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AKNOWLEDGENOTESERIESFORTHEENERGY&EXTRACTIVESGLOBALPRACTICE

AdaptingSpatialFrameworkstoGuideEnergyAccessInterventionsinUrbanizingAfrica

Thebottomline.Theextensionofelectricityintoruralareashasbeenthemainfocusofeffortstoachieveuniversalaccesstoreliable,affordable,andmodernenergyby2030.OntheAfricancontinentandelsewhere,however,rapidurbanizationhasproducednewpatternsofhuman

settlementthatblurthedistinctionbetweenruralandurban.AsacasestudyofKenya

demonstrates,accessmetricsaggregatedattheruralorurbanleveldonotequipgovernmentsandtheirpartnerstoproperlyidentifyortargetsitesforelectrification.Spatializedframeworksanddatathatdefinespacealongarural–urbancontinuumorasurbancatchmentareascanimprovepolicymakers’understandingofthespecificbarrierstoaccessthatcommunitiesface.

PublicDisclosure

Whyiscategorizingareasassimply

ruralorurbaninsufficientforplanningelectrification?

Officialstatisticsbasedonasimplerural-urban

distinctionfailtocapturethecomplexityofAfrica’surbanizationpatterns

Labelinglocalesaseitherurbanorruralassumesthatthereareclearandmeasurabledifferencesbetweenthetwotypesofspaces.Thisideaislosingempiricalmeaning,giventherapidandsustainedurbanizationoccurringacrosshumansocieties.

PatternsofurbanizationontheAfricancontinentarerapid,unevenlydistributed,andcomplex.Contrarytopopularbelief,Africanurbanizationisnotsimplymigrationfromaruralareatoaprimarycity.InformalandspontaneousurbanizationhasalsobecomethenorminmanyAfricancit-ies(Awan2023).Examplesofobservedurbanizationtrendsinclude:

√Theinsituemergenceofnewcitiesandtowncenters(OECD,UNECA,andAfDB2022)

InSub-SaharanAfrica,theurbanpopulationgrewbyafac-torofmorethan20between1950and2015(OECD,UNECA,andAfDB2022).Betweennowand2050,itwillgrowby560milliontoreach1.3billion(UNDESA2022).

√Growthanddensificationinurbanperipheries(McGee2021)

√Theemergenceofsatellitecitiesandurbancorridors(OECD,UNECA,andAfDB2022)

Authors

√Themergingofexistingurbancentersintodense,poly-nucleatedconurbations/mega-agglomerations(OECD,UNECA,andAfDB2022).

JessicaKerseyisaconsultantatESMAPandaPhDcandidateintheEnergyandResourcesGroupattheUniversityofCalifornia,Berkeley.

BryanBonsukKooisanenergyspecialistatESMAP.

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AdaptingSpatialFrameworkstoGuideEnergyAccessInterventionsinUrbanizingAfrica

Statisticsthatarespatiallyaggregatedintoruralorurbancategoriesleveldonotcharacterizethesenewlyurbanandurbanizingcommunities,forseveralreasons(BrennerandSchmid2014).Theofficialdefinitionofurbanvariesfromcountrytocountry,andmostprojectionsarebasedondatafromcensusesconductedinthe1990sor2000s.Itcantakedecadesfornationalgovernmentstorecognizenewcitiesandtownsadministratively.Duringthistime,manyexperi-encerapidgrowthwithoutthesupportandresourcesthatan“urban”designationwouldprovide.

Informalsettlementsarealsooftenomittedfromorunder-representedinofficialstatistics(MitlinandSatterthwaite2013).Itisunclearhowperi-urbancommunitiesarecapturedwithinarural-urbandivision;theirneedstendtobediffer-entfromthoseofboththeirurbanandruralcounterparts.Asignificantnumberofcommunitiesarethushiddeninormissingfromofficialstatistics—bydesignorbecausetheyareobscuredwithindataaggregatedalongabinaryrural-urbancategorization(VanDuijne2019;Ondaandothers2019).

