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How the polio eradication effort in Nigeria led to a quest for global geospatial reference data

Presented by Vincent Seaman, Interim Deputy Director for the Strategy, Data and Analytics, Bill & Melinda Gates Foundation, at the 2017 GIS Working Group Annual Meeting.

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How the polio eradication effort in Nigeria led to a quest for global geospatial reference data

  1. 1. Geospatial Reference Information Database (GRID) How the Polio Eradication Effort in Nigeria led to a Quest for Global Geospatial Reference Data Vince Seaman Deputy Director (Interim) Data & Analytics, Global Development Bill & Melinda Gates Foundation 1
  2. 2. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 2 WHY NIGERIA? AFTER RECORDING ONLY 21 POLIO CASES IN 2010, NIGERIA EXPERIENCED LARGE INCREASES IN 2011 & 2012, MAKING IT A TOP PRIORITY FOR THE GPEI PARTNERS 2 • 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 Reasons for Outbreak…. 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
  3. 3. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 3 Ward Maps from Nigeria - Incomplete - Inaccurate - Out of Date Many missed settlements were not on existing microplans
  4. 4. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 4 Existing Public Databases 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. Creating Ward Vaccination Boundaries Settlement metadata (ward attribute) used with ESRI Thiessen Polygon tool to create Ward “operational” boundary. • Wards aggregated to form LGA Boundaries • LGAs aggregated to form State Boundaries • State Boundaries “fit” to existing National Boundary - No existing formal Ward boundary maps - Polio vaccination campaigns occur at Ward level
  7. 7. Ward/LGA Boundaries later validated by polio H2H team tracks
  8. 8. Gangara Ward Jibia LGA, Katsina State VTS Map, Feb. 2016 Hand-drawn Ward map 2011 Initial GIS trace OSM Map – Feb. 2016 OSM – Gangara A Settlement Mapping – The Reality VTS data uploaded to OSM by eHealth, 2015 > 10 settlements missing from local map Accuracy of settlement geo- location is poor
  9. 9. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** GIS TRACKING OF VACCINATION TEAMS 9 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
  10. 10. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** BUA Polygon divided into 50 meter grid squares Geographic Coverage - BUAs 50 meter buffer around each hamlet Geographic Coverage – Hamlet Areas 75 meter Buffer around SS Point Feature Geographic Coverage – Small Settlements (SS) GIS Tracking: Calculation of Geographic Coverage
  11. 11. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 11 Post Campaign Reports: LGA/Ward Overview • GeoCoverage • Settlements Visited & % of total • Target Population visited & % of total • Heat Map showing visited/missed settlements http://vts.eocng.org/
  12. 12. Improved Coverage of Border Settlements, Tudun Wada LGA, Kano State Jan 2015 tracks Sept 2012 tracks LGA Border
  13. 13. 1 3 DECLINING NUMBER OF CHRONICALLY-MISSED* SETTLEMENTS IN KANO LGAS 17 of the 70 chronically missed settlements are nomadic * Chronically Missed = not visited the last 3 campaigns; same LGAs tracked since November 2013; all LGAs to be tracked from April 2014 70 155 233 4.1% of all settlements tracked 2.7% of all settlements tracked ≈ 3000 Children Missed 4 ≈ 1860 Children Missed ≈ 580 Children Missed 1.2% of all settlements tracked < 0.05% of all settlements tracked
  14. 14. 1 414 Aggregated tracks show road network
  15. 15. Settlement Total U5 Houses Total U5 UNG MAIGADI 5055 1011 45 369 75 State Master List GIS Est Kaduna – Birnin Gwari LGA Local Administrative Population Data Unreliable • Census data unavailable below Admin 1 level • Inflated population counts result in vaccine, bed net, and other critical supply shortages • 2014 H2H enumeration in Kano state found polio target population inflated by a factor of 2, more than 3 million children. • Validation tool needed
  16. 16. December 14, 2017 Demographics & Mobility mapping Andy Tatem, University of Southampton Total Population: 170,123,740 (July 2012 estimate) Administrative units - 774 High Resolution Population Distribution In Northern Nigeria BudhendraBhaduri EddieBright,AnilCheriyadat,AmyRose,JakeMcKee,JeanetteWeaver, MaryUrban,RajuVatsavai
