High-Level Project Summary
We developed a database that'd use important indicators include rainfall percentages, soils quality, pure water amount, agriculture, and weather classified by different countries to rank the environmental injust of each country and revealing the insufficient aspects in a list. By using our database with our own algorithm, we lighting regions where marginalized populations are facing a higher burden from environmental injustice, the list of the potentially influential parameters can be used in a good way by scientists to help really solve the problem. Environmental hazards are a huge problem, our database wil be a good fit gathering people all together to solve it generally!
Link to Project "Demo"
Link to Final Project
Detailed Project Description
Firstly, we choose to use IMR(infant mortality rate) to be the main factor in our algorithm to rank the countries in the background of such environmental injustice. This is inspired by the book "Number Don'y Lie" written by the author Vaclav Smil who treat IMR as a great way determining the quality of life. We crawled the worldwide data in a cvs form by Python and uploaded to Kintone.
Second, by searching on "https://search.earthdata.nasa.gov/search", we got tons of nature data from NASA database. We choosed MERRA-2 Data as our main resource to reveal the nature resource distribution in different area. We use netCDF4.py in Python to parse the nature data with time, latittude and lontittude. By having the library geopy.py, we decrease the data dimention by calculating the mean value of each country's area. Because the data itself is still huge, we plan to seperate in different DB and bind all of them to the ranking data by Country Name.
The problem through the challenge we found is that the environmental injustice impacted by environmental hazards has enormous variables that involved with all kinds of data in NASA earthdate database. Each data point is also huge with multidimention and hard to extract information. Therefore, we design this DATABASE for environmental injustice to highlight the potiential injustice region and provide necessay data support.
This is a great distributed database analyzing environmental injustice in country degree that we have our own modifiable ranking system which highlighting the overall resource lacking regions. By using this database, scientist and relevent researcher can directly use it to get up-to-date data specified in different country, which can be a easier starting point for them to begain their research.
We hope our database can be the entrance database for researchers doing research on envirnoment injustice which will need more iterations on our ranking algoristhm and feed more data to the distribute database.
Space Agency Data
https://search.earthdata.nasa.gov
https://goldsmr4.gesdisc.eosdis.nasa.gov/data/MERRA2_MONTHLY/M2TMNXLND.5.12.4/doc/MERRA2.README.pdf
tavg1_2d_lnd_Nx (M2T1NXLND): Land Surface Diagnostics
We use it as our data support binding with the country ranking which help to provide the necessary data for different country which could show how different the resources are.
Hackathon Journey
It's a brand-new experience for us as we never touch the real data on our earth through satellite data before. It's also excited that we have this chance to try solve the real world problem with those data.
We choose the topic, space for change. Our world is developing faster and faster through the internet. With infomation feeding algorithm, we all easily to forgot that there are still places that need help in basic resources obtain. The survivor bias in our own small world should be broke to let us really see the problem on earth, so we are here.
We first trying to define the issue that what is environmental injustice impacted by environmental hazards through Marginalized communities. It's a challenge that we couldn't find a good threshold on variable to determine it. Inspired by the book "Number Don'y Lie" written by the author Vaclav Smil who treat IMR as a great way determining the quality of life, we change our plan to use the data in other dimention to design the ranking algorithm revealing the environmental injustice issue.
Then, we try to feed relevent data from NASA earth data to our ranking system. We were struggling on resolving such huge data in nc4 file. By doing research, we found using the library netCDF4.py can be a good choose parsing the data. By using numpy.py, we were able to visualize all the data and transform it in to country region and bind to our ranking system.
I want to thanks Eriko who replied us fast and help us to create a slack group.
References
https://www.geeksforgeeks.org/get-the-city-state-and-country-names-from-latitude-and-longitude-using-python/
MERRA2_400.tavgM_2d_lnd_Nx.202108.nc4
https://en.wikipedia.org/wiki/List_of_countries_by_infant_and_under-five_mortality_rates
https://search.earthdata.nasa.gov/search
https://madherst.kintone.com/k/#/portal
python.org
Tags
#software, #database, #NASA_Data, #Kintone, #Python
Global Judging
This project has been submitted for consideration during the Judging process.