Vulnerability of Southern Mediterranean Countries to Exogenous Shocks: Structural VAR Approach
Structural Vulnerability Map of Burkina Faso Feb. 2015 · Structural Vulnerability Map of Burkina...
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Structural Vulnerability Map of Burkina Faso - Feb. 2015 Definition of Structural Vulnerability: Structural vulnerability is a tendency to be in a state of high-risk to negative well-being outcomes(ie. undernutrition, anemia) on account of persistent exposure to various potential shocks (ie. climatic, price) in combination with a chronic resilience deficit (ie. lack of absorptive, adaptive and transformative capacities). GOURMA Vulnerability Estimate Linear Regression Analysis: Scatterplot shows grouping around trendline A linear regression was run to determine how well the Vulnerability Estimate composite model predicts (correlates to) the Well-Being Outcome composite. The assumption is that if Resilience Capacities and Exposure to Shocks and Stresses are combined to form a Vulnerability Estimate, the vulnerable zones identified therein should correlate to geographic zones of negative well-being outcomes (ie . undernutrition). Modeled grid cell values from both geographic models were used for the regression analysis. R-Squared = 0.39 TAPOA Resilience Capacity - - Absorptive , Adaptive, & Transformative Capacities Service Access Literacy Rates Poverty Food Security/ Ag. Productivity Anemia Undernutrition cm.aoe Well-Being Outcome Vulnerability Estimate (left) gets "corrected" here by averaging it with real well-being outcome measurements through the well-being composite above. The result of this correction is the Final Vulnerability Map to the right. Exposure (Shocks and Stresses) Malaria Prevalence Recurrent Price Shocks Historic Sites of Conflict Recurrent Climate Shocks Purpose of Map: The purpose of the final vulnerability map is to better identify "hotspots" of structural vulnerability USAID in order to better geographically target longer term resilience investments (development adapted to vulnerable contexts) where the need is greatest. Improved geographic targetting of the most vulnerable is critical to decision making regarding strategic investments for resilience and redressal of social justice related grievances which may lead to conflict. FROM THE AMERICAN PEOPLE Basic Methodology: Big data analytics were leveraged to identify structurally vulnerable zones. These zones of development need were calculated by averaging together all relevant and available sub-national development indicators across a broad spectrum. In all, 36 datasets, many of which were historical, were aggregated into composites, which were then aggregrated into higher level composites. Geographic areas where most development indicators were negative are more red and areas where indicators were relatively better are more blue. Basic data processing steps are listed below: 1) Identify most relevant sub-national indicators available for the analysis. 2) Convert each geographic dataset to raster format. Use Kriging interpolation in the case of point data. 3) Winsorize data where appropriate based on histogram analysis. This prevents the data from being skewed by outlier data and amplifies geographic variation. 4) Rescale all datasets to a common 0-100 scale so that they are comparable for averaging to create composites. 5) Average datasets using weighting based on consensual subject matter expert judgement to create composites. Mali SO UROU NAYALA BA NWA ZIRO Ghana - - C'A S€-JAli>ES COMOE SUDOUEST PONI Cote d'Ivoire N Niger ES 7' GOUR MA TAPOA KO ULPELOGO KOMPIE NGA Togo Final Vulnerability Map Legend: how to read this infographic 1) Red Zones on all maps are most vulnerable and the blue zones represent least vulnerable relatively. 2) Boxes with blue border contain composite index maps. The + sign between composite maps signifies that they are averaged together to form yet another composite map. Follow the arrow symbol with = sign to see which composite they create when averaged together. 