Exploratory Data Analysis (EDA) is a crucial step in understanding complex relationships between urban green spaces and mental health outcomes. By systematically analyzing data, patterns and insights can be uncovered to assess how access to parks, gardens, and other green areas influence psychological well-being. Here is a comprehensive approach to studying this impact using EDA:
1. Define the Research Scope and Collect Data
Start by specifying the variables of interest related to urban green spaces and mental health. Common data points include:
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Urban green space metrics: area size, proximity to residential areas, type of greenery (parks, trees, gardens), accessibility.
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Mental health indicators: rates of anxiety, depression, stress levels, self-reported well-being scores, clinical diagnoses.
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Demographic and socio-economic factors: age, income, education, employment status, population density.
Data sources might include satellite imagery for green space quantification, health surveys, census data, and local government records.
2. Data Cleaning and Preparation
Prepare the dataset by:
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Handling missing values through imputation or removal.
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Standardizing units and formats.
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Removing duplicates and correcting inconsistencies.
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Creating new variables if needed (e.g., distance to nearest park, green space per capita).
3. Initial Data Exploration
Begin EDA with summary statistics:
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Calculate mean, median, variance for green space metrics and mental health scores.
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Visualize distributions using histograms or density plots to detect skewness or outliers.
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Use boxplots to compare mental health scores across different levels of green space exposure.
4. Analyzing Relationships
To uncover connections between urban green spaces and mental health:
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Scatter plots: Visualize correlations between green space variables and mental health indicators.
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Correlation matrix: Compute Pearson or Spearman coefficients to quantify linear or monotonic relationships.
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Grouped comparisons: Use bar charts or violin plots to compare mental health outcomes by categories of green space accessibility (e.g., low, medium, high).
5. Spatial Analysis
Incorporate geographic information system (GIS) techniques:
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Map green space distribution alongside mental health prevalence in neighborhoods.
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Conduct spatial autocorrelation tests (Moran’s I) to identify clustering effects.
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Use heatmaps to detect hotspots of poor mental health and their proximity to green spaces.
6. Investigating Confounding Variables
Control for socio-economic and demographic factors by:
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Segmenting data into subgroups (e.g., by income level or age).
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Using scatter plots or boxplots to explore how these variables affect mental health independent of green space.
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Checking interaction effects visually and statistically.
7. Time-Series or Longitudinal Data
If available, analyze changes over time:
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Plot trends in mental health measures before and after urban greening projects.
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Use line charts or area plots to show temporal patterns.
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Explore lagged effects where green space improvements influence mental health outcomes after some time delay.
8. Advanced Visualization
Enhance understanding with:
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Pair plots combining multiple variables for multidimensional insights.
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Interactive dashboards to filter data by location, demographics, or green space attributes.
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Regression plots showing fitted lines with confidence intervals to indicate strength and significance.
9. Hypothesis Generation
Use EDA results to form hypotheses, such as:
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Greater proximity to parks correlates with lower anxiety levels.
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Access to larger green spaces improves self-reported well-being more than smaller ones.
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The positive impact of green space is stronger in lower-income neighborhoods.
10. Reporting Findings
Summarize key insights with visuals and statistical summaries, highlighting patterns that warrant deeper modeling or experimental research.
By following these EDA steps, researchers can effectively study how urban green spaces influence mental health. This process uncovers meaningful patterns, guides hypothesis development, and lays the groundwork for more rigorous causal analysis.