When I worked at the Public Curator of Quebec, I witnessed so many troubling things and really started to see growing mental health problems in Montreal. Ever since, I have been trying to find a where to make a more lasting impact on my city. The inspiration for this project came to us after an intense brainstorming session on how to leverage data and artificial intelligence to improve the daily lives of everyone in the city. We started looking through academic articles about what factors within a city actually influence wellbeing and came up with a way to visualise this data and suggest improvements and new places for city and public works projects.
What it does
We leverage open data sets to visualise the data that influences mental wellbeing, this data can include parcs, green spaces, trees, traffic levels, noise levels etc. and we then make suggestions based on perceived levels of wellbeing to improve parts of the city that lack in some of these aspects. We are constantly adding new data sets and are working to perfect our prediciton algorithm
How we built it
We build it using React leveraging various google platforms and Montreal Open Data sets. We built it with machine learning in ming as well as to leverage the power of artifial intelligence to make data driven predictions.
Challenges we ran into
We ran into time constraint challenges, the learning curve on what data is best to leverage and how to determine the importance of this data among the factors that influence mental wellbeing. We also have team specialisation challenges, given that we were only a team of 4 we could accomplish only a small fraction of what we set out to do however the scale and importance of our project is such that a finished product could really make an impact within urban planning.
Accomplishments that we're proud of
We are proud of the heatmap data visualisation app that we could produce within such a limited time frame and we are proud of the social impact our project could have. We really believe that the key to smart cities is smart urban planning.
What we learned
We learned many things related to varying levels of importance given to data and we learned how to effectively leverage many google services to create a great project in a limited time frame.
What's next for ZenZone
What is next is integrating more data sets, perfecting the filters withing the app and making sure that our AI technology makes accurate and logical suggestions for new green spaces. We will also introduce a client facing option to allow people to make more informed decisions on where to live or go that would affect their mental health. We are trying to reduce these issues and if we can use data to determine high risk areas, we can also use data to reduce the risk in those same areas. Sometimes all it takes is a great visualisation of the problem to understand how simple a solution can be.