Inspiration

As recent or soon to be graduates, we personally understand the desire to relocate and expand our world views. There is so much potential out there, but it's hard to know what city is best as we all have unique needs and wants.

What it does

By gathering aspects that students care about when researching a city, we visualize the data based on selected preferences and suggest potential cities. Clicking on a city shows more information about that city and how it compares to others.

How I built it

We initially narrowed our focus to a set of users: recently graduated students. Then, we discussed several user journeys and sought out specific pain points. We conducted some research to find out what type of criterias people look into when deciding where to move, and then found open datasets from statscan and other online sources to support these criteria.

We pulled the 2016 Canadian Census Data information on the biggest cities in Canada. We sorted this data into specific categories, and compiled static JSON files of the cities. We then fed this information into our web app powered by React where we visualized it using Mapbox and different graphing techniques.

Challenges I ran into

Going from a well designed static prototype to an implemented version is a big jump as the data had to be manipulated to fit the visualization library we used. The Stats Canada data was also unreliable and oddly formatted, leading to a lot of difficulties.

Accomplishments that I'm proud of

We managed to design a data visualization that makes use of multiple datasets and combined them in a cohesive way that helps students make an informed decision.

What I learned

We learned that the quality of the data sets was not only dependent on the source it came from, but also the richness of the data in providing value in visualization. In certain fields the data was especially shallow which made it difficult to draw any useful visualizations.

What's next for LeaveTheNest

We would love to explore how students can learn from our visualization and where to expand next. Right now we focused on Canadian data, but the next step would be to include American cities and beyond. We would also love to explore more intricate data visualizations that can dig deeper into the data and provide more value.

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