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Experiences and Lessons▶
1. Project Planning and Management2. Geospatial Mapping and EA Database3. Enumeration Instruments and Tools4. Data Capture and Management5. Census Testing and Pilots6. Recruitment and Training7. Deployment and Supervision8. Enumeration and Logistics9. Quality Assurance and Risk Management10. Census Analysis and Products11. Partnerships and Collaboration12. Advocacy and Publicity13. Procurement and Financial Management14. Post-Enumeration Survey15. Alternative Approaches16. Planning Checklist
Case Studies▶
1. Project Planning and Management2. Geospatial Mapping3. Enumeration Instruments4. Data Capture5. Census Testing6. Recruitment and Training7. Deployment and Supervision8. Enumeration and Logistics9. Quality Assurance10. Census Analysis11. Partnerships12. Advocacy13. Procurement14. Post-Enumeration Survey15. Alternative Approaches16. Planning Checklist
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Implementation areas in a digital census​

Census preparations​

Geospatial population modelling techniques are used to support NSOs in preparation for censuses. By using recent georeferenced nationally representative household surveys and geospatial covariates, population modelling methods provide useful insights on the recent estimates of population to the NSOs thereby helping in resource allocation/management for a successful census.  Specifically, it helps to estimate the required number of digital census materials (e.g., tablets and smart phones) to be deployed especially in countries that have not had census for a long time.

Filling population data gaps in hard-to-count areas​

Despite the known advantages of a digital census, issues of missing data or incomplete census data could still arise, especially in contexts where individuals are unwilling to participate in the census activities or where there may be internet connectivity barriers. Such challenges are further exacerbated by conflicts and insecurity which makes it unfeasible for in-person assistance from the enumerators. In some cases, outright refusal to participate in the census processes also leads to population data gaps.

Disaggregation of population estimates to lower administrative units​

When digital census data is only available at coarser administrative units, geospatial statistical population modelling techniques, in particular top-down approach, could be used to disaggregate the population estimates to more spatially detailed small area scales. Here, Machine Learning techniques are used to provide the population density weights required to disaggregate the coarser units of population data, first to a 100m or 1km grid cell level before aggregating to the small area unit of interest.