High-Level Project Summary
In this task, our aim was to address the problems faced by communities living near the Nile Basin that are affected by environmental injustice. We developed a user-friendly interface- a mobile app that would raise awareness about such communities as well as a website that consists of risk maps formed using numerical algorithms and data obtained from NASA, SEDAC, and JAXA. Using awareness techniques and incentives to people from other countries and NGOs, the plan is to gather donations from people around the world and help use them for the advancement of such marginalized communities. The app is a simple way to present relevant information to the general public and attract fundings.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
With the rapidly evolving world, there have been changes to the world that we, as humans, have little control over, despite being the very reason behind them. Climate change and the natural calamities associated with them are good examples of them. Although environmental phenomena like droughts, water shortage, floods, etc. are ways mother nature expresses its anger, some of its inhabitants are at a greater risk of being affected by them than others. Such are the communities, that we call “marginalized”, that are the victims of environmental injustices all around the world due to their identities.
As an example, the marginalized communities that we chose to focus on were those that consider the longest river in the world, Nile, as their home. The Nile Basin consists of areas that belong to some of the poorest countries in the world such as Burundi, South Sudan, Sudan, Ethiopia, Kenya, Tanzania, Eritrea, Rwanda, and Egypt. The Nile Basin Initiative (NBI) has enabled these countries to advance a shared vision for the development of the region based on sustainable management and equitable use of the Nile water resources. However, some communities in these regions remain under the poverty line and have little to no access to basic human needs.
The purpose of this challenge was to identify such communities and to propose equitable solutions to their problems. As part of our project, we integrated relevant data from NASA, SEDAC, and JAXA as well as from local crowd-sourcing techniques to a risk map that helps identify high-risk regions. To map this data, we used open-source data available from these resources for different risk indicators that included floods, droughts, precipitation, soil moisture, surface air temperature, vegetation index, and dust. These layers were combined as layers onto a single map using different weights for each indicator that helped calculate a total risk factor for different regions. These regions were then color-coded according to the overall risk factor they had.
The risk factor (the Shaikh factor) was calculated using a formula devised by one of our teammates. This consists of an X factor which is the satellite data obtained from NASA’s EarthData datasets provided to us in the Resources tab. The Y factor, on the other hand, is the socio-economic data which was obtained from NASA’s SEDAC, NASA’s Black Marble, JAXA’s Earth-graphy data website. This Y factor will also contain data from the local government census and NGO surveys based on their inputs. Subsequently, the Shaikh factor was computed using the formula shown in the project demo presentation.
To obtain socioeconomic data from local resources, we do not rely on the information from the affected people themselves since people in these areas have little to no access even to electricity, let alone the internet. For instance, only about 1% of the population of South Sudan has access to electricity. Since these people are mostly uneducated and have no technological background, it is extremely difficult to obtain data from them. Therefore, to raise awareness about such communities and identify them, we proposed ideas of using the power of social media. We propose to invite volunteer students from other countries and provide them with the necessary equipment (cameras, batteries, etc.) for them to go to such communities and be their voices. As an incentive, they could be offered certificates, course credits, and stipends from their universities. Moreover, we could use websites like gofundme.org to encourage people to donate to certain families by sharing their personal stories with the volunteers. We could have vlog-making competitions for vloggers who would make documentaries about such communities and raise awareness about their issues. We could hire programmers to build applications and games that would include advertisements about donating to these communities. Besides this, social media trends, such as hashtags and Instagram filters can also help raise awareness and encourage people to donate through the donation website. All of this with the maps will be incorporated into an easily accessible mobile application and website that would give incentives to its users such as discounts, promotions, and deals. Besides these volunteers, local NGOs can also be a huge source of local data and can use the accumulated donations to improve facilities for those communities. The benefits of these platforms are not limited to the marginalized communities but also to the volunteers and NGOs who are receiving funds and credentials for their services of raising awareness and attracting donations from around the world.
We decided to make the most use out of the resources provided by NASA and the other space organizations, therefore we combined the data from different maps and combined them with the local resources to end up with a risk map. The back-end was implemented using Flask. Similarly, the front-end was built using Node.js and the Leaflet library for JavaScript. The main problem we faced was the resolution of the images not being on the same level and some of the maps being too general for our purposes. We had to use photoshop to enhance and edit the maps accordingly.
The prototype of the app was made using the Figma tool that shows the volunteering opportunities available and links for application. It also includes pictures of people from those communities for whom funds are being collected. Merchandise from which all profits would go to donations was also put up on display on this app. Moreover, it also has internship links on which students can apply to be part of this project and get certificates.
In order to combine our visual mapping tool and our mobile application, we decided to make a website using Wix. The website gives a brief introduction to our project and introduces each of the team members. It also briefly describes the EcoApp and EcoMap applications. There is also a merch shop on the website where a discount is offered for the SpaceApps competition.
https://github.com/nsothman/space-for-change-NASA2021
Space Agency Data
Since we worked on a map interface, we used data primarily from NASA and its partner agencies. We first studied the resources available under the resources tab in our task (Space for Change). In this task, we made extensive use of SEDAC and JAXA maps and integrated them into our interface. We chose certain local factors based on articles, newspapers, local news channels, surveys, experiences, past incidents, etc. After studying these factors we combined the remote sensing data and locally collected data to obtain the overall risk factors. Some examples of the risk indicators used include vital data such as Flood risks, Droughts, Precipitation, Surface air temperature, Vegetation Index, Soil Moisture, etc. since these factors characterize regions as being risky or risk-free.
We also used the resources available in other challenges that helped us think outside the box to build our interface.