3) Boxes with brown border contain sub-composite index maps. All subcomposite maps in brown bordered boxes are averaged together to form the composite maps they feed into (see the tip of brown box with = sign in it). It should be noted here that subcomposite maps in this infographic are based on other component maps not visualized on this infographic. The table at right gives the specifics about datasets used and the nature of the components maps that were aggregated using a weighted average to produce the subcomposite maps you see in this infographic. 6) Numbers in red circles represent relative weighting of each composite or subcomposite as it contributes to the average. Weightings are represented as a percentage out of 100%. The reason that some of the weightings are odd numbers is because they were derived from an initial intuitive weighting based on double-weighting, half-weighting, one and a half weighting etc., based on a group consensus of subject matter experts. These informal weightings were then converted to a percentage of 100 to calculating weighted averages to form composites. Top 50 Most Vulnerable Communes "'"' Oe parlmen l ' TIN-AKOH ' DfOU 3 OURSI 4 GOROM-GOROM ' MARKOYE 6 KOUTOUGOU ' TITAEIE ' FALAGOUNTOU ' TANKOUGCUNADIE '" ARl!I NDA 11 SEEIBA " BOUNDORE " GORGI\DJI " DORI " SEYTEi %A " BOURCUM " SOLHAN " NAGEII NG OU " SAMPELGA w TONGOMAY~L H NASSOUMBOU " BANI '" YA LGO " kEl..80 " M ANSILA " PENS,q. " DJIBO " DABLO " DIGUEL " TOUGOURI n SOLLE " BARMOULE " BANH " COALLA " l IMNI " toUIU/l.NGA " BARSALOG HO " OU INDICiUI " POR E·MENGAO " TIT AO " NAM ISSIG UIMA " THlmJ " BOTOU '' FOUTOURI " 8ARTIE80UGOU " TOEGHI N " 80GANOE " LI PTOUGOU " NIDU so ZEGUEDHOUIN Province Oud~IEO Oudolon Oudalao Ouda lao Oudalan Soum Y.igha Seno Y.igha Saum Y.igha Yagha Seno Seno Seno Nom~nkn~ Yagha Nemenl en~ Seno Saum Soum Sen • NamenIen~ Soum Yagha Sanmatenga Saum Sanmat~nga Saum Namentenga LOfoum Soum Lo.-oum Gnagna G, ..,~nJ B,m Sanmat en&,! LOfoum Saum L0<oum Sa11mdl C:!1 f,'l G<1ogna Tapoa Komancljaii Ko m~·ncljar i Kourweogo Gr1<gna G~sna Kcurweogc Namentenga Rq;ion Vul nerability §core POP [2006 Ce n.., 11 SAHEL %3 21,013 SAHEL 95.~ 2'.i,321 SAHEL 91_9 15,.806 S/l.HEL 88.8 106,346 SAHE L 8B.S 27,478 SAHE L 88 .4 18,655 SAHEL 87.S 20.63Sl SA HEL :-l 'i.~ 18,18() SAHEL 85.2 16.453 SAHE L ~4.5 91.(J20 SAHEL 84.1 32.374 SI\HEL 133-9 22,773 SI\HEL 83-7 29,913 SI\HEL 82,6 106,808 SAHEL 82.2 31.S85 QNTRE NORD 81.6 46,232 SAHE L 81.1 25,108 QNTRE NORD 80.9 16,004 SAHEL 80.6 19,127 SI\HfL 80."l 70,3n SAHEL 80.1 20,165 SAHEL 79 .6 S9,27B CENTRE NORD 78.7 31,541 SAHEL 78.3 24,1~7 SI\HEL 78.2. 4l,30S CENTRE NORD 77.3 36.158 SAHEL 76.3 GD,042 CENTRE NOIIO 75.2, 20,707 SAHEL 75.0 8,989 CENTRE NORD 74.4 76.824 NORD 72.8 17,526 SAHE L 72.0 29,383 NORD 70.8 30,332 m 70.8 42,652 m 70 .7 68,4'18 CENTRE NOI\O 69.8 47,75 1 QNTRE NORD 69 .7 78,'}19 NORD 69.4 Z8,l78 SI\HEL 69 .3 Z4,~2 rJO RD 692 66,717 QNTRE NORD 69.0 9,752 m 68.3 23,025 m 68.0 46, "59 m 67.7 H,583 m 67 .S 16,067 PlATEAU CENTRA L 67 .4 16,500 m 66.7 84,&38 m 66.S 41,823 PLATEAU CENTRAL 66.2 26,998 UNTRE NORD 66.0 21,904 For more information: Jeremy Chevrier USAID/Senegal Sahel Regional Office [email protected] or Siaka Millogo USAID/Burkina Faso [email protected] Title fEWSNH "' ~olnor,-s,, H sufli<ienl farm household, :ioilCri1an1< Carbon Con,;ty [ducatlonal l.evt!I Ltter•cv R•les Pov@r-\' Reminonce> Wodhlnd~ L-a<k of Acee" to Health s.,-,1,., on A<<ount of Financial Co nstrain IS t"mpl,a l li•esto,k UnilS lmm~rali<m Rates La<kct ~cce,no Healch s,,..,1,., M .... ,oun, of c;.t.in<a • 1sran,o 10 Heolrh c,,nw ~cf People i,,,rUnlt•<e• Acee., t<> improsed Sanitation Ac,~,, to lmpru"1l0 Orin kins wo,orS<><Jru > 10 minu:es wai. ,o """"" Ctlnklngw;,t.,>Q<Jn;• ~verage «alnfalt Variability Ave••£• Teml"'roture ~"""' l!ainl!iea><>n A,ior,ot,, LOO£\~ Of R•ir,y f,oa5on AveragoTN•IAnnutl Preclplliftl o~ Hi>l6rloar C..allkt Rolo8••• M8lori• P-•l•nc~ •v•r,,g~ niillet prw;e duri"l!I '"""••awn Averngr, '(l' ll <>w earn ,..1ce duri"it 1 ,an _..,n Avera1~whl le <om pri<e d" rinc k,;,,,=on As~rage whlle<0rghum ~" ~U r1"'8 1 Nll-,<on •s•••~~ rod sorghum pr Tee ~ur!ngleal,..,,.,~n Provaten«r~S-k•on Anemia An<!fflQ Prevak>nu! jWtrmen] Ae~rago GAA1 ~ale>ISMUll Aver~il~ GAM Rates (OHS) Avcr.ttte Stun llng