This Power BI dashboard titled “Water Pollution & Disease Exploratory Analysis – Typhoid Cases Dashboard” presents a comprehensive analysis of how water pollution factors correlate with typhoid cases per 100,000 people globally. Here’s a breakdown of its sections and insights:
Dataset is available here: https://www.kaggle.com/datasets/khushikyad001/water-pollution-and-disease
🔷 Top Indicators (KPIs):
- Avg % of Population with Access to Clean Water: 64.61%
- Average Bacteria Count (CFU/mL): 2.49K
- Average Contaminant Level (ppm): 4.95
- Average of Dissolved Oxygen (mg/L): 6.49
- Average of Lead Concentration (µg/L): 10.05
These KPIs summarize the overall water quality and accessibility landscape.
🔷 Pie Charts and Tree Map:
1. Typhoid Cases by Rainfall (mm per year):
- Typhoid is distributed almost evenly across rainfall categories.
- Suggests rainfall isn’t the sole influencer; likely other contributing factors.
2. Typhoid Cases by Water Source Type:
- Highest cases from Spring (18.01%), followed by River, Tap, Well, Lake, Pond.
- Unsafe water sources seem linked to higher typhoid prevalence.
3. Typhoid Cases by Country (Tree Map):
- Top contributors: Brazil, China, Pakistan, Ethiopia, USA, Nigeria, Mexico, India.
- Highlights a global issue affecting both developed and developing nations.
🔷 Line Chart:
Typhoid Cases Over Time (by Year):
- Spikes in some years, like 2015 (~7K cases) and 2019.
- Indicates fluctuating trends, possibly due to seasonal outbreaks or reporting accuracy.
🔷 Bar Charts:
1. Typhoid Cases by GDP per Capita:
- Higher cases observed even in mid-income regions.
- Suggests wealth alone doesn’t guarantee protection without proper infrastructure.
2. Typhoid Cases by Healthcare Access Index:
- Interesting trend: some high cases in lower access regions (as expected), but also notable values in regions with 80+ access index.
- Implies healthcare access might not always translate to prevention if water quality is poor.
🔷 Scatter Plot / Stacked Column:
Typhoid Cases by Urbanization Rate & Treatment Method:
- Multiple treatments shown: Boiling, Chlorination, Filtration.
- Correlation between urbanization and treatment adoption is implied.
- Variability suggests a need for more consistent treatment practices.
🔷 Interactive Filters on Right Side:
- Users can filter by:
- Country
- Region
- Year
- Water Source Type
- Water Treatment Method
This allows dynamic slicing of the data for deeper exploration.
🌐 Footer:
“Learn Power BI @ Slidescope.com”
This suggests it’s a learning resource or demo dashboard by Slidescope.
✅ Overall Insight:
This dashboard is a powerful visualization tool linking water quality indicators and demographic/economic factors to typhoid incidence. It’s useful for:
- Public health planning
- Water resource management
- Policy-making for sustainable development