The document describes using satellite imagery and microcensus data to model population estimates in Nigeria down to the settlement level in order to more accurately plan vaccination campaigns. It finds that existing administrative boundaries, census projections, and survey samples are often inaccurate. High resolution population mapping is able to detect variations in population growth rates between urban and rural areas that national projections miss. A microcensus validation exercise found the granular GIS estimates to more closely match the actual population of a ward than aggregate data.
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IMED 2018: Modeled Population Estimates from Satellite Imagery and Microcensus Data
1. Modeled Population Estimates
from Satellite Imagery and
Microcensus Data
Vince Seaman
Sr. Program Officer, Polio
Global Development Division
Bill & Melinda Gates Foundation
1
9. Settlement Total U5 Houses Total U5
UNG MAIGADI 5055 1011 45 369 75
State Master List GIS Est
Kaduna – Birnin Gwari LGA
Other Use Cases for
Remote Sensing
Resources
Can we validate local
administrative
population data?
• Census data unavailable below
Admin 1 level
• Inflated population counts
result in vaccine, bed net, and
other critical supply shortages
• Validation tool needed
500 people or
5000 people?
11. November 14, 2018
Demographics &
Mobility mapping
Andy Tatem, University of Southampton
Total Population: 170,123,740
(July 2012 estimate)
Administrative units - 774
The Solution: High Resolution
Population Distribution
In Northern Nigeria
BudhendraBhaduri
EddieBright,AnilCheriyadat, AmyRose,JakeMcKee,Jeanette
Weaver,MaryUrban,RajuVatsavai
12. Neighborhood Housing Characteristics Give Different LINE PATTERNS
Local line patterns a good descriptor of the spatial arrangements. Line statistics can representative of structural dimensions
13. All Urban Areas Have 4-6
Neighborhood Types
- Based on size, shape, and
orientation of structures
- Neighborhood type is related to
building use: residential,
commercial, mixed-use, etc.
Local geospatial
neighborhoods are
represented using rich
feature descriptors
composed of edge,
texture, lines and
spectral attributes
14. Managed by UT-Battelle
for the Department of Energy
Neighborhood Classification Scheme – N. Nigeria
Neighborhood Type Layer for Nigeria (based on Kano metro area)
– established 7 residential settlement types (6 Urban, 1 rural) + non-residential
Population density of each neighborhood type determined
from microcensus data (>100 clusters for each type)
M: rural
Z: non-residential
Slums
Slums
16. GIS Population Model Microcensus Methods – Middle and Southern States
Middle/South Total = 1600 clusters in 8 statesMicrocensus Methods:
• All buildings assigned to one of 8 neighborhood
types (Z= non-residential)
• 200 polygons selected randomly for each state
representing all neighborhood types in that state
equally (approx. 10,000 HH/state)
• Each polygon contains approx. 50 residential
structures
• Microcensus team obtains total population and the
U5 count for each HH in polygon
• Decision for which microcensus data is used for
which state based on proximity and demographics
18. 197,696,904 196,477,453
NPC 2017
Census
GIS Estimates
Adjusted, 2017
GIS Estimates Nearly Identical to Census Projections at the National level
What explains the differences at
the sub-national levels?
Census projections assume:
- states grow at a similar rate
(2.7 – 3.4%/yr)
- all LGAs in a state grow at the
same rate.
GIS estimates indicate that yearly
state growth rates range from -
2.8% (Bayelsa) to 16.3% (FCT).
Differences between GIS estimates and Census projections
increase in magnitude from National Local levels
Potential Impact for Polio:
National GIS U5 Estimate = 31,715,584
Mar 2017 NID: Total OPV = 59,445,512
19. 19
Pending
• Haiti – Population estimates and settlement features for entire country;
settlement names and POIs limited to existing data sources.
• DRC – Population estimates, settlements/POIs, and Health Zone catchment
areas for Kinshasa & Bandundu. Bas Congo in process.
• Cameroon – Settlement features for entire country; 80% of settlement
names and POIs collected, population estimates in-process.
