The AfriPop and AsiaPop projects:Mapping people, pregnancies and             births            Andy Tatem     University o...
To discuss•   Population mapping•   Added value•   What next?•   Mash-up questions
Intro to gridded population dataCensus data linked to GISadministrative boundaries  Ancillary data e.g.  Settlements, road...
www.afripop.org                  www.asiapop.orgAims: Build a database of freely-available, detailed and  contemporary spa...
Satellite-derived Census            Admin                                   settlements/land     Subnational, urban/rurald...
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 redis...
>11,000 settlements with pop from:                 UN-OCHA provided populationUnited Nations Development Programme        ...
Refugee/IDP spatial   data example
GRUMP
AfriPop 2010                                                                   C.     A.                                40...
www.asiapop.org
Mapping population            demography             Distribution of children                        under 5 yrs old in 20...
Age-specific fertility rates15-19 years                         35-39 years
Live births in 2010 per 100m   grid cell: 20-24 yr olds                Adjusted to match UN World              Population ...
Live births -> PregnanciesLive births 2010(UN-adjusted)                    Stillbirths = 3.6% of births                   ...
Pregnancies within X hours of        EmONC facilitiesPregnancies 2010                   Travel time to                   n...
Added value?
National estimates vs subnational                     % Population under                                5yrs old
National estimates vs subnational                   Areas >5hrs                   from nearest                   large    ...
National estimates vs subnational
National estimates vs subnational                                       Liberia:                                       tra...
What next?
Satellite-derived Census           Admin                                 Regression       Ancillary                       ...
Population mapping: regression trees       • Forest of regression trees ‘learns’         pop density model weightings     ...
Population mapping: urban growth                      • MODIS satellite urban                        mapping: 2000-2010   ...
Bayesian model-based geostatistics             • Approach to exploit               increasing use of GPS in               ...
Dynamic population mapping• Mapping so far: Static annual average  residential populations• Reality: Regular travel, seaso...
Mobile phone usage data  XUser makes a call   Call routed through   Network operatorfrom location X     nearest tower     ...
Regular, local         Seasonal             Displacement   Permanent  movements              migration                    ...
The Mash-up• Subnational information on fertility rates,  stillbirths, abortions? (SAE / Geostats roles?)• Mapping health ...
Acknowledgements Further information                                               www.afripop.org                        ...
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The AfriPop and AsiaPop projects: Mapping people, pregnancies and births

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This presentation was given at the technical mash-up meeting on "Mapping for Maternal and Newborn Health", hosted by ICS Integrare and the University of Southampton, with the support of the Norwegian Agency for International Development (NORAD) in Southampton (UK), 11-12th March 2013. Further details are available here http://integrare.es/?cat=33
“AfriPop" and "AsiaPop” systems were designed to build a database of freely available population distributions. This presentation describes the process of constructing these population distributions, which include data on pregnancies and births.
By Andy Tatem, University of Southampton.

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  • -A summary intro for those who don’t know the background of how gridded pop data is generally produced and used
  • The main AfriPop aims.
  • First step = Assemble a database of detailed, contemporary census data.For some countries (about 1/3), more recent official estimations were used.We need to match administrative units, in the form of spatial polygons, with population data, which can be tricky.
  • This quickly demonstrates the detail of settlement mapping from Landsat – links to next slide…
  • Shows settlements for all malaria endemic countries.
  • Output comparisons GRUMP vs AfriPop
  • Maps show the original 100 m resolution dataset constructed using the methods described here. (A) Whole Africa database. (B) Close-up for a region in South-East Nigeria. (C) Close-up for the Khartoum area, Republic of the Sudan.Adjusted to 2010 using UN urban and rural growth rates
  • The full AsiaPop website was launched earlier this year.
  • Describe how a variety of subnational datasets on age and sex compositions are brought together, encompassing 1000s of administrative units to give a unique picture of age/sex patterns in Africa. The subnational proportions are then used to adjust AfriPop population maps to enable mapping of any male/female five year age group.
  • Phone data slide, access plot
  • Data anonymized and aggregated to ensure individuals cannot be identified
  • The AfriPop and AsiaPop projects: Mapping people, pregnancies and births

