Global and continental population databases

“Supply side view”

• What has been done

• Related developments

• Possible next steps

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

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)

Continental / global data sets

• Data collection focused

• Cartographic models - pycnophylactic interpolation,

dasymetric mapping

• “Smart interpolation”

– adjustment factors

based on auxiliary

GIS data layers

– accessibility based

weighting

Relationship between district-level mean accessibility and population density - India

1

10

100

1000

10000

100000

1 10 100 1000

Mean Accessibility

Po

pu

lati

on

Den

sity

Accessibility as a predictor of population density

di4

di3

di1

di2

town

currentnode i

node

transport network

V P f di k ik

k

( )

1

4

Access-based smart interpolation(population potential)

0

0.2

0.4

0.6

0.8

11 5 9 13 17 21 25 29 33

e d 2 22/

12d

1

d

we

ight

distance

Distance decay

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

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

– ...

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?

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

GPW II - Europe

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

based on districtlevel totals

based on statelevel totals

Estimated population densities

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

UNSD cities over 100,000 inhabitants

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?

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)

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

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

45-49 50-54 55-59 60-64 65-69

70+ Tota l

P oland - U rban S ex R atios 1994

20-24 25-29 30-34 35-39 40-44

0 1-4 5-9 10-14 15-19

115110105959085

m ales per 100 females

m ore females m ore m ales

45-49 50-54 55-59 60-64 65-69

70+ Tota l

P oland - R ura l S ex R a tios 1994

20-24 25-29 30-34 35-39 40-44

0 1-4 5-9 10-14 15-19

115110105959085

m ales per 100 females

m ore females m ore m ales

Small area statistics from survey data(poverty indicators)

Poverty maps for Ecuador

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

GPW gridding

Administrativeunit

Admin unitdensity

(people / sq km)

Area ofoverlap(sq km)

PopEstimate

Santiago Rodriguez 64.2 5.3 340

Santiago 246.5 2.2 542

San Juan 75.9 12.8 972

Total for cell 91.3 20.3 1854

GPW gridding