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RESEARCHARTICLE
An Economic Geography of the United States:
From Commutes to Megaregions
GarrettDashNelson1☯,AlasdairRae2☯*
1 DepartmentofGeographyandSocietyofFellows,DartmouthCollege,Hanover,NewHampshire,United
StatesofAmerica,2 DepartmentofUrbanStudiesandPlanning,UniversityofSheffield,Sheffield,United
Kingdom
☯Theseauthorscontributedequallytothiswork.
*[email protected]
Abstract
a11111
TheemergenceintheUnitedStatesoflarge-scale“megaregions”centeredonmajormetro-
politanareasisaphenomenonoftentakenforgrantedinbothscholarlystudiesandpopular
accountsofcontemporaryeconomicgeography.Thispaperusesadatasetofmorethan
4,000,000commuterflowsasthebasisforanempiricalapproachtotheidentificationof
suchmegaregions.Wecompareamethodwhichusesavisualheuristicforunderstanding
arealaggregationtoamethodwhichusesacomputationalpartitioningalgorithm,andwe
OPENACCESS
reflectuponthestrengthsandlimitationsofboth.Wediscusshowchoicesaboutinput
Citation:DashNelsonG,RaeA(2016)An parametersandscaleofanalysiscanleadtodifferentresults,andstresstheimportanceof
EconomicGeographyoftheUnitedStates:From
comparingcomputationalresultswith“commonsense”interpretationsofgeographiccoher-
CommutestoMegaregions.PLoSONE11(11):
ence.Theresultsprovideanewperspectiveonthefunctionaleconomicgeographyofthe
e0166083.doi:10.1371/journal.pone.0166083
UnitedStatesfromamegaregionperspective,andshedlightontheoldgeographicproblem
Editor:JoshuaLRosenbloom,IowaState
ofthedivisionofspaceintoarealunits.
University,UNITEDSTATES
Received:July2,2016
Accepted:October22,2016
Published:November30,2016
Introduction
Copyright:©2016DashNelson,Rae.Thisisan
openaccessarticledistributedunderthetermsof Fornumerousreasons,rangingfromthewideningscaleoflabormarketstotheintegrationof
theCreativeCommonsAttributionLicense,which
capitalflows,manyobservershavesuggestedthattheeconomicgeographyoftheUnitedStates
permitsunrestricteduse,distribution,and
isnowbestunderstoodintermsof“megaregions.”Theseareassumedtobelargeregional
reproductioninanymedium,providedtheoriginal
areas,oftencuttingacrossstatelines,thatarenormallycenteredonmajormetropolitanhubs
authorandsourcearecredited.
andincludeanorbitofsmallersub-centers.Todividethecountryintoamosaicofsuchmega-
DataAvailabilityStatement:Thedataareavailable
regions,analyststypicallyrelyonalooselyinterpretivemethodwhichtakesintoaccountphysi-
atthefollowingFigsharelink:https://doi.org/10.
calproximity,morphologicalintegration,andculturalsimilarity,inordertogroupmajorand
15131/shef.data.4110156.LinktoallFigsinhigh
resolution:https://www.dropbox.com/sh/ minorcitiestogetherwithruralareasintocoherentregionalentities;e.g.Hagler[1]orKotkin
tmqry68m80tg8dz/ andSchill[2].
AAAZBaEJxYWcZniBeZnlbWMLa?dl=0. However,theempiricalproblemofdividingspaceintodiscrete,bounded,internally-
Funding:Thepublicationfeeforthisarticlewas homogenousregionshaslongbeenavexedproblemforgeographers,withattemptsatprovid-
providedbytheUniversityofSheffield. inganobjectivemethodforregionalizationstretchingbackmorethanacentury[3–6].While
morerecentscholarlyworkhasemphasizedconceptsofconnectivityandrelationalityastheo-
CompetingInterests:Theauthorshavedeclared
thatnocompetinginterestsexist. reticallinesofinquirywhichleadbeyondtheconfinesofboundedspace[7–10],thegoalof
PLOSONE|DOI:10.1371/journal.pone.0166083 November30,2016 1/23
AnEconomicGeographyoftheUnitedStates
partitioningtheUnitedStatesintofunctionalmegaregionsshowsthattheoldproblemof
regionaldelineationremainsverymuchalive—andunsolved.Ageographybasedsolelyon
whatKhannacalls“connectography”[11]failstoofferapracticalframeworkwithinwhichto
definetheboundedgeographicareaswhichcontinuetomarkoutspheresoflegaljurisdiction,
planningauthority,transportation,andpoliticalrepresentation.
