2017_Counter intelligence- evaluating Wi-Fi tracking data for augmenting conventional public life su by audrc

Australian Planner

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Counter intelligence: evaluating Wi-Fi tracking data for augmenting conventional public space–public life surveys

Julian Bolleter

To cite this article: Julian Bolleter (2017): Counter intelligence: evaluating Wi-Fi tracking data for augmenting conventional public space–public life surveys, Australian Planner, DOI: 10.1080/07293682.2017.1345963

To link to this article: http://dx.doi.org/10.1080/07293682.2017.1345963

Published online: 12 Jul 2017.

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Date: 12 July 2017, At: 18:04

Counterintelligence:evaluatingWi-Fitrackingdataforaugmenting conventionalpublicspace–publiclifesurveys

JulianBolleter

AUDRC(AustralianUrbanPlanningResearchCentre),UniversityofWesternAustralia,Perth,WA,Australia

ABSTRACT

Advancedtrackingtechnologiesareconsideredbysmartcityadvocatestohavethepotentialto transformhowwestudycities.Thispapertests,onedimensionofthesebroaderclaims,through analysingthepotentialcontributionthatWi-FitrackingdataofWi-Fiusersinthepublicrealm cancontributetoconventionalpublicspace–publiclifesurveys.TheconclusionisthatwhileWiFitrackingdatacanmakeimportantcontributionsintermsofcountingpeople,mappingtheir staysandmovementsataprecinctscaleandlarger,itisatpresent,notawholesalesubstitute formoretraditionalmethods – particularlywhenappliedonasitescaletothenuancesof interactionsbetweenpubliclifeandspaces.Inthisrespectisshouldbeconsideredas augmentingsuchtraditionalmethods,ratherthanreplacingthem.

Introduction

Contemporaryglobalcitiestendtoleada ‘doublelife’ , becausetheyare ‘located’ inboththephysicalandthe digitalworlds(Pereiraetal. 2011,353).Accordingly, publicspacehasbeenpervadedbynetworksandsystemsthatproducedigitalbitswhichareconnectedto humanbehaviour, ‘bothinforminghumanactionsas wellasreflectingtheireffects’ (Kloeckl,Senn,and Ratti 2012,90).Thesedigitalbitsortraces,including thoseproducedasa ‘by-product’ fromexistingnetworks,datacollectedwithsensorsanddataactively sharedbypeople(Kloeckl,Senn,andRatti 2012,94), arepotentiallycapableofreflecting,inrealtime,how peoplemakeuseofpublicspace(90).Ithasbeen arguedthatadvancedtrackingtechnologies(ATTs), whichtapintothesedatatraces,havethepotentialto ‘transform’ howwestudycitiesandindeedappearto offeraccess ‘intothecandy-store-of-their-dreams’ for urbanplannersengagedinurbananalysis(Schaick 2008,183).

Method

Thispaperquestionsthispremiseandmethodically measuresthepotentialcontributionthatWi-Fi1 trackingdataofWi-Fideviceusersinurbanpublicspace cancontributetoconventionalpublicspace–public lifesurveys(PSPLSs) – surveysofhowpeoplemove through,dwellandusethepublicrealmdevelopedby JanGehl,andimplementedinmanycitiesaroundthe world(Gehl 2013).Assuch,thispaperisstructured bythefollowingresearchquestion:

ARTICLEHISTORY

Received10October2016

Accepted20June2017

KEYWORDS

Publicspace–publiclife survey;publicrealm;Wi-Fi networks;Wi-Fitracking; pedestriantracking; advancedtracking technologies

How,andtowhatdegree,canconventionalpublic space-publiclifesurveys,atasiteandprecinctscale, beaugmentedbyWi-Fitrackingdatageneratedby Wi-Fideviceusers?

Soastoanswerthisquestionthepaperisdividedinto twosections.The ﬁrstreviewsconventionalPSPLSsso astoprovideabenchmarkagainstwhichWi-Fitrackingdatacanbeassessed.ThesecondreviewsthepotentialofWi-Fitrackingdatatoaugmentaconventional PSPLS.Giventheanswerstotheresearchquestion depend,inpart,onthescaleofapplicationthispaper willfocusonthesitescale,anarearoughly150metres indiameter,andtheprecinctscale,anarearoughly300 metresindiameterandlarger.

Theresearchstrategyadoptedinthispaperisan evaluativestrategy.Evaluativestrategiesaretypically usedtomeasureacertainplanningoranalysispractice againstapre-existingstandard(SwaffieldandDeming 2011,39).Inthispaper,Wi-FitrackingofWi-Fidevice usersinpublicspaceisevaluatedinrelationtoconventionalmanualmodelsofPSPLSssuchasproposedby JanGehl(2013) – amethodwhichhasbecomepredominateinurbandesignandplanning(MatanandNewman 2012,31).

