Chapter 5: Statistics: Describing Data
The trials and tribulations of data visualization for good
“I love big data. It’s got such potential for storytelling.” At DataKind, we hear some version of this narrative every week. As more and more social organizations dip their toes into using data, invariably the conversation about data visualization comes up. There is a growing feeling that data visualization, with its combination of “engaging visuals” and “data-driven interactivity”, may be the magic bullet that turn opaque spreadsheets and dry statistics into funding, proof, and global action.
However, after four years of applying data-driven techniques to social challenges at DataKind, we feel that data visualization, while it does have an important place in our work, is a mere sliver of what it takes to work with data. Worse, the ubiquity of data visualization tools has lead to a wasteland of confusing, ugly, and sometimes unhelpful pie charts, word clouds, and worse.
The challenge is that data visualization is not an end-goal, it is a process. It is often the final step in a long manufacturing chain along which data is poked, prodded, and molded to get to that pretty graph. Ignoring that process is at best misinformed, and at worst destructive.
Let me show you an example: In New York City, we had a very controversial program called Stop and Frisk that allowed police officers to stop people on the street they felt were a potential threat in an attempt to find and reclaim illegal weapons.
After a Freedom of Information Act (FOIA) request by the New York Civil Liberties Union (NYCLU) resulted in the New York Police Department (NYPD) releasing all of their Stop and Frisk data publicly, people flocked to the data to independently pick apart how effective the program was.
The figure below comes from WNYC, a public radio station located in New York City. Here they’ve shaded each city block brighter pink the more stops and frisks occurred there. The green dots on the map indicate where guns were found. What the figure shows is that the green dots do not appear as close to the hot pink squares as one would believe they should. The implication, then, is that Stop and Frisk may not actually be all that effective in getting guns off the street.
But then a citizen journalist created this map of the same data.
By simply changing the shading scheme slightly he notes that this map makes the green dots look much closer to the hot pink squares. In fact, he goes further to remove the artificial constraints of the block-by-block analysis and smooths over the whole area in New York, resulting in a map where those green dots stare unblinkingly on top of the hot-red stop and frisk regions.
The argument this author makes visually is that Stop and Frisk does in fact work.
So who’s right here? Well both of them. And neither of them. These pictures are just that – pictures. Though they “use” data, they are not science. They are not analyses. They are mere visuals.
When data