• Other GRID3 countries to be completed in 2018: Ethiopia, Tanzania, Zambia
http://geopode.world/
21. SELECT USER-DEFINED BUFFER AROUND A POINT
21
Retrieving settlement names and estimated
population/target population using a 2km buffer
around a Health Facility
Other Potential Output Columns:
• H2R/Outreach Settlement? Y/N
• Target Pop: <12mos, <15 years
• Vaccine/Supply requirements
22. Custom polygons can be drawn for catchment areas
Delineating team day areas to estimate resource requirements22
Architecture
http://geopode.world/
24. GIS estimates use U5 population modeled from DHS, MICS and other survey data
Alegana, et al. 2015 http://rsif.royalsocietypublishing.org/
Nigeria uses 20% of total population to estimate the U5 target, but the modeled range is 11 – 23%
MOST COUNTRIES USE A FIXED % FOR KEY DEMOGRAPHIC GROUPS, BUT
THE ACTUAL VALUES VARY WIDELY State %U1 %U5 %U15
Abia 2.5% 13.2% 37.9%
Adamawa 2.9% 18.5% 52.2%
Akwa Ibom 3.2% 13.1% 36.1%
Anambra 2.3% 14.0% 39.7%
Bauchi 3.6% 20.7% 54.2%
Bayelsa 3.5% 14.7% 38.9%
Benue 2.5% 15.3% 44.8%
Borno 3.2% 21.6% 56.5%
Cross River 2.9% 13.3% 37.2%
Delta 2.8% 14.2% 38.8%
Ebonyi 2.4% 14.4% 42.4%
Edo 2.1% 13.0% 36.6%
Ekiti 2.0% 12.2% 35.4%
Enugu 2.2% 14.3% 41.2%
Fct, Abuja 2.2% 15.0% 41.2%
Gombe 3.1% 19.8% 53.1%
Imo 2.5% 13.9% 39.5%
Jigawa 3.6% 22.5% 58.2%
Kaduna 2.9% 18.3% 48.9%
Kano 3.1% 21.1% 54.3%
Katsina 3.3% 21.1% 54.8%
Kebbi 3.4% 19.6% 52.2%
Kogi 2.0% 14.3% 41.9%
Kwara 2.2% 13.3% 37.9%
Lagos 2.0% 13.0% 35.2%
Nasarawa 2.5% 15.7% 44.4%
Niger 3.1% 17.2% 47.1%
Ogun 1.9% 13.3% 38.4%
Ondo 2.1% 13.1% 38.2%
Osun 1.7% 12.6% 37.2%
Oyo 1.8% 12.5% 36.3%
Plateau 2.4% 16.5% 46.0%
Rivers 3.0% 13.4% 36.0%
Sokoto 3.6% 20.7% 52.8%
Taraba 2.7% 16.2% 47.0%
Yobe 3.7% 22.2% 57.2%
Zamfara 3.2% 20.7% 55.4%
National Average 2.7% 16.9% 46.0%
Nigeria Official % 4.0% 20.0% 47.6%
GIS Modeled % U1, U5 and U15
25. Administrative Boundaries
Nearly all existing data is inaccurate !
VTS
Boundary
Published
Census
Boundary
Metro LGAs
Reported sub-national
boundaries do not align
with GIS data collected
in Nigeria
26. New Polio “Operational”
Boundaries (VTS), GADM*
and UN-WHO (Census) all
Differ
Gwale LGA, Kano State
Jan 2015
VTS
Boundary
GADM
Boundary
UN/Census
Boundary
*GADM = internationally-recognized global boundary
resource developed by Robert Hijmans & colleagues at
the University of California, Berkeley and the University
of California, Davis (Alex Mandel)
http://www.gadm.org/
“Official”
Boundaries
Inaccurate?
