Transcript No Slide Title

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
Accessibility as a predictor of
population density
Relationship between district-level mean
accessibility and population density - India
Population Density
100000
10000
1000
100
10
1
1
10
100
Mean Accessibility
1000
Access-based smart interpolation
(population potential)
town
node
transport network
di4
di2
4
current
node i
di3
di1
Vi   Pk f ( dik )
k 1
Distance decay
1
e
 d 2 / 2 2
0.6
1
d
0.4
1
d2
0.2
distance
33
29
25
21
17
13
9
5
0
1
weight
0.8
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
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 neverending 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
Estimated population densities
based on district
level totals
based on state
level totals
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 crosstabulate 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
0
1-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70+
Total
Poland - Urban Sex Ratios 1994
males per 100 females
85
90
more females
95
105
110
115
more males
0
1-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70+
Total
Poland - Rural Sex Ratios 1994
males per 100 females
85
90
more females
95
105
110
115
more males
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
GPW gridding
Administrative
unit
Santiago Rodriguez
Admin unit
density
(people / sq km)
Area of
overlap
(sq km)
Pop
Estimate
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