Ellis Karaca
2024 April 8th

Ranked: Vancouver’s
Canada-Line Stations
Based on 15-Minute City Principles
Background Information
The 15-Minute City is a concept in urban geography whereby the design of cities should aim “to create
self-sufficient neighborhoods with the essential functions of living, working, commerce, healthcare, education, and
entertainment by decentralizing urban functions and services”(Khavarian-Garmsir et al., 2023). One of the key
features of this concept is that the city, through human scaled design, should be built in a way that eliminates the
need for personal automobiles, and long commutes (Khavarian-Garmsir et al., 2023). As established in The

15-Minute City, the underlying principles are; proximity, density, diversity, digitalization, human scale urban design,
connectivity, and flexibility (Khavarian-Garmsir et al., 2023). The scope of my project covers four of these
principles. Both proximity and diversity are covered by my analyses of stores, services, and third places. Density is
covered by my analyses of population density. And Finally, connectivity is covered by my analyses of cycling and
public transportation networks.
I chose to analyze the area around Vancouver’s Canada-Line stations, as I believe the Canada-Line provides
a high level of connectivity within Vancouver. Considering one of the 15-Minute City principles is connectivity,
Canada-Line stations are a great place to start. Additionally, I am very familiar with Vancouver, and the Canada-Line,
and I wanted to pick a topic relevant to my interests. I would have liked to analyze all of the Canada-Line Stations,
but because the City of Richmond’s open data portal is not as extensive as Vancouver's, my analysis would be
extremely limited.

Research Question
Which of Vancouver’s Canada-Line stations best align with principles of the 15-Minute City?

Rationale, and Discussion of Data
I figured the best way to answer this question was by creating a ranking system, and comparing the areas
around each of Vancouver’s Canada-Line stations. I broke down the station ranking into four categories
The first category I ranked the stations based on, was Stores & Services. For this category I used the City
of Vancouver’s storefronts dataset. This dataset contained all the businesses in Vancouver, and importantly, their
business type. The types I was interested in were; convenience goods, food & beverage, service commercial, and
comparison goods.
The second category was Transportation. For this category I used the City of Vancouver’s Rapid transit and
bike network datasets, and a 2019 Translink bus route dataset. I had to make a number of changes to the bus route
data, including the addition of Rapidbus lines.
The third category was Density. For this category I just used the City of Vancouver’s 2021 census data.
Based my analysis on the dissemination areas, and their respective population densities.
The fourth and final category was Third Places. I pulled data about entertainment and leisure from the
previously mentioned City of Vancouver storefronts dataset. I got the data on libraries, parks, and culturals spaces,
directly from the City of Vancouver’s open data portal.
I used a 1 kilometre buffer to represent the area around each station, as this is how far a person could walk
if they travelled at 4 kilometres per hour for 15 minutes. I decided a radius based model of walking distance was
best suited for this project because of its simplicity.

Methodology
The first thing I did before I began any analysis, was source and organize my data layers. As mentioned
before, I got most of my data from the City of Vancouver’s open data portal, but I had to hunt around for my bus
route data. I decided to use BC Albers (3005) for my project’s projection. I then created my 1 kilometre buffer area
around each of the Canada-Line stations. I made sure to leave the buffers undissolved, as I would need to select
individual areas during my analyses. When analyzing Olympic Village Stn, and Yaletown - Roundhouse Stn, I decided
to exclude features on the opposite shoreline.

For my analysis of Stores & Services, I started by exporting all shopfronts features classified as;
convenience goods, food & beverage, service commercial, and comparison goods. I removed any duplicate features
using the dissolve tool, and clipped the features to each station buffer. Then, I counted each area’s features using
the select by expression tool, added the values to an Excel Spreadsheet, and assigned a weight to each value type.
These weight values are visible in the analysis portion of my presentation. Finally, I calculated and graphed the rank
of each station using the sum of their weighted value types. I refer to these sums as "Total Points” in my
presentation document.
For my analysis of Transportation, I started by dissolving duplicate bus and bike routes. Using the “Extract
By Location” tool, I created a new layer for all the transportation modes that intersected each station buffer. I then
counted the number of bike routes, bus routes, and rapid transit lines, through the number of features in their
attribute tables. I added these values to an Excel sheet, and repeated the calculation process from my first analysis.
For my analysis of Density, I began by clipping the dissemination areas to each of the station buffers. I
chose to clip instead of extract because I wanted to measure the population within the buffer areas. I calculated
new areas using the $area expression, and then using the existing population density field, I calculated the new
population. Then, I took the sum of the population in each station buffer using the field statistics tool. I entered the
sum from each station buffer into Excel, and then calculated the rank of each station based off of these sums.
Lastly, my analysis of Third Places. In a similar fashion to my first analysis, I started by exporting all
shopfronts features classified as entertainment, and leisure. I then merged this new layer with the cultural spaces
layer, and dissolved any duplicates. Then, I clipped this, my parks, and my libraries layer, to each station buffer,
calculated the area of each of my clipped parks layer, and counted the instances of each feature on all three layers
using the attributes table. I added these values to an Excel sheet, and again, repeated the calculation process from
my first analysis.
After I had analysed each category at each station, I created a combined ranking by assigning placement
points from 1-10 to each station, and then ranking them based on total placement points.

Possible Improvements
I feel using an isochrone buffer instead of a radius buffer would have provided for a more realistic
representation of what is within a 15 minute walk of each Canada-Line station. In order to do this however, I would
need to source or create a pedestrian network, of which the City of Vancouver doesn’t provide. Additionally, using an
isochrone buffer would mean that I wouldn’t have had to exclude features from opposite shorelines in my analyses.
I believe I could have done a more in-depth analysis if Vancouver’s open data portal also had data about
jobs. This would have allowed me to include occupation as one of the categories I ranked the stations on. It would
also have been really useful if both Richmond, and Translink had a more accessible open data portal. Easy access to
updated bus route data would have been extremely helpful. As mentioned before, in-depth data about Richmond
would have allowed me to analyze the entire Canada-Line.
Finally, perhaps instead of ranking the Canada-Line stations, a more useful approach would be to determine
whether or not they meet the requirements of a 15 minute city.

References
Khavarian-Garmsir, A. R., Sharifi, A., & Sadeghi, A. (2023). The 15-minute city: Urban planning and design efforts
toward creating sustainable neighborhoods. Cities, 132, 104101.
https://doi.org/10.1016/j.cities.2022.104101