THEGLOBALFOODCHALLENGE

GRAPHICREDESIGNPROJECT

STAT515–APPLIEDSTATISTICSANDVISUALIZATIONFORANALYTICS,FALL2016MELISSAA.JOHNSON

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ORIGINALGRAPHICS&CONTENT

TheoriginalgraphicsarefromablogpostthatispartoftheWorldResourceInstituteblogseries,CreatingaSustainableFoodFuture.Theseriesexplorestrategiestosustainablyfeedtheexplosivegrowthinglobalpopulationby2050withvaryinginfographics.I’vechosentoomitsomeofthesediagramstofocusparticularattentiontotheworld’spopulationgrowthandshiftingdiettrendsthatarereshapingthewayourfoodissustainablyproduced.Theworld’spopulationisprojectedtogrowfrom7billionto9.6billionpeopleby2050;inadditiontothe37%increaseinpopulation,theworld’spercapitaconsumptionofmeatanddairyproductsespeciallyinemergingmarkets(ChinaandIndia)areontherise.Thesefoodsaremoreresourceintensiveandenvironmentallyimpactfultoproducethanplantbasedproducts.A“foodgap”ischaracterizedbynutritionaldisparitiesbetweenrichandpoorpopulationsanditisintensifiedwithoverconsumptionofproteinthatwidensthegap.

Diagram1:GrowingPopulation:UnitedNationsDepartmentofEconomicandSocialAffairs,PopulationDivision(UNDESA).2013.WorldPopulationProspects:The2012Revision.NewYork:UnitedNations.

Totalpopulationbymajorarea,region,andcountry.Mediumfertilityscenario.

Diagram2:GlobalConsumptionofMeatandMilkProducts:Bunderson,W.T.2012.

“Faidherbiaalbida:theMalawiexperience.”Lilongwe,Malawi:TotalLandCare.

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Diagram3:AnimalBasedFoodsareMoreResourceIntensivebasedondatafromtheWorkingPaperby,J.Ranganathan.2016.“ShiftingDietsforaSustainableFoodFuture”

REDESIGNDIAGRAM1

Themostimportantpieceofinformationtoconveyaboutthefoodcrisisisperhapstheexplosiveglobalpopulationrisingatastaggeringrateof7billionto9.6billionby2050.Thestackedbarchartindiagram1isnottheworstgraphbutitcanbevisuallyoverwhelmingwithoutdoingthisimportantstatisticenoughjustice.Asanaudience,wearerequiredtodosomecomputingifwewanttomakesenseoftheinformation.Therearenolabeleddatapoints,thusweneedtocontinuouslylookattheverticalandhorizontalaxestocompareandcomputenumbers.Sincethetwo-coloredstackedbarchartshows“Populationin2o12”inyellowand“Populationgrowthfrom2012-2050”inred,wehavetocomputetheestimatedpopulationin2050byaddingtheyellowandredareastogetherforallsevenregions.Wemayalsobeinclinedtoknowtherateofgrowthordirectionofgrowth(increaseordecrease)inpopulationbutsinceitisnotintuitivefromthebarchart,furthercomputingisrequired.Inaddition,barchartstendtoslightlyskewthemagnitudeofthedata,leavingtheaudiencetoconfuseSub-SaharanAfricaashalfthepopulationofAsia,althoughthenumberswouldargueotherwise.

Ichosetoredesignthestackedbarchartasaslopegraphbecausetheyareusefulwhenwehavetwotimeperiodsorpointsofcomparison(populationin2012andestimatedpopulationin2050)andwanttoquicklyshowrelativeincreasesanddecreasesacrosscategoricalvariables(regions)betweenthetwodatapoints.Iaddedabsolutevaluedatapoints(2012population,2050population)andconnectedthepointstoshowvisualincreaseordecreaseinrateofchange(viatheslopeanddirection)sothatitinstantlyjumpsoutatthereader.Iusedcolor,apre-attentiveattributetocodeAsia,Sub-SaharanAfrica,andNorthAfricatodrawattentiontodenselypopulatedregionswherepopulationincreasesexponentiallyovertimewhiletherestofthedataispreservedforcontextwithoutcompetingforattention.Additionally,Icomputedthepercentagechangebetweenpopulationin2012andestimatedpopulationin2050toaddmorecontextadjacenttotheslopegraph.Wecanquicklydiscernthatthemajorityofthe9.6billionpopulationgrowthwilloccurinAsia,Sub-SaharanAfrica,andNorthAfricawheretherearemanydevelopingcountriescurrentlyundernourished.Icreatedtheslopegraphbyextractingdatapoints(estimated)fromthestackedbarchartindiagram1.Iorderedthedataintoatablewiththreecolumns(region,populationin2012,andpopulationgrowth2012-2015)topreparethedataforgraphics.Ifollowedaslopegraphtutorialtotransformalinegraphtoaslopegraph(instructionsinappendix).