  17. 17. 486 features 2 BUAs, 3 SSAs, 9 HA (56 hamlets) 14 settlement features Aggregated Settlement Layer Serves as the Basis for Mapping Raw FE layer Aggregated Settlement Layer Imagery Courtesy of Digital Globe) ORNL Semi-Automated Feature Extraction Captures 95+% Structures
  18. 18. • Power spectrum contours represent 20, 40,60 and 80% energy levels. • Shape of the power spectrum characterizes the semantic category. • Dominant orientations of Downtown, Suburban, Commercial Complex structures captured in power spectrum. Feature Extraction: Different Objects Have Unique Spectral “Signatures”
  19. 19. Structure Edges Give Different LINE PATTERNS Local line patterns a good descriptor of the spatial arrangements. Line statistics can representative of structural dimensions
  20. 20. 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
  21. 21. 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
  22. 22. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 22 Rural residential land use and population density: - Rural areas have less diversity and can be characterized by a single type (M) M M M
  23. 23. GIS Population Model Microcensus Methods Northern 10 States = 900 clusters in 9 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 Densities* (Kebbi, Zamfara) M = 147 Densities* (Kaduna, Kano) A B C D E F M 810 350 257 141 438 61 246 Densities* (Bauchi, Yobe) M = 161 *Population density (per hectare) for each neighborhood type
  24. 24. Microcensus Data Collected in All Neighborhood Types - 150 polygons (492 structures) in the 6 identified “neighborhood” areas - Building features, use, occupancy, and photos collected December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 24
  25. 25. At least 5 Validation Data Sets per State - entire population counted inside of defined area (min. 50 households) - not used to inform model December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 25
  26. 26. Validation Sets Average Variance = 8.7% (Range = 1 – 18%) December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 26 KN0902_5 Microcensus = 334 Model = 366 Variance = +9.3% Density = 5.3 Microcensus = 300 Model = 304 Variance = + 1% Density = 6.25 KN0902_6
  27. 27. OUTPUT: 90-meter population grid with total counts, or selected demographic
  28. 28. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 28 GIS Model and 2006 Census Projections Nearly Identical at National Level – Wide Variations at Sub-National Levels Current Model POP2006 Pop2015 PopUnder5yrs State estimate diff diff (1,000s) diff/projected 4,676,465 6,318,333 1,263,667 Bauchi 5,842,730 -475,603 -475.60 -0.08 4,151,193 5,608,643 1,121,729 Borno 5,173,450 -435,193 -435.19 -0.08 4,348,649 5,624,614 1,124,923 Jigawa 5,288,378 -336,236 -336.24 -0.06 6,066,562 7,915,487 1,583,097 Kaduna 8,521,381 605,893 605.89 0.08 9,383,682 12,568,289 2,513,658 Kano 13,718,523 1,150,234 1,150.23 0.09 5,792,578 7,558,000 1,511,600 Katsina 8,039,212 481,212 481.21 0.06 3,238,628 4,262,742 852,548 Kebbi 4,215,941 -46,801 -46.80 -0.01 3,696,999 4,823,745 964,749 Sokoto 5,537,133 713,388 713.39 0.15 2,321,591 3,164,090 632,818 Yobe 3,566,837 402,747 402.75 0.13 3,259,846 4,328,270 865,654 Zamfara 3,762,484 -565,786 -565.79 -0.13 2006 Census Projections Model vs. Census Projections Std. Dev.: Northern States = 9% All States = 33% Current Model Ratio POP2006 Pop2015 PopUnder5yrs estimate Model/Census 139,983,289 185,847,096 37,169,419 188,451,476 1.01 2006Census Projections National Estimates
  29. 29. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 29 Lack of Accurate Geospatial Reference Data Affects Population Estimates and Spatial Analyses I. Yearly growth projections applied to census data do not accurately reflect urban/rural growth differences below the State Level II. Demographic groups/target populations are not a fixed percentage of total population across country III. Sub-National administrative boundary layers are imprecise IV. Standard Cluster survey methods may not result in a representative sample
  30. 30. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 30 Detail from Ungogo, 2006 Detail from Ungogo, 2015 I. Yearly growth projections are not reliable below the State level due to uneven growth rates in rural & urban areas.