Inthispaper,ourcontributionistoofferanempiricalapproachtodetectinganddefining
megaregionswhichtakestheinsightsofarelational,flowingconceptofgeographyandputs
themtouseinserviceofdelineatingcoherent,boundedregions.Weemployadatasetofmore
than4,000,000commutesasaproxyforpatternsofeconomicinterconnection,giventhe
importanceofcommutesinstructuringthegeographyoflabormarkets[12,13].Thevolume
andresolutionofthisdatasetallowsustodepicttheinterconnectednatureoftheselabormar-
ketsatanationalscaleand,wehope,allowsustomakeasubstantivemethodologicalcontribu-
tiontothestudyofmegaregionsintheUnitedStates.
Weusetheseflowsinordertoprovidearigorousandevidence-basedassessmentof
whether“megaregions”existand,ifso,whatspatialformstheytake.Wedosothroughan
explorationoftwodifferentapproaches:onerequiringvisualinterpretation,andtheother
relyingstrictlyonalgorithmiccomputation.Inthevisualinterpretationmethod,weshowhow
flowmappingcanbeusedinordertoshowthespatialclusteringofurban’megaregions’by
employingcartographictechniquesthataugmentavisualrecognitionofinterrelation.Inthe
algorithmicmethod,weemploynetworkpartitioningsoftwaredevelopedattheMITSenseable
CityLab[14]inordertoassesstheutilityandreliabilityofapurelystatisticalanalysisindeter-
miningthegeographicalbreakpointsbetweencommunities.Suchamethodhintsatapossibil-
itylongpromisedbyspatialscientists:aregionalizationschemewhichreliesentirelyonspatial
laws,ratherthancontestablehumaninterpretation.However,wecautionagainsttheideathat
regionalunitscanbeincontrovertiblydeterminedbyrawmathematicalanalysisalone,and
showhow“bigdata”methodsaredependentnotonlyonthereliabilityofinputsources,but
alsoonchoicesaboutparameters,and“commonsense”checksonresults.
Background
Theimpossibilityofabsolutelydefiningstableterritorialregionshaslongbeenrecognizedby
geographersandthoseworkinginallieddisciplines.Forexample,inhisinfluential1939essay
“TheNatureofGeography,”RichardHartshornewarnedthat“thefaceoftheearthisthevery
antithesisofamosaic”[15].Hartshornepointedtothecomplexandhighlyheterogeneousdis-
tributionoffeatureswhichcharacterizeshumangeography,concludingthat“mandoesnot,
consciouslyorunconsciously,organizearegionasaunit”[15].Thoughthisolddebatehas
nowbeensupersededbyapproachestohumangeographywhichemphasize“relationality,”the
problemofdefiningregionalunitsnonethelessremainsavexingandunsolvedproblem.For
numerouspracticalreasonsitisnecessarytostabilizespaceintodiscreteentitieswithwell-
definededges.Tolistjustafewexamples,electoraldistricts,regionaltransitadministrative
zones,andmunicipaljurisdictionsallrequireanevaluationofwhatterritorialspaceisappro-
priatetotreatasa“single”regionfromapolicyandplanningperspective.Howeverborderless
andintricatelyintermeshedtheactualpatternofhumangeographymaybe,thequestionof
howtoextractfunctionalregionsandedgesfromthefuzzinessofthatpatternremainsan
importanttaskforappliedgeographersandtheircounterpartsinpublicadministration.
