🚀 Geospatial Data Scientist & Urban Intelligence Strategist
I use Python, GIS, and machine learning to make geographic and statistical data accessible, reproducible, and impactful.
My focus: urban accessibility, infrastructure equity, and visual storytelling.

📊 Analyzing Ethiopia’s population distribution and density at the Woreda and Region levels using resampled WorldPop data.

• ✅ Resampled massive 100m raster to 1km for computational feasibility
• ✅ Computed zonal statistics (population totals & mean density)
• ✅ Produced recruiter‑friendly bar charts and choropleth maps
  🔗 View the project here

📈 Statistical and geospatial analysis of Ethiopia Commodity Exchange (ECX) proxies.

• ✅ Built reproducible workflows for market data
• ✅ Applied statistical modeling and visualization
  🔗 View the project here

🗺️ Geospatial analysis at the Admin2 (Woreda) level.

• ✅ Cleaned and standardized administrative boundaries
• ✅ Computed zonal statistics for population and density
• ✅ Produced recruiter‑friendly maps with clear labeling
  🔗 View the project here

🏗️ Data science project plan using a concrete dataset.

• ✅ Designed a reproducible workflow for machine learning analysis
• ✅ Focused on recruiter‑friendly documentation and storytelling
  🔗 View the project here

• Data Science: Python, Pandas, Scikit‑learn, SQL
• Geospatial Analysis: GIS, Rasterio, Geopandas, Choropleths
• Visualization: Matplotlib, Seaborn, Clear labeling for non‑specialist audiences

• Applied data science for urban planning & accessibility
• Climate resilience and equitable infrastructure
• Reproducible workflows & recruiter‑friendly documentation