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REDESIGNEDSLOPEGRAPHFORDIAGRAM1

REDESIGNDIAGRAM2

Inordertocreatemytables,Ihadtofirstrecordallthedatafromtheoriginaltableina.csvfileanduseExceltableeditingfeaturestorestructureandredesignthetables.Thetableindiagram2isattemptingtoconveyalotofnumericalinformationwithpoordesign.Aquickglanceatthetableandtheaudiencemaybeinstantlyoverwhelmedbythedistracting(blackandyellow)heavybordersandshadingwhichonlycompetesforattentionwithoutanyindicatorsastowhichpiece(s)ofinformationshouldstandoutandgrabourattention.Sincethetableisattemptingtocommunicatemultipledifferentunitsofmeasuresuchastime(2006vs.2050),kilocaloriesperperson,perday(1kcal=1000calories),andpercentagechangebetweencurrentandfutureconsumption,itistypicallyeasiertoreadallofthatinformationinatablethanagraph.Ifeltthatcombininglivestockandbeef/muttonnumbersintoonetableiscrowdedandproducesgreatermentalloadforreaderswhoaretryingtoobserveallthenumericalvalues,whichiswhyIseparatedthedataintotwotables.AlthoughIkeptthetableformat,Iredesignedthelayoutsothatthedesignfadesintothebackgroundandthedatadoesallthetalking.Tablesinteractswithourverbalsystem,whichmeanswereadthemtocomparevaluesanddeduceanyparticularpointsofinterest.Mygoalwastouseaheatmaptotargetthesepotentialpointsofinterest.ThisapproachisappropriateformixingdetailsIstillwanttoincludeinthetablewhilealsomakinguseofvisualcuestodirectthereadertothemostimportantaspectofthedata.Aheatmapaidsinvisualizingdataintabularform,whichinadditiontothenumbersIalsoleveragedthefilterfeaturetosortfromdescendingtoascendingtoconveytherelativemagnitudeofthepercentages.Toreducethementalprocessing,Iusedanorange/bluecolorsaturationtoprovidevisualcues,thehighersaturationofblue,thehigherthenumber.Thismakestheprocessofpickingoutthetailsofthespectrum—thelowestnumber(-2%,-19%)andhighestnumber(94%,138%)aneasierandfasterprocessthanintheoriginaltablewherewedidn’thaveanyvisualcuestohelpdirectourattention.Byutilizinglightbordersandwhitespacestosetapartelementsofthetable,IensuredmydesignwasnotdistractingfromtheimportantdetailsIwanttheaudiencetodiscern.

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Wecannowseethattheworld’spercapitameatanddairyconsumptionisgrowing,especiallyinChinaandIndia,andisprojectedtoremainhighinLatinAmericaandAsia.Thesefoodsaremoreresource-intensivetoproducethanplant-baseddiets,whichfurtherexacerbatestheimminentfoodcrisis.

REDESIGNEDTABLESFORDIAGRAM2

AsiaexcludingChinaandIndiaLatinAmericaexcludingBrazil

AsiaexcludingChinaandIndiaLatinAmericaexcludingBrazil

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REDESIGNDIAGRAM3

Inordertoplotthesegraphics,Irecordedallthedata(estimations)fromtheoriginalbarchartina.csvfilewhereIusedRtoreadinthefile.TheClevelanddotplotsarecreatedusingtheggplot2graphicspackage.Rcodesscriptcanbefoundintheappendixsectionofthisreport.Thestackedbarchartindiagram3istryingtoplotacrossmanycategories(land,water,greenhouseemissions)thataremeasuredondifferentscalesandunitswhilesimultaneouslyshowingthesubcomponentpieces.Thisgraphcanquicklybecomevisuallyoverwhelmingbecauseweareconstantlycomparingdifferingunitscalesandsummingupsubcomponentpiecestodiscerntheeffectsofeachcategory.Theuseofcolorissomewhathelpfulsincetheynaturallyadheretotheircategoricalvariables(i.e.,waterisblue).Thischartfurthersplitsthecategoriesintoplantsversusanimalstotrytoconveythemainpurposeofthegraph:animalbasedfoodsaremoreresourceintensivethanplantbasedfoods.Sincethestackedbarchartisstructuresasabsolutenumberswiththethree-unitscalesonthey-axis,itishardertodeducethesenumericalvalues.Ihaddifficultyinswitchingbackandforthwithdifferentunits/scaleswhentryingtorecordthedata,assomebarsmeasureequivalentlytoanotherbarbutbecauseofthescale/unitdifference,Iwouldthenneedtodeduceadifferentvalue.Ichosetoenhancediagram3usingseveralClevelanddotplotsinsteadofabargraphbecauseitreducesthevisualclutterandiseasiertoread.Iwantedtoseparatethethree-units/scalesintosixdifferentplotstoshowthemagnitudeandclearresourceconsumptionpatternsbetweenanimalandplantfoods.Theoriginalgraphicemphasizestheresourceintensityofanimal-basedfoodsaswellasgreaterenvironmentalimpactstoproducethesefoods.Sincemanyanimal-basedfoodsrelyoncropsforfeed,increaseddemandforanimal-basedfoodswidensthefoodgaprelativetoincreaseddemandforplant-basedfoods.Therefore,overconsumptionofproteinleadstoawiderfoodgap.Theredesignedplotgraphsclearlyshowthatourprimarysourcesofprotein(beef,chicken,pork)arethemostresourceintensiveandenvironmentallyimpactfulfoodstoproduceacrossallcategories.