Improvingtheabilitytomeasureandtrackurbanizationeffectsisimportantbecauseurbanareas—andtheinfra-structurethatpowersthem—playacrucialroleineconomicdevelopmentandpovertyreduction.Thankstoeconomiesofagglomeration,theseareasfacilitatetheefficientprovisionofbasicservices,theconcentrationandspecializationoflabor,innovation,andtheavailabilityofawiderrangeofgoodsandservices(UN-Habitat2011).Thesebenefitsextendfarbeyondtheformalboundariesofthecity.Linkagesbetweenurbanareasandtheirsurroundingsprovideopportunitiesforemploymentandincomediversificationaswellasaccesstoservicesdeepinthecountryside(SatterthwaiteandTacoli2002).Spatialframeworksthatcapturethesedynamicscanguideeffortstoprovidebasicinfrastructuretowhereitcanbestsupportbroaderurbansocietalandeconomictransitions.

Inaperiodofrapidurbanizationand

demographictransition,barrierstoenergyaccessareevolvinginwaysthatdonot

cleanlymaptobinaryrural-urbandivisions.

Howdoestheclassificationofanareaaffecteffortstoelectrifyit?

Acoarseclassificationmaynotallowpolicymakersandresearcherstospotbarrierstoenergyaccess,model

andevaluateappropriatetechnologies,orsupporttheeffectivegovernanceofelectrification

Arural-urbanbinaryframinglimitselectrificationeffortsbymakingitdifficultto:

√Understandthebarrierstoenergyaccessandtheenergytransitionpathwaysofdiversecommunities

√Adaptelectrificationstrategiesthatbettermeetcommu-nities’presentandfutureneeds

√Performaccurategeospatialenergymodelling

√Establishcoherentinstitutionaljurisdictions.

Understandingbarrierstoenergyaccess

AccordingtotheSDG7TrackingReport,in2021over83per-centofthe674millionunelectrifiedpeopleinSub-SaharanAfricawererural(IEAandothers2023).Thegeneralunder-standingisthatthemainbarrierinruralareasisthatthegriddoesnotreachthem(Fallandothers2008).Incontrast,urbanenvironmentsareviewedaswell-electrified(albeitwithrecog-nizedchallengeswithservicequality),becauseofthedensityofgridinfrastructure(Blimpo,Postepska,andXu2020).

Thereisaruralbiasintheunderstandingofhowandwhereenergypovertymanifestsitself(Castán-Brotoandothers2017;Singhandothers2015b).Thisargumentdoesnotimplythatruralcommunitiesdonotfacepressingchallengesinaccessingelectricitythatdeservetobeaddressed.Rather,ithighlightsthatmany“rural”communitiesareactuallyinlocationsthatarenewlyurban,densifying,withintheurbanperiphery,orstronglylinkedtoneighboringcities.Thepat-ternsofelectricityaccessinthesespacesbetween,within,andaroundcitiesarecomplex(Silver2023).Understandingthesebroaderprocessesofurbanizationcanbeinstructiveinunderstandingthenatureoftheenergyaccesschallengesthesecommunitiesface.

Inurbanareas,householdsarethoughttotransitionfromtraditionalbiomass-basedenergysourcestoelectricityandgasthroughseveralstagesastheirincomesriseandcleanersourcesbecomemorereadilyavailable(Barnes,Krutilla,andHyde2004).Thesetransitionstakeplacehaltinglyand

3

AdaptingSpatialFrameworkstoGuideEnergyAccessInterventionsinUrbanizingAfrica

unevenly,however,especiallyforthelowest-incomesubsetoftheurbanpopulation(Aungandothers2022;Fallandothers2008;MahumaneandMulder2022).Lackofaffordabilityisthemostwidelyrecognizedbarrier.Onestudyfindsthaturbanizationiscorrelatedwithmoreenergyconsumptionandhigherlevelsofaccesstocleanfuels—butwithincreasedenergypovertyintermsofenergyexpendituresasapercentoftotalincome(MahumaneandMulder2022).

