An exploration of knowledge production through GIS in the ... · 09.05.2016 · An exploration of knowledge production through GIS in the context of the UK food desert debate: Constructing

AnexplorationofknowledgeproductionthroughGISinthecontext

oftheUKfooddesertdebate:ConstructingtheFoodAccessRadar

FHSinGeography2017

CandidateNumber:289123

WordCount:11994

2

TableofContents

TableofAbbreviations...........................................................................................2

Abstract.................................................................................................................3

Acknowledgements................................................................................................3

1.Introduction.......................................................................................................4

2.LiteratureReview...............................................................................................62.1.ScienceandTechnologyStudiesandCriticalGIS........................................................................6

2.2.FoodDeserts..............................................................................................................................................10

3.Methodology....................................................................................................13

4.TheFoodAccessRadarconstructionprocess....................................................16

5.TheinfluenceofGISonthefooddesertdebate................................................285.1Simplicityandrelevancetoevidence-basedpolicy-making..................................................30

5.2.GIS,fooddesertsand‘conventionalspace’...................................................................................31

5.3Objectifyingpowerofmapsandvalidationasascientifictechnology..............................33

6.QualitativeGIStore-envisiontheFoodAccessRadar.......................................36

7.Conclusion........................................................................................................47

8.References.......................................................................................................49

9.Appendix:interviewtranscripts........................................................................55

TableofAbbreviationsEBPM Evidence-BasedPolicy-Making

GIS GeographicalInformationSystem(s)

GUI GraphicalUserInterface

OS OrdinanceSurvey

STS ScienceandTechnologyStudies

3

AnexplorationofknowledgeproductionthroughGISinthecontextoftheUKfooddesertdebate:ConstructingtheFoodAccessRadar

Abstract

IntheUKinthelate1990sdebatesemergedovertheexistenceof‘fooddeserts’

inBritishcities.Bothgovernmentandacademicresearchintogeographicalfood

desertswas characterisedby theuseofGIS to ‘locate’ fooddeserts.Whilst the

partialandoversimplifiednatureofthefooddesertconcepthasbeencritiquedin

the literature, the effect of GIS to project food deserts in a UK government

contexthasnotbeenexplored.Asameansof interrogating theeffectofGISon

theUK fooddesert debate, I construct a government-designed fooddesertGIS

model,theFoodAccessRadar,applyingittoCambridgeshire.Departingfroman

understanding of food deserts as political constructions, the GIS model

constructionprocess is explored through the lensof criticalGISand isused to

supportbroaderclaimsabouttheeffectofGISontheUKfooddesertdebateasa

whole,particularly ingovernmentpolicy.Thedissertation first exploresGISas

bothaconvenientandconvincingtoolforportrayingfooddesertsandsuggests

several implications. Both the objectifying power of GIS and the UK political

contextintheearly2000sareimportantexplanatoryfactors.Havingshownthat

food desert models problematically conceptualise food access, I suggest a

possiblere-envisioningoftheFoodAccessRadar,adoptingtechniquesfromthe

emergingfieldofqualitativeGIS.Reworkingthefooddesertmodelinsuchaway

brings to the fore the situated and partial nature of food desert knowledge

silencedinconventionalGISprojections.

Acknowledgements

IamverygratefultoMichaelAthansonforgoingoutofhiswaytointroduceme

toArcGIS.IwouldliketoextendmythankstoThomasBurgoine,CentreforDiet

andActivityResearch, forhis insightful comments concerning theFoodAccess

Radar.ThankyoualsotoGruiaBadescuforhispassionandsupport.

4

1.IntroductionDuring the late 1990s and early 2000s the concept of the ‘food desert’ gained

significant purchase in UK government as an explanation for growing health

inequalities inUKcities (CumminsandMacintyre,2002a;Beaulacetal.,2009).