JanGehliswidelyacknowledgedforhisemploymentofsocialscienceresearchmethodsinPSPLSsto studyhuman-builtenvironmentinteractionsthatprovidequantitativestatisticalanalysis,whilealsoexplaininginqualitativetermshowpublicspacesarebeing used(MatanandNewman 2012,31).Indeed,theJan GehlPSPLShasbeendeployedinmanycitiesaround theworldovermanydecades(MatanandNewman 2012,31)includingCopenhageninDenmark(1967),

AscoliPicenoinItaly(1965),AlbertslundSyd(south) inDenmark(1969),FitzroyinVictoria(1976),Ontario inCanada(1977),ArendalinNorway(2012)andPerth inWesternAustralia(1994and2010),andcanbeconsideredasareputablebenchmarkagainstwhichto studythepotentialofWi-FitrackingtoaugmentconventionalPSPLSs.Thisisnottosay,however,PSPLS methodsareperfectandindeeditcanbeasubjectto humanerror – ratherthanitformsawell-known andrespectedstandardforcomparison.

ThepotentialofWi-Fitrackingtocontributeto PSPLSsisestablishedthroughextensiveinterviewsof anonymoustechnicalexperts,placemanagersand landscapeandurbanplannerswhohavebeeninvolved incommissioning,interpreting,anddesigningin relationtoWi-Fitrackingdatasourcedfromostensibly publicprecinctssuchasQueenVictoriaMarket(in Melbourne,Victoria),Mandurahcitycentre,andElizabethQuay(inWesternAustralia)amongstothers.Itis throughthisprocessthataverifiableassessmentofthe capabilitiesofWi-FitrackingdatatoaugmentPSPLSs isarrivedat.2

Background

ThepromiseofATTs

Twentyyearsaftertheappearanceofsmartcityliteratureandtheannouncementofthefirst, ‘pioneering’ cases,the ‘smartcitydomainremainsambiguous’ (Anthopoulos 2016,1).Nonetheless,itcontinuesto presentthesamevisionthatplannershavelongaspired to – inshortacitywhichisabletobeanalysedcoherentlyandindetail,enablingthewholecityto ‘function likeclockwork’ (RattiandClaudel 2016,28).

AsitappliestoATTsmartcityproponentshave claimedthatintheurbanrealm, ‘theemergenceof newmappingpracticesmaybeapreludetotheformationofanunprecedentedtypeofcollectiveintelligencethatbringstogetherhumansandnon-humans, algorithmsandcyborgs’ (Picon 2015,106).Indeed, humansareemittinganever-increasingamountof informationintothebuiltenvironment.Thesedata comefromeverywhere:sensorsusedtogatherclimate, pollution,populationdensitiesandflowsinformation (Picon 2015,37),poststosocialmediasites,digitalpicturesandvideos,purchasetransactionrecords,mobile phoneGPSsignalsandWi-Fitrackingtonameafew (Hudson-Smith 2014,42).Throughthe ‘conscientious’ miningofsuchdata,EagleandGreeneclaim ‘it’ spossibletouseBigDatatoengineerbettersystemsand potentiallyabetterworld’ (WallissandRahmann 2016,118)and ‘improvequalityoflife’ (Anthopoulos 2016,3).

WhileATTs,astheyapplytourbanplanning,3 offer excitingopportunitiesthereisanincreasingscepticism ofthesmartcity’soverestimatedpotentialinthis respect(Anthopoulos 2016,1).Indeed,theuseof

data-drivenprocessesinurbananalysisandplanning remainspiecemeal,despitetheongoingpromotionof suchmethodsbysmartcityproponents(Vanky 2015, 177).AsitappliestoATTs,itisregardedthatthere areseveralapplicabilitygapsthathindertheuseof trackingresearchinanurbanplanningandplanning context(Schaick 2010,70).Theseincludethetendency tocollectlargeamountsofdata ‘burdeningthesynthesisingcapacities’ ofurbanplannersandsecondly,that theknowledgegeneratedthroughtrackingresearchis notbeinggearedtowardstheactualneedsofplanners (Schaick 2010,71).

Wi-Fitracking,whichformsthefocusofthispaper, canberegardedasacomponentofalargersubsetof ATTswhichhavebeenusedtostudythemovement anddensitiesofpeopletoinformurbananalysis.In urbanplanning,GPStrackinghasbeenconducted forsometimeusingGPSdeviceswhicharedistributed toparticipantsengagedbyparticularstudies(Spek 2008,27).However,citiesareoftenfarfromideal forusingGPStechnologies.Buildingsblockthereceptionofsatellitesignals,signalsbounceoffbuildings, smallstreetshavelimitedreception,andpeopleof courseenterbuildingswhichcanblockGPSsignals (Spek 2008,28).

Mobilephonescanalsobetracedbasedonthecell towerthemobilephoneisconnectedto,andthrough triangulation,inwhichtheroughpositionofmobile phonesisinterpolatedbycalculatingtherelativesignal strengthfromanumberoftowers(Spek 2008,28). However,thisprocessistimeconsuming,expensive, thereareprivacyissues,and – todate – mobile phonetrackingislessaccuratethanGPStracking (Spek 2008,29).

