images are also permitting to monitor changes toecosystems and natural resources in Africa, like lakeChad. 836Satellite images have become one of the key resources toassess vulnerability to natural disasters, including droughtsand floods. In Africa, satellite images have been used toidentify flood risk areas in Namibia, 837 Senegal (Box 8-9)and Sudan (Box 8-7); and data from satellite imagery hasbeen combined with GIS and precipitation data to producea flood risk map along the Niger-Benue river. 838 Nigeria isparticipating with UK, Spain and China, in the DisasterMonitoring Constellation (DMC), 839 the first Earthobservation constellation of low cost small satellitesproviding daily images for applications including globaldisaster monitoring. The Disaster Monitoring Constellationaims at providing both commercial and free satelliteimagery for humanitarian use in the event of majorinternational disasters.Satellite big data applications are likely to continue toemerge in the years to come. Given the importance ofthese data, a few countries in Africa - Algeria, Egypt,Nigeria, and South Africa - have launched their ownsatellites. 840 These countries negotiated multilateralpartnerships to establish the African ResourceManagement constellation (ARMC), 841 to pool imagery andother remote sensing data from all their micro-satellites.This joint project is meant to form the cornerstone of theAfrican Satellite Constellation dedicated to the monitoringand management of African resources and environment.8.3. New approaches to integrate data8.3.1. Integrating multiple data sourcesMethods to integrate diverse data sources, such as censusand surveys, satellite and ground information, have been inexistence for some years but their usage is not yet widelyspread. These methods attempt to fill data gaps and/orimprove the timeliness and geographical resolution of data,by pulling together information from various sources.A case in point is the production of poverty maps bycombining census and survey estimations (Box 8-6). Thisapproach has not been used widely in African countriesbecause, for some of them, the census and the surveyseither don’t even exist or are too old. As innovative big dataapproaches attempt to fill the poverty data gaps, moreresearch will be needed to further integrate big data withcensus and survey data.population, births, pregnancies, urban change and agestructures. Using this small and big data approach,population maps have been produced for most Africancountries, even those for which census data is very old andofficial population figures are inexistent or unreliable.Box 8-6. High resolution poverty mapsAn understanding of poverty and inequality levels atdetailed spatial scales is a prerequisite for fine geographictargeting of interventions aimed at improving welfarelevels. Similarly, decentralization in many countries hasmeant that decision making for poverty alleviationprograms is shifting from central government to regional orlocal levels. Such decisions should ideally be based onreliable, locally-relevant information on living standardsand the distribution of wealth. In most countries suchinformation is not readily available. Data on material livingstandards generally come from household surveys.Nationally representative household sample surveys rarelypermit a fine disaggregation of the population by place ofresidence.In recent years there has been an accumulation ofexperience with methods to estimate poverty drawing onhousehold survey data alongside population census data,based on statistical techniques. One approach, explored bythe World Bank in collaboration with researchers inacademia, combines household and census data usingstatistical procedures aimed at taking advantage of thedetailed information available in household sample surveysand the comprehensive coverage of a census. An importantfeature of the small area estimation approach is that itproduces confidence intervals for its estimated welfaremeasures. These can be examined to gauge the reliabilityof the estimates. Successful applications of combiningcensus and survey data to obtain poverty estimates 843,844 atthe level of small communities with perhaps only 5,000households have been produced for several Africancountries. 845,846Another audacious example, the Global Forest Watch(GFW), 847 combines satellite technology, open data, andcrowdsourcing to produce timely and reliable informationabout forests worldwide. The platform is used to detectdeforestation – particularly illegal deforestation –, classifyland cover, estimate forest biomass and carbon, and mapthe world’s roadless areas. It functions as a monitoring andalert system that empowers people everywhere to bettermanage forests, including people in Africa.Some examples integrating “big” and “small” data arealready out there. Worldpop 842 combines satellite, censusand cell phone data to create detailed and freely availabledatasets and maps with high resolution on poverty,158
