A Look at Toronto Neighbourhoods: An Infographic
There is a wealth of data currently on Statistics Canada and City of Toronto website collected from the city residents. For example, the Neighbourhood Census of Population, released by Statistics Canada, compiles data on neighbourhood-level information which reveals a profile for each area. The problem here is how the data is presented. The information is data heavy and not easily compared when it is presented on spreadsheets. Data presented in this way is not engaging or appealing for the general population.
A designer was needed to organize the information and produce a piece that was visually interesting and informative for viewers. Statistic Canada wanted a new way of representing data which accurately shows information comparisons but show it in a beautiful way. The piece needed to capture the viewer’s attention and educate at the same time. The topic choice was not limited in the brief.
Target Audience: The aim of this piece is for anyone who is interested in the population breakdown of the city of Toronto. This type of information can be useful to city officials, provincial workers, and statisticians. However, it is certainly not limited to this demographic. The challenge is to create an infographic that will also appeal to the general population who is encouraged to learn about their neighbourhood after seeing this piece.
How can we represent Statistics Canada data in an interesting way to capture the attention of viewers who would not typically view the Neighbourhood Census?
Topic Selection: The initial exploration of topic ideas was the first challenge of this project. Before focusing on data from the Neighbourhood Census, I was exploring a wide range of data available to me both province and nation-wide. I quickly realized I needed to define the scope of the project otherwise it was easy to be buried in data. The topics I explored in this early stage were: looking at ethnicities of the entire Canadian population, immigration in Toronto, and diversity in Ontario. Eventually, to narrow down my topic, I asked a few questions:
What data are relevant to each other and easily comparable?
Which topics do audience care about and can relate to?
Which set of data is complete, recent, and easily accessible for me as a non-government official?
Ultimately, I decided to keep the geographic area small to the City of Toronto. Given the diversity of Toronto, I thought it would be informative to compare different neighbourhoods. This was the best choice given the final piece will be presented in poster form. Toronto has a total of 140 neighbourhoods, each with its distinct characteristics. I was particularly interested in how the population indicators (e.g. ethnicity, education, and occupation) of each neighbourhood compares when you rank them based on average household income.
Data Collecting: Once I decided on the topic, I started collecting data from the 2011 Neighbourhood Census (at the time of this project, the 2016 Census had not been released). This was the most comprehensive collection of data on each of the 140 neighbourhoods in the City of Toronto. I picked 8 neighbourhoods based on average yearly income and hypothesized there would be interesting differences in the demographic diversity based on this disparity. I wanted to answer this question:
How does ethnicity, education level, and occupation differ in neighbourhoods with different average income?
Even if I cannot infer any correlation just based on the Census alone, it would be an interesting comparison to see the difference across neighbourhoods. Additional data included in the collection was the occupational breakdown of each neighbourhoods. That is, what area of occupation is the neighbourhood mostly likely to be employed in different income bracket.
Once all the data was collected for each neighbourhood (on ethnicity, education level, and occupation classification), different graphing and layout options were explored. Namely two version were looked at in more detail: linear layout, and circular layout. Both layouts have their benefits and drawbacks:
Pro: Each indicator would be presented in an individual graph, making the information more intuitive to comprehend.
Cons: The overall infographic will be cluttered with smaller graphs making the data more difficult to compare between neighbourhoods.
Pro: Visually interesting approach. The data is easily compared between neighbourhoods and the overall piece will have a less scattered appearance.
Con: This layout is not as easy to comprehend at a glance and will take effort to fully understand how the data is represented.
Ultimately, I decided a circular layout was the best way to represent the data. The 8 neighbourhoods are each represented by a piece of the pie, and each piece can be further broken down into different indicators resulting in an infographic that would be easy to compare while still interesting to look at. Visually, the linear layout would not work as well. There would be many more smaller graphs scattered on the page, making the end result less impactful.
Graphing this data onto one pie graph presented a unique challenge. For starters, the income disparity between the wealthiest neighbourhood and the poorest neighbourhood was so great it could not fit on a reasonable sized poster at a legible size. To solve this issue, I magnified the lower part of the pie graph to ensure the indicator breakdowns are visible. At the same time, I kept a shadow image of the magnified area so viewers can see pie chart to size.
In order to graph each indicator on the pie graph, I had to first graph each ring separately in MS Excel. I then took the generated graph, extrapolated, and placed in Adobe Illustrator. In MS Excel, each indicator was graphed with the population of the neighbourhood as a control group to ensure sizing consistency.
The infograph was first graphed in black and white in correct proportions using the Excel generated graphs (as seen above). The draft is plotted to scale with each indicator spaced strategically so they are easily identifiable. The bottom 4 pieces of the pie are magnified portion of the graph to compensate for the income disparity between the wealthiest and poorest neighbourhoods.
Once graphing was complete with each indicator nested in the pie graph, the next challenge was colour. The colours of this infographic are a crucial part of having the piece work visually. Each colour is chosen strategically to have each indicator category similar in hue, so the viewer can easily compared this data between neighbourhoods. While the colours between indicators need to work together visually but different enough that users will not confuse the different indicators. My solution to this was to pick different shades of the primary colours and keep the background to neutral colours (gray and pale yellow) to ensure the graph stood out.
For typography, the written portion of the poster is kept to a minimum and confined to the border of the poster. All 140 Toronto neighbourhoods were included and ranked based on average income on the left-hand side so viewers can visually see where the 8 neighbourhoods ranked in terms of income in the city. Background information on each of the neighbourhoods are written along the top of the poster to add extra information to each neighbourhood.
Overall, the poster achieved the visual interested needed to capture the attention of audience who would otherwise not be interested in the Neighbourhood Census. Not only is the piece informative, it captures initial attention with the multitude of bold colours that work cohesively together. Although the piece is filled with information, it does not feel cluttered or overwhelming for the viewer. The strength of this piece is that it gives the viewers the ability to decide how much information they want to take away from the piece.
This poster is unique to Statistics Canada, and outside of the norm for the government to display their information. Although different, it draws attention to information that would have been dull to look at and difficult to compare.
I learned from this process that the initial research for an infographic is just as important as final outcome. The bulk of the work is done even before the decision is made on how the data would be represented visually. Topic selection is crucial for the piece to stay organized, informative, as well as beautifully shown. Information design is unique in that information organization and problem solving is the greatest challenge before layout and colour selection. The process was labour intensive but equally rewarding.