“Clean water and sanitation” — Do we live in clean and safe environment?
This project was carried out as part of the TechLabs “Digital Shaper Program” in Dortmund (summer term 2021).
In a nutshell:
Our project primarily focuses on clean and safe environment indicators aligned to one of the 17 sustainable development goals of the United Nations (UN). We show the current trends and overall situation of water consumption, sanitation, and hygiene around the world. We use several measures of central tendencies and measures of dispersion to dissect the data and prepare the numerous info graphs. You can create your own analysis, charts, and graphs, and maps using simple built-in functions.
Introduction:
According to UNICEF, less than half of the world has access to clean and safe sanitation, i.e., more than half still lack the same. The number of people is around 637 million who use open defecation. Similarly, access to safe drinking water is also limited, and around 2.2 billion people are in that category. Besides these significant numbers, a huge number of global populations lack the basic handwashing facility. Therefore, it is important to know these numbers to better prepare ourselves for a cleaner and safer world together.
Methodology:
We use data from UNICEF on water, sanitation, and hygiene (WASH) for the period 2000 to 2020 for most of the world. The dataset consists of data on a range of variables in three basic categories such as hygiene, sanitation, and water consumption. Each main category has several sub-categories; for example, one can dig into the sanitation area to see the current sanitation trends in the world and compare them with the world average. Users can perform analysis on a regional and continent basis as well as country-wide analysis. We provide numerous tools to make the analysis more interactive. Therefore, scatterplot, bar graph, and map are part of our data analysis tools.
To create a geographical map, we used folium in Python. The entire program is stored on our GitHub depository for reference, and we aim to work on that post our project as it is said, “You’ll never reach perfection because there’s always room for improvement.” We also tried to program the drop-down menu to make it more user-friendly and interactive but had some setbacks, and therefore we decided to leave it for the moment due to time constraints and our limitation to the data science track area. Throughout our project, we used slack for communication and zoom for our weekly meetings to check on the progress of the project.
Results:
Our goal, being the beginners in the Python (data science track), was to create some graphs, charts, and maps and show the overall situation regarding three main indicators water consumption, hygiene, and sanitation around the world. The number of people without having access to clean water, the percentage of the population having no access to toilet at home are a few of many variables’ part of our project. Here you can see the situation around three variables in Yemen using a bar graph, scatterplot, and map.
The overall results show that there is still enough left to do in the areas of sanitation, water consumption, hygiene, especially in countries with poor infrastructure.
GitHub repository:
Team Members:
Data Science (Python):
- Aylin Arzuman
- Irfan Ali
- Tom Schöneweiß
Team Members:
- Florian Zimmer (Project Manager TechLabs)
- Milan Niehaus (Project Manager TechLabs)
- Johannes Heck (Data Science and AI Mentor)
- Tobias Küper (Data Science Mentor)
Sources:
World Health Organization and Unicef (2019). Progress on sanitation and drinking water. WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation (JMP)