  31. 31. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 31 Urban LGAs grow at different rates based on space, location, etc. Census projections result from a growth rate (2.7-3.4%/year) applied at the state level, but highly urban LGAs grow much faster, rural LGAs much slower. 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 Nigeria
  32. 32. Managed by UT-Battelle for the Department of Energy Modeled Population Change – Kano Metro Area
  33. 33. 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)
  34. 34. % Children under 5 Varies from North to South, East to West Alegana, et al. 2015 http://rsif.royalsocietypublishing.org/ II. MODELED DEMOGRAPHICS BASED ON NATIONAL HH SURVEYS INDICATE FIXED FRACTIONS ARE INAPPROPRIATE 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 For the various demographic groups targeted by the GoN, a flat % is used across the entire country: U1 = 4%, U5 = 20%, U15 = 47.6%
  35. 35. III. Sub-National Administrative Boundaries are Imprecise VTS* Boundary Published Census Boundary Kano Metro LGAs Nigeria *VTS Boundary from polio GIS settlement mapping
  36. 36. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 36 LGA (Admin 2) Boundaries Referenced in Census Based on Area No authoritative shapefiles available, however land area matches UN Admin 2 boundaries
  37. 37. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 37 Census Enumeration Areas do not align with published boundaries VTS* Boundary Census/UN Boundary (white) *VTS Boundary from polio GIS settlement mapping Geo-referenced census enumeration area map
  38. 38. • 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/ Commonly used “Official” boundaries do not align with field data & settlement attributes *VTS Boundary from polio GIS settlement mapping
  39. 39. GIS Population Estimates: VTS1, GADM2, UN-WHOBoundaries 2GADM 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 1VTS Boundary from polio GIS settlement mapping
  40. 40. Z = Non-Residential Neighborhood Types - Kano Metro Area National HH Survey 2016 Cluster locations – Kano Metro LGAs 2016 Cluster Survey – HH Points > 90% of Household cluster points from Types B & E, none from Types A & FIV. Cluster Surveys - Are They Truly Representative?
  41. 41. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** http://geopode.world/
  42. 42. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 42 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 GIS POPULATION MODEL – USER INTERFACE OPTIONS http://geopode.world/
  43. 43. SIMPLE CATCHMENT AREA - SELECT USER-DEFINED BUFFER AROUND A POINT 43 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
  44. 44. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 44 GIS SUPPORT TO DEVELOPING COUNTRIES Geospatial Reference Information Database Settlements and Points of Interest Administrative Boundaries Population / Demographics Transportation Network plus Capacity-Building (NSO, National Mapping Agency, etc.) =
  45. 45. ecember 14, 2017Melinda Gates Foundation | DfID-BMGF Partnership to co-fund GRID and other Key Geospatial and Data-related Projects DFID Priority BMGF-DfID GRID Geographies - 2017 • Collect basic geospatial reference data (access geo-referenced national census data where available) • Build capacity within Census/Population Commission, Bureau of Statistics (UNFPA, Flominder) • Develop Population/Demographics & Population dynamics modeling • Build data management/use capacity across all sectors PROJECT 1 (census-based) Support National Statistics Office/Population Council to conduct georeferenced census & manage data Year 1 Countries: Ethiopia, Tanzania, Zambia PROJECT 2 (no census) Support National Statistics Office/Population Council to collect/model geospatial reference data Year 1 Countries: Nigeria, DRC
  46. 46. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 46 GRID PROJECT DELIVERABLES 1. Geo-referenced layer of all settlements and key POIs (from feature extraction layer) 2. Validated sub-national boundary layers (from settlement attributes) 3. Population & demographic estimates at 90 meters (from neighborhood classification and microcensus data) 4. Capacity-building for NSO, NGA, and other government agencies 5. Country and Global Data Platforms
