Open Intelli-Data - Space Apps Challenge

Open Intelli-Data| Planet, With People

Planet, With People

Your challenge is to build on the Human Planet Initiative of the Group on Earth Observations to apply new methods for mapping attributes of human populations. How can Human Planet data from NASA and other sources be used to improve or update maps or other information important to a problem that concerns you? Design or create a tool or service to accomplish this.

Enviromap

Summary

The project is based on using data indicators to create an accurate and weighted scoring structure that will assist in providing a strategic placement system for planting vegetation. The software will compile key environmental datasets such as; air quality, temperature, land cover, etc. That information is used to create a system that prioritizes areas that have a higher need for vegetation in order to improve the local environment. The scoring system acts as a simple numeric visualization for users to compare areas. Each individual dataset, or factor, will have an effect on the overall score of a location and will represent which site has a greater demand for environmental improvement.

How We Addressed This Challenge

The Idea

We developed a software which uses key environmental datasets to help identify geographical locations that require vegetation in order to improve the local environment. Vegetation has many benefits to its local climate such as; improved air and water quality, reduced energy use, enhanced storm water management as well as water runoff control, and a general boost to quality of life. Through Enviromap's data analysis, it can essentially generate a report that will be converted into a scoring system that can help users visualize where planting vegetation would have the greatest positive impact on the local environment.

Significance

This is important because strategically placing new vegetation based upon environmental values rather than geographical purpose has the potential to help local climates, which can grow to a larger scale and have a beneficial impact on the global climate.

How Does it Work?

The software uses python to retrieve surface temperature data and carbon monoxide readings from MOPITT, based on the latitudinal and longitudinal positions specified by the user. Afterwards, it uses land cover and air pollutant data along with the MOPITT data for the given region to sum and allocate points for the selected region.

Our Goal

The goal of Enviromap is to assist in improving the climate on a small scale, which eventually can be translated to a larger scale. Using the software to strategically place new vegetation on a large scale, such as farmland and forests, can help local environments, which can in turn create interconnected networks of local areas being used to help the climate.

How We Developed This Project

Our Inspiration

The modern-day climate crisis is currently a global issue that needs any help it can get. Obviously, there are issues with large corporations and heavy industry polluting the air, but everyone has a role in this crisis. Everything adds up, and we believe that even one person can make an impactful difference, just like how our project may be able to make a difference. Using that logic, we thought about how we could potentially help our local environment first. After some research we discovered that vegetation is something that is both beneficial to human and if placed correctly, beneficial to the climate. Therefore, we thought that we could combine environmental data accessible to us and create something that would show users ideal sites to plant new vegetation on the basis of improving the local climate.

Our Work

Our first target was to find relevant datasets provided by the CSA, specifically, important environmental data. After finding the necessary datasets we set our minds to being able to understand what information we want to use from them and how we would go about using it. We used a collection of Python, VS Code, Matlab, Mapbox API, Python Pandas, and Numpy libraries. Below is a visualization of our work.

Struggles and Victories

Our main problem that we faced was the constraint of time. We have big plans, however it is difficult to integrate everything into the final product because that takes a significantly longer amount of time. We also had difficulties accessing and navigating the data through the python script because the libraries were so unfamiliar. Finally, we also had an issue with getting python to communicate with the web app, therefore we had to use the alternative framework Web to Py. In terms of victories, we were able to fully incorporate a key dataset into the software, which was the surface temperature data. This data worked properly and although we could not incorporate the other datasets, the other relevant data would be shown in similar fashion to the surface temperature data. All that data would come together to show the points system. Another very important victory came through the help of the mentors, who taught us how to properly decipher and use all of the datasets that we needed. This allowed us to continue working with confidence and be able to get the project moving forward.

How We Used Space Agency Data in This Project

We set about retrieving relevant datasets that were necessary for our use, which was mainly environmental data. Our chosen data ended up being datasets of land cover/type, water quality of rivers, air quality including pollution and emission data, surface temperature data, energy use, and data about temperature change across Canada. We chose these datasets by specifically analyzing the benefits of agriculture in local areas. For example, vegetation can improve water quality and storm water runoff naturally. We used land cover data so that the system can dictate if the land is physically suitable to be able to host new vegetation. Additionally, vegetation can help sequester carbon dioxide as well as improve local air quality by removing air pollutants. Furthermore, vegetation can help decrease energy usage, thus in turn reducing any associated pollution or emissions. Each dataset we use has a directly correlated role in tracking how beneficial adding vegetation in a specific area would potentially be. Using those correlations, Enviromap would generate a score-based system that the user could visualize to easily understand precisely how beneficial planting vegetation would be in that region.