Rat es (SMART\ ,o,,-~•Sluolio~ Rat .. (DHS) All datasets used for composites (d atasets averaged based on weightings listed): datasets averaged based on we ig ht ings Ust ed "' historical datasets used when ailallable in order to map structu ral vul nera bility \IS. conjunctural * a ll t ime series datasets have been averaged over entire period to map te nde nc y (structural iss ues) Admin SubCom SubCampoake Fi na l Source Cate Range C@""I Methodotogv noie.. po,lte lltle Woi tht tomPo•lk!Tltl<i Wei:ht FCWS~ ' ET food ••curi1•1 Prcr,ic<f'./ O\Jt lookdala 1000-2014 LivehMod ,,., erai,,d 11' C ..:ore rer ,one o,~r ent ~e t i""' pe-riod . ,~ - F.ood See"rfty =- Sl"'ISme d'Alerte Pr€coce Corcm,ne ,cote gene r ~•d ,V tctalling rar,,t, r of time, (SA/') \IUlnera',le 1009.2014 Cnmmrn< commu11••·;d• n~~od •• , ulr.orable <luring lime pef;,,d, 5r.t: commune, Faad MirMslr. ful ')oriculture - imlf! • .l(fl.q ROgiM ~""""£"d % per r,,ginn n,, ., hnth '/"-'" 4.l'II, Secu r lt¥/Ag . Bu-kin, Faso Th~ snll aganlc ,_..,rboo predlaed mes n furl he l;t Prm:luct iv it.,. lr,temational Soll si:anCard depth (· l-Scmt in,1 ,rn,..,ard dep1h ($,-!Semi A!lril:uMural - R ef~r~l'C1' an,J Prod u,tivity lnlormatlon Cei tre - 2013 Ras:.tr .a nd >rd •l andard '" world >Ool lnf<:rmation dept h (15- >0<m] were summd fur- anappm,cimation <lTth~ s<ill org,, n l: carhan In l"fl soil, which i, n-lD<m. Am.1,ir,, Statisti~ue d, 2a!D-201J Provinco The _, ir.~ rotes for ~••~e, 1 lhru 5 were "'"'r•~~d ,nd w. l' ed "cadon nation, le then IC .e,e averages were avern.ged ~·, e, the 4 years. llrnl ng th<> ~00~ ~"" "~ e,pry,,n~ r,,,,r lh~ •l'JI nl 3 Lite r acy Ra t es cen,os Data 1006 comm....., were as,ed whecher or not t~e ·responden, oould read - ,nci wr~• in anv lae~uag,o. 8urldnobe HO\Jse ho ld lworagod P""•rtv rate< 11r region 1oappm,c;m~ te IJUin~ (Onditicn, 5u(',"@y 1003. 1009 R•i•on '" ti:C3~MI gcr.era l tenden c· 1 Poverw 6a nqtJP,. C,,nt rak> !'tats tad)..,tedl 15¼ A pe r eai:itol ,mount .,,, " lcui,1e<11o, remitta.-.:e, de l" A l~que de l'Oc,e;I 2011 Re BiOn ~• rre{:ion. '" IBcrAO) Oemo;ira phi< and Health 2[1,)3. 2010 CluSler ~nt .. interpo'.ateQ to Raster usin~ Xrlg in~ Method (both JJ¼ SuNe ., IOIISI e•= ,,.,..,r_ , fflr each eara,e ra "' Demographic and Ilea Ith Clusicr Points l nrorpD'ate<l to Raster ..-, I ng ~rtg1n~ Mct hOO (Dnth SuNe.\« (DH<;I 1003, 2010 ~oinr, "'""'' fn, oach year a,orag,,<tl .. Poverty Mini,:,yct Live,toi:k • 2012 6urkN ~ a,o Prtll'ince Proje cted Livesluck figu r~s mnverted lu TW ,~ Du,1 11~ t ile 1006 censu:;, """'Y fan,ily was asbs:111 lhey had mOl'ed la The lasayear, and n so, tmm where ro whoro. lmmi gr,tion ~•••• v1ere u,ee "8 pro,~ for Census D ata coo; Commu-re vul aerabiliC'1 bawd oo 1he a,sumption that generally ,~ ,nne, rhat.arc less •mlncr.1ble .a re ,r.o. '1! ,atlractlw l<Iffcr mo•~ o pportu nltleS1 and thus h '"'" higher rates of imm'8["~.on. Domo~,optic ,n, Ho, lth C1u, 1 or f'oirits 1nterpo!, te,;l t o ~utor wi ng ,;,ig,n~ Method \ooth Survev, [DHSI l003, lOlO Point,. ra sters for each ·1ear a,e ragedl '"' Ellmncuo Yea~y repon from The Ml nlsl,v of Health that ~••ll~Ser,icw, - MCniSln,' of Heatt~ • ,Oil r,o\llnce. calcu!at<es how many people In each """""' e a<e JO ,m - Bu,kiru Fa,o er m~ r• awav from , health ,..,,~, AFRIPCP W14estimote Raster _ .., . .._., ot low,.,- po;wlati <>1 at• oor .,ido,;,,d a,;, po,,•1 to vs lade oJ acc~ss to seMces l=note nessl Serv ice Access Demo!)rophinnd H,.ltt, l.00:1, 2010 Clu,1er f'oints ;n te,- pQ'oted to Ra >t er u, lnR ( ri~ :n; Metliod (both "" $"""' ~ ' [DH>I , .~ ro,i,,,. lot • • <h ·1 .. , o, • ro,;edl Demo;;r ap hic and Health 1t m • .01u Cluster f'oint> interpolated to Raster <£ ins Krigin o Met hod (Dot h l.3 ½ SU s IO!l5i e•~ "''"'" loreach ea r ••• •• · •• oemoarapllk an~ Health l 003, 2010 (1(,,1,r F'Oints 'interp~ ,te<i to Ra ;t,r llSiOg ~~g in~ Met hod (bOl h - 5ur,,ev<[CHSI eo ~ raster< fur e•<h y_ ea r a , ~raged1 CO<ffic,ont oi variation of r,, inf; II Oata w..s ca l <u latod acroz entire ,ime pe riccl for the mon:h cl May [plant"'1: t ime) and !he monrh cf Ottoberi har,,etJ. The rnlRPI .... ,e,, C. funk ot Ian, 1%1 · Rascer variation m ,,.;nf•II du,;o -'l •h.,• tv,:o m'""1\h, 1, - " Sept. 2011 consi, ere d crlicat The lwo ra,ters "" '" lh en averai,-ed to hi~lilii:1,1 t he mu, t vu l,.,,able n,re; i., ,eg,CO, . to ,oinfa'. l ,,.,;,bi! ~v and i t, affep on ag amduc1 ion. 11ve••ll' ,~mi,erature duri,g eaeh reio1· si,a;,,., VJ-.5) Recur rent Uni,eroily -0/ <i6\ k · ,gr ;, •, Z000-2011 <>ve r OOlirtr time i;erio~ wa>~vecago~ :OIIOU li"Mr.l l a:rna ti , l\eser•r<+, Unit (JJt.S-rdilly R"\er rainy ,~ae<n av<,ra~e \ernperaluro. ~l\eni ver•~• ~- C li mate 5hock (UEA,11'.AU) ...,,,on) t»mperat,.