https://github.com/faith99/water_pollution_dashboard
A data visualization project exploring water access, contamination and health outcomes
https://github.com/faith99/water_pollution_dashboard
data-analysis data-visualization powerbi public-health publichealth
Last synced: 4 months ago
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A data visualization project exploring water access, contamination and health outcomes
- Host: GitHub
- URL: https://github.com/faith99/water_pollution_dashboard
- Owner: faith99
- License: cc0-1.0
- Created: 2025-07-02T13:33:17.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-07-02T15:10:53.000Z (4 months ago)
- Last Synced: 2025-07-02T15:43:20.621Z (4 months ago)
- Topics: data-analysis, data-visualization, powerbi, public-health, publichealth
- Homepage:
- Size: 179 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# The Ripple Effect: Water & Public Health in Nigeria
An interactive Power BI dashboard project that explores the link between **water access, contamination**, and **disease outcomes** across Nigeria's regions.
## Project Overview
Unsafe water doesn't just affect hydration, it shapes health, infrastructure, and mortality.
This dashboard was created as a personal data analytics project to investigate how **water quality** and **treatment practices** impact **public health metrics** like disease prevalence, infant mortality, and sanitation access.
Using data from water sources, regional demographics, and health reports, the dashboard uncovers **critical patterns and disparities** across Nigeria’s six geopolitical zones.
---
## Key Questions Explored
- Which regions carry the **highest disease burden**, and why?
- How do **water treatment practices** vary by source (well, pond, river, etc.)?
- Are **contaminant levels** consistently high across sources?
- How do **sanitation coverage** and **healthcare access** influence public health outcomes?
---
## Key Insights
1. **Central Nigeria bears the heaviest disease burden**, despite only moderate healthcare access—indicating deeper infrastructure challenges.
2. **Average contaminant levels remain high** (above 5ppm) across all water sources, especially lakes and ponds.
3. A large percentage of water is left **untreated**, with "None" being the most common treatment method across rivers, lakes, and wells.
4. Regions like **East and Central** report **low sanitation coverage** despite dense populations.
5. **Infant mortality rates** remain consistently high across all zones, reinforcing the urgency for systemic interventions.
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## Tools Used
- **Power BI** – Data modeling, dashboard design, DAX calculations
- **Microsoft Excel** – Data cleaning and prep
- **Canva** – Layout and icon inspiration
- **Markdown** – Documentation and reporting
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## Dashboard Preview
| Dashboard Page | Description |
|----------------|-------------|
| **Overview** | Quick stats on water access, contamination, treatment, and disease count by source |
| **Health Impact** | Visual breakdown of sanitation coverage, disease cases, infant mortality, and access to care by region |
| **Insights** | Key findings and actionable recommendations from the analysis |
[Interact with Dashboard here!](https://app.powerbi.com/view?r=eyJrIjoiODc1MmM2N2ItMDVkNC00ODE3LWE4MWEtMDQwZWY0YzIzYTE2IiwidCI6IjA3YTAwYzJhLTAxZDItNDYwNC04N2YyLTJmN2MwYzQ5ODIwZiJ9)
---
## Report Access
You can find a full PDF version of the project report and recommendation summary in the `/Report` folder. It includes expanded context, detailed insights, and proposed public health strategies.