WoodenRumba| What Is Our Carbon Footprint?
What Is Our Carbon Footprint?
CO2 Wooden Calc
Summary
The aim of this project is to make accessible information from NASA databases to the general public, making the process interactive and enjoyable. That’s why we chose Twitter, as it is one of the biggest social media which allows to integrate such applications so the users can use them and access all the information with no need of any other extension or software on their devices.We have focused on CO2 traffic emissions. For that, we searched the best databases that NASA could offer us, reviewed and analysed the data. With that done, we created a SQL server to save the data. Finally, we designed the Twitter bot to use the data from the server and make it accessible for the user.
How We Addressed This Challenge
Our goal is to bring out the importance of common things in daily life such as commuting on combustion engine cars. We choose to develop a bot for a popular social network like Twitter. We target a young audience who are yet to understand why public transport and alternative methods of transport are so important nowadays. Also electric vehicles are now a thing instead of something in the far future.
The information delivered is relevant to people because we analyzed the most polluted cities caused by high traffic emissions. By establishing a ranking of the dirtiest and cleanest cities in the states people can easily understand what are the possible causes of that problem. Also it encourages people to act in accordance so the levels are reduced. It is easy to share the information and it can reach lots of people really fast.
Interactions between users and the bot happen in the two ways. First one: the user asks information about his city and the bot shows the city’s position in the ranking and the CO2 emissions per habitant along with a photo. For that, it is necessary to add hashtags into the tweet, as the bot needs to detect them to answer correctly. The second depends only on the bot, where it makes a tweet periodically showing the ranking between all the cities in the United States, showing up the CO2 emissions per habitant.
We hope the development of this tool will help the US citizens to become more aware of the CO2 footprint due to transportation and commuting. Also it will help other people to develop more apps to address this problem.
How We Developed This Project
The challenge offered some good points such as being able to work on databases and analysing data. CO2 footprint is a hot topic and also one of the most important things for delaying the Earth Hour.
For this project we decided to make three groups. Each one would be responsible for developing each one of the key parts of the project. One group would focus on databases and data mining, another one would be responsible for the API and the last one would create the code necessary for the bot.
The source files coming from satellite data were processed with a Python script which helped us to extract features linked to the geographic position from the raster file. We addressed some problems that came with that procedure such as the Vulcano projection and the matching XY plot data. Also we got information from US cities from a public database which we linked to the CO2 data. We developed some pipelines which routed the data from the files to the data mining core and then to a server database
For the data mining part of the project we choose to deploy a PostgreSQL database to hold the extracted features. That enhanced the capability of the team to work in simultaneous tasks. Parallel to the data mining process another team was already working on a mock database to ensure data is delivered to the bot in a reliable way.
There is an API in place to allow seamless connection between the database and the Twitter bot, which is our tool to raise awareness about CO2 footprint increase.
The code used to create the bot is based on the Python library Tweepy and other libraries like numpy that allowed us to simplify the code.We had to search for solutions to automate processes like following people on Twitter, answering people's questions or requests. During the code, the main problem was the lack of experience from the members of the group in charge of this task, but it has been a great learning experience.
How We Used Space Agency Data in This Project
After reviewing the databases offered by NASA for this project and considering what the databases could offer us, we thought that the DARTE database could fit very well in our project, as we wanted to analyse CO2 emissions caused by traffic in the United States.
Data was provided on ground level which made it easier to work with. We wanted to use the OCO satellite but it was difficult to understand how they could be related. There are more up to date information on the new satellite database which allows for more interesting and current topics than data analysis from past years.
In this project we access the data from the ORNL DAAC website. It has great options for downloading the GeoTIFF in whatever format and projection we want and also provides us with a visualization tool. Then the data is matched to cities coordinates and then the total sum of the CO2 data per city is calculated.
Project Demo
Link to slideshow
https://docs.google.com/presentation/d/109GZiGwRXohNFKVXmwn3RIN8TrYjCc4S-Pul_PPLPPk/edit?usp=sharing
Link to pdf format
https://drive.google.com/file/d/14xfvIcw5w3S-G6imQgtluW0ageji_Oqz/view?usp=sharing
Project Code
Data & Resources
DARTE Annual On-road CO2 Emissions on a 1-km Grid, Conterminous USA, V2, 1980-2017
Cities List from Simplemaps:https://simplemaps.com/data/us-cities
The 35 Easiest Ways to Reduce Your Carbon Footprint: https://blogs.ei.columbia.edu/2018/12/27/35-ways-reduce-carbon-footprint/