-e durlfll: !9"'Y season can be ,:,insi<leted a i:=1• 1, pl an! ,1,.,, at highor tomporoture,. famine ~• rl v War.si n~ l<lne> With s~orter ,air .~ ;eosons are con,ide/M more s1·srem, Nmwo-1: 1.JOl-2010 Rasrer vui,-.,,a ble. ,~ (ft'WSNHi CHIRP> ,;a,o,e~ C. funk •t Ian, '1961 · Calc l-i•te,J .,.,., wt h ;;....., period.. Zor. e, d l""'er Rascer .Ne rage total precip1:.otion are consioered rn:,re - " Sept. 2014 vu1nr;ra b1e, 41. of incident, per location p!u, ~umbe r cffa,alitle. ~,med ( <infl ict Cc,,;ati,a !'< 1/1/1~,o multipiod t, 1 , t v,o wa, u;ed lo g,,ee,ate, "<o"fti<t Ev;,,~ Dal a (AClECI 7/10/2014 Po1r,1001a l e, .. .. pe, PPinl l"' a l;,,11, All !we, ofWo\ft i Cl fr"1, ,,. Oatahase ~•ta Mse • .11e"' lnilurled (le- prnt,,.;r,, , mien £'0' Jp;, Historic Siles of police , e<mic militie->.etc.). Ca nfl kl I W0tlo Food Froi;ramme Tota l ~• count was used per locat;,,n as• prcxy to (WFl'I 2014 Poir,t Da ta cu,,nid b•c•-~f pu, ulaUon m;l r~><...-e< pres.u,., li¼ crEated by- refuse• preserace. Demo,rophic and H.,lth 2010 Point Dot• Points inl-<roolaled to R"ter us ing ~ri8 iag Met hod (b oth I - Heal t h Shock Sur,e,,. IDl!SI rasle,s fu, each ·1ear a,orago,;1] Puinl Udla repre s ent< a ll mar kets l urveied rnur,lhly !o< ~r ice,. A·,·erage marl<et prices"'"" c, 1 ,u!ated for all m•rl.:.t, ov•-"tim• du~nel••n '"'"'" - LI!• n ~••on i, when t.i ~h pri<es have the biggest ~ti"" impact on SIM}SO~' AGESS 2~04-2014 Pelot Daia house hold fuod se curity. 1'<:trn d:ara was ln:erpolated = 10 ~aster u,li,g Krlglng Me,hod fbnth r;;:ers lor ea ch v.,.r aWtra;i•d). ib>latiWI w•ightieg lor-•ach commodil'/ was ca lc ulated prol>C'1 iooa ll¥ to each comrROO~ie, Recurrent Pr ice production level. Shocks SIM,(SO~AG.SS 2U04•l 014 Point o at• SAME MfTHODOWGV NOTES FOR ALL PlllCE CATA IN COMP051H(SEENOlE>FOR MILlET PRl([S] 5~"1 Carn Prlaes ~, SIM/50 t 1'1GE55 2004·2014 Point D&t• SAME MfTHDOOLOGf NOTES fOR ALl ~ I CE DATIi IN '"' COMPOS I TE jSEE NOTES FOR MI LLET PPJCES] SIM}SO~ ' /\GESS lll04-2014 Puir,l 0~1< SAME MITHOOOLOGV NOTES fOR ALL PlllCE OAT~ IN O:, MFOSI H 15EE NOH:i FOR Mill.ET PRICES] n, SIM/SO~•oi:;c,5 ;w04- :l014 t'<l inlC O> U SAM< METHDOOLOGt NOTES fOR ALL P!l lO: CATA IN ,,. COMPOSITE ]SEE NOUS fOR MILLET PI\ICES] Derno,raphic and Healtti 1 cm, 20,o Clu,lOr Point,. interoo:,teQ to R aster uoiag ~ri8 "1g Method (l>ot h = Surveys [Dl!SI Points r•srer, for each ·1ear auerai:ed) ,t.~emla Demo~raphio an~ Heal th Cluster F'oi,m ioter pd awi to Raster u;in~ Kr.eJae Met hcd (OOl h Prevaleoce l.OO'l, WI• li¼ 5ur,e'/$ (OHSI Po ints rast<erofur each ·1ear a, e,aw,dl 5'8nd~d i,.d Monitorin g Each re gion was polled bf SMARr every oiC.er v ear, all and Asse;sme 11t ol Re :;e f l009 ·2013 Re&; oa inroi-matlim was averaged 1cgether "' AvorageGAM 3n0 Tran sil i>ns ISM~RT I ~7 ?> ~-"· Demop; raphic en~ Health Clu.,•r F'Oiots ·interpo'ated to Ra,ter u;inp; ~•l~ ing Met hod (Oorh 100_3, 2010 '" 5"""''1S IDHSI Points raster, 10, each eara, ora!IOdl Un de rnutrl tlor, St>ndodi>ed MITT tonng Ea ch region was poll eC every othorycoc, all ln'o.'fflation and A,,;,.,,srn~nl ci1 R~:i~f 2~09-2013 Re~i ou ,.,., •v~r~J!<d toAAther '" "'~""'II<' andTra oSltlCHIS !SMART] srun11ng !late< ~¼ Derno.iraphic an:! Health 10)3. WJ.0 Clus1or Points ir,ter po'aleQ. to R aster using Kr1!! "1S Ml!lhod (bnth ~0'6 SUM:V, 101151 Points rasters fur each year auc raf:C(fJ Composite Top lnde• ~., -~In Compooit> W@ij:ht 31% 23% Reslllence '"' Capacity 31% 15% 44% Exposure )1% !Shoc k~ So 21% stresses ) 11% 33% 20% We lHlelng Ouloome ~0% BO%
Transcript of Structural Vulnerability Map of Burkina Faso Feb. 2015 · Structural Vulnerability Map of Burkina...