Thereisconsiderablespatialvariationinthechallengesthatunelectrifiedorunder-electrifiedpopulationsinSub-SaharanAfricafaceinaccessingelectricity(Falchettaandothers2019).Withincities,infrastructuretendstobecon-centratedincommercialdistricts,high-incomeresidentialzones,andotherformalareas(Lallandothers2017).Thequalityandcoverageofservicesalsoworsenmovingdowntheurbanhierarchy;electricitymaynotbeavailableatallinsmallercitiesortowns.Theelectricityaccesschallengesofinformalor“slum”communitieswithincitiesarealsosubstan-tialbutoftenunderrepresented(Buyana2022).Bureaucraticrequirementstoprovelandtenureareadocumentedbarrierinthesecommunities(Singhandothers2015b).

Accesstoinfrastructuredeclinesrapidlymovingintotheurbanperiphery(Lallandothers2017).Insomeperi-urbanareas,communitiesthatlackelectricity,sewerage,andpipedwateraccessexistalongsideformallyplannedneighborhoodsthathaveaccesstofullmodernservices(Silver2015).Otherperi-urbancommunitiesare“undergrid,”andtheirbarriertoaccessisnotthephysicalavailabilityofdistributioninfrastruc-turebutthehighcostofdomesticwiringandtheconnectionapplication(Lee,Miguel,andWolfram2020).

Manyothersareconnectedbutfaceissueswithreliabilityorpowerqualitythatlimitwhenandhowelectricitycanbeused(Ayaburiandothers2020;Jacomeandothers2019;Klugmanandothers2019).Connectionsaresometimespre-carious,meaningthatanintermediarycandisconnecttheenduser.InKampala’sinformalsettlements,forexample,landlordscanandoftendodisconnecttheirtenantsbecauseofunpaidrent,personalconflicts,orasawaytocontrolcosts.Intermediariessometimesimposelimitationsonwhenelectricityisavailableandthetypesofappliancesthatcanbeused(Kerseyandothers2023;Yagumaandothers2024).

Inaperiodofrapidurbanizationanddemographictransition,energyaccessbarriersareevolvinginwaysthatdonotmap

Theabilitytobetterspatializeeconomic

anddemographicdynamicsandmodeltheirtrajectoriescanhelpgovernmentsandtheirpartnersmakestrategicdecisionsabout

howtoprioritizeandtargetelectrificationresources.

cleanlyintosimplerural-urbandivisions.Statisticsandnarra-tivesthatrelyonthesedesignationspoorlyequippractitionerstounderstand—andefficientlyaddress—thesechallenges.AsmoreandmoreofthepopulationinSub-SaharanAfricamovestoorisborninurbanandurbanizingareas,theirneedswillbeincreasinglydifficulttounderstandandmeetwithoutamorespatiallyinformedunderstandingoftheirchallenges.

Adaptingelectrificationstrategiestourbanizationpatterns

Urbanizationproducesfundamentaltransformationsinthesocioeconomicstructureofcommunities.Areasofhighpopu-lationdensitybenefitfromagglomerationeconomies,whichareassociatedwithincreasedincomes,betteraccesstohealthandeducation,andincreasedeconomicproductivity.Surroundingareasbenefitfromrural–urbanlinkages,throughwhichgoods,people,finance,andotherresourcesflow.

Energyisacriticalinputtotheseprocesses.Providedattherightquantity,quality,andprice,electricitycanprovideaclean,low-carbondevelopmentpathwayfortheseemerg-ingeconomies(Goldembergandothers1985).Spatializedeconomicanddemographicdatacanhelppolicymakersmakestrategicdecisionsabouthowtoprioritizeandtargetelectrificationresources.

Adesignationofruralorurbandoesnotyieldinsightintothefollowingfactors,whichareinterlinkedwithurbanizationdynamicsandconsequentialforelectrificationefforts:

√Thetypesofproductiveeconomicactivitiescommunitiesengagein

√Thescaleoftheseactivitiesandexpectedchangesovertime

√Theexistenceandstrengthoflinkageswithnearbyurbancenters.

