Sprare | Devpost

Brief

Sprare is a Python-based software 🐍 that is able to rate whether the climate conditions in a specified area is effective for carbon capture through growing trees based on a scale of 0 to 100.

Inspiration 💡

Climate Change is an extremely prevalent issue in today's environment with over 300,000 deaths associated with the problem each year our team thought it was vital to address this through the use of simple tasks such as planting trees 🌳 in your local area and assisting larger organizations in choosing mass tree-planting sites.

What it does 🌲

Our program makes it quick and effective to see whether an area has the correct indicators such as temperature ☀️ to make an informed judgement to see whether trees can grow safely and efficiently. Users can type a city into the program and a date ranges to view data from. The algorithms 👩‍💻 created will allocate a number to the cities/forest's/location's potential to capture carbon dioxide through mass tree-planting operations.

How we built it 🛠

We built this project using Python as the predominant language in addition to this we used the google maps 🗺 API to source important information we needed for data. Apart from this, we also used Figma in order to enhance our designs and create additional pieces of the visual aspect of our project. We used the Guizero Python toolkit to start developing our GUI, and HTML/CSS/JS to start the development of our webpage.

The following API's were critical and most used for the project:

-Google Geocoding API -GoogleMaps JS API -Google Elevation API -Google Places API -Meteostat API -Python Datetime Package

Challenges we ran into 😤

Trying to implement the GUI version of our game was a challenge as there were a lot of errors that needed to be decoded within the short space of time in addition to this it was challenging implementing the Google Maps JS API.

Accomplishments that we're proud of 🙂

We are proud to have created a project that has the capability to make the climate a much better place via small steps users are able to make with our algorithm. Our main goal 🥅 was to make it more convenient to source information about tree planting and we believe we have fulfilled this goal. We tested our product and found that the algorithm was accurate in predicting the approximate climate and eligibility for tree planting using the data we pull from API's.

What we learned 📔

We learned how to multitask and work together within a limited time frame. We also learned more about the appropriate conditions of growing trees as well as additional skills and debugging techniques we picked up along the way.

What's next for Sprare? 🔜

We would like to complete the GUI as well as test the efficiency of the algorithm in the real world through planting trees to nurture the environment.