Structural Vulnerability Map of Burkina Faso - Feb. 2015 Definition of Structural Vulnerability:
Structural vulnerability is a tendency to be in a state of high-risk to negative well-being outcomes(ie. undernutrition, anemia) on account of persistent exposure to various potential shocks (ie. climatic, price) in combination with a chronic resilience deficit (ie. lack of absorptive, adaptive and transformative capacities).
GOURMA
Vulnerability Estimate
Linear Regression Analysis: Scatterplot shows grouping around trendline
A linear regression was run to determine how well the Vulnerability Estimate composite model predicts (correlates to) the Well-Being Outcome composite. The assumption is that if Resilience Capacities and Exposure to Shocks and Stresses are combined to form a Vulnerability Estimate , the vulnerable zones identified therein should correlate to geographic zones of negative well-being outcomes (ie. undernutrition). Modeled grid cell values from both geographic models were used for the regression analysis.
R-Squared = 0.39
TAPOA
Resilience Capacity
--
Absorptive, Adaptive, & Transformative Capacities
Service Access
Literacy Rates
Poverty
Food Security/ Ag. Productivity
Anemia Undernutrition
cm.aoe Well-Being Outcome
Vulnerability Estimate (left) gets "corrected" here by averaging it with real well-being outcome measurements through the well-being composite above.
The result of this correction is the Final Vulnerability Map to the right.
Exposure (Shocks and Stresses)
Malaria Prevalence
Recurrent Price Shocks
Historic Sites of Conflict
Recurrent Climate Shocks
Purpose of Map: The purpose of the final vulnerability map is to better identify "hotspots" of structural vulnerability USAID in order to better geographically target longer term resilience investments (development adapted to vulnerable contexts) where the need is greatest. Improved geographic targetting of the most vulnerable is critical to decision making regarding strategic investments for resilience and redressal of social justice related grievances which may lead to conflict.
FROM THE AMERICAN PEOPLE
Basic Methodology: Big data analytics were leveraged to identify structurally vulnerable zones. These zones of development need were calculated by averaging together all relevant and available sub-national development indicators across a broad spectrum. In all, 36 datasets, many of which were historical, were aggregated into composites, which were then aggregrated into higher level composites. Geographic areas where most development indicators were negative are more red and areas where indicators were relatively better are more blue.
Basic data processing steps are listed below:
1) Identify most relevant sub-national indicators available for the analysis.
2) Convert each geographic dataset to raster format. Use Kriging interpolation in the case of point data.
3) Winsorize data where appropriate based on histogram analysis. This prevents the data from being skewed by outlier data and amplifies geographic variation .
4) Rescale all datasets to a common 0-100 scale so that they are comparable for averaging to create composites.
5) Average datasets using weighting based on consensual subject matter expert judgement to create composites.
Mali
SOUROU
NAYALA
BA NWA
ZIRO
Ghana
--C'AS€-JAli>ES
COMOE
SUDOUEST PONI
Cote d'Ivoire N
Niger
ES7' GOURMA
TAPOA
KOULP ELOGO KOMPIENGA
Togo
Final Vulnerability Map
Legend: how to read this infographic
1) Red Zones on all maps are most vulnerable and the blue zones represent least vulnerable relatively.
2) Boxes with blue border contain composite index maps. The + sign between composite maps signifies that they are averaged together to form yet another composite map. Follow the arrow symbol with = sign to see which composite they create when averaged together.
3) Boxes with brown border contain sub-composite index maps. All subcomposite maps in brown bordered boxes are averaged together to form the composite maps they feed into (see the tip of brown box with = sign in it). It should be noted here that subcomposite maps in this infographic are based on other component maps not visualized on this infographic. The table at right gives the specifics about datasets used and the nature of the components maps that were aggregated using a weighted average to produce the subcomposite maps you see in this infographic.
6) Numbers in red circles represent relative weighting of each composite or subcomposite as it contributes to the average. Weightings are represented as a percentage out of 100%. The reason that some of the weightings are odd numbers is because they were derived from an initial intuitive weighting based on double-weighting, half-weighting, one and a half weighting etc., based on a group consensus of subject matter experts. These informal weightings were then converted to a percentage of 100 to calculating weighted averages to form composites.
Top 50 Most Vulnerable Communes "'"' Oe parlmenl
' TIN-AKOH
' DfOU
3 O URSI
4 GOROM-GOROM
' MARKOYE
6 KOUTOUGOU
' TITAEIE
' FALAGOUNTOU
' TANKOUGCUNADIE
'" ARl!I NDA
11 SEEIBA
" BOUNDORE
" GORGI\DJI
" DORI
" SEYTEi%A
" BOURCUM
" SOLHAN
" NAGEIING OU
" SAMPELGA
w TONGOMAY~L
H NASSOUMBOU
" BANI
'" YA LGO
" kEl..80
" M ANSILA
" PENS,q.
" DJIBO
" DABLO
" DIGUEL
" TOUGOURI
n SOLLE
" BARMOULE
" BANH
" COALLA
" lIMNI
" toUIU/l.NGA
" BARSALOG HO
" OUINDICiUI
" POR E· MENGAO
" TITAO
" NAM ISSIG UIMA
" THlmJ
" BOTOU
'' FOUTOURI
" 8ARTIE80UGOU
" TOEGHI N
" 80GANOE
" LI PTOUGOU
" NIDU
so ZEGUEDHOUIN
Province
Oud~IEO
Oudolon
Oudalao
Ouda lao
Oudalan
Soum
Y.igha
Seno
Y.igha
Saum
Y.igha
Yagha
Seno
Seno
Seno
Nom~nkn~
Yagha
Nemenl en~
Seno
Saum
Soum
Sen• NamenIen~
Soum
Yagha
Sanmatenga
Saum
Sanmat~nga
Saum
Namentenga
LOfoum
Soum
Lo.-oum
Gnagna
G, .. ,~nJ
B,m Sanmat en&,!
