Mapping Human Footprints; Kenya’s Population Density

If you’ve ever looked at a satellite image of Kenya at night, you’ve seen the clusters of light that define where people live, work, and gather. For my second deep-dive into geospatial visualization, I wanted to turn that “glow” into data. Using population datasets from the Joint Research Centre (JRC), I mapped out the distribution of Kenya’s population to see exactly where the country’s heart beats the loudest.

The Build: How the Map Came to Life Creating a clean, publication-ready density map requires more than just plotting points; it’s about managing scale and contrast. Here’s a quick breakdown of the workflow I used in Python:

Handling the Big Data: I started with high-resolution raster data (GeoTIFF). Because these files can be massive, I used rasterio to mask and crop the data specifically to Kenya’s national boundaries.

Coordinate Alignment: To ensure the population data lined up perfectly with the administrative borders, I reprojected the datasets to a consistent CRS (Coordinate Reference System).

Color Scaling with Logarithms: Population density varies wildly—from nearly empty arid lands to thousands of people per square kilometer in Nairobi. I used a Logarithmic Scale for the colors. This prevents the high-density cities from “washing out” the rest of the map, allowing us to see variations in rural areas too.

Styling for Impact: I chose a dark theme with a “magma” or “inferno” style palette. The bright yellows represent high-density hubs, while deep purples and blacks represent the vast, sparsely populated regions.

Visualizing the Results

The resulting image is a striking look at the “human geography” of Kenya. Against a deep black background, the country’s borders are outlined by the intensity of its inhabitants.

The Hubs: You can immediately spot Nairobi and Mombasa as brilliant beacons of yellow.

The Highlands & Lake Victoria: A thick, bright “crescent” of activity stretches from the central highlands through the Rift Valley towards the Lake Victoria basin (Kisumu), showing the high agricultural and urban density in these fertile regions.

The Arid North: In contrast, the northern and eastern parts of the country appear as vast, dark expanses, punctuated only by small, isolated dots of light representing key towns like Lodwar or Garissa.

This map doesn’t just show where people are; it tells a story of climate, history, and infrastructure. It was a rewarding challenge to transform raw numbers into a visual narrative of a nation.

Technical Stack: Python, Geopandas, Rasterio, Matplotlib. Data: European Commission JRC (GHSL) & FAO (GAUL).

code: Visit my GitHub