TARGET: Mayors Innovation Challenge Discord Team 26 Trenchh#4795 alex9r#1701 Aljug#2101 Michael_T#0692


Employees of the City of Kingston frequently travel across the City for interdepartmental meetings, which is important as the City has many diverse groups, which benefit greatly from information sharing and collaboration. While teleconferencing can be used in some instances, certain meetings require staff attendance. However, the optimal meeting location may not always be chosen for these meetings, leading attendees to use single-occupancy-vehicles, which produce significant GHGs. How can the City of Kingston optimize preferred meeting locations, taking into account the points of origin for each attendee and the requirements of the meeting space in order to reduce the City’s GHG emissions?

What it does

Eco Location takes various preferences (Transportation method, max distance, location type) to find the lowest carbon footprint meeting location among the users in the meeting.

How I built it

Prototype: Figma Implementation: MERN Stack MongoDB: Document-Oriented Database Express: Back-End Framework React: Front-End Library Node.js: JS Runtime Environment

Challenges I ran into

One of our biggest challenges was working with mapbox API and getting it to work as intended. We also had a tough time formulating and completing the algorithm. As with any challenge we face as developers, we just worked them out to the best we could.

Accomplishments that I'm proud of

We are very proud of the effectiveness of the algorithm and the overall UI of the app.

What I learned

We learned a lot about full-stack developing, getting everything to communicate with each other, etc. We also learned teamwork and how to have fun even under constraints.

What's next for Eco Location

  • Optimize Algorithm
  • More responsive UI
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