Ujaval Gandhi

ujaval@spatialthoughts.com

Introduction to QGIS

Introduction to Gridded Population Datasets

CC BY-4.0

Population Datasets

• Population data is typically collected and aggregated by census blocks.
• Vector population datasets include census polygons with population counts.
• Limitations of Vector Population Datasets
• It is not possible to know the distribution of population within each polygon.
• It is not possible to get population of arbitrary regions.

Gridded Population Data

• Many applications require knowledge of distribution of population at finer scales than census blocks.
• One can disaggregate the population counts from census blocks to a uniform grid.
• Common techniques include modeling the population distribution based on landuse/landcover, building counts, satellite imagery etc.
• The output is a uniform grid with population counts. This is stored and distributed as a raster dataset.
• Raster data model allows for aggregating counts for any region.

Gridded Population Datasets

• GPW (Gridded Population of the World) - NASA SEDAC
• 1 km grid cells
• Available for 2000, 2005, 2010, 2015, and 2020
• GHSL (Global Human Settlements Layer)
• 250m grid cells
• Available from 1975, 1990, 2000, 2015, 2020, (2025, 2030)
• WorldPop
• 100m grid cells
• Available for each year from 2000 - 2020
• Facebook’s High Resolution Population Data [download link]
• 30m grid cells
• Available for 2019/2020
• Landscan Global
• 1km grid cells
• Available yearly from 2000-2022

Calculating Total Population

Aggregating Population in Regions

• Each pixel in a gridded population dataset represents the population count.
• To count the total population within a polygon, we can calculate the ‘sum’ of pixel values within that polygon.
• This analysis is known as ‘Zonal Statistics’.
• Zonal Statistics allows us to compute raster statistics in vector ‘zones’.

Processing Toolbox → Raster Analysis → Zonal statistics

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