Global and continental population databases

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Global and continental population
databasesSupply side view

• What has been done
• Related developments
• Possible next steps

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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

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Continental / global data sets

• BUCENs 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)

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Continental / global data sets

• Data collection focused
• Cartographic models - pycnophylactic
  interpolation, dasymetric mapping
• Smart interpolation
• adjustment factorsbased on auxiliaryGIS data
  layers
• accessibility basedweighting

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Accessibility as a predictor of population density
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Access-based smart interpolation(population
potential)
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Distance decay
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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

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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
• ...

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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?

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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

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GPW II - Europe
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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

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

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UNSD cities over 100,000 inhabitants
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Resolve modeling issues

• Potential circularity
• e.g., for environmental applications, cant 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?

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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)

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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

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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

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Small area statistics from survey data(poverty
indicators)
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Poverty maps for Ecuador
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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

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GPW gridding
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GPW gridding
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