    1. 1. The AfriPop and AsiaPop projects:Mapping people, pregnancies and births Andy Tatem University of Southampton
    2. 2. To discuss• Population mapping• Added value• What next?• Mash-up questions
    3. 3. Intro to gridded population dataCensus data linked to GISadministrative boundaries Ancillary data e.g. Settlements, roadsSpatial modeling rules todisaggregate census countsEstimates of number ofpeople in each grid cell
    4. 4. www.afripop.org www.asiapop.orgAims: 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
    5. 5. Satellite-derived Census Admin settlements/land Subnational, urban/ruraldatabase boundaries use growth ratesPopulation distributions UN national estimate adjustments Sub-nationalage/sex proportions Population distributions by age/sex HouseholdAdmin 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 databasespecific fertility rates Births Cost-distance model: travel time estimates Abortion, stillbirth rates Pregnancies Births, pregnancies, WOCBA access to services
    6. 6. Input population data: year/spatial detail GRUMP
    7. 7. Landsat Enhanced Thematic Mapper (ETM)
    8. 8. Landsat derived mapped settlements
    9. 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. 10. >11,000 settlements with pop from: UN-OCHA provided populationUnited Nations Development Programme estimates by district for the year 2011(UNDP), the German Agency for TechnicalCooperation (GTZ), the Kenya MedicalResearch Institute (KEMRI), the Food SecurityAnalysis Unit (FSAU), and the UN Office for theCoordination of Humanitarian Affairs (OCHA) UN High Commission for Refugees (UNHCR) refugee camp locations and sizes Landsat derived settlement extents 2005
    11. 11. Refugee/IDP spatial data example
    12. 12. GRUMP
    13. 13. AfriPop 2010 C. A. 400 AfriPop GRUMP GPW 300 LandScan RMSE% UNEP 200 B. 100 0 Mali Namibia Swaziland TanzaniaLinard et al (2012) PLoS ONE
    14. 14. www.asiapop.org
    15. 15. Mapping population demography Distribution of children under 5 yrs old in 2015Source of subnationalage/sex data Proportion of the population <5yrs old
    16. 16. Age-specific fertility rates15-19 years 35-39 years
    17. 17. Live births in 2010 per 100m grid cell: 20-24 yr olds Adjusted to match UN World Population Prospects national total estimates
    18. 18. Live births -> PregnanciesLive 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. 19. Pregnancies within X hours of EmONC facilitiesPregnancies 2010 Travel time to nearest health facility
    20. 20. Added value?
    21. 21. National estimates vs subnational % Population under 5yrs old
    22. 22. National estimates vs subnational Areas >5hrs from nearest large settlement
    23. 23. National estimates vs subnational
    24. 24. National estimates vs subnational Liberia: travel time to nearest health facilityNot accounting for subnational differences in demographiccomposition can result in significant differences in metrics
    25. 25. What next?
    26. 26. Satellite-derived Census Admin Regression Ancillary settlements/landdatabase boundaries tree mapping data use Urban growthPopulation distributions mapping/ simulation Sub-nationalage/sex proportions Population Dynamic population mapping distributionsAdmin boundaries by age/sex Women of childbearingSubnational, urban/r age: 5 yr groups ural age-specific fertility rates Births Bayesian model-based Abortion, stillbirth geostatistical mapping rates Pregnancies
    27. 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. 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. 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. 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. 31. Mobile phone usage data XUser makes a call Call routed through Network operatorfrom location X nearest tower records time and tower of call for billing YUser travels to Yand makes a call
    32. 32. Regular, local Seasonal Displacement Permanent movements migration migrationBharti, Tatem, Ferrari et al (2011) Science
    33. 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. 34. Acknowledgements Further information www.afripop.org www.asiapop.orgCatherine 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
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