Geovis Project Assignment @RyersonGeo, SA8905, Fall 2022
Toronto’s rapid transit system has been constantly growing throughout the decades. This transit system is managed by the Toronto Transit Commission (TTC) which has been operating since the 1920s. Since then, the TTC has reached several milestones in rapid transit development such as the creation of Toronto’s heavy rail subway system. Today, the TTC continues to grow through several new transit projects such as the planned extension of one of their existing subway lines as well as by partnering with Metrolinx for the implementation of two new light rail systems. With this addition, Toronto’s rapid transit system will have a wider network that spans all across the city.
Based on this, a geovisualization product will be created which will animate the history of Toronto’s rapid transit system and its development throughout the years. This post will provide a step-by-step tutorial on how the product was created as well as showing the final result at the end.
Toronto is the largest metropolis in Canada, attracting people from far and wide. As such, there are many forms of transportation that pass through the city including cars, bicycles, public transit, regional trains and many more. The Toronto Transit Commission (TTC) is one of the main methods that people rely on, as millions ride their services each and every day. All of these forms of transportation must share the roads, and from time to time collisions occur. This project aims to animate collisions between TTC surface vehicles such as a bus or streetcar, with another form of transportation (not including pedestrians). This visualization will be on the web-mapping service Carto, where a time series map will be produced on the various TTC related collisions.
The collision data for this project was obtained from the Toronto Police open data portal. The “TTC Municipal Vehicle” dataset that was used is a subset of the “Killed and Seriously Injured” dataset, as these are the specific types of collisions that were collected. The data is available for the years 2008-2018, but only the past five years from 2014-2018 were used for the sample size of the project. Information on the collisions provided in the dataset include the latitude, longitude, intersection, vehicle collision type, time, date, year and neighbourhood it occurred in.
The first step of getting the time series web map to work is to create a map and import the data into Carto. The collisions data was downloaded from the Toronto Police as a .csv file, which can easily be uploaded to Carto. Other supporting data used for this map includes the City of Toronto boundary file retrieved from the City of Toronto open data portal and the TTC routes which were retrieved from Scholars Geoportal. In order for these shapefiles to be imported into Carto, they must either be uploaded as .ZIP files or converted to another supported format such as JSON file. Once all the data was ready, it was uploaded through the “Connect Dataset” label shown below.
The next step was to geocode the collision locations with the latitude and longitude provided in the collisions .csv file. This was done through Carto’s geocode feature shown below. To do this, the layer with the data was selected and the geocode option was chosen under the “Analysis” tab. The fields for latitude and longitude were then input.
Once geocoded, the “Aggregation” method for the data needed to be chosen. As this is a visualization project over a span of years, the time series option was chosen. The “Style” also needed to be set, referring to how the points would be displayed. The dataset contained information on the different vehicle types that were involved in the collisions, so the “point colour” was made different for each vehicle. These functions are both shown below.
The same “Style” method for visualization was also applied to the TTC Routes layer, as each type of transportation should be shown with a unique colour. The last part in animating the data is to choose the field for which the timer series is to be based on. The date field in the collisions .csv file was used in the “Widgets” function on Carto. This allows for all the data to be shown on a histogram for the entire time span.
To finalize the map, a legend and basemap were selected. Once happy with my map, I made it public by enabling the “Publish” option at the bottom of the interface. This generated a shareable link for anyone to view.
A snapshot of the final time series map is shown below.
Thank you for viewing my blog post!
To access the full web map on Carto, the link is provided here:
An animated look at TTC’s subways and LRT expansion by when they first opened. Includes 2017’s Finch West subway expansion and 2021’s Eglinton LRT expansion.
By Khakan Zulfiquar – Geovis Course Assignment, SA8905, Fall 2015 (Rinner)
As a course assignment, we were required to develop a professional-quality geographic visualization product that uses novel mapping technology to present a topic of our interest. I chose to create an animated-interactive map using CartoDB to visualize the construction of Toronto Transit Commission (TTC) stations from years 1954 to 2021. The interactive map can be found at https://zzzkhakan.cartodb.com/.
This idea was inspired by Simon Rogers who animated the London’s Rail System. It was interesting to see an animated map slowly draw together a footprint of an entire infrastructure system. A number of (non-interactive) animations of Toronto’s subway system development were collected by Spacing magazine in 2007 and can be viewed at http://spacing.ca/toronto/2007/09/21/ttc-subway-growth-animation-contest/.
A feature within CartoDB called “torque” was used to create the envisioned map. Torque is ideal for mapping large number of points over time. Torque has been famously used in media for mapping tweets as pings.
As a beginner to CartoDB, I had to go through tutorials and online courses to get familiar with the interface. As I became comfortable with CartoDB and its features, I recalled an example I had seen in the CartoDB gallery. It was Simon Roger’s London Rail System map. I knew exactly the kind of data I would need to make a similar map for TTC stations. There was an instant halt as the data was not readily available. Using Wikipedia, ttc.ca, and OpenStreetMap I was able to compile the data I required. The data was uploaded into CartoDB and the following map was created.
Tutorial / How-to-Use
For the heading numbers above, please find the associated instructions below.
Title and Subtitle – Speaks for itself.
Toronto Subway/ LRT Map [full resolution]- A Map of Toronto’s Subway and future LRT produced by the TTC. This map is the most common visual representation of TTC’s subways and LRT. The map’s color scheme was mimicked to help viewers, especially those familiar with TTC, make the transition to the animated map smoothly.
Timeline – The timeline is in a continuous-loop. You can press pause to stop the animation and resume to start the animation again. You can also control the speed of the animation by sliding the play-bar back-and-forth.
Hover Window – As you hover over the stations, a window will pop up automatically with the name of the station. No clicks required. The names will only appear if the “ttc_station” layer is switched on (more on this in step 7).
Info Window – If you would like further information on a certain station, simply click on the station and you will be presented with the station’s name, line #, grade (above, at, or underground), platform type, and etc. The info window will only appear if the “ttc_station” layer is switched on (more on this in step 7).
Legend – as the name implies…
Layer Switch – a tool to turn on or off the layers being used in this map. The map was created with the intent to be both animated and interactive. The animated bit is the stations being plotted and the interactive part was for the user to find further information about the station. However, the animated bit is both intrusive and resource-heavy. Because of this, an option is being included to turn layers on-or-off as required. Be sure to try out the combinations.
MAIN SHOW – the main map area has a beautiful CartoDB Dark Matter basemap with all of the TTC stations plotted. Feel free to zoom in and out.