Post on 27-Jan-2015
description
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