LOfoum
Saum
L0<oum
Sa11mdl C:!1 f,'l
G<1ogna
Tapoa
Komancljai i
Ko m~·ncljari
Kourweogo
Gr1<gna
G~sna
Kcurweogc
Namentenga
Rq;ion Vulnerabi lity §core POP [2006 Ce n..,11
SAHEL %3 21,013
SAHEL 95.~ 2'.i,321
SAHEL 91_9 15,.806
S/l.HEL 88.8 106,346 SAHE L 8B.S 27,478
SAHE L 88 .4 18,655
SAHEL 87.S 20.63Sl SA HEL :-l 'i.~ 18,18()
SAHEL 85.2 16.453 SAHE L ~4.5 91.(J20
SAHEL 84.1 32.374
SI\HEL 133-9 22,773
SI\HEL 83-7 29,913 SI\HEL 82,6 106,808
SAHEL 82.2 31.S85
QNTRE NORD 81 .6 46,232
SAHE L 81.1 25,108
QNTRE NORD 80.9 16,004
SAHEL 80.6 19,127
SI\HfL 80."l 70,3n
SAHEL 80.1 20,165
SAHEL 79 .6 S9,27B CENTRE NORD 78.7 31,541
SAHEL 78.3 24,1~7
SI\HEL 78.2. 4l,30S
CENTRE NORD 77.3 36.158
SAHEL 76.3 GD,042
CENTRE NOIIO 75.2, 20,707
SAHEL 75.0 8,989
CENTRE NORD 74.4 76.824
NORD 72.8 17,526
SAHE L 72.0 29,383 NORD 70.8 30,332
m 70.8 42,652
m 70 .7 68,4'18
CENTRE NOI\O 69.8 47,75 1
QNTRE NORD 69 .7 78,'}19
NORD 69.4 Z8,l78
SI\HEL 69 .3 Z4,~2
rJO RD 692 66,717
QNTRE NORD 69.0 9,752
m 68.3 23,025
m 68.0 46,"59
m 67.7 H,583
m 67 .S 16,067
PlATEAU CENTRA L 67 .4 16,500
m 66.7 84,&38
m 66.S 41,823
PLATEAU CENTRAL 66.2 26,998
UNTRE NORD 66.0 21,904
For more information: Jeremy Chevrier USAID/Senegal
Sahel Regional Office [email protected]
or Siaka Millogo
USAID/Burkina Faso [email protected]
Title
fEWSNH
"' ~olnor,-s,,H sufli<ienl farm
household,
:ioilCri1an1< Carbon Con,;ty
[ducatlonal l.evt!I
Ltter•cv R•les
Pov@r-\'
Reminonce>
Wodhlnd~
L-a<k of Acee" to Health s.,-,1,., on A<<ount of
Financial Constrain IS
t"mpl,a l li•esto,k UnilS
lmm~rali<m Rates
La<kct ~cce,no Healch s,,..,1,., M ....,oun, of
c;.t.in<a
• 1sran,o 10 Heolrh c,,nw
~cf People i,,,rUnlt•<e•
Acee., t<> improsed
Sanitation Ac,~,, to lmpru"1l0 Orin kins
wo,orS<><Jru
> 10 minu:es wai. ,o """"" Ctlnklngw;,t.,>Q<Jn;•
~verage «alnfalt Variability
Ave••£• Teml"'roture ~"""' l!ainl!iea><>n
A,ior,ot,, LOO£\~ Of R•ir,y f,oa5on
AveragoTN•IAnnutl
Preclplliftlo~
Hi>l6rloar C..allkt
Rolo8•••
M8lori• P-•l•nc~
•v•r,,g~ niillet prw;e duri"l!I
'"""••awn
Averngr, '(l' ll <>w earn ,..1ce duri"it 1,an _..,n
Avera1~whl le <om pri<e
d"rinc k,;,,,=on As~rage whlle<0rghum ~"
~Ur1"'8 1Nll-,<on •s•••~~ rod sorghum pr Tee
~ur!ngleal,..,,.,~n
Provaten«r~S-k•on Anemia
An<!fflQ Prevak>nu! jWtrmen]
Ae~rago GAA1 ~ale>ISMUll
Aver~il~ GAM Rates (OHS)
Avcr.ttte Stunllng Rates (SMART\
,o,,-~•Sluolio~ Rat .. (DHS)
All datasets used for composites (datasets averaged based on weightings listed): ~ datasets averaged based o n weight ings Usted
"' historical datasets used when ailallable in orde r to map structu ral vulnerability \IS. conjunctural * all t ime series datasets have been averaged over entire period to map tendency (struct ural issues)
Admin SubCom
SubCampoake Fina l Source Cate Range
C@""I Methodotog v noie.. po,lte
lltle Woitht tomPo•lk!Tltl<i
Wei:ht
FCWS~' ET food ••curi1•1 Prcr,ic<f'./
O\Jt lookdala 1000-2014 LivehMod ,,., erai,,d 11'C ..:ore rer ,one o,~r ent~e t i""' pe-riod . ,~ - F.ood See"rfty =-Sl"'ISme d'Alerte Pr€coce
Corcm,ne ,cote gene r~•d ,V tctalling rar,,t, r of time, (SA/') \IUlnera', le 1009.2014 Cnmmrn<
commu11••·;d•n~~od • • , ulr.orable <luring lime pef;,,d, 5r.t:
commu ne, Faad MirMslr.ful ')oriculture - imlf! • .l(fl.q ROgiM ~""""£"d % per r,,ginn n,,., hnth '/"-'" 4.l'II, Secur lt¥/Ag . Bu-kin, Faso
Th~ snll aganlc ,_..,rboo predlaed mes n furl he l;t Prm:luct iv it.,.
lr,temational Soll si:anCard depth (·l-Scmt in,1 ,rn,..,ard dep1h ($,- !Semi
A!lril:uMural -Ref~r~l'C1' an,J Prod u,tivity lnlormatlon Cei tre -
2013 Ras:.tr .a nd >rd •landard '" world >Ool lnf<:rmation
depth (15- >0<m] were summd fur- anappm,cimation <lTth~ s<ill org,, n l: carhan In l"fl soil, which i, n-lD<m.
