1HlG51J

More documents

Recommendations

Info

losses; predicting species distribution using a modellingtool; application of regional climate models in theprediction of surface temperature and precipitation; modelanalysis of non-CO 2 greenhouse gases; bio-dieselperformance and emissions; mobile health technology; andAfrican schistosomiasis control and drug research.7.5. Big data applications for sustainable developmentSo-called “big data” is another area in which scientists haveapplied new tools to provide information and analysis onaspects of sustainable development. Much of this emergingwork lies outside the official statistics systems and employsapproaches from the natural sciences to analyse social,economic and environmental questions. While chapter 8 ofthe present report provides a more in-depth account ofapplications of big data in the specific case of Africa, herewe provide a glimpse of the emerging broad picture of bigdata applications for sustainable development.“Big data” have been defined along divergent lines.According to one definition, big data are: high in volume,velocity, and variety; exhaustive in scope; fine-grained intemporal or spatial resolution, and inherently relational. 627Data might be user-generated (e.g. call records data,Twitter, Flickr), gathered by sensors (e.g., satellites, videos),or draw on data repositories made public by governmentsor corporations (e.g. real estate prices, subway records).Table 7-5 provides an overview of the wide range ofemerging big data applications and how they can supportthe whole range of SDGs at various geographical and timescales. At present, big data applications tend to be used insectors that are either correlated with big data productionof some kind, or that are currently monitored throughtraditional means. Some uses, such as for environmental orland-use monitoring through satellite data analysis havebecome commonplace. 634 Others like monitoring illegalfishing with satellites or literacy with cell phone recordshave just started.144“Big” may refer to gigabytes, terabytes or even topetabytes. 628 It is important to note that natural scientistshave long used these methods for production and analysisof scientific data. However, their diffusion into corporationsand the social sciences has attracted great attention by thewider public and earned the new label “big data”. Recentlybig data has been described as a complex ecosystem ofdata crumbs, capacity (tools and methods, the hardwareand software requirements) and community (producersand users of the data crumbs and capacities). 629The geographies of user-generated information are veryuneven between and within countries, and informationalpoverty has increased in some places. 630 In contrast, manydata gathered via sensors show a geographic coverage thatdoes not stop at national borders. For example, satellitedata is often global in scope - leaving no country “datapoor”, even though socio-economic, infrastructural, andeducational barriers to using these data remain.In the context of the SDGs, recent deliberations at the UNhave focussed on the question of whether big data couldcontribute to the monitoring of progress and theeffectiveness of policies, programmes andactivities. 631,632,633 They are envisaged to complementofficial statistics. This is in contrast to the monitoring ofMDGs which focussed exclusively on official statistics.A number of big data sources may be representative only ofparticular segments of society. 635 Data analysis methodsare being developed that aim to correct for sample bias andvarious types of data gaps, and to separate the sought-aftersignals from noise. For example, a Twitter-mining algorithmused to detect changes in food prices in Indonesiapredicted a food crisis where there was none. 636 In the2010 Haiti earthquake aftermath, social media dataproduction was only weakly (and inversely) correlated with637 638damage.Table 7-5. Big data applications in areas covered by the SDGs and in topics relevant for sustainable developmentSDGs What is measured? Data source Geographic scope of applicationSatellite images (night-lights) 639Global mapPovertyCell phone records 640Côte d’IvoirePoverty (SDG1)Price indexes 641 Online prices at retailers websites ArgentinaSocio-economic levels Cell phone records City in Latin America 642 ; UK 643Food price crises 644 Tweets IndonesiaHunger and foodsecurity (SDG2)Health (SDG3)Money spent on food 645,646 Cell phone data and airtime credit purchases A country in East-Central AfricaCrop productivity 647 Satellite images AfricaDroughtRemote sensingAustralia 648 ; Afghanistan, India, Pakistan 649 ;China 650Online searches US 651 ; China 652InfluenzaTwitter Japan 653 ; US 654Voluntary reporting through the internet 655,656 Belgium, Italy, Netherlands, Portugal, UnitedKingdom, United StatesMalaria 657 Cell-phone records KenyaPopulation movements658during an epidemicCell-phone records West Africa
SDGs What is measured? Data source Geographic scope of applicationCholera 659 Social and news media HaitiDengue 660,661Argentina, Bolivia, Brazil, India, Indonesia, Mexico,Web search queriesPhilippines, Singapore, Thailand, VenezuelaFlu, gastroenteritis and662chickenpoxOnline searches FranceVaccine concerns 663Media reports (e.g., online articles, blogs,government reports)144 countriesIllnesses Twitter 664 USVaccine concerns Twitter US 665 ; Indonesia 666HIV 667 Twitter USTwitter 668USWastewater analysis 669EuropeDrug usesocial media and web platform scans; emergencyroom and poison centre calls; arrestee drug testing; USlistservs 670Perceptions towards671contraception methodsFacebook and U-report UgandaEducation (SDG4) Literacy 672 Cell phone call and SMS records SenegalWomen (SDG5)Women’s well being 673 Twitter MexicoDiscrimination of women 674 Twitter IndonesiaPrecipitation measurements, water level and waterquality monitors, levee sensors, radar data, modelWater flows, quality ofWater and sanitation drinking water 675predictions as well current and historicNetherlandsmaintenance data from sluices, pumping stations,(SDG6)locks and dams.Leaks, clogs and water676quality issuesSensors SingaporeElectric power677Energy (SDG7) consumptionSatellite images 21 countriesEnergy use 678 Smart meters CanadaEconomic growthand employment(SDG8)Infrastructure,industrialization andinnovation (SDG9)Inequality (SDG10)Cities (SDG11)SCP (SDG12)Climate change(SDG13)Oceans (SDG14)GDP growth 679 Satellite images 30 countries;GDP at sub-national levels 680 Satellite images China, India, Turkey, USInflation 641 Prices from online retailers Argentina, Brazil, Chile, Colombia, VenezuelaUnemploymentBlogs, forums and news 681Ireland, USOnline searches 682,683USTourism 684 Mobile phone records Finland-SwedenMap with internet devicesby location 685Internet tools to scan all addresses of the fourthversion of the internet protocolWealth and inequality 686 Airtime credit purchases Cote d’IvoireMigration Social media 687 , online searches 688 Several countriesUrban extent and689,690populationSatellite images GlobalTransport use andjourneys 691 ; SubwayTransport cards data London, UKflows 692,693Travel patterns 694 Cell phone records Cote d’IvoireCommuting time 695,696 Traffic sensors FinlandCell phone records 697Cote d’Ivoire, Portugal, Saudi Arabia, USA (Boston)Flood hazard and risk Satellite imagesNamibia 698 ; Global 699 ; Nigeria, Niger-BenueRiver 700 ; Chamoli district, Uttarakhand, India 701Flood impact Cell phone records Mexico 702Perceptions of fuel subsidy703reformTwitter El SalvadorNet primary704,705productionSatellite images Greater Mekong sub-regionPopulation and energy706related GHG emissionsSatellite images WorldwideMethane 707,708 Satellite measurements USPerceptions on climate709changeTwitter WorldwidePerceptions on climate710changeTwitter WorldwideVessels conducting illegalfishingSatellite dataWorldwide 711 ; covers 75% of the globe 712 ;Ocean measurements 713Worldwide145World
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: Figure 7-5. Concentrations of plast
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 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
show all

1HlG51J

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?