Acontemporaryapproachtosolvingthisproblemcanbefoundinliteratureonmegare-
gions[16,17].Theconceptofthe“megaregion”—likesimilarprecedentconceptssuchasthe
“conurbation”[18]or“megalopolis”[19]—hasdevelopedinresponsetonewpatternsofeco-
nomicintegrationoperatingacrosswidergeographicscales.Asthefunctionalpatternsof
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AnEconomicGeographyoftheUnitedStates
humangeographyhavetransformedandrenderedolderterritoriesinadequateorobsoletefor
thepurposeofmanagingpublicservices,oneofthekeyconcernsinstudiesofthesekindshas
beenthepracticalquestionofhowtodividespaceintonewadministrativeregionswhosegeo-
graphiclogicfollowstheunderlyingfunctionalstructureofthesenewpatterns[20,21].
“Megaregions”maywellpassacommon-sensetest,basedonarecognitionofthewaysin
whichlargerareashavebeensubstantivelytiedtogetherbytheforcesofurbandevelopment,
telecommunications,thefrictionlesscirculationofcapital,andtheconsolidationofbothpublic
andprivateinstitutions.However,theproblemofhowtoempiricallydeterminetheexistence
andpreciseshapeoftheseareasisanextensionoftheoldgeographicproblemofdividing
spaceintounitareas.Inthispaper,weattempttoshowthefunctionalexistenceofmegaregions
intheUnitedStatesbyturningtoalargedatasetofcommutingpatternsandshowinghow
“natural”patternsofcommunityclusteringcanbedemonstratedboththroughvisualinterpre-
tationaswellasthroughstatisticalanalysis.
Webelievethatourresultsofferanew,moreempiricallyrigorousevaluationofmegare-
gionsanddemonstratetheutilityofthisapproachingainingabetterunderstandingofthe
functionaleconomicgeographyoftheUnitedStatesatamacro-spatialscale.
DataandMethods
Wefocusonjourneystowork(commutes)owingtotheirimportanceinthefunctioningof
localandregionallabormarkets,andbecausethevolumeofdataavailableprovidesarobust
testcaseforthecommunitypartitioningalgorithmwedescribebelow.Insection4ofthe
paper,wereportourresultsbasedonananalysisofmorethan70,000spatialnodesandmore
than4,000,000connectionsbetweenthem,usingacombinationofdesktopandcloud
computing.
GiventhegeographyoftraveltoworkintheUnitedStates,thecountyscaleofaggregation
wasdeemedtoocoarseandtoovariabletobeofuse.Themostpopulouscounty,LosAngeles
County,ishometomorethan10,000,000peoplewhiletheleastpopulous,LovingCounty,
Texas,ishometofewerthan100.Themeancountypopulationatthetimeofthe2010USCen-
suswasalmost100,000.Further,manyofthenation’smorethan3,000countiesarefarlarger
orfarsmallerthanthemetropolitanareaswithinwhichtheyarelocated,makinganyanalysis
ofjourneystoworkatthisscaleproblematic,inthesensethatthegeographicscaleofanalysis
wouldbetoolargetoidentifytheunderlyingphenomenonofjourneytoworkpatternswe
seektoinvestigate.
Therefore,wedecidedtofocusouranalysisatafinergeographicresolutioninordertocap-
ture,asfaraspossible,thevolumeanddiversityofflows.Forthispurpose,theCensusTract
wasdeemedthemostappropriatespatialunit,sinceithasanaveragepopulationofjustover
4,000andCensusTractsaregeographicallylargeenoughtocontainmajornumbersofemploy-
ees,unlikesmallerCensusunitssuchasBlocksorBlockGroups.Dataonjourneystoworkare
reportedforjustover74,000CensusTractsintheUnitedStatesandareavailablefromtwodif-
ferentsources:theAmericanCommunitySurvey(ACS)commutingandworkplacedataand
theLongitudinalEmployer-HouseholdDynamicsOrigin-DestinationEmploymentStatistics
(LODES).Thesecomplementarydatasetscanhelpusanswerimportantquestionsaboutthe
economic,demographicandspatialrelationshipsassociatedwithdailyjourneystoworkfor
morethan130millionAmericansbuttheydifferinseveralimportantrespects[22],aswe
explainbelow.