The term ‘food desert’ was used in government and academic spheres to

describe areas, specifically those already characterised by high levels of

deprivation,wherephysicalaccesstotheprovisionofhealthandaffordablefood

wasinadequate(Wrigleyetal,2003:151).Poorfoodaccess intheseareaswas

thoughttocause‘deprivationamplification’(Cummins,2007:355),wherelackof

accesscompoundedexistingsocialproblems.Eradicating‘fooddeserts’through

government policy was therefore seen as a way of reducing social exclusion

(Wrigley, 2002: 2031), in line with the 1997 New Labour government’s

commitmenttotacklesocialissuesintheUK(CumminsandMacintyre,2002b).

However, by the late 2000s, mounting evidence against the existence of food

deserts in UK epidemiology and public health research suggested that the

concept,whilst increasingly applicable in theUS,might not be appropriate for

theUK(Whiteetal.,2004;Beaulacetal,2009).Yet,despitescepticismoverthe

existenceinspaceoffooddesertsduringthis ‘fooddesertdebate’(Clarkeetal.,

2004), thesubstanceof thefooddesertconcept itselfreceived littlecritique. In

almost all public health debates concernedwith the existence of food deserts,

only empirical evidence was sought, using scientific methods, to prove or

disprove their existence. Only a small minority of academics critiqued the

construction of the food desert concept itself and its relevance to food access

(Cumminsetal.,2007).

Thisdissertationbuildson the fewcritiquesof theconceptduring theUK food

desert debate. Whilst these critiques questioned the applicability of the food

desertanalogy,withits‘conventionalviewofspace’,totherealitiesofthesocial

worldandsocialpolicy(CumminsandMacintyre,2002a;Cumminsetal.,2007),

whattheydidnotexplorewastheinfluenceofgeographicalinformationsystems

(GIS)invisualisingthefooddesertconcept.Departingfromanunderstandingof

fooddesertsassocialandpoliticalconstructions(Sismondo,2010),Iexplorethe

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widespreaduseofGIStoproduceknowledgeaboutfooddeserts,informedbythe

“blackbox”motif fromScience andTechnologyStudies (Sismondo,2010:120)

andanunderstandingoftheobjectifyingpowerofGISfromcriticalGIS(Warren,

2011; Elwood et al., 2011). By constructing a government-designed GIS food

desert model, the Food Access Radar, for Cambridgeshire, I show how

projectionsoffooddesertsinGIScanhidesocialisedandpoliticisedknowledge

production processes behind a veneer of objectivity. In the context of

government food desert policy, I link the Food Access Radar’s objectivity-

reinforcing role with the ‘evidence-based policy-making’ paradigm of the late

1990s. In relation to the wider food desert debate, I suggest that GIS’s

associations with positivist science reinforced the ‘seeming objectivity’ of the

food desert concept (Pavlovskaya, 2002), contributing to the lack of critical

studies during the food desert debate, and perhaps explaining why the

problematic conceptentertainedUKacademicsandpolicymakers foralmosta

decade.

AsameansofinvestigatingtheuseofGIStovisualisefooddesertsIexploreda

particularGISmodeloffooddeserts.ThisGISmodel,theFoodAccessRadar,was

designedbytheUKnationalgovernmentin2003,intendedtolocatefooddeserts

at localauthority level. IconstructedandappliedthemodeltoCambridgeshire.

Cambridgeshire was a unique opportunity because, without a previous food

access study, the County Councilwelcomedmy application of the FoodAccess

Radarasapracticalresource.Formypart,constructingthemodelenabledmeto

illuminatethewaythattheFoodAccessRadar’sresultsrepresentfooddesertsas

something objective, whist masking the subjectivities of the knowledge

production process. The dissertation explores how this seeming objectivity

might have had policy implications by building onmy experience of the Food

AccessRadar constructionprocess and throughkey informant interviewswith

thosewhopilotedthemodel.

