Investigating the Impact of Urban Design on Mental Health Outcomes Using Data Science

Overview

This project explores how urban design elements—such as green spaces, building density, and air quality—impact mental health outcomes. Using data science methodologies, including geospatial analysis and machine learning, the study integrates spatial, environmental, and mental health data to identify correlations and generate actionable insights for healthier urban planning.

Problem Statement

Urban environments influence mental health, but specific design features that contribute to improved or deteriorated mental well-being remain underexplored. Issues like lack of greenery, noise pollution, and poor air quality are associated with higher rates of anxiety and depression, particularly in cities. This research investigates how urban design can promote better mental health outcomes, leveraging modern data science tools to uncover critical insights.

Objectives

• Primary Objective: Analyze how urban design features affect mental health outcomes using data science techniques.
• Secondary Objectives:
  • Identify correlations between green spaces, air quality, and mental health statistics.
  • Develop predictive models for assessing the mental health impact of urban design changes.
  • Provide actionable recommendations for urban planners and policymakers.

Methodology

1. Data Collection:

   • Spatial Data: Urban structures, green space accessibility, and transportation networks.
   • Environmental Data: Air quality (PM2.5, NO2) and noise pollution metrics.
   • Mental Health Data: Prevalence of anxiety, depression, and stress from public health records or surveys.

2. Data Analysis:

   • Use GIS tools to map spatial relationships.
   • Employ statistical methods and machine learning (e.g., random forests, XGBoost) to model mental health outcomes.

3. Scenario Testing:

   • Simulate the effects of hypothetical urban interventions (e.g., increased green space, reduced pollution).

4. Evaluation:

   • Metrics: Correlation coefficients, predictive accuracy (F1-score, R-squared).

Technologies and Tools

• Data Analysis: Python (pandas, numpy, scikit-learn), R.
• Geospatial Tools: QGIS, GeoPandas, ArcGIS.
• Machine Learning Frameworks: TensorFlow, PyTorch, XGBoost.
• Visualization: Tableau, Matplotlib, Seaborn.

Expected Outcomes

• Identification of urban design features most strongly associated with mental health outcomes.
• Predictive models to assess potential mental health benefits of urban planning changes.
• Evidence-based recommendations for urban design strategies to improve mental well-being.

Ethical Considerations

• Data Privacy: Compliance with regulations like HIPAA and GDPR for sensitive health data.
• Equity: Address disparities in access to green spaces and environmental quality.
• Community Involvement: Collaborate with local stakeholders for validation and interpretation of findings.

How to Use This Repository

1. Clone the repository:

   git clone https://github.com/your-username/urban-design-mental-health.git