Asthenamesuggests,theAmericanCommunitySurveycommutingandworkplacedata
arederivedfromanationwidesurveyofemployees,askingwheretheyworked“lastweek.”
TheACSisalarge,continuoussurveyofaround3,500,000addressesperyearanddataare
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availableinone,threeorfiveyearestimateperiods.Thelatterisbasedon60monthsofdata
andreflectsthecharacteristicsofanareaovertheentireperiod.Bycontrast,intheLODES
dataset,employmentlocationisreportedbyemployers,and,iftheemployerhasmorethan
onelocation,thismaynotbethesamelocationwhereanemployeeactuallyworks[22].
LODESjobcountsarereleaseddowntotheCensusBlockLevel,whereasACScommutingand
workplacedataareavailabledowntoCensusTractlevel.
OurconsideredviewisthatLODESdatamaybemostappropriatefortheanalysisofcom-
mutepatternsatmorelocallevels(e.g.withinasinglemetropolitanarea)ratherthanatthe
nationalscale,aswereportinthispaper.Inshort,eachdatasetisusefulinitsownrightand
couldbeusefullydeployedtocarryouttheanalysisreportedhere.However,wehaveusedthe
ACS5-yearestimatesinordertoprovidethemostrobustmeasureofnationwidecommute
patterns,intheabsenceofthenow-discardedtraveltoworkquestionsfromtheLongForm
Census.
TheACS2006–2010CensusTractFlowDataset
Thedatasetweusedcontainsoriginanddestinationinformationfor4,156,426journeyto
workflowsbetween74,002CensusTractcentroidsintheUnitedStatesandPuertoRico.We
computedtheEuclideandistancebetweenoriginanddestinationtractcentroids,inorderto
provideadistanceestimatebetweenworkplaceandresidence.Additionalfieldswereadded
fortract,countyandstateFIPScodesinordertoallowforsubsequentfilteringofthedata
usingthesegeographies.WithintheUnitedStates(minusPuertoRico),thisleftatotalof
4,155,548tract-to-tractflowsassociatedwith130,163,663totalcommuters.Whenfilteredby
distance(d<160km)andplottedonamapoftheLower48states,thisproducesageographic
representationoftheurbanfabricoftheUnitedStatesthatshowshowindividualareasare
connected,asseeninFig1.Adistanceof160km(c.100miles)waschosenbecauseitincludes
allbutthemostextremecommutes,andaccountsfor97.4%ofallpoint-to-pointcommuter
flowsinthedataset.Fig1illustratestheinterconnectednessofsomelargergeographicareas,
suchasthenortheasternseaboard,whichevokesGottmann’soriginal“megalopolis,”spanning
theareafromBostoninthenorthtoWashington,D.C.inthesouth.Thisrepresentation
requiresfurtherscrutiny,andisthereforethesubjectoftheremainderofthepaper.
Avisualheuristicapproachtoregionaldelineation
Whenattemptingtomakesenseoflargeandcomplexspatialdatasets,itispossibletotakea
numberofdifferentmethodologicalapproaches.Hereweexaminetwoofthemostcommon:a
visualheuristicapproachandanalgorithmicapproach.AccordingtoSmelcerandCarmel
[23],thecognitiveworkwedowithmapsissimplifiedusingvisualheuristics,andthisiscer-
tainlythecasewhenwecomparealargecommuterdatatablewithmillionsofcellstothekind
ofcartographicrepresentationshowninFig1.However,wedonotconcurwithSmelcerand
Carmelwhentheystatethat“visualheuristicsdifferfromalgorithms,whichareguaranteedto
provideacorrectsolution”‘ (emphasisadded;cf.Anderson,[24]).Asweshallseebelow,the
extenttowhicha“correctsolution”canbederiveddependsuponuser-definedparameters,the
natureofthealgorithmusedandtheunderlyingepistemologicalpositionoftheresearcher.