This dissertation begins by providing an overview of the concepts and

approaches used in the study, discussing the utility of critical GIS as a sub-

disciplineinwhichtobasemyanalysisandoutliningtheexistingcritiquesofthe

food desert concept. I then offer an account of the Food Access Radar

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constructionprocessanditsapplicationtoCambridgeshire,beforediscussingthe

influenceofGISasapowerfulvisualisingmediumontheUKfooddesertideain

governmentpolicyandtheimplicationsofitsinfluence.Finally,havingdiscussed

theeffectof conventionalusesofGIS toproject fooddeserts, suchas theFood

AccessRadar,IsuggestqualitativeGISasameansofbetterconceptualisingfood

accessthroughGIS.

2.LiteratureReview

2.1.ScienceandTechnologyStudiesandCriticalGIS

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AcriticalanalysisoftheFoodAccessRadar,andtheeffectofGIStoprojectfood

desertsmoregenerally,departs fromanunderstandingof scientificknowledge

as socially constructed. Since Kuhn’s (1962) initial theorisation of social

involvementintheconstructionofscientificfact,adepthofliteratureinScience

andTechnologyStudies(STS)hasscrutinisedtheroleofactors(researchersand

policy-makersinmycase)inknowledgeproduction(LatourandWoolgar,1986),

whoare influencedby their socio-political valuesandenvironment (Sismondo,

2010). By pointing to the influence of these actors, STS is critical of scientific

objectivityassociatedwiththeScientificMethod,especiallywhenappliedtothe

social world. Instead, STS research argues that ‘objectivity’ is something

developed in the right social circumstances (Sismondo, 2010: 140). Inmaking

claimstoobjectivity,aresearcherisseparatedandelevatedabovetheobjectof

study (Haraway, 1988). This “god trick”, Haraway argues (1988: 582), is

problematic because it enables the researcher to cede responsibility and

accountabilityfortheresearch,hidingthesituatednessofknowledgeproduction

(1988:581).Ausefulwaytoconceptualisetheobjectifyingpowerofscienceand

technology is through the “black box” motif (Sismondo, 2010: 120).

‘Blackboxing’, as described succinctly by Latour, is “the way scientific and

technical work is made invisible by its own success. When a machine runs

efficiently,whenamatteroffactissettled,oneneedfocusonlyonitsinputsand

outputsandnotonitsinternalcomplexity.Thus,paradoxically,themorescience

and technology succeed, themore opaque and obscure they become” (Latour,

1999).Theblackboxmotifisusefulforthisdissertationbecauseitcanbeusedto

understand how the social and political processes that create food desert

knowledgearehiddenbehindaveneerofobjectivitywhenconstructedinGIS.

BeforediscussingcriticalGISasasub-disciplineofSTS,itisimportanttobriefly

outline GIS. Geographical Information Systems are computer systems used to

capture, store, check and display data at particular positions on the earth’s

surface. They emerged out ofmilitary technology in the 1960s andwere first

used to record landuse information inNorthAmerica (Tomlinson, 1962). The

term‘GeographicalInformationSystem’wascoinedbyDaceyandMarble(1965).

By the early 1990s, GIS had become commercialised by the Environmental

Systems Research Institute (ESRI) and widely adopted by governments and

8

other organisations (Nyerges et al., 2011). GIS literature remained (and still

remains) highly technical, with textbooks focussing on tool development and

skillbuilding(StMartinandWing,2007).

CriticalGISemergedasareaction tomainstreamGIS,and isoneapplicationof

STS in geography. The sub-discipline critiques the widely held assumption in

‘conventional’ Geographical Information Science (Openshaw, 1991; Goodchild,

1992; Elwood and Cope, 2009) that GIS is an unproblematic, positivist tool

producingobjectiveknowledge(StMartinandWing,2007;Pavlovskaya,2009).

CriticalGISattributes thispositivistunderstandingofGIS-producedknowledge

to the visualizing power of maps and the association of GIS technology with

science (Pickles, 1995; Pavlovskaya, 2006). Firstly, aswith conventionalmaps

(Crampton, 2001), the visualizing power of GIS is at the root of its fetishized

objectivity.Mapsmakespatialdataimmediatelyaccessibletoourmind,andthis

seductive featuremeans they carry a discursive power as both an expert and

accuraterepresentationofreality(Pavlovskaya,2006:2012;Elwood,2009:17).

