Sustainability Metrics Predictions

Inspiration

Inspired by our shared interest in sustainability and walkability, and our hatred for the suburbs and giant parking lots.

Goals

Our project seeks to improve the human condition in urban centers across the United States by giving cities and citizens the information they need to make their city sustainable. According to "Sustainable Cities" by the Princeton Student Climate Initiative "As cities continue to grow, cities should consider practices that will lead to sustainability. Such practices can create career and business opportunities, safe and affordable housing, and most importantly develop resilient societies and economies". In the United States 86% of the population lived in urban areas in 2020, and that percentage is only set to grow in the future. Improving the sustainability of cities is important to improve the quality of life of current and future urban residents.

What it does

In our project, we aimed to calculate the relationship between several urban metrics and the sustainability of a city, determining which metrics are most important to the quality of life of city residents. By working to improve the metrics with the most impact on quality of life communities can maximize the effects of their resources.

How we built it

We started to build it using Jupyter Notebook to do data analysis in Python. We also planned to have a website, an app, or a dashboard.

Challenges we ran into

One of the main challenges that we faced was time and obtaining quality data that contained all of the metrics that we’ve determined were important in sustainability. We spent a lot of time deciding on what these metrics should be, what our project should do, and trying to find datasets that had data on the metrics that we could also analyze. Next time, we would probably start with the actual finding of data, and data analysis, sooner, so that we may course correct earlier.

Another challenge was our newness to data science. We were both just beginners in data science, and with an ambitious task, we couldn’t do what we set out to do with the given time constraint. We also started rather late, and perhaps allocated more time on initial planning than is ideal.

Accomplishments that we're proud of

We're proud of having gone through this hackathon. Even though we didn't reach our goal, we still learned a lot, in sustainability, energy, and metrics of sustainability, as well as in managing a time constraint in a project.

What we learned

We learned a lot about sustainability, sustainable cities, and the factors and metrics against which cities are measured to be sustainable. We also learned about the difficulties that come up in solving closer to real-world problems, where the datasets are not cleaned and preprocessed for you.

What's next for Sustainability Metrics Predictions

We would like to continue working on this project, as we believe that it could be valuable to cities and communities wanting to be more sustainable in concrete ways.

Built With

• jupyter
• python