Video-basedComputerVisioncanbeusedto monitorpedestriansandsophisticatedsoftwareis availableforautomaticpeoplerecognitionandanalysis (Spek 2008,30).Despiteitsapplication,Video-based ComputerVisionis ‘notperfect’ despiterapid improvementofthistechnologyinrecentyears(Nielsen 2014,3).Automaticpeoplecountersarealsosimilarinthattheyeffectivelymeasuretheintensityof pedestrianactivityatalimitednumberoflocations (Spek 2008,26).Othersimilartoolsinusetorecord themovementofpeopleincludeincreasinglyubiquitousCCTV.Thesetechniques,thougheffectivefor studyingthebehaviourofindividualswithina restrictedspatialsetting,areoflittleuseoncethepedestriansstepbeyondtheobservationpoint’slineof sight(Shoval 2008,22).

Giventhecomparativeshortfallofthesetechnologies,atthesiteand – tosomedegree – precinct scale,Wi-Fitrackinghasbecomethedominanttechnologicalmeansfortrackingpedestrianmovements (Hampton,Livio,andSessionsGoulet 2010,704),in partbecauseWi-Fi-enabledsmartphonesalesand usehaveseenexplosivegrowthinthepastseveral

years(MusaandEriksson 2012,281).TherearegenerallytwowaysoftrackingpeopleusingWi-Fi,passive andactive.Passivedataareabletobecollectedif thereisaWi-Fidevicegoingpastasensor,oraccess point,butnotconnectedtoit.ThisisbecauseWiFi-enableddevices ‘sendout’ regularlyintheirsearch forWi-Fiaccess(Kalogiannietal. 2015,2).Active dataarecollectedwhenaWi-Fiuseractivelyconnects toaWi-Finetworkandagreestotheconditionsset outbytheprovider.Anactiveconnectionoften requirestheusertofilloutan ‘opt-in’ questionnaire, whichprovidesanaddedlayerofdataconcerninga user ’sassessmentofaplaceorevent.4

WhileactiveandpassiveWi-Fitrackingtechnologiesinindoorenvironmentssuchasshopping centres,airports,cafes,bars,bookstoresandhostels (Schauer,Werner,andMarcus 2014,171)have becomeverypopularoverthelastfiveyears,more andmorepublicplacesandurbanprecinctsarealso offeringWi-Fi(Kalogiannietal. 2015,1),meaning thatthistrackingabilityisbeingextendedintothe broaderurbanrealm(Lambert,McQuire,andPapastergiadis 2013,3).Thedatageneratedbytrackingof Wi-Fiusersinsuchspacesarebeginningtobeused inplanningforanumberofapplicationsincluding thepredictivedemandfortransport,way-finding, theplacementofpathways,ensuringpermeabilityto pedestrianflowsand,andinembryonicform,toassess theeffectivenessofurbanplanningandmanagement interventionsonuserexperience,visitationand dwell5 times.

Researchgap

Inresponsetotheclaimswhichhavebeenmadeby advocatesaboutthepotentialofATTstohelpengineera ‘betterworld’ (EagleandGreeneinWallissand Rahmann[2016],118),thispaperaimstotestthe potentialofWi-Fitracking(aprominentATT)of peopleinpublicspacetoevaluatepubliclife – ina waythatisofpracticalassistancetourbanplanners whoareorchestratingPSPLSs.Throughatargeted andsystematicapproach,thepaperisintendedto avoidthe ‘twinpitfallsofunbridledenthusiasmfor technologyandblanketcriticism – twoattitudes whichareunfortunatelyalltoocommoninrelation todigitalmatters’ (Picon 2015,20).Moreover,despite awealthoftechnicalliteratureonWi-Fiandpositioning – indeedacomparativelyrecentsearchforWi-Fi and ‘positioning’ onGooglescholarreturnsover 10,000papers(Kjærgaardetal. 2013,1) – arguably thereisalacunaofpublicationinrelationtothe practicalapplicationofWi-Fitrackingforurban plannersengagedinproject-relatedurbananalysis –thispaperwilldirectlyaddressthislacuna.

Onereasonforthissituationisthatwhilethereis ‘ a greatdealhappening’ intheareaofWi-Fitrackingof Wi-Fideviceusers(CliffordandHardy 2013,1), ‘alot

ofitisbelowtheradar’ largelyforreasonsofconfidentiality.InshortbecauseoftheprivacyissuesaroundWi-Fi trackingsomeorganisationstrynottodrawattentionto themselvesdoingit.Indeed,inmanycases,thosebeing trackeddonotknowthesystemsareinuse,andtheproducersoftheWi-Fitrackingsystemswillgenerallynot revealtheirclients(Sturmer 2013).

RegardlessknowledgeaboutthepotentialofATTs isimportantbecauseifurbanplannersproceedwithoutdetailedknowledgeofpeople’smovementand activitypatterns,urbanplanners ‘runariskofspatial interventionscreatingconditionsforundesirable behaviour,limitingdesirablebehaviourorexcluding groupsofpeoplewithlimitedpossibilitiesfromdesirableorevennecessarydailyactivities’ (VanSchaick andVanderSpek 2007,89).Moreover,fromadisciplinaryperspective,ifurbanplanningdoesnotgrapple withATTsitpotentiallyleadstothedisciplinenot beingabletodomorethanjust ‘tagalongwiththe developmentsintheInformationandCommunicationsTechnology(ICT)sector’,asopposedto beingableto ‘formulateandexecuteitsownresearch agenda …’ (Schaick 2010,81).

