How to Use EDA to Study the Relationship Between Housing Quality and Health Outcomes

Exploratory Data Analysis (EDA) is a critical step in understanding the underlying patterns, trends, and relationships within datasets. When examining the relationship between housing quality and health outcomes, EDA enables researchers, data analysts, and public health officials to uncover correlations and insights that might inform policies and interventions. The intersection of housing and health is well-documented, as factors like ventilation, crowding, pest control, and structural safety can significantly influence physical and mental well-being. EDA facilitates a structured approach to exploring these dynamics using both visual and statistical tools.

1. Understanding the Variables

Before diving into analysis, it’s essential to define the scope and type of variables involved:

• Housing Quality Indicators may include:

  • Structural integrity (presence of cracks, leaks, dampness)

  • Indoor air quality (ventilation, presence of mold)

  • Clean water availability and plumbing

  • Overcrowding metrics (persons per room)

  • Heating and cooling systems

  • Pest infestations

  • Noise levels

  • Neighborhood conditions (pollution, green spaces, crime rate)

• Health Outcomes can be measured through:

  • Incidence of respiratory diseases (asthma, bronchitis)

  • Mental health conditions (depression, anxiety)

  • Infectious disease prevalence

  • Chronic illnesses

  • Self-reported health status

  • Hospital admission rates

  • Child development metrics

2. Data Collection and Preparation

Data sources might include:

• Government health surveys

• Housing and population census data

• Hospital records

• Environmental monitoring datasets

• Longitudinal studies or panel datasets

After gathering data, the preprocessing phase includes:

• Cleaning the data: handling missing values, correcting inconsistencies

• Transforming variables: categorizing continuous variables, creating binary flags (e.g., presence/absence of mold)

• Merging datasets: aligning housing data with individual or regional health data

• Feature engineering: deriving new metrics like a composite housing quality index

3. Descriptive Statistics and Univariate Analysis

Start with univariate analysis to understand the distribution of each variable:

• Numerical variables: Use histograms, box plots, and summary statistics (mean, median, standard deviation)

• Categorical variables: Use bar plots and frequency tables to explore distribution

For instance:

• Analyze how many households report structural issues

• Assess the spread of health outcomes by region or age group

4. Bivariate Analysis to Identify Relationships

Once individual variables are understood, bivariate analysis can help explore the relationship between housing quality and health:

• Correlation analysis: Use Pearson or Spearman correlation to test linear relationships between numerical housing quality indicators and health metrics

• Cross-tabulations: Explore categorical relationships, such as between poor ventilation and presence of asthma

• Group comparisons: Use box plots or violin plots to compare health scores across different housing conditions

• Statistical tests:

  • T-tests or ANOVA to compare means of health outcomes across housing quality groups

  • Chi-square tests for independence between categorical variables

5. Multivariate Visual Exploration

EDA often relies on rich visualizations to observe patterns:

• Heatmaps to display correlation matrices

• Scatter plots with trend lines to detect linear or non-linear relationships

• Pair plots to explore multiple variable interactions

• Geospatial visualizations if data includes geographic information — useful for detecting regional disparities

Interactive visual tools (like Tableau or Plotly) can enhance the understanding of large, multi-dimensional datasets, especially when exploring time-series or demographic subgroups.

6. Dimensionality Reduction

In datasets with many housing quality indicators, dimensionality reduction helps uncover latent factors:

• Principal Component Analysis (PCA) can be used to reduce multiple housing attributes into fewer, uncorrelated components representing overall quality.

• Factor analysis may help group related variables, e.g., indicators of poor structural conditions, into single dimensions.

This helps in simplifying the analysis and reduces noise when studying correlations with health outcomes.

7. Detecting Outliers and Data Quality Issues

Outliers may indicate either data entry issues or rare but important phenomena (e.g., extremely high hospital admissions in a particular housing block). Use:

• Box plots to detect univariate outliers

• Scatter plots to spot multivariate anomalies

• Isolation Forests or DBSCAN for more complex outlier detection

Cleaning or contextualizing these points ensures that downstream analysis remains robust.

8. Clustering and Segmentation

Clustering algorithms can segment households or individuals based on housing and health profiles:

• K-Means or Hierarchical Clustering can group similar housing conditions or health outcomes

• These clusters might reveal hidden patterns, such as a particular cluster with both high overcrowding and elevated rates of infectious diseases

Segmentation can help target policy interventions more effectively.

9. Time-Series Exploration

When longitudinal data is available, EDA can be used to observe trends over time:

• Line charts to show the progression of health outcomes in areas with improving or degrading housing conditions

• Lag analysis to determine if there’s a delayed impact of housing improvements on health

This provides evidence for the effectiveness of housing policy changes or social programs.

10. Hypothesis Generation and Model Preparation

EDA serves as a precursor to modeling by helping form hypotheses, such as:

• “Households with poor ventilation have significantly higher asthma rates”

• “Regions with higher housing quality scores report fewer mental health issues”

These hypotheses can later be tested using regression models, classification algorithms, or causal inference methods. But without EDA, it would be difficult to understand which variables matter or how they interact.

11. Ethical and Social Considerations

When analyzing sensitive topics like housing and health, ethical data handling is crucial:

• Ensure data privacy and anonymization

• Avoid overgeneralizations or deterministic conclusions

• Use findings to inform equitable and inclusive policies

EDA should be a tool for insight and advocacy, not just academic discovery.

12. Case Study Example: Urban Housing and Child Respiratory Health

A hypothetical case study might involve:

• Collecting data on housing conditions in urban neighborhoods (e.g., dampness, heating)

• Linking it with hospital records of children under 10 reporting respiratory issues

• Using EDA to identify which housing factors most correlate with illness frequency

• Mapping findings geographically to identify high-risk zones

The EDA might reveal that households with mold and inadequate heating have 3x higher child hospitalization rates, prompting local government to fund home repairs and heating subsidies.

Conclusion

EDA is a powerful, iterative process that can yield critical insights into how housing quality impacts health outcomes. Through data visualization, statistical exploration, and pattern recognition, it allows stakeholders to identify at-risk populations, support public health strategies, and justify housing reform policies. While EDA itself does not prove causality, it provides a robust foundation for deeper analysis and data-driven decision-making.