  47. 47. Intensive Capacity-Building (minimum 24 months) December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 47 NATIONAL STATISTICS OFFICE/POPULATION COMMISSION • Training, software & hardware provision, technical support • Manage, use and curate census data and other national statistics NATIONAL GEOSPATIAL AGENCY • Training, software & hardware provision, technical support • Manage, use and curate national geodatabase • Regular updates of boundaries, settlements, & POIs OTHER GOVERNMENT MINISTRIES/AGENCIES (FINANCE, ELECTORAL, EDUCATION, UTILITIES, ETC.) • Identify priority use-cases & applications • Assist NSO and NGA in supporting other agencies REGIONAL WORKSHOPS & TRAINING • Additional opportunities to enhance GIS skills • Network and share best practices with other AFRO country teams
  48. 48. Other BMGF-Supported GIS Projects
  49. 49. Share the VISION! Contact Info: Vince Seaman Senior Program Officer Country Support, Polio Global Development V +1.206.770.2351 C +1.206.669-7259 E Vincent.Seaman@gatesfoundation.org
  50. 50. © 2013 Bill & Melinda Gates Foundation | *** Confidential – for internal use only *** 50 EXTRA SLIDES GRID Data: Applications and Use Cases 1. Health Facility/service area catchment mapping 2. Cluster maps for fixed post vaccination campaigns 3. DEM maps show water flow/accumulation and catchment areas/populations from sampling point – WASH, polio, malaria, Cholera, typhoid? 4. Imagery change analysis to determine settlement status in Borno state
  51. 51. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 51 1. Define HF Catchment Areas and Target Populations 1. Obtain Ward map from VTS – verify all HFs are accurately represented 2. Obtain Ward Settlement populations from VTS (Total and selected demographics). Adjust populations if recent enumeration is available. 3. Add local names to settlements, or new settlements where needed. 4. Assign settlements to HF based on location, access, and services 5. Add populations (total and demographics) of selected settlements to determine estimated catchment area population. Note: A “Microplan” can be printed from the VTS that includes the Ward map, a list of primary and sub-place names, and
  52. 52. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 52 HF Catchment area must be determined by local HF and LGA staff
  53. 53. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 53 2. Cluster maps for fixed post vaccination campaigns. 1. Fixed post (FP) locations identified by 1 km settlement clusters 2. Target population in each cluster determines # of FPs and days 3. Ward target population can be used to estimate vaccine/personnel requirements 4. Daily vaccination totals at each FP collected with smartphone to track progress
  54. 54. 5 4 2. Cluster maps for fixed post vaccination campaigns. Settlements within 1km clustered # of Health Camp days calculated Problem: IPV 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
  55. 55. 55 Measles Campaign Northern States, Oct. 2015 Microplanning Map (5 day campaign) - Rural Fixed Post = 125/day - Urban Fixed Post = 175/day - Settlements grouped in 1km clusters - Target Populations (< 1 year) used to calculate # of fixed post days
  56. 56. © 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. 3. DEM maps to Assess Environmental Surveillance Sites - catchment area size, location and population estimates
  57. 57. Jakarta Police StnKuma Masallachi-Fagge Gogau Fagge Kano Environmental Surveillance Sites DEM Layers Used to Assess Polio Environmental Surveillance Sites
  58. 58. Junction Point Drainage Line Watershed-Catchment Est. Population Collected at Junction Tablet-based maps used in the field to locate optimum locations for ES sites
  59. 59. 59 Assessing Environmental Surveillance Catchment Areas http://maps.novel-t.ch/#/catalog/all 17 Countries 300 sites
  60. 60. 2013 2016 4. Imagery change analysis to determine settlement status in Borno state. CDC/GRASP Change Analysis of 2013 vs 2016 Imagery Identified Damaged/Destroyed Settlements by Boko Harum.
  61. 61. December 14, 2017 © 2013 Bill & Melinda Gates Foundation | 61 GIS Settlement maps and population used as base- layer for analysis - output used to direct vaccination teams and also by humanitarian response partners (UN-OCHA, WFP)
  62. 62. 5. Population U1 living > 1km from a Health Facility Requested by NPHCDA ED for Public Health Strengthening Assessment

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