How to Map Population Density Using Python + WorldPop (Full Guide) 📊
How do you actually map population density at scale? In this video, I break down the complete GIS workflow for creating a high-resolution population density map using Python, WorldPop data, and a 500m analysis grid.
You’ll learn exactly how to load population rasters, clip areas of interest, build fishnet grids, run zonal statistics, and calculate both total population and density per grid cell. Whether you're working on urban planning, smart cities, disaster response, utilities, or GeoAI, this workflow works for any city or region in the world.
Area of Intrest : How crowded is Indirapuram, really? In this video, I break down the complete Population Density Mapping workflow using Python, WorldPop 2025, and GIS zonal statistics to reveal the real numbers behind one of NCR’s busiest residential hubs.
In this video, you’ll learn:
How Indirapuram’s population is distributed across 195 grid cells
Why some clusters exceed 30,000–38,000 people per km²
How to compute density using Python, GeoPandas, Rasterio, and RasterStats
How to read hotspot maps and interpret real urban crowding
How to use this workflow for any other city in India or globally
This workflow is widely used in urban planning, smart cities, disaster response, electric/water utilities, and GeoAI analytics.
If you want the full notebook + code + maps, grab it here 👇
📂 GitHub: [https://github.com/saswat97/GIS_Small...]
If you want to check the full coding video of it , please check
• Population Density Mapped: What 500m Grids...
If you want more GIS + Python projects like this, let me know in the comments!
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📚 Tools Used
Python (GeoPandas, Rasterio, RasterStats) • WorldPop 2025 • Folium • Contextily • Google Colab
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