Global and continental population databases “Supply side view”

1. Global and continental population databases“Supply side view” • What has been done • Related developments • Possible next steps

2. Population data in raster format • Gridding pop data is not a new idea • Population map of West Africa (John Adams, LSE 1968) • Statistical Offices (e.g., Japan, Sweden) • Population Atlas of China • ... • Individual country or regional level • Methods not well-documented • Mostly not available in digital form

3. Continental / global data sets • BUCEN’s CIR database • Africa (UNEP/GRID, 1991) • Global Demography Project (NCGIA & CIESIN, 1994) • 1 degree global grid (Environment Canada, 1995) • Europe (RIVM, 1995) • Africa update and Asia (NCGIA, UNEP/GRID & WRI, 1996) • Latin America (CIAT) • Landscan (ORNL, 1999) • GPW II (CIESIN, 2000)

4. Continental / global data sets • Data collection focused • Cartographic models - pycnophylactic interpolation, dasymetric mapping • “Smart interpolation” • adjustment factorsbased on auxiliaryGIS data layers • accessibility basedweighting

5. Accessibility as a predictor of population density

6. Access-based smart interpolation(population potential)

7. Distance decay

8. Related developments - source data • Initial data sets and applications have created large demand for these types of data (gridded and small area data) • National statistical offices are adopting GIS for census mapping; in developing countries supported by UNSD and donors • Availability of national and regional high resolution and high quality databases; NSOs, CIESIN - China & Mexico, ACASIAN, MEGRIN

9. Related developments - modeling • Innovative modeling approaches • Kernel estimation • Fractal cities • Behavioral models (settlers) • NASA/USGS work on land cover change / urban growth patterns • ... • New global data sets that can support population modeling • USGS elevation and land cover data • NOAA “city lights” • WCMC protected areas • ...

10. Next steps • Accuracy assessment of existing data sets • User survey • who benefits from these data? • can we get better feedback from users? • do current data sets address expressed needs? • is it worth the cost?

11. Improve quality of source data • Largest quality improvements will come from better input data, not from modeling improvements • Collection of pop figures and boundary data is a never-ending task (e.g., 2000 round data available soon) • Improve base pop estimates - extrapolation to common base year, recent pop displacements • For boundaries: focus on highest possible resolution or on best possible positional accuracy? • Identify new and improve existing auxiliary data sets

12. GPW II - Europe

13. Improve smart interpolation methods • Calibration of parameters! • currently determined ad hoc, but should be based on observed patterns (both accessibility and other auxiliary factors) • adjustment factors should be determined statistically • importance of factors unlikely to be constant across countries • accuracy assessment

14. Estimated population densities based on district level totals based on state level totals

15. Improve smart interpolation methods • Make more explicit use of city information • location and size of many cities available • urban extent approximated by “city lights” data • may address urban / rural issue better than official statistics

16. UNSD cities over 100,000 inhabitants

18. Resolve modeling issues • Potential circularity • e.g., for environmental applications, can’t use land cover data to predict pop distribution, if users will then cross-tabulate pop with land cover types • but for “pop at risk” studies (e.g., health, disaster response) we might want to use any available meaningful auxiliary factors • family of data sets?

19. Resolve modeling issues • What is an appropriate output resolution? • average GPW admin unit resolution is 33 km, average area is about 1070 sq. km • pixel size is 2.5 min, or about 4.6 km at equator with an area of about 21 sq. km • so “modeling ratio” is about 50 output cells per admin unit • but large variability across countries (resolution) • Switzerland 3.7 • Luxembourg 4.7 • … • Chad 302.8 • Saudi Arabia 374.2 • Same with population per unit (1.5 thousand to 3.4 million)

20. Resolve institutional issues • Coordination between groups • pool input data sources • agree on coding schemes (FAO proposal) • division of tasks • Get endorsement from National Statistical Offices and UN • Determine distribution status of admin boundaries • Funding plans

21. Expand scope of database • Time series / projections or scenarios • Rural / urban • Demographic components (age-sex) • Living standards • High resolution databases for specific regions/countries • Work closer with application projects

24. Small area statistics from survey data(poverty indicators)

25. Poverty maps for Ecuador

26. Clarke and Rhind 1991 • Variety of databases with different levels of spatial resolution • made compatible with gridded data • no more than a few years out of date • time series of data for different resolutions • ability to distribute freely for scientific purposes

28. GPW gridding

29. GPW gridding