The AfriPop and AsiaPop projects provide freely available, detailed spatial data on population distributions in Africa and Asia to support epidemiological modeling and health metrics. The projects map people, pregnancies and births at a high resolution by linking census and satellite data. They estimate populations in grid cells, map age/gender demographics, and model accessibility to health services. Future work may involve mobile phone data, health worker mapping, and projecting population changes out to 2030. The goal is to improve understanding of population dynamics and access to services over time and space.

1. The AfriPop and AsiaPop projects:
Mapping people, pregnancies and
births
Andy Tatem
University of Southampton

2. To discuss
• Population mapping
• Added value
• What next?
• Mash-up questions

3. Intro to gridded population data
Census data linked to GIS
administrative boundaries
Ancillary data e.g.
Settlements, roads
Spatial modeling rules to
disaggregate census counts
Estimates of number of
people in each grid cell

4. www.afripop.org www.asiapop.org
Aims: Build a database of freely-available, detailed and
contemporary spatial data on African/Asian population
distributions to support epidemiological modelling and
health metric derivation.
Initial focus:
1. Database of detailed, contemporary census data
2. Fine scale, accurate mapping of settlements
3. Sub-national mapping of age/gender structure
4. Low cost, easily updated

5. Satellite-derived
Census Admin
settlements/land Subnational, urban/rural
database boundaries
use growth rates
Population distributions
UN national estimate
adjustments
Sub-national
age/sex proportions Population
distributions
by age/sex Household
Admin boundaries Infrastructure
surveys:
, topography,
travel times,
land use data
Women of childbearing mode
Subnational, age: 5 yr groups
Friction Facility GPS
urban/rural age-
surface database
specific fertility rates
Births
Cost-distance model: travel
time estimates
Abortion, stillbirth
rates
Pregnancies Births, pregnancies, WOCBA
access to services

6. Input population data: year/spatial
detail
GRUMP

7. Landsat Enhanced Thematic Mapper (ETM)

8. Landsat derived mapped settlements

9. Redistributing census count data
• 80-90% population covered through mapped
settlements
• Remaining rural populations redistributed by
land cover specific weights
• 5 countries with detailed census data
spanning range of ecological zones used to
derive empirical weights

10. >11,000 settlements with pop from: UN-OCHA provided population
United Nations Development Programme estimates by district for the year 2011
(UNDP), the German Agency for Technical
Cooperation (GTZ), the Kenya Medical
Research Institute (KEMRI), the Food Security
Analysis Unit (FSAU), and the UN Office for the
Coordination of Humanitarian Affairs (OCHA)
UN High Commission for Refugees (UNHCR)
refugee camp locations and sizes
Landsat derived settlement
extents 2005

11. Refugee/IDP spatial
data example

12. GRUMP

13. AfriPop 2010
C.
A.
400
AfriPop
GRUMP
GPW
300
LandScan
RMSE%
UNEP
200
B.
100
0
Mali Namibia Swaziland Tanzania
Linard et al (2012) PLoS ONE

14. www.asiapop.org

15. Mapping population
demography
Distribution of children
under 5 yrs old in 2015
Source of subnational
age/sex data Proportion of the
population <5yrs old

16. Age-specific fertility rates
15-19 years
35-39 years

17. Live births in 2010 per 100m
grid cell: 20-24 yr olds
Adjusted to match UN World
Population Prospects national total
estimates

18. Live births -> Pregnancies
Live births 2010
(UN-adjusted) Stillbirths = 3.6% of births
(http://www.who.int/pmnch/media/news/201
1/stillbirths_countryrates.pdf)
+ Abortions = 28 per 1000 women age 15-44
(http://www.guttmacher.org/pubs/journals/Se
dgh-Lancet-2012-01.pdf)
Pregnancies 2010
=

19. Pregnancies within X hours of
EmONC facilities
Pregnancies 2010
Travel time to
nearest health
facility

20. Added value?

21. National estimates vs subnational
% Population under
5yrs old

22. National estimates vs subnational
Areas >5hrs
from nearest
large
settlement

23. National estimates vs subnational

24. National estimates vs subnational
Liberia:
travel time to
nearest
health facility
Not accounting for subnational differences in demographic
composition can result in significant differences in metrics

25. What next?

26. Satellite-derived
Census Admin Regression Ancillary
settlements/land
database boundaries tree mapping data
use
Urban growth
Population distributions mapping/
simulation
Sub-national
age/sex proportions Population Dynamic population mapping
distributions
Admin boundaries by age/sex
Women of childbearing
Subnational, urban/r age: 5 yr groups
ural age-specific
fertility rates
Births
Bayesian model-based
Abortion, stillbirth geostatistical mapping
rates
Pregnancies

27. Population mapping: regression trees
• Forest of regression trees ‘learns’
pop density model weightings
• Enables inclusion of a variety of
types of spatial dataset
• Substantial accuracy improvements

28. Population mapping: urban growth
• MODIS satellite urban
mapping: 2000-2010
• Boosted regression tree
spatial urban growth
simulation model: 2010-2030
Observed urban Predicted urban
growth 1990-2000 growth 1990-2000
Casablanca,
Morocco

29. Bayesian model-based geostatistics
• Approach to exploit
increasing use of GPS in
national household surveys
• Space-time models with
structured relationships with
covariates
• Rigorous handling of
uncertainty

30. Dynamic population mapping
• Mapping so far: Static annual average
residential populations
• Reality: Regular travel, seasonal migration,
displaced populations
• Redefine travel times/catchment areas/facility
network improvement beyond static pictures
• Built on cutting edge data and methods

31. Mobile phone usage data
X
User makes a call Call routed through Network operator
from location X nearest tower records time and tower
of call for billing
Y
User travels to Y
and makes a call

32. Regular, local Seasonal Displacement Permanent
movements migration migration
Bharti, Tatem, Ferrari et al (2011) Science

33. The Mash-up
• Subnational information on fertility rates,
stillbirths, abortions? (SAE / Geostats roles?)
• Mapping health workers?
• Models for projecting 10, 20 yrs ahead?
• Comprehensive, accurate and contemporary
geolocated health facility datasets?
• Quantify/map seasonal differences in access to
services?
• Quantify/map rapidly changing population
distributions?

34. Acknowledgements Further information
www.afripop.org
www.asiapop.org
Catherine Linard, Andrea Gaughan, Forrest
Stevens, Zoe Matthews, Jim Campbell,
Pete Gething, Marius Gilbert, Dave Smith,
Amy Weslowski, Caroline Buckee, Carla
Pezzulo, Nita Bharti, Bryan Grenfell, Clara www.ameripop.org
Burgert
E-mail: A.J.Tatem@soton.ac.uk