REDESIGNEDPLOTSFORDIAGRAM3

ha=onehectarecontainsabout2.47acres.primarilyusedinthemeasurementofland

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ha=onehectarecontainsabout2.47acres.primarilyusedinthemeasurementofland

1meter3=1000liters

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1meter3=1000liters

Tonnesofcarbondioxideequivalent(tCO2e)isameasurethatallowsyoutocomparetheemissionsofothergreenhousegasesrelativetooneunitofCO2.

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Tonnesofcarbondioxideequivalent(tCO2e)isameasurethatallowsyoutocomparetheemissionsofothergreenhousegasesrelativetooneunitofCO2.

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APPENDIX

SlopeGraphTutorialonExcel-http://peltiertech.com/slope-graphs-in-excel/

1) Converteddatafromdiagram1toanExceltable(datamaybeestimatesbasedoninformationfromthebarchart)

a. Formatteddataintoaslopegraphtoredesigndiagram1

2) RedesignedTableData(.csvfile)fordiagram2

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3) RscriptforClevelandDotPlots

mydata <- read.csv("highresourcefood.csv", header=TRUE)head(mydata)require(ggplot2)require(plyr)ce <- ddply(mydata, "Resources", transform,Percent_Landuse.Cropland = Landuse.Cropland / sum(Landuse.Cropland) * 100)ggplot(ce, aes(x=Resources, y=Percent_Landuse.Cropland, fill=Landuse.Cropland)) + geom_bar(stat="identity")

ggplot(mydata, aes(x=Landuse.Cropland, y=reorder(Resources,Type))) + geom_point(size=3) + theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line(colour="grey60", linetype="dashed"))

mydata[, c("Resources", "Type", "Landuse.Cropland")]Resourceorder <- mydata$Type [order(mydata$Resources, mydata$Landuse.Cropland)]mydata$Type <- factor(mydata$Type, levels=Resourceorder)

p<- ggplot(mydata, aes(x=Landuse.Cropland, y=Resources)) + geom_segment(aes(yend=Resources), xend=0, colour="grey50") + geom_point(size=3, aes(colour=Resources)) + scale_colour_brewer (palette="Set1", limits=c("R","T"), guide=FALSE) + theme_bw() + theme(panel.grid.major.y = element_blank()) + facet_grid(Type ~ ., scales="free_y", space="free_y")p + ggtitle("Cropland Use (in Hectare)") + theme(plot.title=element_text(size=rel(1.3), lineheight=.9, family="Arial",face="bold", colour="darkgreen"))

mydata[, c("Resources", "Type", "Landuse.Pasture")]Resourceorder <- mydata$Type [order(mydata$Resources, mydata$Landuse.Pasture)]mydata$Type <- factor(mydata$Type, levels=Resourceorder)

p<- ggplot(mydata, aes(x=Landuse.Pasture, y=Resources)) + geom_segment(aes(yend=Resources), xend=0, colour="grey50") + geom_point(size=3, aes(colour=Resources)) + scale_colour_brewer (palette="Set1", limits=c("R","T"), guide=FALSE) + theme_bw() + theme(panel.grid.major.y = element_blank()) + facet_grid(Type ~ ., scales="free_y", space="free_y")p + ggtitle("Pasture Land Use (in Hectare)") + theme(plot.title=element_text(size=rel(1.3), lineheight=.9, family="Arial",face="bold", colour="aquamarine4"))

mydata[, c("Resources", "Type", "Water.Consumption.Rain")]Resourceorder <- mydata$Type [order(mydata$Resources, mydata$Water.Consumption.Rain)]mydata$Type <- factor(mydata$Type, levels=Resourceorder)

p<- ggplot(mydata, aes(x=Water.Consumption.Rain, y=Resources)) + geom_segment(aes(yend=Resources), xend=0, colour="grey50") + geom_point(size=3, aes(colour=Resources)) + scale_colour_brewer (palette="Set1", limits=c("R","T"), guide=FALSE) + theme_bw() + theme(panel.grid.major.y = element_blank()) + facet_grid(Type ~ ., scales="free_y", space="free_y")p + ggtitle("Rainwater Use (in Meter Cube)") + theme(plot.title=element_text(size=rel(1.3), lineheight=.9, family="Arial",face="bold", colour="blue4"))

mydata[, c("Resources", "Type", "Water.Consumption.Irrigation")]Resourceorder <- mydata$Type [order(mydata$Resources, mydata$Water.Consumption.Irrigation)]mydata$Type <- factor(mydata$Type, levels=Resourceorder)

p<- ggplot(mydata, aes(x=Water.Consumption.Irrigation, y=Resources)) + geom_segment(aes(yend=Resources), xend=0, colour="grey50") + geom_point(size=3, aes(colour=Resources)) + scale_colour_brewer (palette="Set1", limits=c("R","T"), guide=FALSE) + theme_bw() + theme(panel.grid.major.y = element_blank()) + facet_grid(Type ~ ., scales="free_y", space="free_y")