ReliefRadar

Inspiration

Our team found that we had trouble finding toilets in the public so we decided to make an app that allows us to find the toilets within our vicinity. It will help with city efficiency through the changes of output between.

What it does

It finds the nearest toilets and gives all the details of a toilet and allows you to filter through different options and sorts by recommended (AI that learns user habits to suggest optimal toilet locations).

How we built it

We built our web app integrated with FireBase using data through official government sources.

Challenges we ran into

We ran into challenges with linking our Firebase database to the front end. We also ran into problems with the sourcing of data through our government databases.

Accomplishments that we're proud of

We're proud of the integration of inclusivity in our app and hope to expand its accessibility to all types of people.

What we learned

We learned team management skills, time management skills, and improved our technical skills in relation to the MEAN/MERN stack that includes Google's Firebase.

What's next for ReliefRadar

We plan to have a more solid foundation in terms of app development and would expand the scope and scalability of this project to various countries, and integrate various technologies in the Internet of Things scope and the inclusion of Artificial Intelligence through machine learning for our recommendations.