Secondly, GIS amplifies the power of maps by placing them into the realm of

informationtechnology,whichvalidatesGISasascientificconstruct(Sheppard,

1993; Lake, 1993; Pavlovskaya, 2006). GIS, therefore, “unveils worlds to

researchers, policymakers and the public,worldsmade ‘true’ by the assumed

legitimacyofdataandvisualdisplays”(Pavlovskaya,2009:13).AtthelimitGIS

can“evenproducetheplacesthataretheretoknow”(ElwoodandCope,2009:

10).AnunderstandingofhowGIScancreate ‘truth’makescriticalGISauseful

framework throughwhich toexplore theeffectof representing fooddeserts in

GIS.ThepowerofGISisparticularlysalientinpolicymaking,throughtoolssuch

as the Food Access Radar, where “administrators and lawmakers are naively

unaware of the limitations of maps and other geographic data” (Monmonier,

1998).

Critical GIS takes issuewith the understanding of GIS as a positivist tool, and

arguesthatallGIS-basedknowledgeissociallyconstructed,situatedandpartial

(Sheppard,2001;Kwan,2002;Warren,2011).Socialconstructivistcritiquesgo

right down to the development of the algorithms that make up the software

(Chrisman, 2001), far deeper than this dissertation ventures. Most directly

9

relevant,arecritiquesoftherelationshipbetweentheGISuserandtheGraphical

User Interface (GUI), or what we see on the screen. Critical GIS researchers

critique theassumptionheldbyconventionalGIS that theGISuser isapassive

actorintheknowledgeproductionprocess,whoputsdataintothemachineand

gets factsout(Warren,2011).Assumptionof thispassiverelationshipremoves

the effect of social and political factors in the knowledge production process.

ProponentsofcriticalGISarguethattherelationshipismuchmoredynamic(Sui

and Goodchild, 2001, 2003): “in GIS the user is actively engaged in the

knowledgeproductionprocess.Whathe/sheputs inaffects theGISasmuchas

what it represents on the GUI” (Warren, 2011: 72). Therefore, the knowledge

thatGISproducesisaffectedbythevaluesoftheresearcherandtheenvironment

inwhichhe/sheis.Forthisdissertation,theorizationsofthe‘active’GISuseras

socially implicated (Elwood, 2006) problematize the objectivity of the GIS

constructionprocessthatcreatesknowledgeaboutfooddeserts.

QualitativeGISbuildsoncriticalGIStoproblematizetheconstructionofGISasa

necessarilyquantitativetechnology.ProponentsofqualitativeGISarguethatGIS

hasalwaysbeen‘non-quantitative’:attributingitsquantitativeportrayaltohow

ithasbeenhistoricallyconstituted(Kwan,2002;KwanandKnigge,2006).Asa

result, there are “openings” in GIS that “enable” qualitative research, data and

analysis(Pavlovskaya,2006:2003).Bytargetingtheseopenings,qualitativeGIS

“intersects GIS and qualitative research with the goal of integrating multiple

forms of evidence or ways of knowing, in order to explain how spatial

knowledge, patterns, relationships, and interactions are produced, and with

what sorts of social and political impacts” (Elwood and Cope, 2009: 6).

Qualitative GIS does not deny that some fixity is inherent to GIS (in that GIS

projections ‘fix’ knowledge and meanings at particular moments). Rather, it

engages with this fixity through strategic deployments and iterations of fixed

representations, to account for ‘situated knoweldges’ (Harraway, 1988).

