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Presents a data-driven framework to understand patterns in drinking water accessibility and quality within low and middle-income countries. By analyzing data from 77 countries across fifteen accessibility and two quality metrics, the research employs clustering analysis to group countries into three distinct typologies, moving beyond geographical classifications. A subsequent decision tree model is used to identify which variables are most predictive of a country's typology and to explore the relationships between water quality indicators. The study concludes that investments in improved water accessibility, particularly piped water and protected wells, are crucial for enhancing water quality and overall public health outcomes in these regions.

⏰ 🕐Timestamp ⏲ 🕝 🕰

00:00 - Intro

00:44 - The Global Water Crisis

01:28 - Research Methodology

02:56 - Water Accessibility Variables

04:24 - Country Typologies

06:36 - Statistical Analysis Results

08:48 - Decision Tree Analysis

11:44 - Key Findings

13:12 - Recommendations

#water #waterengineering #education #sdg6 #waterforall #waterquality #waterpipeline #pipedwater