Part1:areviewofconventionalPSPLS

JanGehl’sPSPLmethodinvolvesbothqualitativeand quantitativesurveysofurbanspacesprimarilyusing humanobservationaltechniquescentredonpedestrian andactivitycounts(MatanandNewman 2012,31). ThisreflectsGehl’sbeliefthatinsurveyingpubliclife peopleneedtoseethe ‘interactionoflifeandcity spacewiththeirowneyes’ becausethisprovidesa ‘deepunderstanding’ (Gehl 2013,6).

ThecontentofGehl’spubliclifestudiesvariesfrom studytostudyandfromplacetoplace,however,some ‘tools’ areconsistentlyapplied(Gehl 2013,126).The followingsectionbrieflysetsoutthesehumanobservation ‘tools’,whichconstituteaconventionalPSPLS. Thiswillprovideabenchmarkagainstwhichthe potentialofWi-FitrackingasaPSPLStoolcanbe assessedinpart2ofthispaper.

Counting

InaPSPLSmanualcountingconductedbyobservers registershowmanypeoplearemovingandhow manyarestaying,andwhatstationaryactivitiesthey areengagedin(Figure1).Byconductingpeoplecounts priorandsubsequenttoinitiativesinurbanspaces,itis understoodthaturbanplannerscanevaluatewhether theinterventionresultedinincreased ‘life’ inthe space,widerrepresentationofagegroups,amongst others,andtogenerallyassessprojects(Gehl 2013, 25).Tothisend,countingistypicallyconductedover settimeperiodsinordertocomparedifferenttimes ofday,weekoryear(Gehl 2013,25).

StationaryactivitiesmappingconductedasapartofPSPLSofPerthin2008(Imagesource:GehlArchitects,2009).

Mapping

Manualmappingconductedbyobserversregistersthe patternsofwhereinasurveyareapeoplearestaying (typicallystandingorsitting)andwherepeopleare moving.Thelocationsofwherepeoplearestaying aremappedonaplanatdifferenttimeswithina 24-hourperiodoroverevenlongerdurationsoftime. Theresultantmapscanthenbelayered,atechnique whichprovidesapictureofthegeneralpatternofstayingactivities(Gehl 2013,26).

Manualmappingofmovementsconductedbyan observerinvolvesdrawingthemovementsaslines onaplanduringaparticular,limitedtimeperiod (Gehl 2013,28).Thegoalcanbetocollateinformationsuchas ‘walkingsequence,choiceofdirection, flow,whichentrancesareusedmost,andwhichleast’ (Gehl 2013,28).Trackingpeopleinthiswayis regardedtoprovideknowledgeconcerninggeneral movementpatternsinaspecificarea.Furthertoan observerstandinginonespottoregistermovement, observerscanalso ‘track’ specificpeoplesoastoregistertheirmovements – anactivityreferredtoas ‘shadowing’ or ‘tracking’ (Gehl 2013,29).

Recordingqualitativedetails

Thetoolsexplainedintheabovesectionsprovidegenerallyquantitativesnapshotsoftheinteractions betweenpubliclifeandpublicspace(Gehl 2013,32).

Thesesamplesofwhatistakingplacerarelyprovide allthequalitativedetails.

TobolsterthiswithqualitativeinformationphotographsarefrequentlyusedinPSPLSstoillustrateparticularsituationsandrevealthe ‘interactionorlack thereofbetweenurbanformandlife’ (Gehl 2013,31). Inasimilarmanner,WilliamWhyte(1980)wasable tousephotographytoeffectivelyillustratesubtleties suchaspeople’spreferenceforsunnyandprotected microclimatesandaseatingpositionwhichbacks ontoapillar,andthewaystreetperformers,sculpture andimpressiveviewscanprovideanexternalstimulus whichpromptsstrangerstotalktoeachotheras thoughtheywerenot … (p.94).Variationstostandard photographyincludetime-lapsephotographyorvideo sequencestoshowsituationsunfoldingovertime(Gehl 2013,31)amethodalsousedextensivelybyWilliam Whyte.

Toestablishqualitativedetail,thePSPLSmethod proposesthattheobserverkeepsadiarywhichregistersdetailsandnuancesabouttheinteractionbetween publiclifeandspace(Gehl 2013,24).AsJanGehl explains: ‘Notingdetailsandnuancescanincrease knowledgeabouthumanbehaviourinpublicspace forindividualprojectsaswellastoaddtoourmore basicunderstandinginordertodevelopthefield’ (2013,32).Duetothis,themethodisregularlyused asaqualitativesupplementtomorequantitative materialsoastoexplainandelucidatedata(Gehl 2013,32).

Figure1.

Part2:thepotentialofWi-Fitrackingto augmentaconventionalPSPLS

Thefollowingsectionconsidersthedegreetowhich passiveandactiveWi-FitrackingofWi-Fiusersin publicspacescanaugmentaconventionalPSPLS. ThevisualoutputofWi-Fitrackingtendstoconsist ofaheatmapshowingconcentrationsofWi-Fienableddevicessuperimposedoverabaseplanoraerial photo(Figure2).

Counting

AsJanGehlexplains,countingcanbebrokendown intocountingthenumberofpeoplewhoaremoving, countinghowmanyarestationary,anddetermining whatactivitiesthestationarypeopleareengagedin.

