High Resolution Population Density Maps

This dataset is external to the Digital Earth Africa platform.


Africa is the second most populous continent in the world and has a rapidly growing population. Population growth and its spatial distribution are important factors to be considered for sustainable development. For example, spatially distributed population and demographic information can be used to support infrastructure planning and understand potential impact of environmental changes and disasters on lives.

High Resolution Population Density Maps + Demographic Estimates by CIESIN and Meta provides population data at approximately 30 m spatial resolution, i.e. estimate of the number of people living within 30-by-30-meter grid cells. The dataset is created by Meta and the Center for International Earth Science Information Network (CIESIN) at Columbia University. It combines information from census and building identification from high resolution satellite imagery. In addition to overall population density, data are available in the following demographic categories: * Male * Female * Women of reproductive age (ages 15-49) * Children (ages 0-5) * Youth (ages 15-24) * Elderly (ages 60+)

More information on the method used can be found at https://dataforgood.facebook.com/dfg/docs/methodology-high-resolution-population-density-maps


This notebook will demonstrate how to load and visualize population data.

Getting started

To run this analysis, run all the cells in the notebook, starting with the “Load packages” cell.

Load packages

Import Python packages that are used for the analysis.

import rioxarray as rxr
import matplotlib.pyplot as plt

from deafrica_tools.plotting import display_map
/usr/local/lib/python3.8/dist-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.10.3-CAPI-1.16.1). Conversions between both will be slow.

The population data is hosted in AWS region us-east-1, therefor we set the AWS region to us-east-1 when running this notebook. The AWS region needs to be changed to af-south-1 to access DEAfrica data.

# set s3 region for HRSL data access
import os

Analysis parameters

This section defines the analysis parameters, including:

  • central_lat, central_lon, buffer: center lat/lon and analysis window size for the area of interest

The default location is in Johannesburg, South Africa.

# Set the central latitude and longitude.
central_lat = -26.2041
central_lon = 28.0473

# Set the buffer to load around the central coordinates.
buffer = 0.05

# Compute the bounding box for the study area.
xmin, xmax = central_lon - buffer, central_lon + buffer
ymin, ymax = central_lat - buffer, central_lat + buffer
bbox = (xmin, ymin, xmax, ymax)

View the selected location

The next cell will display the selected area on an interactive map. Feel free to zoom in and out to get a better understanding of the area you’ll be analysing. Clicking on any point of the map will reveal the latitude and longitude coordinates of that point.

display_map(x=(xmin, xmax), y=(ymin, ymax))
Make this Notebook Trusted to load map: File -> Trust Notebook

Loading genenral population data

The gridded population data are provided in Cloud-Optimized Geotiffs (COGs) covering 10 by 10 degree tiles. We will access the latest version of the COGs through a virtual raster (VRT).

By setting the chunks parameter, data is lazy-loaded as a dask array first.

ds_general = rxr.open_rasterio('s3://dataforgood-fb-data/hrsl-cogs/hrsl_general/hrsl_general-latest.vrt', chunks ={'x': 1000, 'y': 1000})
# load the data within area of interest

pop_general = ds_general.isel(band=0).sel(x=slice(xmin, xmax), y = slice(ymax, ymin)).compute()

Loading population data in different demographic categories

In the example below, we will load and visualize population for Youth (ages 15-24) and Women.

ds_youth = rxr.open_rasterio('s3://dataforgood-fb-data/hrsl-cogs/hrsl_youth_15_24/hrsl_youth_15_24-latest.vrt', chunks ={'x': 1000, 'y': 1000})
pop_youth = ds_youth.isel(band=0).sel(x=slice(xmin, xmax), y = slice(ymax, ymin)).compute()
ds_women = rxr.open_rasterio('s3://dataforgood-fb-data/hrsl-cogs/hrsl_women/hrsl_women-latest.vrt', chunks ={'x': 1000, 'y': 1000})
pop_women = ds_women.isel(band=0).sel(x=slice(xmin, xmax), y = slice(ymax, ymin)).compute()
fig, ax = plt.subplots(2, 2, figsize=(22, 16), sharey=True)

pop_youth.plot.imshow(ax=ax[0, 0], robust=True)
(pop_youth*100/pop_general).plot.imshow(ax=ax[0, 1], robust=True)

pop_women.plot.imshow(ax=ax[1, 0], robust=True)
(pop_women*100/pop_general).plot.imshow(ax=ax[1, 1], robust=True)

ax[0, 0].set_title("Youth population")
ax[0, 1].set_title("Percentage of youth population")

ax[1, 0].set_title("Female population")
ax[1, 1].set_title("Percentage of female population")
Text(0.5, 1.0, 'Percentage of female population')

Additional information

License: The code in this notebook is licensed under the Apache License, Version 2.0. Digital Earth Africa data is licensed under the Creative Commons by Attribution 4.0 license.

Contact: If you need assistance, please post a question on the Open Data Cube Slack channel or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with this notebook, you can file one on Github.

Compatible datacube version:

import datacube

Last Tested:

from datetime import datetime