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IMED 2018: Modeled Population Estimates from Satellite Imagery and Microcensus Data

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Vincent Seaman Ph.D., Senior Program Officer, Polio Eradication Program, Bill and Melinda Gates Foundation: Modeled Population Estimates from Satellite Imagery and Microcensus Data.

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IMED 2018: Modeled Population Estimates from Satellite Imagery and Microcensus Data

  1. 1. Modeled Population Estimates from Satellite Imagery and Microcensus Data Vince Seaman Sr. Program Officer, Polio Global Development Division Bill & Melinda Gates Foundation 1
  2. 2. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 2 WHY ALL THE RESOURCES FOR GIS WORK IN NIGERIA? AFTER RECORDING ONLY 21 POLIO CASES IN 2010, NIGERIA EXPERIENCED LARGE INCREASES IN 2011 & 2012… 2 798 388 21 62 122 53 6 0 0 100 200 300 400 500 600 700 800 900 2008 2009 2010 2011 2012 2013 2014 2015 Polio Cases in Nigeria, 2009-15 • Many cases from settlements not visited by vaccination teams • Microplans were incomplete • Target populations were inaccurate and grossly inflated in many areas, leading to data falsification and vaccine waste • Quality of monitoring and coverage data was poor What was behind this?
  3. 3. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 3 Ward Maps from Nigeria - Incomplete - Inaccurate - Out of Date
  4. 4. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 4 Existing Public Geodata are Limited to Urban Centers Automated Feature Extraction (FE) Settlements (ORNL)Adamawa State, Nigeria (OpenStreet Maps)
  5. 5. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 5 Manual & Automated Feature Extraction of Satellite Imagery Field Data Collection Settlement Attributes used to create Ward Boundaries Points of Interest 2013-16: GIS Base Layers Collected for 11 Northern States
  6. 6. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** BASEMAP SUPPORTED GIS TRACKING OF VACCINATION TEAMS 6 Given to Ward Focal Person (WFP) at LGA HQ each morning WFP Returns to Ward take-off point and gives phones to vaccinators WFP returns to LGA-HQ where GPS tracks are downloaded Vaccinators return phone to WFP at the end of their day Feedback for daily coverage provided to WFPs and LGA team at daily meeting 5a-6:30a 7a-8a 11a-5p 2p–8p Tracks uploaded to EOCs/Dashboard via MiFi Missed Settlement Report generated at end of days 4 & 5 GPS – enabled Android phone Collects time-stamped GIS coordinates every 2 minutes
  7. 7. Team Movements Visualized = Improved Accountability http://vts.eocng.org
  8. 8. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 8 Post Campaign Reports at all Levels: LGA/Ward Overview • GeoCoverage • Settlements Visited & % of total • Target Population visited & % of total • Heat Map showing visited/missed settlements http://vts.eocng.org/
  9. 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?
  10. 10. WorldPop: Redistributing census count data All Public Population Density Resources based on National Census Data
  11. 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. 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. 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. 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
  15. 15. November 14, 2018 © 2013 Bill & Melinda Gates Foundation | 15 NHtypeState Nigeria A 8.1 B 19.3 C 0.3 D 2.7 E 0.3 F 0.9 M 67.3 Z 1.1 23% of Nigerians live in Urban areas. Of those, 36% live in slum (Type A) neighborhoods. Cross River = 64% of the urban population lives in Type A Sokoto =46% of the urban population lives in Type A
  16. 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
  17. 17. OUTPUT: 90-meter population grid with total counts, or selected demographic
  18. 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. 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/
  20. 20. November 14, 2018 © 2013 Bill & Melinda Gates Foundation | 20 Select the Layers tab to see the drop-down Map layers and Total Population or < 5 population can be selected here Type in coordinates to go to a specific place Custom Demographics slider: 0-12 mos, 5 year intervals Polygon and point buffer options Print Screen Change Basemap Scale Bar POPULATION MODEL – USER INTERFACE OPTIONS
  21. 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. 22. Custom polygons can be drawn for catchment areas Delineating team day areas to estimate resource requirements22 Architecture http://geopode.world/
  23. 23. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 23 Challenges Identified using Population Estimates • Flat national demographic fractions are not accurate • Administrative Boundary shapefiles are imprecise • Census Projections are Not Reliable Below the State Level • Cluster Surveys may not sample representative neighborhoods
  24. 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. 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. 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. 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
  28. 28. November 14, 2018 © 2013 Bill & Melinda Gates Foundation | 28 NIE - Yearly growth projections are not reliable at sub-national levels Census projections result from a flat growth rate of (2.7-3.4%/year) applied at the state level, but urban population growth has rapidly outpaced rural growth. SOLUTION: GIS estimates, based on 2015-16 imagery building footprint, reflect actual urban and rural population distribution down to the settlement level 2006 and 2014 Kano Settlement Extents Percent Change in Settled Area by LGA, 2006-2014
  29. 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. 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. 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
  32. 32. * * Ward boundary from Vaccination Tracking System (VTS)
  33. 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. 34. Populated Portion of Magarya Ward (VTS Operational Boundary) Boundary does not follow street or natural feature
  35. 35. Magarya Ward (Wurno LGA) selected for small size, ease of access CDC aligned boundaries to closest road
  36. 36. Enumeration Output • Residential status of all HHs collected • Age/gender for all residents • All buildings geocoded • Some points fell outside VTS boundary
  37. 37. Ward Boundary Adjusted • New boundary captures all enumerated HHs • Represents “true” Ward boundary
  38. 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
  39. 39. 8673 282 1548 2931 8708 337 1887 4833 26,205 396 2546 5241 16,994 680 3399 8089 3584 TOTAL U1 U5 U15 MAGARYA ENUMERATION GIS estimate <1% variance from enumerated value *
  40. 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. 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. 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. 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. 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
  45. 45. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** Digital Elevation Map (DEM) layers can detect changes in elevation based on the resolution. For 30 meter resolution, the contour lines are spaced 30 meters apart. Environmental Surveillance Site Assessment - catchment area size, location and population estimates
  46. 46. Junction Point Drainage Line Watershed-Catchment Est. Population Collected at Junction http://maps.novel-t.ch/
  47. 47. Mobile ODK Tools Available for ES Data Collection BLUE LINE Field data Collection
  48. 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. 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
  50. 50. Nigeria Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) Project 29/06/2018 2018-19: DRC, Tanzania, Zambia, Mozambique
  51. 51. Ecopia Building Footprint Layer (© DigitalGlobe) Tanzania, Zambia, Mozambique, Sierra Leone, Somalia*) New Developments in Automated Feature Extraction
  52. 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. 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
  54. 54. 54 | Copyright © 2015 Intellectual Ventures Management, LLC (IV). All rights reserved. Remote Sensing to Locate HIV High-Risk Populations Primary goal • Automatic identification of fishing boats • Small boats such as dugout canoes, not large commercial vessels Secondary factors • Season and date • Water hyacinth • Proximity to roads and settlements Boat in water (Asembo Bay, Kenya) Boats on shore A large fish market (Kisumu, Kenya)
  55. 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. 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
  57. 57. 57
  58. 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. 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

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