Data Wrangling: Population and Air Quality Analysis
Population and Air Quality Analysis
From August to December 2024, our team conducted a Python-based data analysis to examine the relationship between population density and air quality in major cities across California and Texas. Using real-time API data and publicly available population statistics, we explored how urbanization affects air pollution.
Through data wrangling, visualization, and statistical analysis, we identified key insights, such as the influence of industrial activity and agriculture on air quality beyond population size alone. The project highlighted the importance of sustainable urban planning and the need for better data accessibility to improve environmental research.
Python Learning
February 4, 2025 at 8:03:53 AM
Population and Air Quality Analysis
From August 2024 to December 2024, our team worked on a data-driven project analyzing the relationship between population density and air quality in major cities across California and Texas. Using Python, we integrated real-time air quality data with population statistics to assess how urbanization impacts environmental conditions.
Project Scope
Objective: To determine the correlation between population size, density, and air quality metrics.
Data Sources:
Air Quality Data: Collected via a public API, focusing on 10 major cities in California and Texas.
Population Data: Gathered from publicly available datasets, cleaned and processed for analysis.
Key Steps and Approach
Data Wrangling:
Used Pandas for data cleaning and manipulation.
Merged air quality and population datasets for consistency.
Exploratory Data Analysis:
Applied Matplotlib and Seaborn to visualize trends and correlations.
Used NumPy for statistical calculations.
API Handling:
Managed API requests using Requests.
Handled rate limits and optimized data retrieval.
Geospatial Insights:
Leveraged Geopandas to map population density and AQI variations across cities.
Key Findings
Population density impacts air quality, but industrial activity and agriculture play significant roles.
Some high-density cities maintained better air quality, suggesting effective environmental policies.
The study reinforced the need for sustainable urban planning and industry regulations.
Impact & Insights
This project demonstrated how data science can provide actionable insights into environmental challenges. The findings support the idea that improving air quality requires a combination of policy enforcement, technological solutions, and sustainable practices.
Population and Air Quality Analysis
From August 2024 to December 2024, our team worked on a data-driven project analyzing the relationship between population density and air quality in major cities across California and Texas. Using Python, we integrated real-time air quality data with population statistics to assess how urbanization impacts environmental conditions.
Project Scope
Objective: To determine the correlation between population size, density, and air quality metrics.
Data Sources:
Air Quality Data: Collected via a public API, focusing on 10 major cities in California and Texas.
Population Data: Gathered from publicly available datasets, cleaned and processed for analysis.
Key Steps and Approach
Data Wrangling:
Used Pandas for data cleaning and manipulation.
Merged air quality and population datasets for consistency.
Exploratory Data Analysis:
Applied Matplotlib and Seaborn to visualize trends and correlations.
Used NumPy for statistical calculations.
API Handling:
Managed API requests using Requests.
Handled rate limits and optimized data retrieval.
Geospatial Insights:
Leveraged Geopandas to map population density and AQI variations across cities.
Key Findings
Population density impacts air quality, but industrial activity and agriculture play significant roles.
Some high-density cities maintained better air quality, suggesting effective environmental policies.
The study reinforced the need for sustainable urban planning and industry regulations.
Impact & Insights
This project demonstrated how data science can provide actionable insights into environmental challenges. The findings support the idea that improving air quality requires a combination of policy enforcement, technological solutions, and sustainable practices.