Nonetheless,wedobelievethatthealgorithmicapproachwedevelopconfersmanyadvantages
aboveandbeyondthevisualheuristicandhassignificantpotentialinaddingvaluetothevisual
approach,particularlywithrespecttodiscriminatingthedividinglinesoroverlapsbetween
closely-integratedregions.
Aheuristicapproachis,putsimply,onewhichinvolvesexplorationthroughtrialanderror
toproduceresultswhichareuseful,butnotnecessarilyoptimal.Simon[25]referstothisasthe
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Fig1.Tract-to-TractCommutesof160kmorless.
doi:10.1371/journal.pone.0166083.g001
conceptof“heuristicsearch”inwhichsub-optimalbuteffective“satisficing”solutionsare
arrivedat.WetakethisvisualapproachwiththeACScommutingandworkplacedataby
workingiterativelytowardsrepresentationswhichidentifycorelabormarketareasbasedon
networksofflows.Inadditiontoprovidingavisual‘sensecheck’ontheunderlyingdata,this
alsoprovidesausefulreferencepointforthealgorithmicapproachdescribedlater.
Inordertooperationalizethevisualheuristicapproach,weemployedRae’s“principlesfor
theorderlylossofinformation”inwhichiterativelossandoptimalcompromisearekeytenets
[26].Intheformer,differentparametersareappliedinordertofilteroutthe“noise”inadata-
set;akintoBoulding’smaximthat“knowledgeisalwaysgainedbytheorderlylossofinforma-
tion”[27].Thelatterprinciple—optimalcompromise—isanexplicitrecognitionthatevenin
empirically-drivenstudiestheresultsareneverperfect,whichagainechoesSimon’s‘satisfi-
cing’principle.ThesealsoproveausefulwaytooperationalizePolya’smathematicalproblem
solvingapproachinwhichthevisualapproachtounderstandingisgivenaprominentrole
[28].
Aswereportinthenextsectionofthepaper,thevisualheuristicapproachinpractice
involvedfilteringtheACSdatabasedonEuclideandistanceandflowvolumethresholdsin
ordertoproducealabormarketregionalizationoftheUnitedStateswhichwasbothempiri-
callydrivenandvisuallyeffective.Ideally,suchanapproachwouldalsoincludenetwork
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AnEconomicGeographyoftheUnitedStates
distanceandtraveltimeparameters,buttheseintroducealevelofcomputationalcomplexity
beyondthatwhichcanbeaccommodatedhere.
Fundamentally,weareseekingtotesttheefficacyofthevisualheuristicapproachvis-a-vis
thealgorithmicapproachtoregionaldelineation.Weacknowledgethatempiricalevidence
suggestsacommutingtoleranceof30to45minutesformostworkers[29]butsinceweare
focusedontheidentificationoflargereconomicregions,ourvisualheuristicapproachuses
the50milesfigure(80.5km)for“longdistancecommutes”citedbyRapinoandFieldsintheir
analysisofmegacommutingintheUnitedStates[30].Weusethislongerdistanceinan
attempttocapturethevastmajorityofcommuterswithinmetropolitanareas.
