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funding, national statistical systems will further have tocompete with other institutions for scarce resources.Increases in process efficiency e.g. through innovation canhelp producing better data and increasing theattractiveness for funding and recruiting. However, veryfew national statistical systems in low-income countries,especially in Africa, have either the technical or thefinancial strength to undertake research and development.Even when innovations are ready to be implemented,required initial investments of both money and humanresources are often not available. Budget dependence oninstable domestic funding and tied international supportcomplicates medium term planning to account for positivefuture returns from these initial investments. Regular andpredictable funding will be needed to implement and scaleup data innovations.168
Figure 8-13. Data innovations covering African countriesPovertyGUINEAMADAGASCARMOROCCOEducationDAKAR, SENEGALBOTSWANAEGYPTMOZAMBIQUENAMIBIASOUTH AFRICAWater resourcesLAKE VICTORIATerrestrial ecosystemsRainfall data, landcharacteristic andpopulation densitycombined in a soilerosion indexdcccxcix Combining census andsurvey data to produceonline poverty maps atUGANDAFood securityETHIOPIAERITREIADJIBOUTISOMALIARWANDABURUNDIUGANDAKENYASUDANSOUTH SUDANUsing SMS to track stocks ofessential medicine (Uganda)mTracTANZANIASatellite, survey and cellphone dataand geospatial modellingto produce highresolution poverty mapsAir time creditpurchases toestimate moneyspent on foodUNDISCLOSEDCOUNTRY IN EASTAFRICAPerceptions towardsimmunization andcontraceptionfollowed throughsocial mediadistrict levelCOTE D’IVOIREAnalysis of cell phonerecords to producepoverty mapsHealthUsing geospatial informationallocate health resourcesSmartphone apptakes land and soilscharacteristics andreturn potentialproduction anderosion for the siteEbola transmissionthrough populationmobility estimatedwith cell phonerecordsMost importantsources of malariainfections with cellphonedata analytics*These examples are illustrative and do not include all data innovations covering African countries.Highly granular literacymaps based on SMS andcell phone use (use ofSMS indicates literacy)SatellitesIntegrating data sources(e.g. traditional andmobile phone surveys)ALGERIAEGYPTNIGERIASOUTH AFRICAHandheld deviceswith GPS tocollect dataWater accounts toprovide information onwater use and associatedcostsCAPE VERDEMOZAMBIQUENIGERIASENEGALHealth and climateFree SMS-basedsystems to report dataOpen dataportalsKENYASOMALIATANZANIALocations and monthssuitable for malariatransmission based onclimatological averagesSatellite data ondesert aerosols usedas predictive indicatorto meningitisepidemics Satellite and grounddata on land cover todevelop GHG169inventoriesNAMIBIARWANDATANZANIAZAMBIABURUNDI ETHIOPIAKENYA MALAWIMAURITANIA NIGERIASENEGAL SOMALIAUGANDA ZAMBIANovel strategies forimpact evaluationstudiesClimateNAMIBIANIGERIASUDANSatellite data, groundstation measures andclimate models toimprove climate dataEcosystems maps usinggeospatial and satelliteinformationUsing satellite imagery todetect flooding in advanceETHIOPIAGAMBIAMADAGASCARRWANDATANZANIANatural disastersMixing social with biophysicalindicators to map vulnerabilityA geographicalapproach to landaccounts to analyseecosystem servicesMOZAMBIQUESENEGALFlood warning polesmonitor water levels andalert communities ofeminent floods
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GLOBAL SUSTAINABLEDEVELOPMENT REPOR
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ForewordIn September 2015, world le
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3.1. Interlinked issues: oceans, se
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7.2.1. Open call for inputs to the
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Box 5-10. Operationalizing inclusiv
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Figure 8-8. Location of ambulance u
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Hentinnen (DFID); Annabelle Moatty
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Friendship University of Russia, Ru
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List of Abbreviations and AcronymsA
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IRENAIRIISEALISSCITCITU-TIUCNIUUIWM
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USAIDVPoAVSSWBGUWCDRRWEFWFPWMOWTOWW
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Figure ES-0-1. Possible roles for t
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Figure ES-0-2. Links among SDGs thr
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increase either the availability or
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Chapter 1.The Science Policy Interf
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Complex relationship between scienc
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Communication between scientists an
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1.2.1. Highlighting trends and prov
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International, Marine Stewardship C
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limited. There is a relative dearth
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educe the time lag between science
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Chapter 2. Integrated Perspectives
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2.1.4. Recommendations by the Inter
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ultimate idea is systems design - t
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2.2. Integrated SDG perspectives in
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Hunger andagriculturePovertyWorld B
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IIASA-GEAPBLSEIOECDRITE-ALPSFEEMGSG
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Table 2-4. Number of models capturi
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In order for oceans, seas and marin
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fully integrated scientific assessm
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While some efforts are undertaken t
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Table 3-3. Impact of important clas
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Marine pollution from marine and la
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While the scientific coverage of th
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managementinitiative in BancoChinch
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equired, with natural and social sc
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Table 4-1. SDGs and DRR linkagesSDG
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poverty forces low-income household
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Figure 4-1. Economic losses relativ
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OECD countries and, if they are ava
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4.3.4. Baseline setting and assessi
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Using assessed levels of risk as ba
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Table 4-3. Disaster management cycl
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New sensor data also includes unman
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Chapter 5. Economic Growth, Inclusi
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Table 5-1. Industrial policy waves
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Figure 5-3. Number of Y02 patents p
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increasingly production specific an
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5.3. Industrialisation and social s
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education will either make it hard
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Table 5-3. UNEP’s five key types
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5.6. Concluding remarksThe precedin
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occurs despite the lower share of e
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LLDCs face several development chal
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technology-innovation (STI) policie
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6.2.3. Relevant publications for LD
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- A patent bank would help LDCs sec
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In comparison to the Almaty Program
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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 and 162: GabonNamibiaNigerSenegalRep CongoC
Page 163 and 164: Figure 8-5. Poverty map for Guinea,
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: epidemics. Some African countries a
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
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