27. GIS Population Estimates: VTS, GADM1, UN-WHO Boundaries
1GADM Version 2.8, March 2016. http://www.gadm.org/
VTS Boundaries
Pop. Est. = 678,198
GADM Boundaries
Pop. Est. = 372,703
UN-WHO (Census) Boundaries
Pop. Est. = 484,934
Gwale LGA, Kano State, Nigeria
Use of incorrect
boundaries impacts
population estimates
29. Managed by UT-Battelle
for the Department of Energy
Calculated Rates of Annual Population Change for Both Methods (2006-2014)
0
1
2
3
4
5
6
7
8
9
Prorating
Modeling
(Census Projections)
(GIS Estimates)
30. Z = Non-ResidentialNeighborhood Types - Kano Metro Area
Cluster Survey Samples –
Are they representative?
2016 National HH Survey Cluster
locations Kano Metro LGAs
Survey Cluster –
HH Points
> 90% of Household cluster points from
Type B & E, none from Types A & F.
70% of population lives in Type B & E,
17% in Types A & F.
31. Micro-Census of
One Ward in Sokoto State
Sokoto SERICC / EOC
2 May 2018
Center for Global Health
Global Immunization Division/Polio Eradication Branch/Nigeria Team
GIS Estimates – Validation Data
Goals:
• Validate the enumeration results of the Demand Generation project
for children younger than 1 in one ward
• Obtain a more accurate count of the total population living in the
selected ward
• Validate the GIS estimates for different age groups in the selected
ward
33. Magarya Ward
Boundary
• from polio Vaccination
Tracking System (VTS)
• “operational” status –
not authorized by GoN
• no other shapefiles
exist for Ward
boundaries (Admin3)
34. Populated Portion of Magarya Ward (VTS Operational Boundary)
Boundary does
not follow street
or natural feature
35. Magarya Ward (Wurno LGA) selected for small size, ease of access
CDC aligned
boundaries to
closest road
36. Enumeration
Output
• Residential status of all
HHs collected
• Age/gender for all
residents
• All buildings geocoded
• Some points fell
outside VTS boundary
38. Ward Population
Queried from GIS
Raster
• Landscan V1.1 gridded
layer (90 meter resolution)
• Dots represent centroid of
each 90 meter grid square
• Value is estimate of total
population w/in each
square
• Values are counted if
centroid is within Ward
boundary
40. Technical and Programmatic Guidance
Adjustment of target population in the context of the
Global Polio Eradication Initiative (GPEI)
Version 1.1 13 Feb, 2018
Total projected savings
in 2018:
~$143m
41. • Microplanning requirements for fixed-post vaccination campaigns
(e.g. Polio-IPV, Measles, Yellow Fever, etc.)
– The activity should take place over “X” days maximum
– Children should not have to walk more than 1km to get vaccinated
– Fixed-Post capacity (# of children vaccinated per day)
• 60 children a day in rural areas
• 100 children a day in urban areas
• Question: how many fixed posts are required and where should they
be located?
Fixed-Post Vaccination Microplanning:
Determining Optimum post location and resource requirements
42. 4
2
Fixed Post Microplanning
Settlements
within 1km
clustered
# of Health
Camp days
calculated
Problem:
Vaccination Posts or
Health Camps (HCs)
had to be located no
further than 1km from
any resident.
Solution:
An automated tool was
created that clustered
settlements within 1km
of one another.
Target populations were
then used to determine
the number of days the
HC would work in a
cluster.
Result:
>95% coverage overall,
no missed settlements
43. 2017-18 National
Measles Campaign
Nigeria
• GIS maps and
population estimates
used to support
microplanning and
locating vaccination
posts in the North.
• Traditional
microplanning used in
the south (GIS
mapping in-process).
