Maji Ndogo Water Crisis 1
This focuses on the water crisis in Maji Ndogo and gender inequality in water access.
Maji Ndogo is an African community facing a water crisis due to poor infrastructure, overcrowded water sources, and contaminated water supplies.
The end goal of this project is to make clean water finally accessible to all in the said community, eliminating gender inequalities, eradicating crimes at water sources (most especially against women and children), and fixing poor infrastructure.
This approach exemplifies the power of data in revealing underlying societal issues.
Our emphasis is on efficiently conveying precise findings to our primary stakeholders, including national and local governments and our funders. The report we create must be comprehensive, clear, and impactful, effectively conveying the full scope of our research and its implications. Therefore, commencing below:
Starting with Part 1 here, which focuses on water crises in Maji Ndogo and gender inequality in water access.
Working on a large dataset of 60,000+ records and continuing from our SQL analysis, below are the main points that are visually represented for better communication to our stakeholders;
Average of time in queue by days of the week show that:
• Queues are very long on Saturdays.
Average of time in queue by hour of day and day of the week show that:
• Queues are longer in the mornings and evenings.
• Wednesdays and Sundays have the shortest queues.
The percentage of women available across the different water sources are the highest across all days of the week, compared to the percentage of men and children.
We have an average number of 2000 people who often share one tap
Most water sources dominated by people are shared taps, followed by wells.
Most water sources are from rural locations in Maji Ndogo
The count of crimes committed against individuals at the different water sources is predominantly against women compared to men. Harassment being the leading crime committed against them
This leads us to part 2 of the project where we added more data, worked on data modeling and relationships to further drill down to the below information:
The population split between urban and rural and a map of provinces.
Crime-related and gender disparity data.
Total number of people per source type.
Total number of each source of water type, for every town.
Number of wells and their pollution status, etc…