1. Remote Sensing Technology Complementing Official Statistics Population Density Mapping in Afghanistan
Paris21 Marketplace
05 April 2017
Tapiwa Jhamba, PhD
United Nations Population Fund

2. What is it?
An innovative methodology to estimate population counts for a country where a full census is not feasible
Statistical modelling requires some data from surveys, GIS data and satellite imagery
Partnership of UNFPA, WorldPop/Flowminder, and Afghanistan National Statistics Office
The last census of Afghanistan was done in 1979, and security concerns have prevented a more recent one.
Current estimates are mostly based on the 1979 census as the baseline population;
Since 2011 CSO has been conducting a form of rolling census, the Socio-Demographic and Economic Survey (SDES) which includes enumeration for 50% of households (as of this date, the survey already covered 12 out of 34 provinces);
UNFPA Country Office in Afghanistan received a request from President Ghani to help in mapping and estimating populations for election planning support.
Under the leadership of UNFPA, we collaborated with Flowminder to generate population estimation based on satellite images.

3. Generates overall population counts and density data.
How does it lead to strengthening national statistical capacity, and/ contribute to strengthening the SDGs and 2030 Agenda?
The last census in Afghanistan was conducted in 1979 and current population estimates are mostly based on that baseline;
Generates overall population counts and density data.
These can be used to generate SDGs - ~ 96 of indicators require some form of population data
Objective
To generate population counts disaggregated by age and sex at district level for entire country;
Data sources
Survey data (SDES and micro census), GIS data and Satellite imagery;
Population estimation
The mapping of the population of Afghanistan was done by combining satellite imagery, other remote sensing data, socio-demographic survey data, urban data, and GIS statistical modelling.
Statistical modelling was used to estimate population counts for areas with no population data - “fill in the gaps”

4. How does it work? Core Steps:
Enumeration of population with ground-based surveys (SDES & micro-census)
Prediction of EA population in un-surveyed areas using statistical models integrating population survey data and ancillary data sets (GIS and satellite imagery)
Spatial disaggregation of population estimates
Output dataset includes:
Population counts at Enumeration Area level;
100mx100m gridded population estimates;
Population estimates disaggregated by sex and large age groupings