Capitalcostpercapitaofinfrastructure

provision($/capita)

4

Inurbanizing

hoodsimprove

temsmustbe

oftheirusers.

economicgrowthsiveandpollutingwilldependin

nomicactivitiesthatwillbest

theelectricityoverlookedandrentleast-costMalik2022).

Extendingthismunities,local

sifyingintomanufacturing,

transitionsare

electrified,affects

ofthedemand

technologiesand

puttotimeof

shifts)indemandThesedynamics

whooftenseek

lightmanufacturing

daytimeloadsthatcanserveasanchorcustomers(GuillouandGirard2022).

TheUtilities2.0TwaakepilotinUgandaisanexampleofaminigridwhosebusinessmodelispremisedonrealizingthelatentdemandofeconomicallydiverseperi-urbancommu-nities.Theobjectiveofthepilotistodemonstratethatminigridscanbeusedtostimulatedemandbeforethearrivalofthecentralgrid.

UnderTwaake,a40kilowatt(kW)peakminigridwascon-structedinthevillageofKiwumu.ThelivelihoodsofresidentstherearestronglylinkedtocommerceinKampala,whichis45minutesawaybytruckorbus.Overhalfofbusinesscus-tomersreceiveddedicatedassetfinancingforcommercialandagriculturalproductive-useappliances.TheminigridwasconnectedtothecentralgridinJune2023.Despiteearlychallengeswithinstitutionalcoordination,thismodelappearstohavebeeneffectiveinimprovingthecosteco-nomicsofelectrification(RockefellerFoundation2021).

Fromthesupplyside,thecost-effectivenessoftechnologyoptionsisstronglyinfluencedbytheformanddensityofhumansettlementpatterns.Thecostofelectrificationpercapitavariessignificantlydependingonthetechnologytypeandpopulationdensity(figure1).

Figure1.Capitalviaacentralgrid,minigrid,andsolarhome

system,based

1,000

900

800

700

600

500

400

300

200

100

Categorizationanddensity(people/km2)

Largecity(30,000)Largecity(20,000)Largecity(10,000)

Largecity(5,008)

Largecity(3,026)

Largecity(1,455)

Secondarycity(1,247)Ruralhinterland(38)Deeprural(13)

0

GridMinigridSolarPV

Technology

Source:Adapted

AdaptingSpatialFrameworkstoGuideEnergyAccessInterventionsinUrbanizingAfrica

areas,demandforelectricitywillgrowasliveli- andbecomemorediversified.Electricitysys- abletosupporttheshiftingdemandgrowthUnreliableorpoor-qualitysupplycanconstrain andcreateorincreaserelianceonexpen- fuelsources.Howdemandgrows—which largepartonthenatureoflocalizedeco- —hasimplicationsforthetypesofsystemsmeetusers’needs.Arecentstudyarguesthat demandofagriculturalactivitieshasbeen thatitsinclusioncouldsignificantlyaltercur-electrificationplanning(Korkovelos,Koo,and

argumenttothecontextofurbanizingcom-economiesarerapidlygrowinganddiver-service,andtrade.Howthese

occurring,andtheextenttowhichtheycanbe therateofdemandgrowthandtheshape profile.Bothareconsequentialforselecting businessmodelscapableofmatchingout-useandaccommodatingfuturegrowth(and whileremainingfinanciallysustainable. arewell-understoodbyminigriddevelopers,urbanizingsiteswithnicheindustries,suchasandagriculturalprocessing,withhigh

costpercapitaofelectrificationonsettlementdensity

fromFosterandBriceno-Garmendia(2010).

5

AdaptingSpatialFrameworkstoGuideEnergyAccessInterventionsinUrbanizingAfrica

byabout25percent(FosterandBriceno-Garmendia2010).Thesefindingsunderscoretheneedtoincreasecoordinationbetweenentitiesresponsibleforurbanplanningandelectric-ityserviceprovision.Througheffectiveurbanplanning,citiescangrowinwaysthatminimizethecostsofbasicserviceprovision,avoidlock-inofhigh-carbonfuelsources,andalignwithotherobjectivesofsustainableurbangrowth.

