gridded population of the world version 2: 1995 un adjusted population density gridded population...
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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)