The most recent operating statistics I could find is from 2010. Some of their layoffs last fall included communications jobs, which probably plays into this.
First I keyed in the number of trips made at the busiest subway stations. Since it's about volume, clutsters seemed like a natural choice. Here's what I came up with:
I then took some of my own data from last week's post mapping out a week of TTC delays. I sorted the causes out in a pie chart, and isolated the mechanical issues (the only section the TTC has responsibility over). It was interesting to see that only 20 per cent of the issues fell under TTC jurisdiction, while the rest were situational or a result of Toronto's generally crummy infrastructure:
For a third chart, I did a Wordle. These are fun: you plug in a block of text and it sorts words by size based on prominence. I took the text from the past 24 hours of TTCing, a Twitter account that aggregates commuter's thoughts on the TTC. Processing social media is more work than a report; you have to strip out the data about when/where the posts were made, and you have to check for libel. But I think it's much more interesting to see what the average person has to say. Here's what I came up with:
It was easier than I thought to make these, though these programs does much of the processing work. It's easy to see why data visualization is becoming so popular, as it's no longer statisticians who can observe trends and suggest improvements to our public systems.
No comments:
Post a Comment