Personal explorations into climate change data, environmental trends, and sustainable urban development using open datasets and Python notebooks.
-
Passionate about using data to understand and address global challenges like climate resilience, urban heat, green spaces, and low-carbon cities.
-
Builds on my data analysis skills (EDA, anomaly detection, visualization) from other projects.
-
Focus: Reproducible analysis with public datasets — no proprietary tools.
-
climate_trends_exploration.ipynb→ Basic analysis of global temperature, CO₂ emissions, extreme weather (work in progress). -
More coming soon: Urban heat islands, sustainable city indicators, green space mapping, renewable energy transitions.
-
NASA / NOAA global temperature & CO₂ (GISS, NOAA NCEI)
-
World Bank Urban Indicators (population density, green space, emissions)
-
UN-Habitat City Prosperity Index / Urban Data
-
Copernicus / ESA satellite data for land use & urban expansion
-
Kaggle / Our World in Data collections on climate & sustainability
pip install -r requirements.txt
jupyter notebook
Or run notebooks directly in Google Colab (
Temperature: NASA GISS (https://data.giss.nasa.gov/gistemp/)
CO₂: NOAA (https://gml.noaa.gov/ccgg/trends/data.html)
Urban/sustainable cities: World Bank (https://data.worldbank.org/topic/urban-development), UN-Habitat (https://data.unhabitat.org/), or Kaggle "Sustainable Urban Planning" dataset.
Python · Pandas · Matplotlib/Seaborn/Plotly · Geospatial basics (GeoPandas if added) · Data Visualization · Time-Series Analysis
Inspired by open-source sustainability projects like Open Sustainable Technology (https://opensustain.tech/) and World Bank urban datasets.
Questions, ideas, or collabs? Open an issue or reach out!
Main profile: https://github.com/S33mi
Casual thoughts: @Seemi_Rauf on X 🌍🌿