Analgorithmicapproachtoregionaldelineation
Newmanidentifiestheproblemofdetectinganddescribingcommunitystructureswithinnet-
workdataas“oneoftheoutstandingissuesinthestudyofnetworkedsystems”[31].A“com-
munity”inanetworkeddatasetreferstoasetofnodesinwhichthedensityofrelationsis
strongerinternallywithinthecommunitythanitisexternallywithmembersofdifferentcom-
munities,or,asNewmandescribesit,“theappearanceofdenselyconnectedgroupsofvertices,
withonlysparserconnectionsbetweengroups”[31].Inavisualrepresentation,networkdata
ofthistypeisoftenrenderedasdots(verticesornodes)connectedbylines(variouslycalled
connections,edges,orarcs),andcommunitiesareshownasdotswithmanylinesconnecting
betweenoneanother,oftencircledbyaprovisionalborderindicatingthelimitsofthecommu-
nity.Althoughthisusuallythismeansthatthenodesinacommunityarevisuallyrepresented
asspatiallyproximate,themathematicaldefinitionof‘community’inthissensedoesnotrely
onthespatialpositioningofthenodes,onlyonthedensityofconnections.Communitystruc-
turedefinedinthiswayistopological,nottopographical.
Thevalidityandstrengthofaproposedcommunitystructurecanbemeasuredbythevariable
modularity,whichNewmanandGirvandefineasafunctionofthetotalnumberofconnections
withinanetworksetwhichlieentirelywithinaproposedcommunityboundary(asopposedto
acrosscommunityboundaries)[32].AsNewmanandGirvannote,however,thiscannotbeeval-
uatedasasimplefractionalmeasure,sinceassigningallofthemembersinafulldatasettojust
onesinglecommunitywouldthereforeresultinaperfectscore.Theirproposedmodularityvari-
ablethereforetestsintra-communityconnectionsagainstaperfectlyrandomassignmentofcom-
munities.Avalueof0indicatesapartitioningofnodesintocommunitieswhichis“nobetter
thanrandom,”whileavalueof1indicates“networkswithstrongcommunitystructure”[32].
BytreatingtheACScommuterdatasetasanetworkconsistingofnodes(censustracts)and
edges(commutes),wecanapplythealgorithmictechniqueswhichstatisticiansanddatascien-
tistshavedevelopedforcommunitydetectionandtestwhetheritispossibletodetectthe
signatureof“natural”communitygroupingsinthepatternofcommutergeography.Several
softwarepackagesexistwhichemployvariousalgorithmicapproachestocommunitydetec-
tion;wechosetousethe‘Combo’packagedevelopedbySobolevskyetal.atMIT’sSenseable
CityLab[14].
Comboachieveshigh-accuracypartitioning,measuredbythemodularityscoreoftheout-
putcommunities,atthepriceofarelativelymoreintensecomputationaltask.Sobolevskyetal.
reportgoodresultsforComboonadesktopcomputerwithdatasetsofupto30,000nodes;our
setcontainsslightlyover70,000nodes,andconsequentlyrequiredtheuseofhigh-performance
computinghardware.WethereforecompiledCombofromsourceonanAmazonWebSer-
vicesEC2(memory-optimized)virtualmachinewith122GBofRAMrunningUbuntuLinux.
ComborequiresitsinputdatainthePajeknetworkformat.Thisformatrequiresnodesto
beidentifiedbysequentialnaturalnumbers.WewroteaPythonscripttoiteratethroughthe
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databaseofcommutes,assigningidentifierstoeachcensustractnodewithatleastonecom-
muteoriginordestinationasthe“vertices”ofthenetwork,andusingtheseidentifiers,together
withtheflowvolume,tolistthe“arcs”ofthenetwork.WethenusedthisPajekfileasaninput
sourcefortheCombosoftware.Computingtimefordifferentversionsofthedatasetranged
from8to12hours.Aftereachcompute,wemergedtheoutput,inwhichComboreturnscom-
munityidentifiersforeachinputnode,backintoaspatialfilewithcoordinatedataforeach
censustract.Thisgaveusmappabledataofcensustractsandtheircommunitiesasassignedby
thecommunitydetectionsoftware.
Again,suchanapproachmeansleavingoutthespatialdataattachedtoeachnodeduring
thecomputationalphase.Unlike,forexample,thepartitioningalgorithmdevelopedbyGau-
dartetal.,whichanalyzescommunityclusteringbasedonnodes’proximityinspace[33],net-
workanalysisinComboconsidersonlythestrengthofaconnectionbetweentwonodes,not
theirphysicalnearnesstooneanother.Inourcase,the‘strength’ofaconnectionisdetermined
bythevolumeofthecommuterflowbetweentwocensustracts.