Am.1, ir,, Statisti~ue d, 2a!D-201J Provinco
The _,ir.~ rotes for ~••~e, 1 lhru 5 were "'"'r•~~d ,nd w. l'ed"cad on nation, le then IC.e,e averages were avern.ged ~·,e, the 4 years.
llrnlng th<> ~00~ ~"""~ e,pry,,n~ r,,,,r lh~ •l'JI nl 3 Liter acy Ra t es
cen,os Data 1006 comm....., were as,ed whecher or not t~e ·responden, oould read -,nci wr~• in anv lae~uag,o. 8urldnobe HO\Jseho ld
lworagod P""•rtv rate< 11r region 1oappm,c;m~te IJUin~ (Onditicn, 5u(',"@y 1003. 1009 R•i•on '" ti:C3~MI
gc r.era l tenden c·1 Poverw
6a nqtJP,. C,,nt rak> ~ !'tats tad)..,tedl 15¼
A per eai:itol ,mount .,,, " lcui,1e<11o, remitta.-.:e, de l" Al~que de l'Oc,e;I 2011 Re Bi On
~•rre{:ion. '" IBcrAO) Oemo;ira phi< and Health 2[1,)3. 2010
CluSler ~nt .. interpo'.ateQ to Raster usin~ Xrlg in~ Method (both JJ¼
SuNe ., IOIISI e•= ,,.,..,r_, fflr each eara,era "' Demographic and Ilea Ith Clusicr Points lnrorpD'ate<l to Raster ..-, Ing ~rtg1n~ McthOO (Dnth
SuNe.\« (DH<;I 1003, 2010 ~oinr, "'""'' fn, oach year a,orag,,<tl
.. Poverty
Mini,:,yct Live,toi:k • 2012
6urkN ~a,o Prtll'ince Proje cted Livesluck figur~s mnverted lu TW ,~
Du,111~ t ile 1006 censu:;, """'Y fan,ily was asbs:111 lhey had mOl'ed la The lasayear, and n so, tmm where ro
whoro. lmmigr,tion ~•••• v1ere u,ee "8 pro,~ for Census Data coo; Commu-re vul aerabiliC'1 bawd oo 1he a,sumption that generally ,~
,nne, rhat.arc less •mlncr.1ble .a re ,r.o.'1! ,atlractlw l<Iffcr
mo•~ o pportu nltleS1 and thus h'"'" higher rates of imm'8["~.on .
Domo~,optic ,n, Ho, lth C1u,1or f'oirits 1nterpo!,te,;l to ~utor w ing ,;,ig,n~ Method \ooth Survev, [DHSI
l003, lOlO Point,. rasters for each ·1ear a,e ragedl '"' Ellmncuo
Yea~y repon from The Mlnlsl,v of Health that ~••ll~Ser,icw, -MCniSln,' of Heatt~ • ,Oil r,o\llnce. calcu!at<es how many people In each """""'e a<e JO ,m -Bu,kiru Fa,o
er m~r• awav from , health ,..,,~,
AFRIPCP W14estimote Raster _.., . .._., ot low,.,- po;wlati <>1 at• oor.,ido,;,,d a,;, po,,•1 to vs lade oJ acc~ss to seMces l=note nessl Service Access
Demo!)rophinnd H,.ltt, l.00:1, 2010
Clu,1er f'oints ;n te,- pQ'oted to Ra >ter u, lnR ( ri~ :n; Met liod (both
"" $"""'~' [DH>I ,.~ ro,i,,,. lot • • <h ·1 .. , o,• ro,;edl Demo;;raphic and Health
1tm • .01u Cluster f'oint> interpolated to Raster <£ ins Krigin o Method (Doth
l.3½ SU s IO!l5i e•~ "''"'" loreach ear ••••• · ••
oemoarapllk an~ Health l 003, 2010
(1(,,1,r F'Oints 'interp~ ,te<i to Ra ;t,r llSiOg ~~g in~ Method (bOlh -5ur,,ev<[CHSI eo~ raster< fur e•<h y_ear a , ~raged1
CO<ffic,on t oi variation of r,, inf; II Oata w..s ca l<ulatod acroz entire ,ime periccl for the mon:h cl May
[plant"'1: t ime) and !he monrh cf Ottoberi har,,etJ. The rnlRPI .... ,e,, C. funk ot Ian, 1%1 ·
Rascer variat ion m ,,.;nf•II du,;o-'l •h.,• tv,:o m'""1\h, 1, -" Sept. 2011 consi, ere d crlicat The lwo ra,ters ""'" lhen
averai,-ed to hi~lilii:1,1 t he mu, t vu l,.,,able n,re; i., ,eg,CO, .to ,oinfa'.l ,,.,;,bi! ~v and i t, affep on ag
amduc1 ion.