ArguablyWi-FitrackingishighlyeffectiveincountingmovingorstationarypeoplewithaWi-Fi-enabled device;however,therearetechnicallimitationsin termsofhowthedataarecollected – inshortitis onlycollectedfrompeoplewithWi-Fi-enableddevices, andthereforerelyingonthesedataalonepotentially introducesasignificantcompositionalbias(Schaick 2008,189).Forinstance,thedemographiccomposition ofpeoplewithWi-Fidevicescantendtowardsbeing youngandwelleducated(Hampton,Livio,andSessionsGoulet 2010,709)andassuchrelyingonWi-Fi

trackingdataalonetocalculatehowmanypeopleare movingorstationarycouldmeanthatsignificantproportionsofthetotalnumbersofpeoplemovingcould bemissing.

Whiletherearequitewell-establishedstatistical modelsforcalculatingthebias,andtraditionalsurveys canbeundertakentotestthese,inevitablythereisa slippagebetweenthemodeland ‘reality’.Indeed accordingtotechnicalexperts,insomecases,thenumberofpeoplewhoare ‘invisible’ inWi-Fitrackingcan beroughlycommensuratewiththosepassively observedthroughWi-Fitracking.

Moreover,thebiasbecomesparticularlypronouncedifonlyactive,notpassive,trackingisbeing carriedout.Indeed,insomeurbanprecincts,only aboutfivepercentofpeopleopt-intoactiveWi-Ficonnectionsmeaningthatasubstantialproportionofthe totalnumberofpeoplemovingisnotrepresented.As aresultoftheseproblems,theoperatorsofthe QueenVictoriaMarketinMelbourneAustraliafound thatWi-Fitrackingdatacouldonlybeusedincombinationwithotherpeoplecountingsystems.6 Asthey arguethatWi-Fitrackingdataare ‘notworthanything ifitisnotabletobevalidatedwithothermethods’

Countinghowmanypeoplearemoving NotwithstandingitsbiasWi-Fitrackingdatagenerated bythemovementofpeoplewithWi-Fi-enabled

Figure2. TypicalWi-FitrackingheatmappingofWi-Fi-enableddevicesinapublicspaceatdifferenttimes. Note:ContextualinformationhasbeenremovedfromtheWi-Fiheatmappingtoensuretheanonymityoftheprojectfeatured.Thismappingisnotnecessarilyspatiallyaccurateandcanbe ‘out’ byasmuchas10–15metresdependingonthefrequencyofsensors(Imagebyauthor).

devices,particularlyattheprecinctscale,havesignificantpotentialasitallowsafargreatersamplesize andspatialdistributiontobeanalysed(Sevtsuketal. 2009,334)overamuchlongertimeframe,thanwhat isgenerallyfeasibleinaPSPLSwhichwouldrequire asubstantialnumberofobserversrecordingpeople movementstocoverthesamegeographicareaand time.

Moreover,theimmediacyofthedatagarnered throughtrackingofthemovementofpeoplewith Wi-Fi-enableddevicespotentiallyallowsfortherealtimemanagementofthepublicrealm(Schaick 2008, 191)somethingwhichaconventionalPSPLSwould struggletoreplicate.Asoneanonymoustechnical expertexplainedWi-Fitrackingofaneventorfestival inanurbanprecinctcanbedoneonminutebyminute daybyday assuchyoucanprogramonaminuteby minutebasisandbeshiftingassetsaroundaccordingly. Andyoucancertainlybeexperimentingwithshade, parking,allthethingsthatyoucanmovearound. Moreover,theprocessofimplementingurbanplanningandmanagingurbaneventsmaybecomemuch moreiterativeasdesigners/managersengagewith real-timedataandfeedbackfromusers.

Countinghowmanypeopleareengagedin stationaryactivities

Wi-FitrackingispotentiallyhighlyeffectiveforcountingpeoplewithWi-Fidevicesengagedinstationary activities,particularlywhensuchapopulationgroup hasahighsmartphonepenetrationrate.Oneexample ofitspotentialistheMassachusettsInstituteofTechnology(MIT)whereWi-Fitrackingdatageneratedat acampusscale7 informspaceplannersabouttheefficiencyofspaceusage.Thisisdonebychartingthe locationpatternsoftypicallystationarypeoplewitha Wi-Fi-enableddeviceagainsttheoriginalassigned useofspace;forinstance,lecturehall,cafeteriaorseminarroom(Sevtsuketal. 2009,328).Thesamplesize, spatialareaandtemporalperiodofthisstudywould beextremelydifficulttoreplicateinaconventional PSPLS.However – asmentionedpreviously – those withoutsuchaWi-Fi-enableddeviceareeffectively invisible.

