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. Dr. Mark Iliffe @markiliffe Geospatial Lead, N-LAB MAPPING THE NEXT: MAPPING FOR THE SUSTAINABLE DEVELOPMENT AGENDA
- 5. - 200 400 600 800 1,000 1,200 1990 2000 2010 2020 2030 2040 Population(millions) African population growth Urban Rural RapidUrbanizationandUnplannedGrowthBringsChallenges DaresSalaamContext
- 6. RapidUrbanizationandUnplannedGrowthBringsChallenges DaresSalaamContext Traffic Congestion Solid Waste & Waste Water management Safe Drinking water Youth employment 1 2 3 4 5.5 Million People
- 8. “Wealsocallfora datarevolutionforsustainabledevelopment,witha newinternationalinitiativetoimprovethequality ofstatisticsand informationavailabletocitizens.We shouldactivelytakeadvantageof newtechnology,crowdsourcing,andimprovedconnectivitytoempower peoplewithinformationontheprogresstowardsthetargets.“ TheReportoftheHigh-LevelPanelofEminentPersonsonthePost-2015DevelopmentAgenda,United Nations DevelopmentAgenda2015-2030
- 9. “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
- 11. Confidential15
- 12. Confidential16
- 13. 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 + + +
- 14. 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
- 17. 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
- 18. 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
- 19. MappingCampaignsinDaresSalaam RamaniHuria Started March 2015: 165 Students, 100+ Community Members, 100 Red Cross Volunteers
- 21. 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)
- 22. MappingOutputsinDaresSalaam RamaniHuria 160,000 Building Footprints, 500km+ of waterways, rivers and drainage, 1000s of toilets, water points
- 23. Target Areas: 2012 Population: 1,127,729 | Target Areas: 2015 Population est: 1,296,888 (15% growth) MappingOutputsinDaresSalaam RamaniHuria
- 24. TandaleandNdugumbiWards,KinondoniMunicipality RamaniHuria Before (August 2015) After (October 2015)
- 25. 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
- 30. 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
- 35. FusingDataStreams RamaniHuria • 745,989 Building Footprints • 88km of Imagery and Surface Models • 2091km of Roads
- 45. • 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
- 46. • 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
- 48. • 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
- 55. ERS&ENV S1 • Before • After SupportingResponse BukobaEarthquake
- 56. ERS&ENV S1 Level of Change potential damage areas 0 very low 1 very high ChangedetectionanalysisoverBukoba BukobaEarthquake
- 58. •
- 60. 60 Discrepancy between distributions hypothesized to be due to large repairs on metal rooftops, which the algorithm detects as individual buildings. MachineLearning BuildingaGeospatialPlatform
- 61. 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
- 63. ResearchasaPlatform Towards In an analogue world, policy dictates delivery. In a digital world, delivery informs policy.“ ” Mike Bracken
- 64. 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
- 65. Dr Mark Iliffe @markiliffe THANK YOU
Notas do Editor
- Who am I? What do I do?
- Mungo Park
- John Cary in 1805
- Mungo Park, 1798 to 1890
- 85.2% population increase in 15 years. The primary cities of emerging countries are growing rapidly.
- History of Dar es Salaam – moving from 3.5 million to 5.5 million residents. Massive strain on delivery of public services
- READ QUOTE
- Half of humanity – 3.5 billion people – lives in cities today By 2030, almost 60 per cent of the world’s population will live in urban areas 95 per cent of urban expansion in the next decades will take place in developing world 828 million people live in slums today and the number keeps rising The world’s cities occupy just 3 per cent of the Earth’s land, but account for 60-80 per cent of energy consumption and 75 per cent of carbon emissions Rapid urbanization is exerting pressure on fresh water supplies, sewage, the living environment, and public health But the high density of cities can bring efficiency gains and technological innovation while reducing resource and energy consumption
- Stress the importance of data driven development generally Inform decisions and support policy generation Recall the Addis Ababa UNGGIM declaration with data [NEXT SLIDE TO CONTINUE MESSAGE]
- However, this data is often scant/missing The causes of flooding are not localized, but spread throughout a regional area. Therefore, mass data collection is needed to make sense of the scale of flooding
- Animation: Text boxes automatically appear sequentially
- Drainage Map Transportation Map Tandale Schools Msasani Village
- UAV Image Appears on click. UAV image can drill further down, though due to movement of vehicles the orthorectification could be improved. Aerial Imagery is 30cm / UAV is 4cm
- Participatory mapping Allows the mapping of risk reduction priorities at a hyper-local level Connects local government officers with citizens to identify
- Generated through basic tools (pens/paper)
- Using flood inundation software, such as Inasafe, identify at-risk infrastructure/population
- Leads to traditional outputs, leveraged by community leaders, city planners and other government/non-governmental organizations
- Building Footprints Digital Surface Model / 3D Buildings Flood Risk Identified “At Risk” Buildings
- Flood risk and inundation scenarios
- Flood risk and inundation scenarios
- Scale the most flood prone neighbourhoods of a city Combine with Red Cross volunteers Identify and create action plans to improve resilience to flooding and plans for disaster management
- Constant, iterative, engagement and iteration with policy and decision makers Fail forward
- 50cm Aerial Imagery derived (unknown origin, assumed ~2005) Very high resolution drone imagery, digital elevation models; Sentinel 2 The fusing of these streams has applications in urban planning, landuse detection, vegetation etc
- Translated to looking at the infrastructure, we can identify areas quickly which have a high change, showing places that could be very damaged. From here we can look at where to commit our resources. We can use data to make decisions.
- This is just one method where data and maps can support us in the Disaster Management Department. We can work with mapping communities, both in Tanzania and working with volunteers around the world
- Working with the Ramani Huria community enabled us to go from no map of Bukoba, to the map of infrastructure and buildings as you can see on the right
- Convoluted Neural Networks, Automatic Building Detection
- Fitness for purpose Reuse of data Challenges of repurposes and reusing data – let our digital world inform and support policy