Qualitative GIS also accommodates an understanding “that any effort to fix

meanings (or todisrupt them) is inherentlypower-laden, inseparable fromthe

performative, representational, or analytical practices through which these

meaningsareproduced” (ElwoodandCope,2009:8).Byapproaching the food

desert(andwiderfoodaccess)issuethroughaqualitativeGISlens,Ican,having

10

shownthatthereareissuesassociatedwithconventionalGISrepresentationsof

food deserts, propose ways to re-envision the Food Access Radar so that it

incorporatesasubjectiveandheterogeneousunderstandingoffoodaccess.

2.2.FoodDesertsYet,despiteunderstandingsincriticalGISandqualitativeGISofthecomplexand

situated relationship between the GIS software, the user and representations,

muchofconventionalGIScontinuestotreatGISasatoolforpositivistscience(St

MartinandWing,2007;Pavlovskaya,2009).DuringtheUKfooddesertdebate,

bothinpublichealthresearchandpolicy-making,theconventionalviewofGISas

a positivist tool prevailed. Researchers and policy-makers alike used GIS,

seemingly unproblematically, for spatial analysis of food store distribution

across fixed areas (Shannon, 2015). This spatial analysis had the intention of

provingordisproving theexistenceof fooddeserts (Donkinet al., 1999,2000;

Cummins andMacintyre, 1999; Clarke et al., 2002;White et al., 2004; O’Neill,

2005;Pearceetal.,2006;ElinderandJansson,2008).Althoughslightlydiffering

in the way they used the GIS software, these studies assumed that their

representationsoftheurban‘foodscape’(Lakeetal.,2010,2012),aswellastheir

understanding of the choices that consumers make, reflected reality; they

thereforeassumedthatthefooddesertstheyrepresented(ordidnotrepresent)

were objective. Market-based food desert studies, as distinguished from

geographical food desert studies by Beaulac et al. (2009), also rested on an

oversimplified and rational understanding of human choice and activities (for

example:Mooney,1990;Soomanetal.,1993).

Steve Cummins and Sally Macintyre were the two main academics to offer a

sustainedtheoreticalcritiqueofthefooddesertidea,ratherthanjustinvestigate

itsexistencethroughempiricalresearch.Bothconsistentlyrefutedtheideaofthe

food desert, analysing it variously as a convenient political construction for

policy-makers (Cummins andMacintyre, 2002a) and as an oversimplified and

problematicunderstandingoffoodaccessibilityandlivedexperience(Cummins

andMacintyre,2005;Cumminsetal.,2007).Theysupportedtheiranalysiswith

qualitative and mixed-method evidence of the complex relationship between

11

geographicalaccessibilityandfoodchoice(Whiteetal.,2004;Clarkeetal.,2004;

Cummins et al, 2005). They repeatedly suggested dropping the concept in

academiaandpolicybecauseitwas“oflimitedfurtheruse”(Cummins,2005:3).

For Cummins et al. (2007), the food desert model is problematic because it

departsfroma‘conventional’understandingofspace.Inthisunderstanding,the

space between places is interpreted as physical Euclidian distance to fixed

points,withgeographicalboundariesdrawnata specific scale.Throughsucha

conceptualisation of space, focussing on the single spatial scale of the

“neighbourhood”,proponentsofthefooddesertarguethatvariationinphysical

distance from food stores causes health inequalities (Cummins et al., 2007:

1833).Cumminstakesissuewiththis“localtrap”(Cummins,2007:355),arguing

that it is an oversimplification of food access that “assumes that individuals

behaveinrathersimilarwaysandrangeovertheirneighbourhoodsinalimited

manner to purchase food items” (Cummins et al., 2007: 1833). The

oversimplification is problematic and unproductive, for Cummins and others,

becauseitcreatesabinarybetweentheroleofindividualsandtheroleofplace

(or context) as explanations for health inequalities, with food deserts

championing place. The concept therefore misunderstands the nuances and

complexityoffoodaccess.Theysuggestapproachingfooddesertstudiesthrough

a ‘relational’ approach that can capture the “tight interrelationships between

individuals and context” (Cummins et al., 2007: 1829). The theorisation of

‘conventional’ relationships across space is important for my dissertation

becausetheFoodAccessRadar,andotherfooddesertprojectionsinGIS,employ

thisunderstandingofspace.So,fromthisbasis,Icanarguethatconceptualising

fooddeserts inGISreinforcestheunderlyingunderstandingof foodaccessasa

function of ‘conventional’ space, giving furtherweight to the food desert idea.