Thegeography-blindnessofthealgorithmthereforealsoallowsustoprovideatestcasefor
Tobler’s“FirstLaw”ofgeography:thepremisethat‘nearthingsaremorerelatedthandistant
things’[34].Censustractsneartoeachothershould,accordingtothislogic,havestronger
commuterconnectionstooneanotherthancensustractsfarapart.Consequently,eventhough
thepartitioningalgorithmisnotconsideringnodes’locationsinspace,itshouldproduce
communitieswhicharespatiallyclustered,ifthestructureofcommuterpatternsobeysthe
expectedruleofmoreconnectionsbetweenspatially-proximatenodes,andfewerconnections
betweenspatially-distantnodes.
Results
Inthissectionofthepaperwepresenttheresultsofourvisualandalgorithmicanalyses.Inthe
firstpart,welookattheextenttowhichavisualapproachtounderstandingtheACScommut-
ingandworkplacedatacanhelpusidentifynatural“communities”or“regions”ofinteraction
withinwhichjourneystoworktakeplace.Theseprovideavisualapproximationoflabormar-
ketareasofthekinddiscussedintheacademicliteraturefordecades[20,35–38].Ratherthan
attempttodothisatthescaleofthewholeUnitedStates,wefocusourvisualheuristiconCali-
forniaandtheMinneapolis-St.Paulmetroarea.Theformerprovidesagoodexampleofan
apparentlypolycentricurbannetwork,withmultiplecentersandamorecomplexcommuting
structure,asdescribedpreviouslybyCerveroandWu[39,40].Thelatterprovidesagood
exampleofanapparentlymonocentricurbancommutestructure,dominatedbyacore
employmentzoneinthecenter,ofthekindstudiedinthepastby,interalia,BogartandFerry
[41]andRichardson[42].Bothapproachescanhelpusunderstandmoreabouttheunderlying
economicgeographyofmajormetropolitanareasoftheUnitedStates.
Thesecondpartofthissectioniscomprisedoftheresultsofouralgorithmicapproachto
assigningcensustractsinto‘natural’communitiesbasedontheirrelationalpositionwithin
thenetworkdataset.Weshowthepromisingresultsofsuchanalgorithmicevaluationand
thesuccessoftheCombosoftwarepackageinmakingfine-scalediscriminationbetweenthe
edgesofmegaregions.Wealsoshowsomeofthedifficultiesanddataoutliersproducedby
suchanapproach,andsuggestsomequalificationswhichmustbeattachedtopurelystatisti-
calanalyses.
Identifyingpolycentricandmonocentricspatialstructures
Thesimplequestionwefirsthopetoanswerbytakingavisualheuristicapproachtocommute
dataiswhetheritispossibletodividegeographicspacebytakinganiterativeapproachto
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filteringandvisualizingtheACScommutingandworkplacedataset.Fig2representsafirst
steptowardsthisobjective.Wepresentalljourneytoworkflowsof50miles(80.5km)orless
whichbeginorendinCalifornia.Immediately,wecanseewhatappeartobeanumberofsepa-
ratefunctionaleconomiczones.Forexample,wecanseealargeinterconnectedurbanregion
spanningfromSanLuisObispoontheCaliforniacoast,extendingthroughLosAngelesand
SanDiegointhesouthandPalmSpringsintheeast.Wecanalsoobserveanotherlargecom-
muterregionwhichtakesinthemetropolitanareasofSanFrancisco,Sacramento,SanJose
andMonterey.Additionally,weobservesmaller,separateinterconnectedcommuterregions
includingEurekaandReddingintheNorth,FresnoandBakersfieldintheCentralValleyand
ElCentrointhesouth.Thesepatternsdo,ofcourse,mapontounderlyingpatternsofpopula-
tiondensitybuttheyalsoprovidevaluableadditionalinformationinrelationtotheconnectiv-
ityoftheseareaswitheachother.