Moreover,Wi-FitrackingofpeoplewithaWi-Fienableddeviceengagedinastationaryactivityforces researcherstoquestionsuppositionsonhumanbehaviour,astheprovisionofaWi-Finetworkchanges people’sactivitybehaviour(Schaick 2008,185) – in short,oneofthestationaryactivitiesthatpeoplewith aWi-Fi-enableddevicemaybeengagedinwillbe usingthepublicWi-Fiitself(Schaick 2010,75).Indeed, theprovisionofaWi-Finetworkinanurbanprecinct canreducethesociabilityofpublicspaces;inonestudy thelargemajorityofWi-Fiusersinpublicspacehada lowdensityofco-locatedacquaintanceships(Hampton,Livio,andSessionsGoulet 2010,710).The

provisionofWi-FinetworksforthepurposeoftrackingpeoplewithaWi-Fi-enableddevicecontradicts JanGehl’sideathattheobservermustbeasneutral asa ‘flyonthewall’ ,an ‘invisiblenon-participant whotakesinthebigpicturewithouttakingpartin theevent’ (Gehl 2013,5).

SuchissuesasideWi-Fitrackingdoesallowthe accurateassessmentoftheamountofWi-Fiusers areengagedinstationaryactivities,something whichisdifficulttodowithaconventionalPSPLS inwhichthetimeperiodofthesurveymaybe exceededbyaperson’sdwelltime.Thisabilityhas beenpioneeredinshoppingcentreswhereitserves asapowerfulanalyticaltoolforshoppingcentre operators(Figure3).Inthepublicdomain,theassessmentofthetimepeoplearestationarycanoccurfor regulardurationsoveranextendedtimeperiod –days,weeks,monthsoryears,andcanrecordwhether peoplehavebeeninalocationpreviouslyorhave mademultiplevisits.Inthisrespect,Wi-Fitracking ispotentiallyapowerfuldiagnostictooloftheeffect ofurbaninterventionsthroughtheanalysisofboth visitationnumbers(ofvisitorswithWi-Fi-enabled devices)butalsohowwhethertheinterventions haveencouragedpeopletoengageinstationary activities.

Countingwhatstationaryactivitiespeopleare engagedin(culturalorcommercialactivities, playing,lyingdown,seated,seatedincafeseating orstanding)

PassiveoractiveWi-Fitrackingisoftenusefulfor tellingtheresearcherthe ‘ what ’ butnotthe ‘ why’ andinthatrespectWi-Fitrackingdepartsfromthe moreconventionalstudiesofPSPLS(Sevtsuketal. 2009,334).Inshort,Wi-Fitrackingofpeoplewith Wi-Fi-enableddevicescantellthosecollectingthe datathatthosepeoplearestationarybutnotwhy theyarestationary,i.e.whatstationaryactivitythey areundertaking. 8 Incontrast,whenobserversare manuallyconductingthecountingofpeopleundertakingstationaryactivities,theycanaddinformation fromthesite,suchastheactivitybeingundertaken orthedemographicprofile(Gehl 2013,6).Given thatWi-Fitrackingisnotable(initself)toreveal suchdetailJeroenSchaickconsidersthatitis ‘ unable toprovidetheanswerstotheresearchquestions mostrelevanttospatialplanning,forexamplewhat typeofactivitypeopleareundertaking,whothe activitywascarriedoutby’ (2008 ,192),andwhat theirmotivationsunderlyingtheactivitiesmaybe (Shoval 2008,22).Theabsenceofsuchinformation canleadtoasituationwhich ‘ suggeststhatpresence ofpeople’ isalwaysapositivething,while ‘ absenceof peopleisalwaysnegative ’ (Schaick 2010 ,87),without understandingthedeeperreasonsforsuchpatterns.

Dwelltimesofananonymousprojectillustratedonatypicalweb-basedWi-Fitrackingdashboard.

Mapping

Mappingstays

PassiveandactiveWi-Fitracking – notwithstandingits inherentbias – isabletoindicateapproximatelywhere ‘stays’9 areconcentrated.Nonetheless,theabilityof Wi-Fitrackingtoaccuratelymeasurethespatial locationofstaysisquestionable.Indeed,insomepublic spaceexamples,technicalexpertsacknowledgethatthe locationaldataofapersonwithaWi-Fi-enableddevice canbe ‘out’ byasmuchas10–15metresdependingon thefrequencyofsensors(Figure4).AssuchusingWiFitrackingtodeterminethelocationofstaysatthesite scale,itislikelytobesoinaccurateastobeofquestionableuse,however,attheprecinctscale – wheresuch inaccuraciesareoflesssignificance – itisstillpotentiallyinstructive.Tocombatthisinaccuracyatthe finerscale,shoppingcentrestendtousealargenumber ofWi-Fitrackingsensors,typicallyonathree-metre gridhiddeninceilings.Accordingtotechnicalexperts, thisresultsingreateraccuracyandcangenerallyaccuratelylocateWi-Fiuserswithinahalfmetreradius, however,deployingthisnumberofsensorsthisunlikelytobefeasibleinthepublicdomain.

Mappingmovement

ThespatialinaccuracyofWi-Fitrackingofstaysalso inhibitstheuseofWi-Fitrackingdataatthesitescale formappingmovementsofpeoplewithWi-Fi-enabled devices(Nielsen 2014,2).Thisisconfirmedbythe experienceoftheoperatorsofQueenVictoriaMarket inMelbournewhoexplainedthatthenumberofsensors requiredtoproduce ‘anttrails’ showingthatpeople’ s

movementsrequiredaprohibitivelyexpensivenumber ofsensors.Again,thisissueisparticularlyscaledependent;whilemappingmovementsthroughWi-Fitrackingisproblematicatthesitescale,attheprecinctscale itispotentiallyveryusefulasspatialinaccuracieshave lesssignificance.