Hackathon Journey
There is something majestic about Space Apps which brings us back every year. Our team consisted of 6 people from 3 different nationalities. Our diversity, coupled with the environmental injustice occurring in central Africa, enabled us to have a truly international experience. We became more aware of the social, political, economic, and environmental injustices committed against minorities all over the world. The struggle for the rights of the weak and the poor is highly noble and that drew this challenge to our attention. Moreover, it was convenient for us to discuss the Nile basin since one of our team members belongs to that region and we could feel the problem better. Our project was organized using a Kanban board on the Miro application. First, we identified the problems, then we brainstormed the solutions. This was followed by implementing the solution and identifying risks using a website or a mobile application. Once we broke these tasks down, we assigned them to volunteering members, and one member would peer-review and add them to the presentation and the project tab of the competition.
References
Datasets for the Visual Mapping:https://worldview.earthdata.nasa.gov/ & https://sedac.ciesin.columbia.edu/mapping/viewer/
Water Balance Visualization:https://svs.gsfc.nasa.gov/4044
Measurements of Pollution: https://donnees-data.asc-csa.gc.ca/en/dataset/ef42819f-35bb-49c0-a368-1e61fa876ee6
Water quality of the Nile:https://www.sciencedirect.com/science/article/pii/S1687428516300917
Water Development for Egypt and Sudan:https://efdinitiative.org/sites/default/files/guariso_whittington_1987.pdf
Human Development Report on South Sudan:http://hdr.undp.org/sites/default/files/Country-Profiles/SSD.pdf
Human Development Report on Burundi:http://hdr.undp.org/sites/default/files/Country-Profiles/BDI.pdf
Water Inequality Index:https://www.mdpi.com/2073-4441/12/4/931/pdf
Sources and Sinks in Nile Basin:https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR015231
Challenge of Nile Basin Initiative:https://gwp.org/globalassets/global/toolbox/case-studies/africa/transboundary.-swot-analysis-and-challenge-of-nile-basin-initiative-393.pdf
Nile Basin Research Program:https://core.ac.uk/download/pdf/30823565.pdf
Burundi ETOA:https://pdf.usaid.gov/pdf_docs/pnaeb430.pdf
Burundi Datasets:
https://data.humdata.org/dataset/who-data-for-burundi
https://data.humdata.org/dataset/highresolutionpopulationdensitymaps-bdi
https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-burundi
https://data.humdata.org/dataset/world-bank-poverty-indicators-for-burundi
https://data.humdata.org/dataset/world-bank-health-indicators-for-burundi
https://data.humdata.org/dataset/wfp-geonode-ica-burundi-land-degradation
https://data.humdata.org/dataset/wfp-geonode-ica-burundi-flood-risk
https://data.humdata.org/dataset/wfp-geonode-ica-burundi-landslide-risk
Egypt Datasets:
https://data.humdata.org/dataset/who-data-for-egypt
https://data.humdata.org/dataset/who-data-for-egypt
https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-egypt-arab-rep
https://data.humdata.org/dataset/world-bank-poverty-indicators-for-egypt-arab-rep
https://data.humdata.org/dataset/world-bank-environment-indicators-for-egypt-arab-rep
https://data.humdata.org/dataset/world-bank-health-indicators-for-egypt-arab-rep
https://data.humdata.org/dataset/world-bank-education-indicators-for-egypt-arab-rep
https://data.humdata.org/dataset/worldpop-population-density-for-egypt
https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-egypt-arab-rep
Kenya Datasets:
https://data.humdata.org/search?q=kenya+&ext_search_source=main-nav
https://data.humdata.org/dataset/world-bank-combined-indicators-for-kenya
https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-kenya
https://data.humdata.org/dataset/world-bank-health-indicators-for-kenya
https://data.humdata.org/dataset/world-bank-education-indicators-for-kenya
https://data.humdata.org/dataset/world-bank-economy-and-growth-indicators-for-kenya
https://data.humdata.org/dataset/who-data-for-kenya
Rwanda Datasets:
https://data.humdata.org/dataset/income-activitie
South Sudan Datasets:
https://data.humdata.org/dataset/income-activitie
Sudan Datasets:
https://data.humdata.org/dataset/health-facilities-in-sub-saharan-africa
https://data.humdata.org/dataset/income-activitie
Tanzania Datasets:
https://data.humdata.org/dataset/relative-wealth-index
https://data.humdata.org/dataset/income-activitie
Uganda Datasets:
https://data.humdata.org/dataset/relative-wealth-index
Eritrea Datasets:
https://data.humdata.org/dataset/relative-wealth-index
https://data.humdata.org/dataset/health-facilities-in-sub-saharan-africa
DR Congo Datasets:
https://data.humdata.org/dataset/dr-congo-health-0
https://data.humdata.org/dataset/dr-congo-settlements
https://data.humdata.org/dataset/worldpop-population-counts-for-congo
Ethiopia Datasets:
https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-ethiopia
https://data.humdata.org/dataset/world-bank-poverty-indicators-for-ethiopia
https://data.humdata.org/dataset/world-bank-environment-indicators-for-ethiopia
https://data.humdata.org/dataset/ethiopia-languages
https://data.humdata.org/dataset/ethiopia-infant-mortality-rate
https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-ethiopia
https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-ethiopiahttps://data.humdata.org/dataset/who-data-for-ethiopia
https://data.humdata.org/dataset/worldpop-population-density-for-ethiopia
Worldwide Data
Environmental data:
https://www.servirglobal.net/#data\&maps
Agriculture:
Vegetation greenery index worldwide:
Tags
#Injustice #Inequality #EJ #Africa #NileBasin #WaterCrisis #Dams #WeShowedUpAgain #EcoTopia

