The AfriPop and AsiaPop projects: Mapping people, pregnancies and births

Andy TatemUniversity of Southampton

To discuss

• Population mapping • Added value• What next?• Mash-up questions

Intro to gridded population dataCensus 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

www.afripop.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 data2. Fine scale, accurate mapping of settlements3. Sub-national mapping of age/gender structure4. Low cost, easily updated

www.asiapop.org

Census database

Admin boundaries

Satellite-derived settlements/land

use

Population distributions

Population distributionsby age/sex

Sub-national age/sex proportions

Admin boundaries

Women of childbearing age: 5 yr groupsSubnational,

urban/rural age-specific fertility rates

Births

Abortion, stillbirth rates

Pregnancies

Subnational, urban/rural growth rates

UN national estimate adjustments

Infrastructure, topography, land use data

Household surveys:

travel times, mode

Friction surface

Cost-distance model: travel time estimates

Facility GPS database

Births, pregnancies, WOCBA access to services

Input population data: year/spatial detail

GRUMP

Landsat Enhanced Thematic Mapper (ETM)

Landsat derived mapped settlements

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

>11,000 settlements with pop from:United Nations Development Programme (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-OCHA provided population estimates by district for the year 2011

Landsat derived settlement extents 2005

UN High Commission for Refugees (UNHCR) refugee camp locations and sizes

Refugee/IDP spatial data example

GRUMP

Linard et al (2012) PLoS ONE

AfriPop 2010A.

B.

C.

Mali Namibia Swaziland Tanzania

AfriPopGRUMPGPWLandScanUNEP

RM

SE

%

01

00

20

03

00

40

0

www.asiapop.org

Proportion of the population <5yrs old

Source of subnational age/sex data

Mapping population demographyDistribution of children under 5 yrs old in 2015

Age-specific fertility rates

15-19 years35-39 years

Live births in 2010 per 100m grid cell: 20-24 yr olds

Adjusted to match UN World Population Prospects national total

estimates

Live births -> Pregnancies

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)

+

=

Live births 2010 (UN-adjusted)

Pregnancies 2010

Pregnancies within X hours of EmONC facilities

Pregnancies 2010

Travel time to nearest health facility

Added value?

National estimates vs subnational% Population under

5yrs old

National estimates vs subnational

Areas >5hrs from nearest large settlement

National estimates vs subnational

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

What next?

Census database

Admin boundaries

Satellite-derived settlements/land

use

Population distributions

Population distributionsby age/sex

Sub-national age/sex proportions

Admin boundaries

Women of childbearing age: 5 yr groupsSubnational,

urban/rural age-specific fertility rates

Births

Abortion, stillbirth rates

Pregnancies

Ancillary data

Regression tree mapping

Urban growth mapping/simulation

Dynamic population mapping

Bayesian model-based geostatistical mapping

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

Population mapping: urban growth• MODIS satellite urban

mapping: 2000-2010• Boosted regression tree

spatial urban growth simulation model: 2010-2030

Observed urban growth 1990-2000

Predicted urban growth 1990-2000

Casablanca, Morocco

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

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

Mobile phone usage data

User makes a call from location X

User travels to Y and makes a call

X

Y

Call routed through nearest tower

Network operator records time and tower of call for billing

Bharti, Tatem, Ferrari et al (2011) Science

Regular, local movements

Seasonal migration

Displacement Permanent migration

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?

Further information

www.afripop.org

www.asiapop.org

E-mail: A.J.Tatem@soton.ac.uk

www.ameripop.org

Acknowledgements

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 Burgert