AQI Visualization for Oregon#

Python Cartopy Pandas xarray NumPy Seaborn Matplotlib GeoPandas

01/2023 – current

Overview#

Analyzed satellite data on carbon dioxide levels and air quality index (AQI), visualizing spatial distribution and time-series trends across Oregon counties using Python.

  • Mapped CO2 values to counties throughout Oregon using satellite NetCDF data and shapefiles

  • Cleaned and integrated air quality, temperature, and wind data from multiple sources

  • Developed custom functions for plotting geospatial data

  • Created interactive visualizations with Jupyter ipywidgets

AQI spatial distribution map

Air Quality Index spatial distribution across Oregon counties.#

Methodology

The project processes satellite CO2 data from NetCDF files alongside EPA air quality index measurements. County-level shapefiles enable geographic mapping of environmental indicators.

Data Sources:

  • Satellite CO2 data (NetCDF format)

  • EPA daily AQI by county (2021–2022)

  • Daily temperature and wind data for Oregon counties

  • PM 2.5 measurements for Oregon (1999)

Processing Pipeline:

  1. Load and parse NetCDF satellite data with xarray

  2. Clean and merge EPA AQI tabular data with Pandas

  3. Join environmental data to county geometries via GeoPandas

  4. Generate choropleth maps and time-series plots

Technologies#

Category

Tools

Data Processing

NumPy, Pandas, xarray, netCDF4

Visualization

Matplotlib, Seaborn, Cartopy

Geospatial

GeoPandas, Shapely

Interactive

Jupyter ipywidgets

View on GitHub

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