“Food insecurity, urbanization, and climate change are interconnected and rapidly rising globally. In the early 2020s, a summer of heatwaves, floods, and increased use of food banks nearly broke the Great Lakes city of WateryTrees’ budget. The city incentivized its various districts to develop solutions and in response, the Lakefront district embarked on a journey to become a spongy food oasis in 15 years. This is a recount of that district’s efforts, including their ups and downs, between the years 2024 and 2039.”
- taken from the main page of my website.
My name is Saachi and today is September 30th; I’m five months into a 12-month sabbatical. If you’re new here, I’m taking a break from work - data viz analytics for a financial institution - to focus on a research and communication design project. The aim of this project is to:
This month, I aimed to complete at least 80% of the coding, function, and ‘look-and-feel’ for the main page of my website. I also tried to determine which major events in WateryTrees’ 15-year fictional timeline could happen based on real-world information and policies.
If you're on a desktop, I'd love it if you could take a look and send feedback via Facebook/linkedin messaging:
Unfortunately, it's not optimized for mobile...yet
On the main page, I wanted the user to get a basic summary of the lakeshore district in WateryTrees - a brief intro, the main historical events from 2024 to 2039, and a top-down view of a map changing over time. The map was inspired by urban planning models and the wooden world maps people use as wall decor.
The images are sourced from Pinterest, and are not mine.
All the user needs to know is: “Did the Lakeshore district achieve the spongy food oasis?”. To help answer this, there are two important metrics:
Of course, there are other important metrics like flood risks, lake health, permeability, thermal comfort etc. which I will address elsewhere on the website.
The images are sourced from Pinterest, and are not mine.
A line graph is super easy to understand, even with minimal labelling, so I placed one at the bottom for the two key metrics above along with a scrollable timeline. The timeline demonstrates specific actions/events rather than evenly spaced out years. Weather apps were my main inspiration for this
Image credit: Saachi Sadcha (the main page of this interactive website, with the final model of WateryTrees' Lakeshore District's map)
A user might scroll through the entire timeline in a few seconds to watch the map change. But if they want to understand why and how a specific event impacted food security or emissions locally, I provide big KPI numbers and short paragraphs on the sides.
I don’t want to overwhelm the page with words, but I want to provide proper explanations AND highlight the real-world inspirations for my decisions. I’m still trying to figure out how to achieve that balance, as well as figure out the tone of the written section of this page.
Toronto’s food insecurity issues, which are connected to barriers of wealth and an affordability crisis rather than just food availability, are growing and made worse by climate change and urbanization. While the entire city contends with extreme and more frequent heat waves, ice storms, and flooding, it’s the most marginalized citizens that are most impacted.
For example, a single female parent is at very high risk of food insecurity due to the high cost of childcare and housing but is managing to scrape by. A massive flood occurs and the insurance premiums in her apartment go up, which leads to an increase in monthly rental payments. To compensate, she starts to skip meals.
This bit of research was a downer, but thankfully there are always people working on solutions to hard problems.
This includes people like Claire Perttula at the Malvern Family Resource Centre (interviewed in the video link above by CBC). It was so heartening to learn about the many existing, active solutions to rising food insecurity, driven by local community members. If you’d like to learn more, please use the links below.
For this project, rather than focusing on the problems, I want to highlight and speculate on the potential impact of community-level solutions.
With all the information noted above, a small part of me wonders if I could make a proper predictive analysis of Toronto’s food insecurity with a couple of “what-if” scenarios. But many years ago, I created 3D and predictive models of a spatially tiny but complex ecosystem with reliable 30+ year data . I already know from that experience that predictive analysis is not something one does in a one-year sabbatical or with the amount of data I’ve collected thus far.
So I needed to find the “line of accuracy”. How closely does the city of WateryTrees need to mimic Toronto or similar cities? How much fantastic detail can I add before the solutions cease to have meaning? Can I create real value in a pretend space?
I’m still struggling with these questions, but ultimately my job as a data viz analyst is to visualize data, in the hopes that a complex story can be made simple and engaging. It doesn’t matter if all the data are not real, only that it’s obvious to the user when they are not.
Image Credit: Saachi Sadcha (an early model of WateryTrees, based on Toronto's layout)
That’s why I decided to call this pretend city ‘WateryTrees’. Toronto may have received its current name from the Mohawk word ‘Tkarón:to’, while ‘WateryTrees’ refers to the meaning ‘tree in the water there’. Both refer to the ancient fishing weirs at Atherly Narrows used by Indigenous peoples almost 5000 years ago. Thus, Toronto and WateryTrees have nearly identical layouts, histories, and key features, but the first is real and the second is not.
Image Credit: Saachi Sadcha (a preview of the area of interest)
The function of the main page is 80% done and requires only content and some user interaction testing. So I’ll have to bribe and beg some willing victims to help me slowly improve this page over the rest of the sabbatical.
Besides testing, my main focus will be the main chapter of Summer 2024, which will include the following:
Thank you kindly,
Saachi
Image credit: referred to in text
Thank you to Erik Chan for reading this blog post and suggesting edits prior to publishing
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