Modelingleast-costelectrificationaccurately

Mostelectrificationmodelsexplicitlyorimplicitlyincorporaterural-urbanbinaryclassifications.Dependingonhowtheseassumptionsfactorintothemodel,theymayyieldrecom-mendationsfortechnologiesthatareinappropriatetotheon-the-groundrealitiesofurbanizingcommunities.

Demandestimationishighlysensitivetoforecastingmeth-odsandinputassumptions,whichcanproducedramaticdifferencesinmodelingoutcomes(Kemausuor2014).Twoofthemostcommonlyusedleast-costelectrificationmod-elsbaseseveralkeydemandmodelingassumptionsonarural-urbansplit(table1).InsomeoftheearlyapplicationsoftheOpenSourceSpatialElectrificationTool(Onset),forexample,demandwasbasedonestimatesofpercapitaruralandurbanconsumption(Moksnesandothers2017).

Minigridsareoftenassumedtobearelevantoptionforiso-latedandunelectrifiedruralareas.Inhigher-densityareas,theyarealsocost-competitivewithgridexpansion.ThroughfieldworkinIndia,Senegal,andTanzania,GuillouandGirard(2022)findthatminigriddevelopers“tendtofavorlargervillagesandplacesthat,thoughofficiallyrural,haveeco-nomicandsocialdynamicsthatbringthemclosertourbanareas.”Thisfindingisconsistentwithagrowingbodyofworkthatadvocatesforthepotentialofunder-gridminigridsandotherhybridizedorgrid-interactivesystemstoimproveaccessinareaswherethegridwillsoonreachorispresentbutofferspoorservice(Graber,Mong,andSherwood2018).

Energydemand,thecostofserviceprovision,andurbangrowthpatternsareinterlinkedprocesses.Whilewehavediscussedmatchingenergyservicestofittheneedsofurbanizingareas,thereverse–shapingplanningtoenablehigh-efficiencyformsofurbanliving–isalsopossible(MadlenerandSunak2011).Urbanplanningcanbeanimportanttoolinpromotinggrowthpatternsthatareenergyandcost-efficient.Healthyurbanexpansionisaparticularareaofconcern.Urbansprawloftenleadstotheinefficientuseofresourcesandariseinthepercapitacostofservicedelivery.Doublingurbandensityhasbeenestimatedtoreducethepercapitacostofinfrastructureimprovements

Model

Urbanclassification

Assumptionsthatdependonruralorurbandesignation

Technologyoutputoptions

Source

NetworkPlanner

Userdefinesthresholdbelowwhichanareaisconsideredruraland

abovewhichitisconsideredurban.

Populationgrowthrate,aaveragehouseholdsize,fractionoftotaldemandduringpeakhours

Grid,minigrid,off-grid

Kemausuorandothers(2014);Ohiare(2015)

Reference

ElectrificationModel(REM)

Noexpliciturban-ruralassumptionswithinmodelingparameters,butsomeinputassumptionsareinformedbynationaldatasetsthataredisaggregatedbyruralorurban.

Smallhomesolar,minigrids,grid-

connectedsystems

Cillerandothers

(2019);Borofsky

(2015)

OpenSourceSpatial

Userdefinesthresholdbelowwhich

Averagehouseholdsize,

Gridconnection,mini

Mentisandothers

ElectrificationTool

anareaisconsideredruraland

populationgrowthrates,

gridsystems,stand-

(2017);Korkovelos

(OnSSET)

abovewhichitisconsideredurban.

accesstiersb

alonesystems

andothers(2019)

Table1.Assumptionsinleast-costelectrificationmodelsthatareinformedbybinaryrural-urbanclassifications

a.Aftera“rural”communitysurpassesthesetthreshold,itupgradesto“urban”growthrates.

b.Demandestimationhasimprovedasthesemodelsbecomemoresophisticatedandhigher-resolutionsocioeconomicdatabecomemorewidelyavailable.TheGlobalElectrificationPlatform,forexample,whichisbasedonOnSSET,allowsforabottom-upestimationmodethatusesaspatializedpovertyindex

andincomedatatoestimatedemand.