44. Unweighted Measles Vaccination Post-Campaign Coverage Survey, by EA
– Nigeria, 2017-18
Source: Nigeria MVC Post Campaign Coverage Survey, 2017-18
90% to <95%
≥95%
>0% to <90%
0%
Proportion of Surveyed
Children Vaccinated
GIS
Microplanning
States
47. Mobile ODK Tools Available for ES Data Collection
BLUE LINE Field data
Collection
48. Sample Data Collection Options to Improve Specimen Tracking
Each Kit has bar code
to identify specimen
1. Select Site Name from drop-down list
2. Scan Bar Code
3. Collect GPS Coordinates (date, time stamp)
4. Enter site specific info
5. Select Name of S.O. from drop-down list
6. Upload Form (to Lab, CO, RO, POLIS?)
Surveillance Officer
Collects Specimen
ODK App on Android
Phone Documents
Sample Collection
1. Bar Code scanned upon arrival in Lab
2. Updates ODK Data Form
3. Transit time auto-calculated
4. Results of Lab Analysis added
5. Upload Form (to CO, RO, POLIS?)
Scan Bar Code with
Android phone
49. Public Health & Other Applications
Ebola Outbreak Response SupportPolio Environmental Surveillance
IPV & RI Microplanning
LQAS Cluster Selection and Validation
Imagery Change Analysis in Conflict Settings
Bed net Distribution - CRS
Electric Power Grid Planning - WB
Health Zone Catchment Areas, DRC
(NTD-HAT)
eSURV – AFP Surveillance Tracking
AVADAR – Community-Based
AFP Surveillance
Others
52. Country Acq.Date Format Acq. Date Vendor As Of Source
Nigeria 2016 TPK Tiles 2016 ORNL 2016-18 ORNL-Flowminder
Nigeria 2014-16 ESRI Cache
Cameroon 2015 ESRI Cache 2016 ORNL
Afghanistan 2015 TPK Tiles 2016 ORNL 2017 Flowminder-UNFPA
Pakistan 2015 TPK Tiles 2016 ORNL
Lake Chad Region 2017 ORNL 2017 ORNL-Flowminder
Ethiopia1
2017 ORNL
Zambia1
2017 ORNL
DRC2
2016-17 2016-17 ORNL 2018 ORNL-Flowminder
Haiti 2016 TPK Tiles 2016 ORNL
Ghana 2018 TIF 2018
Tanzania 2018 Ecopia
Mozambique 2018 Ecopia
Sierra Leone 2018 Ecopia
Somalia-South 2018 Ecopia
Somalia-North 2016 DG
1
Awaiting licensing 2
partial
*All imagery is 4-6 band, color, 0.5m, <10% cloud cover
Imagery* Settlement Layer GIS Population Estimates
BMGF Polio - Imagery & Derivative Products Resources
53. Share the
VISION!
Contact Info:
Vince Seaman, Ph.D.
Sr. Program Officer, Polio - Global Development
Bill & Melinda Gates Foundation
V +1.206.770.2351
C +1.206.669-7259
E Vincent.Seaman@gatesfoundation.org
55. DiSARM is a tool
being used at national
scale in Namibia and
Botswana to help
malaria programs plan,
implement and monitor
indoor residual spray
campaigns.
56. Observed Predicted
• Ensemble machine learning used to predict which buildings are residential and non-residential
• ~85-90% of residential and non-residential correctly classified
• Sturrock et al (2018)
Predicting residential structures for Malaria IRS
58. Working with 38 wards, containing in total 141,812 rooms
(structures)
Operational units can be selected for inclusion in the campaign and an
estimate of the number of structures in those areas is presented
59. Nigeria GRID3 priority focal areas for now include: Health, Education, Water Resource, Agriculture, Population/Demography and
Survey. Some of the potential Use cases considered include:
Potential Use-cases for GRID3 Data in Nigeria
S/N Thematic Area Use-Case
1 Survey Testing Traditional vs Gridded population survey methodology with NBS.
2 Health 1. Developement of National geo-mapped Health facility Registry.
2. Emergency refferel care services (Linking ambulances to Facilities).
3 Education 1. School maps visualization.
2. School catchment area maps.
3. Gender ratio, Student-Teacher ratio maps etc
4 Water Resurces 1. Access to water per settlement
2. Flood disaster prediction
5 Agriculture 1. Land Use management
2. Farm-Market distance
3. Sustainability Oriented Agrological Production Mapping (SOAPM)
6 Census 1. Support to hybrid census (including micro-census)
2. Verifying EADs using high resolution imagery;
3. Real time monitoring of enumeration
7 Governance 1. community focused monitoring and tracking implementation of projects