Further, Cummins et al.’s ‘relational’ approach to food access is useful to re-

envisionaqualitativeGISrepresentationoffoodaccess.

CumminsandMacintyre(2002a)alsocritiquedtheevolutionofthefooddesert

metaphor in government policy. For them, the food desertwas a construction

arising out of a convenient political context: a “factoid” created in UK

governmentbecause“it fitted inwithbroaderpolicyobjectives”(Cumminsand

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Macintyre,2002a:438). In the late1990sa shift occurred inhealth-promoting

activity towards spatial strategies (Lang and Caraher, 1998), reflecting wider

trends towards area-based anti-exclusion policies in theUK (Mohan, 2002). In

parallel,the1997NewLabourgovernmentmadeacommitmenttofightingsocial

exclusion (Social Exclusion Unit, 2004). In this political context, the

‘construction’ of food deserts, and a commitment to ‘eradicate’ them, was a

convenientwayofmeetingbothsocialexclusionpoliciesandmeetingarea-based

targets.

Thefooddesertwasalsoparticularlyappealingbecauseitwas“aplausibleand

attractive theory with seemingly straightforward solutions” (Cummins and

Macintyre, 2002a: 437). The simple causal relationship between food deserts

and health/exclusion outcomes meant the concept was well suited to New

Labour’s new policy paradigm of ‘evidence-based policy-making’ (EBPM) for

socialissues(Fafard,2015).Intendedtomodernisethedecision-makingprocess

for social policy (Wells, 2007), EPBM made policy-making dependent on

identifyingaprobleminordertosolveit.Forfooddeserts,policy-makers,armed

withnewGISsoftware(Monmonier,1998),couldprovideevidenceby‘locating’

areassufferingfromlowaccessbeforeimplementingstrategies.TheFoodAccess

Radar, aswill be shown, is a clear exampleof one suchgovernment-led effort.

However, the opening of a political space in which the food desert idea was

rapidly accepted is clearly problematic when food deserts are understood as

oversimplifiedconceptualisationsoffoodaccess.Whenreallivesareatstake,the

resting of a concept on a plausible and attractive theory is clearly not enough

(MacintyreandPetticrew,2000).

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3.MethodologyThe main method I employ to explore GIS-produced knowledge about food

desertsisconstructingafooddesertmodelmyself.Idosoinordertounpackthe

technological ‘blackbox’ thatGISproduces.As theSTS theoryof ‘blackboxing’

(Sismondo,2010)appropriatelydescribes,GIShasatendencytoobscurepartial

andsituatedknowledgeproductionprocesses. In thecaseof fooddeserts,only

the inputs (data) and outputs (visualised food deserts) are clear, whilst the

esoteric GIS processes remain invisible (Sismondo, 2010: 120). Therefore, to

uncover the social and subjective process involved in the construction of food

desert knowledge inGIS, and understand theway this knowledge can become

treatedasobjective,itisnecessarytointerrogatehowGISmodelsarebuilt.The

GISblackboxinthisunderstandingistreatedlikeanaeroplane’sblackbox.Asa

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technicianwoulddo,IlookinsidetheGISblackboxtoseewhattheproblemsare

withprojectionsofthefooddesertconcept.Insum,bycreatingtheFoodAccess

Radar,Iamabletotakeaparttheprocess,unpackingthe“blackbox”(Sismondo,

2010)inordertolookinsideit.

