Inspiration
Climate change, mostly. Our team is most concerned about the impending death of our planet, and observing how Florida, a region particularly vulnerable to the effects of climate change, handles these events serves as an interesting blueprint for what might happen at a larger scale across the world further down the road. Moreover, it was a very good opportunity for us to work with datasets and libraries like pandas for the first time.
What it does
Our project is a visualization of Florida’s housing trends graphed against weather-based indicators. We also make an attempt to analyze said trends and draw conclusions about the driving forces behind them.
How we built it
We found several datasets about various indicators of Florida’s housing market and climate events. We condensed these down into a single database which we used to visualize the data we gathered, before finally creating a website to host our key findings.
Challenges we ran into
Our single biggest challenge was to acquire datasets that were both relevant and compatible with each other. Specifically, detailed housing data was hard to acquire, and we were only able to find trends from a quarter century ago. Second, we could not figure out how to incorporate a lot of the data we gathered for our project. These factors prevented us from conducting the in-depth analysis we set out to accomplish.
Accomplishments that we're proud of
We collected a large amount of data about Florida's houses and weather trends, and managed to condense our important findings from it into an easy-to-digest format. We did this using platforms that were previously completely alien to us, and I’m glad that we took this hack as an opportunity to learn.
What we learned
We learned to work with Python libraries like pandas, matplotlib, and Plotly for the first time. This was also our first time building a web application with Flask which we found to be pretty awesome.
What's next for The Floridian House
If we work on our hack after the hackathon, we would like to incorporate much more of the data we gathered. Furthermore, we’d like to create a more sophisticated searching/sorting algorithm to organize our website better.
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