11ve••ll' ,~mi,erature duri,g eaeh reio1· si,a;,,., VJ-.5) Recur rent
Uni,eroily -0/ <i6\ k ·,gr ;, •, Z000-2011 <>ve r OOlirtr time i;erio~ wa>~vecago~ :OIIOU li"Mr.l l a :rna ti , l\eser•r<+, Unit (JJt.S-rdilly R"\er rainy ,~ae<n av<,ra~e \ernperaluro. ~l\eniver•~• ~- Cli mate 5hock
(UEA,11'.AU) ...,,,on) t»mperat,.-e durlfll: !9"'Y season can be ,:,insi<leted a i:=1• 1, plan! ,1,.,, at highor tomporoture,.
famine ~• rl v War.sin~ l<lne> With s ~orter ,air.~ ;eosons a re con,ide/ M more
s1·srem, Nmwo-1: 1.JOl-2010 Rasrer vui,-.,,a ble . ,~
(ft'WSNHi
CHIRP> ,;a,o,e~ C. funk •t Ian, '1961 · Calc l-i•te,J .,.,., wth ;;....., period.. Zor.e, d l""'er
Rascer .Ne rage total precip1:.otion are consioered rn:,re -" Sept. 2014 vu1nr;ra b1e ,
41.of incident, per location p!u , ~umbe r cffa,alitle.
~,med ( <inflict Cc,,;ati,a !'< 1/1/1~,o
multipiod t,1, tv,o wa, u;ed lo g,,ee,ate, "<o"fti<t Ev;,,~ Dala (AClECI
7/10/2014 Po1r,1001a l e, .. ~ .. pe, PPinl l"'a l;,,11, All !we, ofWo\ft iCl fr"1, ,,.
Oatahase ~•taMse •.11e"' lnilurled (le- prnt,,.;r,, , mien £'0'Jp;, Historic Siles of
police, e<mic militie->. etc.). Canfl kl
I W0tlo Food Froi;ramme Total ~• count was used per locat;,,n as• prcxy to
(WFl'I 2014 Poir,t Da ta cu,,nid b•c•-~f pu,ulaUon m;l r~><...-e< pres.u,., li¼
crEated by- refuse• preserace.
Demo,rophic and H.,lth 2010 Point Dot•
Points inl-<roolaled to R"ter us ing ~ri8 iag Method (both
I - Heal th Shock Sur,e,,. IDl!SI rasle,s fu, each ·1ear a,orago,;1]
Puinl Udla repre sent< a ll markets l urveied rnur,lhly !o< ~r ice,. A·,·erage marl<et prices"'"" c, 1,u!ated for all
m•rl.:.t, ov•-"tim• du~nel••n '"'"'"- LI!•n ~••on i, when t.i~h pri<es have the biggest ~ti"" impact on
SIM}SO~'AGESS 2~04-2014 Pelot Daia house hold fuod se curity. 1'<:trn d:ara was ln:erpolated = 10 ~aster u,li,g Krlglng Me,hod fbnth r;;:ers lor each
v.,.r aWtra;i•d). ib>latiWI w•ightieg lor-•ach commodil'/ was ca lc ulated prol>C'1 iooa ll¥ to each comrROO~ie,
Recurrent P r ice production level.
Shocks SIM,(SO~AG.SS 2U 04•l 014 Point o at•
SAME MfTHODOWGV NOTES FOR ALL PlllCE CATA IN COMP051H(SEENOlE>FOR MILlET PRl([S]
5~"1 Carn Prlaes ~,
SIM/50t1'1GE55 2004·2014 Point D&t• SAME MfTHDOOLOGf NOTES fOR ALl ~ ICE DATIi IN
'"' COMPOSITE jSEE NOTES FOR MILLET PPJCES]
SIM}SO~' /\GESS lll04-2014 Puir,l 0~1< SAME MITHOOOLOGV NOTES fOR ALL PlllCE OAT~ IN
O:,MFOSI H 15EE NOH:i FOR Mill.ET PRICES] n,
SIM/SO~•oi:;c,5 ;w04-:l014 t'<linlCO>U SAM< METHDOOLOGt NOTES fOR ALL P!l lO: CATA IN ,,.
COMPOSITE ]SEE NOUS fOR MILLET PI\ICES]
Derno,raphic and Healtti 1cm, 20,o
Clu,lOr Point,. interoo:,teQ to Raster uoiag ~ri8"1g Method (l>oth = Surveys [Dl!SI Points r•srer, for each ·1ear auerai:ed) ,t.~emla Demo~raphio an~ Heal th Cluster F'oi,m ioterpd awi to Raster u;in~ Kr.eJae Methcd (OOlh Prevaleoce l.OO'l, WI• li¼
5ur,e'/$ (OHSI Po ints rast<ero fur each ·1ear a, e,aw,dl
5'8nd~di,.d Monitoring Each re gion was polled bf SMARr every oiC.er vear, all
and Asse;sme 11t ol Re :;e f l009·2013 Re&;oa inroi-matlim was averaged 1cgether "' AvorageGAM
3n0 Transil i>ns ISM~RT I ~7 ?> ~-"· Demop;raphic en~ Health Clu.,•r F'Oiots ·interpo' ated to Ra,ter u;inp; ~•l~ ing Method (Oorh
100_3, 2010 '" 5"""''1S IDHSI Points raster, 10, each eara,ora!IOdl Undernutrl t lor,
St>ndodi>ed MITTtonng Each region was pol leC every othorycoc, all ln'o.'fflation
and A,,;,.,,srn~nl ci1 R~:i~f 2~09-2013 Re~iou ,.,., •v~r~J!<d toAAther '" "'~""'II<' andTra oSltlCHIS !SMART]
srun11ng !late< ~¼
Derno.iraphic an:! Health 10)3. WJ.0
Clus1or Points ir,ter po'aleQ.to Raster us ing Kr1!! "1S Ml!lhod (bnth ~0'6
SUM:V, 101151 Points rasters fur each year auc raf:C(fJ
Composite Top lnde• ~., -~In Compooit> W@ij:ht
31%
23%
Reslllence
'"' Capacity
31%
15%
44%
Exposure
)1% !Shock ~ So 21% stresses )
11%
33%
20%
WelHlelng Ouloome
~0%
BO%