InChapter4IhaveproducedanaccountoftheFoodAccessRadarconstruction

process.Throughout theprocess Ikeptadiaryofall thesteps Imade towards

creatingtheoutput, frominitialsourcingofdatatoanalysingthefinalresults. I

recorded comments and reflections on any assumptions, amendments and

simplificationsmade tomake the software ‘work’ (Warren, 2011).Overall, the

construction process lasted two months, from July to September. Although it

wouldhavebeenpossibleforanexperiencedGISprofessionalworkingfulltime

toconstructthemodel inunderaweek,IhadtoteachmyselfGIS, familiarising

myself with the software, jargon and processes. This made the construction

processconvolutedandarduous.Locatingappropriatedataalsoprovedtobea

longprocess.

InthecontextofunveilingtheinnerworkingsoftheGIS ‘blackbox’,mylackof

previous GIS knowledge meant it was important to learn how to use the

software.AcommonthemerunningthroughthefieldofcriticalGISisthattogain

anuancedandcriticalunderstandingofthesubjectivitiesinvolvedinknowledge

production,onemustengagewiththeGIS itself (Kitcher,1998;Warren,2011):

look upon itwith an ‘insider-gaze’ (Elwood, 2006).Without the ‘vocabulary of

thetechnology’(Kwan,2002)itisimpossibletomakeaninformedcontribution

to any debate. With no previous GIS knowledge, it was therefore entirely

necessary forme to learnGISbycreating theFoodAccessRadar. In sodoing I

stepped away from being what Openshaw aptly termed a “technical cripple”

(1991:464),towardsbeingabletodevelopeffectivecritiquesofGIS’sroleinthe

food desert debate, on its own terms (Sheppard, 2001). As a result, all maps,

unlessotherwisestated,aremyown.

I chose to use the Food Access Radar model rather than other models for a

numberofspecificreasons.Firstly,CambridgeshireCountyCouncilinformedme

that no countywide food access study had been carried out so far,makingmy

15

studyoriginalanalysis.Secondly,asagovernment-createdmodel,theknowledge

thattheFoodAccessRadarproducedaboutfooddesertswouldhavehadamore

direct impact on policy than knowledge produced by academic models,

especially so if results were treated as evidence in an ‘evidence-based policy-

making’paradigm.Warren(2011:82)arguesthatexposingthesubjectivitiesof

“so called non-enlightened studies”, a category under which the Food Access

Radar falls, is an essential part of critical GIS. Thirdly, the different scales at

which the government model was designed and applied gave room for

misinterpretation, which merits further evaluation. Having been created by a

national body, but designed for local authority use, there was a risk that the

social context of results could bemisunderstood in local policy. Fourthly, on a

more pragmatic level, itwas possible forme, as a GIS novice, to construct the

FoodAccessRadarthankstotheavailabilityofconstructionguidelinesprovided

bythegovernment(ViningandDay,2005),whichhelpedmydatasearchesand

GISlearning.

To build upon the interrogation of the GIS model, I also conducted two key

informant interviews. These were with informants from Staffordshire and

OxfordshireCountyCouncil:theonlytwoCountyCouncilsforwhomIcouldfind

evidence of them having used the Food Access Radar. Both informants were

directlyinvolvedintheirrespectivefoodaccessprojectsin2005and2006.Given

thattheseprojectswereoveradecadeago,theinterviewsweredesignedtoadd

contexttomystudy,abouthowGIS,theresultsthatitproducedandtheconcept

of food deserts were understood at the time. They were carried out over

telephone, lasting approximately 25minutes andwere semi-structured.While

core interview questions were “predetermined” they remained “open ended”

(Ayres2008:810),allowingtherespondenttofullyexplorethequestionwhilst

allowing the researcher the flexibility to interrogate emerging themes

(Flowerdew and Martin, 2005). The interviews were recorded with verbal

consent and subsequently transcribed (See appendix). Information garnered

fromtheGISconstructionprocessandtheinterviewswasalsocomplementedby

analysis of the government-provided construction guidelines (Vining and Day,

2005), lookingat thewaycertainphrases couldbemisinterpreted to conveya

falsesenseofobjectivityintheGIS.

