CarbonCare

Inspiration

The team pursued this idea as many of our peers in undergraduate programs are finishing their post secondary education and deciding to make a purchase for a new car. Although many of our peers seem to take into account of practicality and features, not many are concerned about how much pollutants their new vehicles can emit to the atmosphere. This is a growing trend everywhere, as many people are gravitating towards SUV's rather than giving some insight on how much their purchase can affect the environment.

What it does

Our product will take the user inputs of the make/model for new 2021 cars and give the emissions of the product, with 3 alternative vehicles lower or equal to the user's inputted car and 3 alternative vehicles higher or equal to the user's inputted car. This will let the user decide if they have different buying options for vehicles that will have the same emission amount or if their vehicle options have differing emission amounts.

How we built it

We used amazon rds and postgresql to build a clean database, which uses the dataset provided by statscanada in this link: https://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64#wb-auto-6 . We then used node.js with fastify framework to parse the data, with GraphQL allowing us to use query responses that are decided by the user. Finally, the front-end was done in angular.js and uses the ngx-charts framework to display the final visualization for the user.

Challenges we ran into

The initial challenges were setting up the relational database, cleaning up the data for usability, query logic, and using an appropriate framework with our GraphQL/Node.js API to parse the data. We initially tried to learn front-end React and Angular from scratch, however this proved to be difficult due to the time limit. The later issues were formatting with angular.js and having to connect all the features cohesively.

Accomplishments that we're proud of

We were able to allow users to input any vehicle in the current year and learn new tools that we had little familiarity with beforehand. Also, by using a relational database we will be able to extend the schema by year and have more flexibility with data tools for showing more in depth environmental factors.

What we learned

Although we we able to delegate the project in parts, the issues were raised when we had to connect the features altogether to make a cohesive product. While we are building out each part of the project, we will need to do iterative testing next time to have a less amount of issues.