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BackgroundPurposeRationaleStructureTarget Audience
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
Recommendations
Resources
Checklist
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Challenges and Lessons Learnt​

  • The use of modelled data does not replace the census process. Instead, modelled estimates are a supplement to a more traditional census by filling gaps in the data, and providing high-resolution estimates during the intercensal period.

  • Modelled population estimates are probabilistic. They are model-based and should therefore not be treated in the same way as true population counts. Instead, they are an opportunity to refine census planning when the most recent census is largely outdated, or fill gaps where a full enumeration cannot be delivered.

  • Producing modelled population estimates relies on the availability of household surveys or microcensus data to provide the statistical input to the models. The higher the quality of the statistical input data, the higher the quality of the modelled outputs will be, and the smaller the measure of uncertainty around the estimates.

  • Producing high-resolution gridded population estimates requires access to a number of geospatial covariate datasets. This is best delivered through data sharing across different parts of government, where a national mapping agency and/or national space agency may provide access to authoritative geospatial datasets that can be used in the modelling process.

  • Geospatial boundaries (administrative or census) should be maintained without topographic errors (such as slivers) to support modelling. The process will allocate points (households, buildings, addresses etc.) to geographic areas, so any topographic errors will lead to points being allocated to either the wrong area, or no area.

  • Modelling large geospatial and statistical datasets requires significant processing capacity, and consideration needs to be given to the processing environment in advance of any modelling activity. Recommendations on the hardware specifications are included in the recommendations section of this chapter.