Thisinitialplottingofcommutesisquiteusefulinthatitprovidesasimplevisualdepiction
ofeconomiclinkagesandwecanbegintounderstandthespatialstructureofcommutingin
California.Ifwerefinethisrepresentationstillfurther,asinFig3wherewefocusontheSan
FranciscoBayArea,amoredetailedrepresentationofapolycentricurbanregionemerges.In
thisrepresentationwedisplaylonger,lowervolumecommutesindarkershadesofredand
shorterjourneysinlightershadesoforange,inordertohelpthevieweridentifythemain
employmentcenters.InFig3theyincludeSanFrancisco,OaklandandSacramento,butalso
Stockton,ModestoandSantaRosa.Nonetheless,itisdifficulttodeterminefromthisviewthe
extenttowhichtheselinksarestatisticallysignificantandwhetherthisnexusofeconomic
activityconstitutesasinglefunctionalzoneinandofitself.Wedealwiththisissueinthenext
sectionofthepaperthroughthealgorithmicmethod.
Beforedoingso,wepresentthecaseofMinneapolis-St.Paul,whichrepresentsaratherdif-
ferentcasetothatofCaliforniaandtheBayArea.InFig4wehaveplottedtracttotractcom-
mutescenteredonMinneapolis-St.Paul,whichappearstoformamajormonocentric
employmentzoneinMinnesota,alsoextendingintowesternWisconsin.Moredistantsatellite
citiessuchasSt.CloudtothenorthwestandRochestertothesoutheastarelessstronglycon-
nectedtothisdominanturbanemploymentdestination,butitisdifficulttoknowtheextentto
whichtheyarefunctionallyseparatefromavisualinspectionalone,andthisisthepoint:cogni-
tively,theviewermakesassumptionsaboutthemodularityofanetworkbasedonvisualrepre-
sentationslikethecommutemapsshownhere,butthisisimpreciseandsomewhatsubjective.
Themappingofflowsalonecanonlytakeussofarifweareinterestedinknowingmoreabout
theunderlyingnetworkstructureofthedata.
Havingsaidthis,avisualapproachprovidesaveryusefulmethodinandofitselfbecauseit
allowsustointerpretdataspatially,quicklyidentifyanomalousconnectionsandpossible
sourcesoferror(cf.Anselin[43]).Thenextsectionofthepapernowfocusesonalgorithmic
communitypartitioningandallowsustogaugewhethertheprocessesofvisualcognition
(whichwecanthinkofhereasa‘manualmodularity’approach)initiatedbytheflowmaps
abovearereflectedintheresultsofthealgorithmicapproach.Itwouldappearthatwecan
identifynaturaleconomiccommunitiesorregionsfromavisualinspectionalone,butforreal-
worldapplicationssuchasregionaltransitplanning,wherestatisticalaccuracyisrequired,this
isnotsufficient.
Algorithmiccommunitypartitioning
Intheiralgorithmicpartitioningofdatafromtelephonecalls,Rattietal.[44]andSobolevsky
etal.[45]foundpromisingresultswhichexhibitedahighdegreeofgeographiccontiguity.Kal-
lusetal.reportsimilarresultsusingsocialmediainteractions[46].Wesoughttotestwhether
PLOSONE|DOI:10.1371/journal.pone.0166083 November30,2016 8/23
AnEconomicGeographyoftheUnitedStates
Fig2.Tract-to-TractCommutesof80km/50milesorlessinCalifornia.
doi:10.1371/journal.pone.0166083.g002
PLOSONE|DOI:10.1371/journal.pone.0166083 November30,2016 9/23
AnEconomicGeographyoftheUnitedStates
Fig3.Tract-to-TractCommutesof80km/50milesorlessintheBayArea.
doi:10.1371/journal.pone.0166083.g003
PLOSONE|DOI:10.1371/journal.pone.0166083 November30,2016 10/23