Oneexampleofthesuccessfuluseof ‘Trendwise’ Wi-Fitrackingtotrackmovementattheprecinct scalewasattheannualWestAustralian ‘Mandurah CrabFest’ heldinMandurah,southofPerth.Inearlier years,therewasaperceptionbysitebusinessowners thatthefestivalwasnotresultinginincreasednumbers ofpeoplemovingpast,orvisiting,theirpremises.This wasattributedtothefactthatthefestivalwasheldon theMandurahforeshoreandassuchwasperceived ashavingits ‘backtotheotherpartsoftown’ where theshopswerelocated.Asaresultofestablishing throughWi-Fitrackingdatathatthisperceptionwas generallytrue,theCityofMandurahrelocatedsome CrabFestactivitiesandatthesametimetheyexpanded ittoincorporatethecommercialproviders.Moreover, theCityofMandurahwasableto ‘ prove’– through Wi-FitrackingofthesubsequentCrabFestevent –thatthevisitationofshopshadincreasedmarkedly.

NotwithstandingitsbiasWi-FitrackingdatageneratedbythemovementofpeoplewithWi-Fi-enabled devices,particularlyattheprecinctscale,havesignificantpotentialasitallowsafargreatersamplesize andspatialdistributiontobeanalysed(Sevtsuketal. 2009,334),thanwhatisgenerallyfeasibleinaPSPLS whichwouldrequireasubstantialnumberofobservers recordingpeoplemovementstocoverthesamegeographicarea.

Figure3.

Figure4. TheabilityofWi-Fitrackingtoaccuratelymeasurethespatiallocationofstaysisquestionable.Indeed,insomepublic spaceexamples,thelocationaldataofapersonwithaWi-Fi-enableddevicecanbe ‘out’ byasmuchas10–15metresdependingon thefrequencyofsensors(Imagebyauthor).

Recordingqualitativedetails

Inshort,Wi-Fitrackingprovidesquantitativedatabut notanunderstandingofthequalitativereasonsfor whycertainpatternsinthedataarebeingproduced; forexamplepeoplearecongregatinginoneareaofa spacebecauseitissunny,shelteredfromthewind, hasa ‘nice’ view,providesagoodpositiontopeople watchorenablespeopletositwiththeirbacktoa wallofpillar(Whyte 1980).Despitethis,thereis potentialforsensors10 (WallissandRahmann 2016, 119)usedinconjunctionwithWi-Fitrackingto fleshoutthereason ‘why’ patternsmayforminthe data.

Photography

ConventionalPSPLSoftenemploysphotographyto illustratesituationsandinteractions,orlackthereof, betweenpublicspaceandpubliclife(Gehl 2013,31). Wi-Fitrackinghasnomechanismforrecordingthe subtletiesofpubliclifethatahumanobservercan observeandacameracanrecord.WhileWi-Fitrackingcouldbeusedinconjunctionwithanautomated camerasystem – triggeredbyparticularpatterns appearingintheWi-Fitrackingdata – suchasystem isunlikelytobeabletocapturethesubtletieswhich canbediscernedbythehumaneyeandrecorded throughphotography.

Keepingadiary

Finallythrough ‘keepingadiary’ inaconventional PSPLSobserverscannoteobservationsandregister detailsandnuancesabouttheinteractionsbetween publiclifeandpublicspace(Gehl 2013,24).Inthis respect,Wi-Fitracking,consideredinisolation,haslittlecapacityforrecordingthenuancesofinteractions betweenpubliclifeandspacesotherthanthosethat manifestthemselvesindata.Thisshouldnotbeconsideredafatalflawthough – indeedthequantitative toolsofaconventionalPSPLSalsoneedtobeaugmentedbyqualitativeinterpretationsofotherwiseabstract data.Moreover,itcanbeassumedthatsensornetwork deploymentswillincreasedramaticallywithinthecomingyearsduetoincreasingfeasibilityandaffordability (Resch,Britter,andRatti 2012,175).Arguablythe deploymentofsignificantnumbersofsensorsinpublic space,whenusedinconjunctionwithWi-Fitracking, mayprovidetheanswersastowhycertaintracking patternsareappearing.Forinstance,sensorscouldbe deployedtorecordsunandshade,windandother environmentaldatawhichcanbelatercorrelated withWi-Fitrackingdata.

