Gridded Population of the World

Version 2: 1995 UN adjusted population density

Gridded Population Workshop

May 2-3, 2000

GPW Version 2 Characteristics:

• Based on national and sub-national spatial and population data, best available or ‘affordable’

• Data set of quadrilateral grids (2.5’, or 20 km2 at

equator) that contain estimates of:– population (1990 and 1995, raw and adjusted)– land area (administrative unit area net of lakes)

• Six continents covered (Antarctica was not gridded)

Source Data CharacteristicsSummary of administrative levels

Administrative Level Frequency Cumulative % US equivalent0 47 21.1% Nation1 68 51.8% State2 88 91.4% County3 18 99.5% Tract4 1 100.0% BlockTotal 222

• Best available ‘matched’ data used (population and boundaries must match)

• Commercial, government and other institutional sources– over 100 sources

– roughly 40 data suppliers

Population Data Adjustments

• Annual rate of change calculated:

• Population estimates adjusted to

1990 and 1995:Px = P2 ert

Definitions

r Annual rate of growth

P1..2 Census estimate

t number of years between census enumerations

Px Year of Estimate (90 or 95)

Pun UN Estimate

Padj Adjusted estimate

tP

Pe

r

1

2log

Population Data Adjustments

Definitions

a Adjustment factor

Px Year of Estimate (90 or 95)

Pun UN Estimate

Padj Adjusted estimate

• Adjustment factor for matching national estimates to UN estimates calculated:

a = (Pun - Px) / Pun

• Adjustment factor applied at the national level :Padj = Px * a

Source Boundary Data

Administrative unit centroids shown for the approximately 127,000 units collected for GPW v2

Boundary Data Adjustments

• International boundaries and coastlines matched to the Digital Chart of the World (DCW):– completed without data loss– some countries left unmatched (e.g., SABE data)

• Lakes and ice from DCW added to boundary data

Gridding Algorithm

• Proportional allocation used to spread the population over grid cells

• Virtually all data work completed on vector data; gridding is the last step.

• National grids created, global grids assembled by adding national grids together– country grids are created with collars so that

they start and end on even degrees; therefore the assembly of the grids without interpolation is possible

Gridding Algorithm: Proportional Allocation

• Adjusted boundary data is unioned with a blank fishnet coverage of grid cells

• Resulting small polygons have areas and densities calculated

Gridding Algorithm

• Population densities (input and adjusted) are multiplied by the polygon areas to allocate population

• Result is a detailed coverage with population estimates for each polygon

• Population and area information are then gridded

Issues in Adjusting Population Data

• Variable quality source census data

• Timeliness of input data varies

• Additional demographic data are not available to improve estimates– available in sample surveys

• limited coverage (although tend to be strong where census data are weak)

• nationally or sub-nationally representative, but usually not representative beyond administrative level 2

Issues in Adjusting Boundary Data/Gridding

• Small rounding error introduced

• Variable quality and detail of input data degrades final product

• Process intensive (cpu and storage)

Possible Improvements

• Better source data (boundary and census)

• Additional spatial inputs, e.g.,:– parks (constraint)– roads, populated places (‘attractors’ in a

heuristic model)

• Additional population inputs, e.g.,:– Survey data

• Grid other variables– demographic, socioeconomic, etc

• Custom grids

Asia:Population density (1995 UN adjusted

values)

Africa:Population change (1990 - 1995)