How we built the Gardiner map

By Leslie Young

(crossposted from GlobalToronto.com)

Building our interactive map of the Gardiner Expressway was a long process.

It started with an idea, of course. After hearing so many reports of concrete falling from the Gardiner during the summer of 2012, I wondered if maybe there was something we weren’t hearing. Was there more concrete falling than we knew about? Were there other problems?

This sort of thing tends to be well-documented by cities, so Freedom of Information seemed like the answer.

I made two Freedom of Information requests to the City of Toronto, one for all emails and communications products dealing with falling concrete on the Gardiner, and the other for all engineering and inspection reports.

When I got the information, it was a little more than 2000 pages long. So, I of course had to read it. It was at this stage that words like “punch-through” really jumped out at me.

The feeling was that if we were going to release a story like this online, it would be a shame not to visualize it. The obvious choice for a visualization seemed to be a map, since people were going to want to know where the problems were.

So we wanted to make a map.

The first step was to catalogue each event. This involved deciding on our criteria (loose concrete that presented some kind of risk. Either it had a high chance of falling, or it was above a high traffic area). I took my cues on the criteria from the documents themselves. Many of them are specifically categorized in this way.

This involved putting every document we had into Document Cloud, a tool for sharing and annotating documents, for easy categorization.

So, I read through every report that fit those criteria, looking for any mention of a location. I then entered those details into a spreadsheet and created a Document Cloud reference for every incident.

Almost all of the locations were referred to by “bent number” instead of an intersection. This meant that you would see a reference to “Bent 23-25” for example. So, changing this into a useable latitude/longitude coordinate for mapping purposes required some extra work.

We purchased a technical drawing of the Gardiner that listed bent locations from the City of Toronto. Then I went through each incident, first placing it on a bent, then manually assigning coordinates.

Once the spreadsheet was ready to go, it was loaded into a Google Fusion Table. Global News’ newest web developer, Kate Grzegorczyk, built the interface in Javascript, drawing upon the Fusion Table data to create an interactive in-depth experience.

Since we published the story, the Gardiner has been a hot topic at city hall, with city officials responding with press conferences and councillors debating the merits of different plans for the Gardiner. The problems we raised were news to both the public and to councillors, so the debate might have been very different without it.  

Toronto’s bees

By Leslie Young

Last week, we put together a map of beeyard locations in Toronto. They’re all over downtown and the west end, which is pretty neat – people seem to be really taking up this hobby.

You can read the story here and download the data from Buzzdata (ha!) here.

We got the idea to show not just the beeyard locations, but also the foraging areas of those bees, so that people would know where they are likely to encounter bees from a given hive. It seemed simple enough: find out the range of a bee (about 3km) and draw circles with that radius around each point.

Finding an effective way to do this was surprisingly difficult. We hit upon this great tool from freemaptools.com, which allowed you to set a radius and bulk upload a set of points. It then draws a circle around each point and lets you export the resulting polygon.

It’s very easy to use and something we’ll be keeping in our toolbox for future projects.

Mapping marijuana grow houses

by Patrick Cain

We’ve mapped marijuana grow operations in Toronto before, but this year’s iteration is more ambitious: 

- First, we took the fresh 2011 census data and our census tract boundary set, mashed that up with several years’ worth of grow operation addresses, and created a map showing grow house rate by census tract. If you have the ability to count points within a polygon (Leslie showed me how to do this) it opens the door to interesting off-the-grid uses for census data.

- The other map shows grow ops by location for several years:

Toronto’s worst intersections, redux

by Patrick Cain

Various media outlets have taken a swing at a Toronto’s Worst Intersections story at some point. It’s not an exact science - most seem to be based on reader nominations. Ours have been statistical: a ratio of reported accidents to the traffic volumes at the intersection.

Last summer, we looked at Toronto’s worst pedestrian intersections using this system (link, link). The results were revealing - most of the most dangerous places to walk in the city were suburban intersections where heavy, fast traffic on roads designed for cars met high residential density.

Here’s the worst:

View Larger Map

Last week we went though the same process for motorists, crunching a decade’s worth of collision data, or about 500,000 accidents.

Lakeshore and Jameson and Eglinton and the Allen were #2 and #3 on the list (not surprising for people who have spent time driving in Toronto) but the sleepy T-intersection of Old Finch Ave. and Sewells Road topped the list. It has little traffic, but in proportion to that, lots of accidents. Drivers have to deal with a steep hill, two blind corners in opposite directions - hard to keep an eye on both - and the semi-official gravel lane for traffic turning north, and a one-lane Bailey bridge, a leftover from Hurricane Hazel now more than half a century old.

View Larger Map

Here are screenshots of the maps:

Cross-posted at patrickcain.ca

Relational diagrams of Toronto city council votes

by Patrick Cain

Last week we published a series of relational diagrams of voting patterns in Toronto city council using NodeXL. It’s a direct result of Peter Aldhous’s excellent presentation at the NICAR conference in St. Louis.

(The context, for those out of town, is a shift of votes away from Toronto’s mayor on city council. A stable majority supported him from elections in October 2010 to earlier this year, when between one thing and another he lost enough councillors to start losing votes. We show how likely any given member of council was to vote the same way as any other member in any given month, which over time shows the change in power dynamics. Councillors who tend to vote together will be clustered closely together in the graphic.)

This version is baby steps - I’d like to make them more attractive and animation-friendly. I’ve always meant to move into other areas of visualization beyond maps - hopefully this is the first of many projects.

January 2012 is below:

Cross-posted at patrickcain.ca.