Discussionandconclusion

AsthepriorsectionsindicateacombinationoftheWiFitrackingwithmoreconventional,particularly

Figure5. ThistablesummarisestheabilityofWi-FitrackingtoaugmentPSPLSs.AcombinationoftheWi-Fitrackingwithmore traditional,particularlyqualitative,PSPLSmethodscanfillinmostmethodologicalgapsaseachapproachhasitsadvantagesand disadvantages.

qualitative,PSPLSmethodscanfillinmostmethodologicalgapsaseachapproachhasitsadvantagesand disadvantages(Figure5).AsGehlhimselfexplains ‘ a singletoolisrarelysufficient.Itisusuallynecessary tocombinevarioustypesofinvestigation’ (2013,22). Thedecisionofwhethertoprioritiseaparticular methoddependsonthescaleofthestudy(sitetoprecinct),theintendedtimeframeofthestudy,thepeople poweravailabletoundertakeaPSPLS,andwhether moneyisavailableforinvestmentsintherequired equipment.Nonetheless,asageneralsummation, broaderprecinctscalePSPLSs,withlongerdurations aremuchmoresuitedtoWi-Fitrackingmethods whilefinerscale,shortertermandmorenuanced studiesaremoresuitedtoconventionalPSPLS methodsbasedonhumanobservation.Ineithercase, bothrequirequalitativeassessments,throughphotography,humanobservationandothers,tointerpret whyparticularpatternsinthecountingandmapping dataarebeingproduced(Schaick 2010,82).AsAurigi cautionsus ‘wearestilldealingwithrealplacesand theirqualities’ andthatshouldremaincentraltoour urbananalysisandplanningefforts.Asheexplains:

Theoverlynonchalantjettisoningofwhatweknow abouturbanplanninginthenameofanunexplored ‘digital’ worldanditsallegednewrulesisnotagood startingpointtowardsshapingthedigitally-augmentedcity.(Aurigi, 2016,20)

Giventhenatureofthisresearchareaitisentirelyimaginablethatasnewtechnologiesemergeandevolvethe

ﬁndingsofthispaperwillbecomeoutdated.Indeed,in 2013,therewereapproximately9billiondevicesinterconnectedbyvirtueoftheinternet,withthisexpected toincreaseto24billionby2020(WallissandRahmann 2016,129).Moreover,itispredictedthatwithinthe nextdecadethemobilephonewilllikelybedefunct, withsystemssuchasGoogleGlassandinevitableiterations ‘bringingaugmentedrealitytotheforefrontof datacollectionandviewingthecity’ (Hudson-Smith 2014,47).Withsuchsystems,wearemovingtowards aneraof ‘alwaysbeingon’,wheretracking,tagging andscanningwillsimplybetakenasgiven(HudsonSmith 2014,47).Forurbanplanners,promisestoprovidearichsourceofdatato ‘ﬁndouthowpeopleuse, feelabout,work,liveandplayinsmartbuildingsand, ultimately,thesmartcity’ (Hudson-Smith 2014,47).

Whileindeedthismaybecomethecase,thispaper hasbeendirectedtowardsprovidingabetterunderstandingofoneaspectofthesmartcityintermsofcontemporarypotentialforurbanplanners,nota promisedfuture.

Notes

1.Wi-Fireferstothe ‘familyof801.11technological standardsthatallowdevicestoestablishawireless siteareanetworktransmittedviaunlicensed2.4GHz spectrum’ (Lambert,McQuire,andPapastergiadis 2013,5).

2.Ideally,theauthorwouldhaveconductedadirectcomparisonofWi-FiandPSPLSanalysisonanactualsite,

however,privacyconcernsofthosecollectingWi-Fi datahaverenderedthisimpossible – inshortnoorganisationscurrentlyconductingWi-Fitracking(thathave beencontacted)wouldagreetothepublicationofsuch data.Assuch,thesefindingsofthispaperaredrawn fromnumerousinterviewswithexpertsinthefield andtheWi-Fitrackingdata(intheformof ‘heat maps ’ representedonananonymoussite).

3.Theterm ‘urbanplanning’ inthispaperisusedtoalso encapsulatetherelateddisciplinesofurbandesignand landscapeplanning.

4.ThispaperwillnotincludeusethisaspectofWi-Fi provisioninthisevaluationas,accordingtotechnical experts,oftenonlyabout5%ofpeopleopt-inonthe Wi-Fiportalsplashpage,meaningthatasignificant biasispotentiallyintroducedintothesamplegroup.

5.Theoverallaverageamountoftimespentbyvisitorsat alocationforaselectedtimeperiod.

6.TheQueenVictoriaMarketisa7hectarehistoricopen airmarketinMelbourne.Themarketisintheprocess ofbeingrenewedtocreatea ‘worldclass’ marketprecinctwithbetterfacilitiesandpublicopenspaces.

7.In2000,MITdecidedtoundertakeavastoperationof buildingacampus-wideWi-Finetwork.By2005,this campushasover3000activewirelessaccesspoints providingfullcoverageofWi-Fiinallacademicand residentialbuildingsreferredtoasiSPOTS(Sevtsuk etal. 2009,328).

8.Thiscouldbegarneredfromopt-inquestionnairesfor Wi-Fiaccess,however,againarelianceonthisdistorts thesample,astypicallyonlyalowpercentageof peopleactivelyconnecttoaWi-Finetwork.

9.Again ‘stays’ arewherepeoplearestandingandsitting withinaspace.

10.Asensorisanelectroniccomponentcapableofdetectingeventsorchangesinenvironmentalconditions andtoelectronicallytransmitthisinformation.

Acknowledgement

TheauthorwouldliketoacknowledgeBrettWood-Gushfor hisgenerousassistanceinthepreparationofthispaperand JillPenterforhereverpatientcopyediting.

Disclosurestatement

Nopotentialconflictofinterestwasreportedbytheauthor.

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