6

AdaptingSpatialFrameworkstoGuideEnergyAccessInterventionsinUrbanizingAfrica

Rural-urbanassumptionsalsoenterdemandforecastingalgorithmsasaveragepopulationgrowthratesand/orhouseholdsize.Theseassumptionsareoftenbasedonaver-agedhistoricalvaluesandusuallydonotaccountforthelocation-specificdynamicsthatdrivethesedemographicshifts.Populationgrowthisusuallymuchhigherin“rural”areasthatarenearlargecities.Areasmayalsoexperiencepopulationdeclinesaspeopleresettleinareaswithmoreeconomicopportunityorpoliticalandenvironmentalstabil-ity(Blackandothers2008).Modelsthatdonotaccountforthesedynamicsmaynotbeabletoidentifyareasofhighpotentialgrowthanddensificationandcouldthereforerec-ommendsystemsthatarenotwell-suitedtolocaleconomictrajectories.

Adiverseecosystemofelectricityprovisionmodelsnowexists,includingthecentralgrid,minigrids,meshgrids,metrogrids,andsolarhomesystems.Manyofthesesystemsarecom-merciallyavailableatsizesandpricepointsthatareincreas-inglytailoredtoincome-constrainedusers.Mostmodelsincludejustthreepossibilities:solarhomesystems,minigrids,andgridextension.Theydonotincorporateevolvinghybridtechnologiessuchasmeshgridsandunder-gridminigrids,whichmaybecost-effectiveindensifyingruralandperi-ur-bancommunitiesorareasthataregrid-connectedbutexperiencepoorreliabilityandservicequality(Menon2022).Futureiterationsofthesemodelscouldintroducepenaltiesforpoorgridreliabilityandqualityandallowgrid-tiedorunder-gridsystemstocompeteagainstgridextensionwithinoptimizationalgorithms.

Establishingcoherentinstitutionaljurisdictions

Rural-urbandesignationscanpresentachallengetoeffec-tivegovernanceofelectricityprovision.Inmanycountries,aruralelectrificationagencyorsimilarinstitutionistaskedwithexpandingaccessintoruralareas.Withincities,themunic-ipalityortheutilityisoftenresponsibleforelectrification.Peri-urbanareasmaynotfallcleanlyunderthejurisdictionofeitherinstitution.Emergingurbanzonesmustwaituntilthenextcensuscycletobeformallyincorporatedandreceivetheresourcesnecessarytoplanandadministerurbanser-vices.Theseandotherrapiddemographicshiftshavegonelargelyunrecognizedintheofficialstatisticsthatareusedforplanningandpolicymaking.

Senegalprovidesanextremeexampleofthisinstitutionaldisconnect.Inthe1990s,areformintheelectricitysectordesignatedanewelectrificationagency,theSenegaleseRuralElectrificationAgency(ASER)forruralareas;urbanareasremainedunderthejurisdictionofthenationalutility,SENELEC.Underthisframework,peri-urbanareasfellintoneitherruralnorurbanclassificationsandwerenottargetedbyeitheragency’spolicies(Singhandothers2015a).

ReevaluatingKenya’sMulti-TierFrameworkSurvey:Whatinsightsdoesacontinuum

approachoffer?

SpatialdisaggregationofKenya’sMulti-TierFrameworkSurveyrevealslargeaccessdeficitsinperi-urban

communities,drivenprimarilybyhighconnectioncosts

Rural-urbanasacontinuum

Severalestablisheddatasetscanbeoperationalizedinlieuofabinaryrural-urbanclassification.Table2outlinesfourofthemostwidelyusedorrecentlydevelopeddatasets;fig-ure2showsimagesbasedonthem.

