would you describe ‘Known to police’ in an elevator pitch?
‘Known to police’ is about who police stop, question and document in encounters that typically involve no arrest or charge, where they do this, and why. What we’ve shown, using Toronto police data, is that, in every part of the city, black and “brown” people are being stopped at rates disproportionate to the populations of black and brown people living in these areas. This is even more so with young males. The analysis allows for a provocative question: Is it possible that police in certain areas of the city have documented every young male of colour who lives there? And, what does that do to a community?
What inspired you to make ‘Known to police’?
The nominated work is the latest in several looks we’ve had at Toronto police arrest and charge data, and data of who they stop in mostly non-criminal encounters. It all dates back to 2000, when I made an FOI request for raw police data. The inspiration continues to be the stories we hear involving negative encounters with police and perceptions that blacks
are singled out.
Did you work by yourself or in a team?
This has always been a team effort. Here are the people involved in the short-listed work, including their roles:
- Andrew Bailey, Toronto Star, data specialist
- Hidy Ng, Toronto Star, map specialist
- Jim Rankin, Toronto Star, reporter-photographer and data analyst
- Sharis Shahmiryan, Toronto Star, designer and animator
- Patty Winsa, Toronto Star, reporter and Flash designer
- Randy Risling, Toronto Star, video production
- Brian Hughes, Toronto Star, graphics
- Brett Smith, Toronto Star, web editor
- Aneurin Bosley, Toronto Star, web editor
How did you get a hold on the data you needed?
As mentioned, through a Freedom of Information request. Here are more details on what we got and how we did it.
Which tools were used?
Our team members, collectively, used the following skills: reporting, photography, editing, data analysis, mapping, flash and HTML knowledge, print and online design, video production, animation, FOI knowledge.
- Video: Flash animation, Photoshop, Final Cut Pro, Illustrator
- Photography: DSLRs
- Data: Microsoft Access, SPSS, Excel
- Mapping: Map Marker, MapInfo Professional, Generation 5 Allocate,Flash, HTML and techniques included spatial analysis, multivariate correlation and interpolation.
- Demographic data came from Statistics Canada, census data.
How long did it take to make ‘Known to police’?
Once we had the data, about three months.
Do you have a useful tip for starting data journalists?
For starters, getting the data through freedom of information laws is always a time-consuming process. Police, traditionally, have not been open to sharing and have used every tool at their disposal to deny access. Major court cases in this ongoing series, involving important decisions that will benefit others seeking data from government sources, paved the way for this latest chapter. Our newsroom is constantly looking to explore new storytelling techniques as well. The animated movie produced for this project was a first for us. We are learning as we go, which is a great thing to be able to say. As for the analysis, we went to police early with our findings, which they checked themselves. We continued to refine our analysis and hone it down to key findings that would resonate. This all takes time, patience and perseverance.
As far as tips, this is always the big one: Be sure of your analysis. “Interview” your data for weaknesses. Maintain an open dialogue with the source of your data and when in doubt, ask questions. Never assume. You don’t need to be a rocket scientist to do data journalism. Know what data journalism can do, and if you lack expertise, seek it out from colleagues and other journalists. Finally, people never remember the big numbers from your findings. They remember people. You must bring your analysis to life with real people and stories.