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Mapping the Next - Mapping for the Sustainable Development Agenda

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Presentation given to the Satellite Catapult in December 2016 around the next generation of mapping, earth observation and participatory geography.

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Mapping the Next - Mapping for the Sustainable Development Agenda

  1. 1. Dr. Mark Iliffe @markiliffe Geospatial Lead, N-LAB MAPPING THE NEXT: MAPPING FOR THE SUSTAINABLE DEVELOPMENT AGENDA
  2. 2. - 200 400 600 800 1,000 1,200 1990 2000 2010 2020 2030 2040 Population(millions) African population growth Urban Rural RapidUrbanizationandUnplannedGrowthBringsChallenges DaresSalaamContext
  3. 3. RapidUrbanizationandUnplannedGrowthBringsChallenges DaresSalaamContext Traffic Congestion Solid Waste & Waste Water management Safe Drinking water Youth employment 1 2 3 4 5.5 Million People
  4. 4. Toendpoverty,protecttheplanet,andensureprosperityforall SustainableDevelopmentGoals
  5. 5. “Wealsocallfora datarevolutionforsustainabledevelopment,witha newinternationalinitiativetoimprovethequality ofstatisticsand informationavailabletocitizens.We shouldactivelytakeadvantageof newtechnology,crowdsourcing,andimprovedconnectivitytoempower peoplewithinformationontheprogresstowardsthetargets.“ TheReportoftheHigh-LevelPanelofEminentPersonsonthePost-2015DevelopmentAgenda,United Nations DevelopmentAgenda2015-2030
  6. 6. “embrace open data and standards, innovative and creative approaches and platforms that are fit-for-purpose to collect and collate, share and distribute geospatial information” “ ” 2016 UNGGIM Addis Ababa Declaration FuturePolicyFrameworks DataRequirements
  7. 7. SDGGoal11:Makecitiesinclusive,safe,resilientandsustainable DaresSalaam: RapidandUnplannedGrowth
  8. 8. Confidential15
  9. 9. Confidential16
  10. 10. Hazard,Exposure,VulnerabilityandRisk TypicalDataRequirements Hazard Analysis: • Elevation Model • Land Use/ Land Cover • Drainage network • Rainfall Intensity Duration frequency Exposure mapping: • Buildings, Roads • Critical facilities • Population distribution day/night Vulnerability Assessment • Disabled • Livelihoods • Shelter access • Early Warning Hazards Exposure Vulnerability Risk + + +
  11. 11. Challenges DataRequirements Insufficient Data • Elevation Model 5% areas LIDAR 30cm • Lack of Met data • Rapid Hydrodynamic changes Informal Data • 80% Unplanned Growth • Inconsistent census and admin boundary data Socio-Cultural Factors • Informal economy / livelihoods • Rentals Local Capacity • Data Management • Data Analysis
  12. 12. Research DirectionsandOpportunities GeospatialResearch CROWDSOURCING REMOTE SENSING POLICY DATA-DRIVEN DEVELOPMENT
  13. 13. CrowdsourcinginDaresSalaam:RamaniHuria CaseStudy
  14. 14. 2011PilotinTandaleshowedthatStudentandCitizencanbeasourceofUsefulData HowweStarted Collect very Local Data – eg. drain type, business types, etc Fast changing features – eg. rubbish sites, flooding areas Citizen can voice Issues on the map – eg. children play areas 1 2 3
  15. 15. CitizenDatainDaresSalaam:RamaniHuria RamaniHuria In September 2011 25 Town Planning Students worked with 25 community members to map Tandale Ward in 3 weeks August 2011 September 2011
  16. 16. MappingCampaignsinDaresSalaam RamaniHuria Started March 2015: 165 Students, 100+ Community Members, 100 Red Cross Volunteers
  17. 17. CitizenDatainDaresSalaam:RamaniHuria RamaniHuria Goal: 1 million residents in flood prone vulnerable communities / Currently: • Target Areas: 2012 Population: 1,127,729 • Target Areas: 2015 Population est: 1,296,888 (~15% Growth)
  18. 18. MappingOutputsinDaresSalaam RamaniHuria 160,000 Building Footprints, 500km+ of waterways, rivers and drainage, 1000s of toilets, water points
  19. 19. Target Areas: 2012 Population: 1,127,729 | Target Areas: 2015 Population est: 1,296,888 (15% growth) MappingOutputsinDaresSalaam RamaniHuria
  20. 20. TandaleandNdugumbiWards,KinondoniMunicipality RamaniHuria Before (August 2015) After (October 2015)
  21. 21. UsingParticipatoryMappingwithStudents,CitizensandWardOffices KeyAdvantages Affordable Data Collection for local level – approx. $10,000 per ward Hyper-local details – trees, businesses, water points, facilities, drains Community Context – digitizing critical features for citizens Culture of participating in mapping strengthens relationship of officials with community 1 2 3 4
  22. 22. CaseStudy RemoteSensing
  23. 23. LowCostMappingDrones RemoteSensing
  24. 24. AerialImagery, UAVComparison UAVs