Figure 8-5. Poverty map for Guinea, 2002/3.Figure 8-6. Flood zone levels and the related risk in Sudan: (1)high risk, mainly from the river Nile; (2) high risk, mainlyfrom the Valleys; (3) rarely affected by the Valleys.Source: H. Coulombe (2008), 848 © World Bank.By integrating as many sources of data as possible, maps ofterrestrial ecosystems in Africa were recently produced at a90m resolution. These maps represent the finest spatialresolution data of its kind ever produced for the entirecontinent. Several layers of data on climate regions,landforms, geology, and land cover were combined toproduce these maps. Some of those layers were originallydeveloped using satellite imagery. 849 These maps areuseful for biodiversity conservation, for assessments of thevalue of ecosystem goods and services and to betterunderstand how and which ecosystems are being impactedby climate change and other disturbances.Box 8-7. Using satellite data in Sudan for flood predictionand detectionHundreds of villages line the banks of the river Nile, andbecause of their proximity to the river banks are adverselyaffected in years of above average floods. In recent years,the Sudan Survey Authority has been using geospatialtechnologies to monitor the flow of the River Nile usingsatellite imagery data (MODIS provided by NASA) on a dailybasis to ensure the flooding risk to their citizens isminimised. Sudan has developed hazard and risk maps as abasis to run multiple flood scenarios, depending on thespecific water levels of the River Nile. The scenarios areable to approximate the level of impact to citizens and theireconomic resource base such as agricultural land. It isdifficult for governments to impose restrictive accesspolicies to sources of livelihood such as water, even if thosesame sources create risks to the population residingnearby. Therefore, it is important for countries such asSudan to have continuous monitoring capabilities to beable to warn their citizens when natural hazards such asflooding may take place, and to ensure that there is aneffective and efficient emergency response.Source: Alhussein (2014). 8508.3.2. Integrating geographical informationGovernments now rely on comprehensive and accurate,location-based information to support strategic priorities,making decisions, and to measure and monitor outcomes.Overall, the use of geospatial information and technologyby African countries is increasing, with many innovations innumerous areas (see Box 8-7 to Box -8-9). This expansion ofgeospatial initiatives has been grounded on the spread ofmobile devices for data collection with geo-positioning (seesection 8.1) and an enhanced accuracy of GPS data inAfrica. The improved accuracy of the GPS data in Africa isdue to a rise in the number of GPS based stations, as part ofthe African Geodetic Reference Frame (AFREF) project. 851 .Recent survey results indicate that there are 116 GPS basestations and a total of 43 stations broadcasting data to beused for computing position data. All African countries havestarted utilizing Global Navigation Satellite Systems (GNSS),in particular GPS, in various geospatial applications. 852 Withthe recent UN General Assembly’s adoption of a resolutionon the Global Geodetic Reference Frame for SustainableDevelopment, the region can benefit further by having aglobal framework to improve the positional accuracy ofdata in Africa. 853159
- Page 1 and 2:
GLOBAL SUSTAINABLEDEVELOPMENT REPOR
- Page 3:
ForewordIn September 2015, world le
- Page 6 and 7:
3.1. Interlinked issues: oceans, se
- Page 8 and 9:
7.2.1. Open call for inputs to the
- Page 10 and 11:
Box 5-10. Operationalizing inclusiv
- Page 12 and 13:
Figure 8-8. Location of ambulance u
- Page 14 and 15:
Hentinnen (DFID); Annabelle Moatty
- Page 16 and 17:
Friendship University of Russia, Ru
- Page 18 and 19:
List of Abbreviations and AcronymsA
- Page 20 and 21:
IRENAIRIISEALISSCITCITU-TIUCNIUUIWM
- Page 22 and 23:
USAIDVPoAVSSWBGUWCDRRWEFWFPWMOWTOWW
- Page 24 and 25:
Figure ES-0-1. Possible roles for t
- Page 26 and 27:
Figure ES-0-2. Links among SDGs thr
- Page 28 and 29:
increase either the availability or
- Page 30 and 31:
Chapter 1.The Science Policy Interf
- Page 32 and 33:
