This project explores the relationship between NDVI (Normalized Difference Vegetation Index) /GVI（Green View Index） indices, and various health and environmental variables, socioeconomic variables to assess the need for increased urban HEALTH greening initiatives.
01. NDVI (Normalized Difference Vegetation Index) /GVI（Green View Index）indices
02. Public Health Outcomes (e.g., asthma, death, obesity, mental health)
03. Environmental Factors (e.g., heat, air pollution, noise reduction)
04. Socioeconomic Factors (e.g., poverty, unemployment)
Dataset
Input Data Into GIS for Distribution Map
Correlation
Multi-Model Validation
1. Green Index significantly reduces pollution, confirming its environmental benefits.
Green Index is negatively correlated with exercise rates, raising questions about whether green spaces are effectively promoting physical activity.
No direct relationship is found between tree coverage and obesity or asthma, indicating other factors may be more influential.
SEM Analysis
The SEM results show that green index significantly reduces pollution, while has no significant effect on obesity, exercise, or asthma. This suggests that urban greenery improves air quality but does not directly influence health.
Poverty plays a key role, significantly increasing obesity and reducing exercise, suggesting that economic development, not greenery, drives health.
Strong spatial dependence (lambda) confirms that health and environmental factors are geographically clustered. This supports the hypothesis that socioeconomic factors overshadow the direct impact of green spaces on health.
Are the Environment variables Spatial Self-Correlated?
Environmental variables exhibit a certain degree of spatial self-correlation, particularly among pollution index, O₃ concentration, and walking distance to parks, which show strong correlations with statistical significance. This indicates that environmental conditions in specific areas are likely to influence each other rather than being independently distributed.
What is the Relationship between Environment-Health, Environment-Economy, and Economy-Health variables?
Urban environmental factors—particularly pollution, heat, and green space accessibility—significantly influence public health. Policies that reduce pollution and heat exposure while increasing urban greenery and walkability could help improve health outcomes.
Analysis
Hot-Spot Analysis Summary
Key Pattern: Northeast regions tend to have higher health risks, while western and northern areas show better health indicators.
Asthma: Hotspots in the northeast and central areas; cold spots in the southwest.
Death: Hotspots in the northeast and central areas; cold spots in multiple locations.
Depression: Strong hotspots in the southeast and northeast; cold spots in the north.
Exercise: Hotspots in central areas; cold spots in the west and north.
Obesity: Hotspots in the southeast; cold spots in the north.
Serious Distress: Hotspots scattered in the northeast, central, and southwest; cold spots mainly in central areas.
Bivariate LISA Cluster Analysis Summary
Key Pattern: Pollution strongly correlates with distress, while green spaces alone may not guarantee mental health improvements.
Green Index vs. Serious Distress:
High-High (Red): Areas with high green index and high serious distress, indicating green spaces may not effectively mitigate distress.
Low-Low (Blue): Areas with low green index and low serious distress, suggesting other factors might play a role in reducing distress.
Pollution Index vs. Serious Distress:
High-High (Red): Areas with high pollution and high serious distress, confirming pollution as a major stressor.
Low-Low (Blue): Areas with low pollution and low serious distress, indicating better environmental conditions support well-being.
Low-High (Light Blue): Areas with low pollution but high distress, suggesting non-environmental factors contribute to distress.
Results
What’s the Final Solution? Risk Map or Priority Map?
Based on the risk assessment, priority interventions include local infrastructure needs in high-risk areas, more parks in low-income communities, and incorporating health indices into planning.
The intervention priority map translates risk assessment into actionable priorities for urban planning and policy-making. Overlapping patterns between the comprehensive risk and intervention maps validate the effectiveness of risk modeling in identifying high-need areas.
Spatial Effect Decomposition
Noise pollution (Noise_z) primarily impacts health through direct pathways, making it an immediate concern.
The urban heat island effect (Heat_z) mainly exerts its influence through indirect pathways, implying it has long-term systemic consequences.
Environmental pollution indicators (Pollution_index) show strong indirect effects, suggesting their impact accumulates over time rather than being immediately noticeable.
Green space and walkability (Green_index, Walk_to_Park_z) exhibit a relatively balanced direct and indirect effect, emphasizing their role in both immediate and long-term urban health benefits.
Green Index & Obesity (Moderated by Poverty)
Interaction plot shows a clear distinction between High and Low SES groups, with a stronger effect for High SES.
Effect sizes are relatively large for both groups, with moderate error bars, making this a more robust finding.
Interactive Map
Link: https://experience.arcgis.com/experience/8acec853ba8f4806a270e71fbfe7ebfa/?draft=true
Data Dive Award
Spring 2025 Global Data Dive: Sustainability:
https://engineering.nyu.edu/research-innovation/centers/cusp/research/data-dives
Participating in the CUSP London Data Dive 2025 at King’s College London was an incredible experience. This four-day competition brought together 13 teams from top institutions, including King’s College London, University of Warwick, University of Glasgow, New York University, and Peking University, to tackle urban sustainability challenges through data-driven insights.
I am truly honored that Team 13 received the 🏆 Highly Recommended Award! Huge thanks to my amazing teammates for their collaboration and dedication.
My primary contributions to the project included preprocessing noise data, GIS visualization, and building an interactive experience using GIS Experience Builder. This journey has been both challenging and rewarding, reinforcing the power of data in shaping more sustainable and livable cities.
