Urban Climate Analysis

    • Analyze the urban climate impact of modern urban planning.

    • Compare population density and vegetative health in Reston, VA, to neighboring towns.

    • Utilize H3 indices to align datasets with different resolutions for accurate comparison.

    • Assess how urban design influences environmental sustainability.

  • Independent researcher

  • August-October 2023; awaiting publishing

    • Python for data processing and analysis.

    • H3 spatial indexing for uniform dataset alignment.

    • Geographic and climate data sources for urban planning insights.

    • Data visualization to interpret population density and vegetative health trends.

    • Successfully aligned disparate datasets for a consistent comparison.

    • Identified key urban planning factors affecting climate sustainability.

    • Demonstrated the effectiveness of H3 indexing in urban analysis.

    • Provided insights on balancing urban density with environmental health.

Introduction

I had long heard that Reston is recognized for its urban planning, with green spaces legally defined in its development framework. However, I was curious to examine the quantitative environmental impact of these design choices.

Study Overview

  • To analyze this, I conducted a study using NASA and GCIS indices, overlaying temperature and vegetative health data with population density in Reston and a neighboring town.

  • Since some areas of both towns are unpopulated (e.g., highways, airports, etc.), they were removed from the dataset (Figure 3) to ensure a more accurate analysis.

Key Findings

  • For the same population density, Reston’s green development correlates with better vegetative health and lower temperature averages.

  • This suggests that planned green spaces can mitigate urban heat effects and support ecological health.

Challenges & Methods

  • Data Disparity: NASA and GCIS data sets had different spatial resolutions, making direct comparisons difficult.

  • Solution: I used H3 hexagon spatial indexing, averaging values within each hexagon to standardize the data across both datasets.

  • Tools: Python, H3 indexing, geographic data visualization.

Conclusion

By using a spatially consistent method, I demonstrated that Reston’s urban planning positively impacts environmental sustainability. This study highlights the importance of integrating green infrastructure into urban development.

Figure 1: An instance of modern urban development lacking green space—correlating with low vegetative health and higher temperatures despite having the same population density as the neighboring town, Reston.

Figure 2: Demonstration of data compilation for both towns, with unpopulated areas removed for accurate analysis.