Complex relationship between scienc
- Page 34 and 35:
Communication between scientists an
- Page 36 and 37:
1.2.1. Highlighting trends and prov
- Page 38 and 39:
International, Marine Stewardship C
- Page 40 and 41:
limited. There is a relative dearth
- Page 42 and 43:
educe the time lag between science
- Page 44 and 45:
Chapter 2. Integrated Perspectives
- Page 46 and 47:
2.1.4. Recommendations by the Inter
- Page 48 and 49:
ultimate idea is systems design - t
- Page 50 and 51:
2.2. Integrated SDG perspectives in
- Page 52 and 53:
Hunger andagriculturePovertyWorld B
- Page 54 and 55:
IIASA-GEAPBLSEIOECDRITE-ALPSFEEMGSG
- Page 56 and 57:
Table 2-4. Number of models capturi
- Page 58 and 59:
In order for oceans, seas and marin
- Page 60 and 61:
fully integrated scientific assessm
- Page 62 and 63:
While some efforts are undertaken t
- Page 64 and 65:
Table 3-3. Impact of important clas
- Page 66 and 67:
Marine pollution from marine and la
- Page 68 and 69:
While the scientific coverage of th
- Page 70 and 71:
managementinitiative in BancoChinch
- Page 72 and 73:
equired, with natural and social sc
- Page 74 and 75:
Table 4-1. SDGs and DRR linkagesSDG
- Page 76 and 77:
poverty forces low-income household
- Page 78 and 79:
Figure 4-1. Economic losses relativ
- Page 80 and 81:
OECD countries and, if they are ava
- Page 82 and 83:
4.3.4. Baseline setting and assessi
- Page 84:
Using assessed levels of risk as ba
- Page 87 and 88:
Table 4-3. Disaster management cycl
- Page 89 and 90:
New sensor data also includes unman
- Page 91 and 92:
Chapter 5. Economic Growth, Inclusi
- Page 93 and 94:
Table 5-1. Industrial policy waves
- Page 95 and 96:
Figure 5-3. Number of Y02 patents p
- Page 97 and 98:
increasingly production specific an
- Page 99 and 100:
5.3. Industrialisation and social s
- Page 101 and 102:
education will either make it hard
- Page 103 and 104:
Table 5-3. UNEP’s five key types
- Page 105 and 106:
5.6. Concluding remarksThe precedin
- Page 107 and 108:
occurs despite the lower share of e
- Page 109 and 110:
LLDCs face several development chal
- Page 111 and 112: technology-innovation (STI) policie
- Page 113 and 114: 6.2.3. Relevant publications for LD
- Page 115 and 116: - A patent bank would help LDCs sec
- Page 117 and 118: In comparison to the Almaty Program
- Page 119 and 120: Box 6-6. ASYCUDA and Landlocked Cou
- Page 121 and 122: 6.4.5. The landscape of SIDS relate
- Page 123 and 124: Table 6-2. Example of science-polic
- Page 125 and 126: Figure 6-9. Data availability for i
- Page 127 and 128: Review Focusing on the Least Develo
- Page 129 and 130: Table 6-5. Coverage of SDGs in publ
- Page 131 and 132: - SYLWESTER, Kevin. Foreign direct
- Page 133 and 134: SIDS:- UNCTAD. Improving transit tr
- Page 135 and 136: Chapter 7.Science Issues for the At
- Page 137 and 138: 7.2.1. Open call for inputs to the
- Page 139 and 140: implementation (SDG17), peaceful an
- Page 141 and 142: percentage of women holding a leade
- Page 143 and 144: environment, in order to make stron
- Page 145 and 146: technology transfer. Respect for ea
- Page 147 and 148: Figure 7-5. Concentrations of plast
- Page 149 and 150: SDGs What is measured? Data source
- Page 151 and 152: UN SystementityECLAC Drafted and re
- Page 153 and 154: Figure 7-6 shows very wide ranges f
- Page 155 and 156: Table 7-8. Factors that promoted or
- Page 157 and 158: Chapter 8. New Data Approaches for
- Page 159 and 160: These novel Internet- and SMS-based
- Page 161: GabonNamibiaNigerSenegalRep CongoC
- Page 165 and 166: Figure 8-9. Map of internet connect
- Page 167 and 168: Box 8-11. A geographical approach t
- Page 169 and 170: There are many well established met
- Page 171 and 172: epidemics. Some African countries a
- Page 173 and 174: Figure 8-13. Data innovations cover
- Page 175 and 176: issues” in respective areas of ex
- Page 177 and 178: Notes1 United Nations, Prototype Gl
- Page 179 and 180: 51 Contributions sent by national l
- Page 181 and 182: 112 The 72 models are: AIM, ASF, AS
- Page 183 and 184: 201 For more information, please vi
- Page 185 and 186: 276 A. R. Subbiah, Lolita Bildan, a
- Page 187 and 188: 354 Information available at: http:
- Page 189 and 190: African Economic Outlook, Structura
- Page 191 and 192: 512 Report Of The International Min
- Page 193 and 194: 595 Jessica N. Reimer et.al, Health
- Page 195 and 196: 671 Pulselabkampala.ug, 'UNFPA Ugan
- Page 197 and 198: 732 Climate Change timeline: (a) Sc
- Page 199 and 200: 790 Oxfam. ICT in humanitarian prac
- Page 201 and 202: 863 T. Dinku. New approaches to imp