  25. 25. UsingUAVsforUrbanMapping KeyAdvantages Simple & Affordable – approx. $1,000 for phantom, $25,000 for ebee – low running costs High resolution – up to 3cm Basemap, 8cm Elevation model Timeliness – can choose exact day of mapping to suit project needs for baseline Cloud free – advantages over satellite and manned aircraft as drone fly under clouds 1 2 3 4
  26. 26. ParticipatoryInundationModelling:MappingRiskReductionPriorities CaseStudy
  27. 27. MappingRiskReductionPriorities:ParticipatoryInundationModelling MapsasaPlatform
  28. 28. MappingRiskReductionPriorities:ParticipatoryInundationModelling MapsasaPlatform
  29. 29. MappingRiskReductionPriorities:ParticipatoryInundationModelling MapsasaPlatform
  30. 30. FusingDataStreams RamaniHuria • 745,989 Building Footprints • 88km of Imagery and Surface Models • 2091km of Roads
  31. 31. LowCostMappingDrones CitizenData
  32. 32. FusingHydrologicalModelswithParticipatoryMapping MapsasaPlatform
  33. 33. MappingRiskReductionPriorities:ParticipatoryInundationModelling MapsasaPlatform
  34. 34. GeospatialPolicyDevelopment PolicyandProcess
  35. 35. AssessingPublicPolicy MapsasaPlatform
  36. 36. AssessingPublicPolicy MapsasaPlatform
  37. 37. ZanzibarMappingInitiative BuildingaGeospatialPlatform
  38. 38. • Creating a map of Zanzibar Islands at very high resolution, released as open data • Introduction of a cost effective technology for land monitoring • Building different projects around the data (Conservation, Land tenure, Urban Planning, etc…) • Local Capacity Building • Increasing the efficiency in data colection from the Commission of Lands • Creating opportunities for new local businesses to develop around the technology ZanzibarMappingInitiative BuildingaGeospatialPlatform
  39. 39. • 9 drones are deployed in 3 different teams of local operators • 2 power full computer for processing data at a high speed • 3 field computers for flight planning and control • NAS for storing over 10TB of Data • 2’400sq/km to map • 239 zones unguja and 182 in Pemba • 3 teams of 4-5 composed of local surveyors with support of students of State University of Zanzibar • Mission kick-off August 15th 2016 for 2 months Equipment,TeamandMission BuildingaGeospatialPlatform:ZanzibarMappingInitiative
  40. 40. Scope BuildingaGeospatialPlatform:ZanzibarMappingInitiative
  41. 41. • Each grid covers an area of 3km x 3 km (9km²). • In optimal conditions (no wind), one zone can be covered in 6 flights (at a GSD= 7 cm). • In order to facilitate data management, each grid has been assigned a unique Zone ID. • There are currently 239 zones in Unguja and 182 Zones in Pemba. In the future, it will be possible to add more zones. Important is to keep the Zone_ID as a unique identifier. • This has been done in order to manage size of data per square and being able to work with it. Scope BuildingaGeospatialPlatform:ZanzibarMappingInitiative
  42. 42. Zanzibar BuildingaGeospatialPlatform
  43. 43. UrbanPlanning BuildingaGeospatialPlatform
  44. 44. 3DModels BuildingaGeospatialPlatform
  45. 45. BuildingVolumeCalculation BuildingaGeospatialPlatform
  46. 46. LandTenure BuildingaGeospatialPlatform
  47. 47. TowardsSustainableSkills BuildingaGeospatialPlatform
  48. 48. ERS&ENV S1 • Before • After SupportingResponse BukobaEarthquake
  49. 49. ERS&ENV S1 Level of Change  potential damage areas 0 very low 1 very high ChangedetectionanalysisoverBukoba BukobaEarthquake
  50. 50. WorkingwithaGlobalMappingCommunity BukobaEarthquake
  51. 51.
  52. 52. 59 MachineLearning BuildingaGeospatialPlatform
  53. 53. 60 Discrepancy between distributions hypothesized to be due to large repairs on metal rooftops, which the algorithm detects as individual buildings. MachineLearning BuildingaGeospatialPlatform
  54. 54. ParticipatoryMapping KeyChallenges Coordination: Mix of Universities, COSTECH, City and Disaster Management Department UAV Permits: require Ministry of Defense, Lands and Survey, Aviation Authority Data Processing: flying is easy, processing takes trial and error for good outputs Community Mapping: low cost but labour intensive – relies on steady supply of students 1 2 3 4
  55. 55. ResearchasaPlatform Towards In an analogue world, policy dictates delivery. In a digital world, delivery informs policy.“ ” Mike Bracken
  56. 56. AnAgendaforMappingtheNext Towards Policy and legislation for government use of citizen generated open data Outreach to policy/decision makers on how ‘maps’ can provide efficiency Optimize local and international communities with new forms data and methods Mapping where there are no opportunities for maps – NeoDemographics 1 2 3 4
  57. 57. Dr Mark Iliffe @markiliffe THANK YOU

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