TheanalysispresentedhereusestheUrban-RuralCatchmentArea(URCA),whichdisaggregatesspaceinto30categories.AparticularstrengthoftheURCAmethodologyisitsabilitytoidentifyeconomiclinkages.URCAisbasedonCentralPlaceTheory,whichpositsthatpeoplelivingclosertoacityortownhavebetteraccesstoitsgoods,services,andopportunitiesandthatbiggercitiesofferabroaderrangeofurbanbenefits(Christaller1933).UndertheURCAframe-work,spaceisunderstoodasacatchmentareasurroundinganurbancenter(Cattaneo,Nelson,andMcMenomy2021).Catchmentareasaredefinedbythelengthoftimeitwouldtakesomeonelivinginthatareatoreachthenearestcityortownofaparticularsize.Itisestimatedbycalculatingtheleast-costpathoveracostsurface.Thiscostsurfacequantifiestheeaseandspeedoftraveloverdifferentroutes,accountingfortransportnetworks,landcoverdata,andinternationalborders(Weissandothers2018).Animportantlimitationisthatthisalgorithmdoesnotexplicitlyaccountforthemodeoftravel.Catchmentareadesignationsmayvaryacrosscountryandregionalcontexts,asdifferentcon-textsfavordifferenttransportationmodes.

7

AdaptingSpatialFrameworkstoGuideEnergyAccessInterventionsinUrbanizingAfrica

Table2.Featuresofdatasetsthatcanbeusedingeospatialanalysisandplanning

Dataset

Description

Type

Areacovered

Yearscovered

Custodian

Datasetaddress

Africapolis

Providesextentsofurban

agglomerations;includes

statisticsonpopulationandsettlementdensity.

Vector

African

continent

2015,2022

OECD

https://oecd.us4.list-manage.

com/subscribe?u=5aa4680

998eddebe5f4ce7065&id=

17db0c31dd

Degreeof

Urbanisation(DEGURBA)

Classifiesspaceascities,

townsandsuburbs,and

ruralareasbasedona

combinationofgeographicalcontiguityandpopulation

density.Thisfirstlevelof

theclassificationmaybe

complementedbyarangeofmoredetailedconcepts,suchasmetropolitanareas,commutingzones,dense

towns,semi-densetowns,suburbanorperi-urban

areas,villages,dispersedruralareas,andmostlyuninhabitedareas.

Raster

Global

1975–2030,atfive-yearintervals

European

Commission

(UNendorsed)

https://ghsl.jrc.ec.europa.eu/

download.php?ds=smod

Open

Buildings

Includesoutlinesofbuildingsandcharacterizesthe

densityofsettlements;

footprintsofbuildingsaredeterminedfromhigh-

resolutionsatelliteimageryviaadeeplearningmodel.

Vectorsand

points

Centraland

SouthAmerica,Africa,IndianSubcontinent,SoutheastAsia

Variesby

location(basedonavailabilityofsatellitedata)

Google

https://sites.research.google/open-

buildings/#download

Urban-RuralCatchmentArea(URCA)

Thirtycategoriesshow

catchmentareasaroundandcitiesandtowns,defined

intermsoftraveltimeto

closestcityortown(less

than1hour,1–2hours,2–3hours,morethan3hours)ofdifferentsizes.

Raster

Global

2015

Authors

/

articles/dataset/

Urban-rural_

continuum/12579572

Sources:Florczykandothers(2019);OECD,UNECA,andAfDB(2022);Sirkoandothers(2021);Cattaneo,Nelson,andMcMenomy(2021).

Thisanalysisre-evaluatestheresultsofKenya’sMulti-TierFramework(MTF)surveyusinganadaptedversionoftheURCAframework.TheobjectiveistorevealtheadditionalinsightsthatamoresophisticatedspatialframeworkcanofferininterpretingtheMTFresults.

TheURCAframeworksegmentspopulationsintocategorieswithsimilarsocioeconomicdemographicsandqualityofinfrastructureservices.Thistypeofspatialdisaggregationisnecessarytounderstand