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4.TheFoodAccessRadarconstructionprocess

In this section I explain the construction of the Food Access Radar in GIS.

Initially, Iprovideanoverviewof theFoodAccessRadarasagovernmenttool,

before explaining the construction process in a step-by-step manner. The

constructionprocessisakintounpackingSismondo’s‘blackbox’(2010:120).

TheFoodAccessRadarisastandardisedmethodforidentifyingfooddesertsata

local authority level, designed for construction in GIS software. The Food

StandardsAgencyandNationalConsumerCouncil(NCC)devisedtheGISmethod

in 2003, amid growing government interest in food deserts as a contributing

factortosocialexclusionandhealthinequalities.Itsaimwastocontributetothe

government’s commitment to eradicate food deserts, as specified in the Food

Poverty Eradication Bill in 2001 (O’Neill, 2005). The NCC, a national-level

organisation,producedconstructionguidelinesforlocalauthoritiestofollow,to

17

encourage them tocarryFoodAccessRadar studiesata county leveland then

useresultstoinformlocalpolicy.

TheFoodAccessRadar labelsparticularOutputAreas, the lowestgeographical

levelatwhichgovernmentdata iscollected,as ‘fooddeserts’ if they fulfilgiven

accessibility criteria (referred to as ‘food-access-poor’ areas in the guidelines).

Three parameters are used tomeasure physical accessibility to food: distance

fromfoodoutlets,distance frompublic transportandtheprevalenceofaccess-

inhibiting characteristics in each Output Area. By overlaying these three

variables in GIS, the guidelines claim that the model can locate Output Areas

(OAs) in a local authority that suffer from accessibility issues resulting from

geographical distance to food outlets and public transport, and where a high

occurrenceofindividualsexhibitaccess-inhibitingcharacteristics.

Forthisdissertation,IdecidedtoconstructtheFoodAccessRadarforthecounty

of Cambridgeshire, as a basis fromwhich to interrogate ‘objective’ knowledge

productionthroughGISinthecontextoffooddeserts.IappliedtheFoodAccess

RadarmethodtoCambridgeshireforseveralreasons.Asnotedinchapter3,no

FoodAccessRadarstudyhasbeenundertakenforthecounty,makingmystudy

original research. I am also familiar with the county, making it easier to find

contacts fordataandhelpingme interpret themapsanddata Iwas lookingat.

Finally,asshowninfigure1a,althoughCambridgeshireisagenerallyprosperous

county, there are pockets of deprivation (Figure 1b) and it experiences a

significantnorth/southdivide(Figure1c).Itwasinterestingtoseewhetherthe

Food Access Radar also identified these areas as food-access-poor, supporting

‘deprivationamplification’hypotheses(Cummins,2007:355).

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IwillnowdescribeandexplaintheFoodAccessRadarconstructionprocessina

step-by-stepmanner,recordedasaflowchartinFigure2.Significantly,although

theflowchartpresentsthestepsasdiscreteandtheprocessaslinear,thisvastly

oversimplifiestheconstructionprocess.

Step8:Identifying‘food-access-poor’outputareas

Step7:Identifying‘highconcentration’outputareas

Step6:Amalgamationoflayersintoonemapdocument

Step5:Masstransitdataprojectionandnetworkanalysis

Step4:Foodoutletdataprojectionandnetworkanalysis

Step3:IntegratedTransportNetworkprojectionandgeodatabase/networkdatasetcreation

Step2:CensusdataprojectioninGIS

Step1:Datacollection(Census,foodoutlet,publictransport,IntegratedTransportNetwork)

Figure1Threemaps(1a,1b,1cfromlefttoright)showingdeprivationlevelsacrossCambridgeshireusing2015IMDdataattheLSOAlevel.Figures1band1crepresent

theredsquaresinFigure1a.Figure1bshowspocketsofdeprivationinnortheast

CambridgeCity.Figure1cshowshighratesofdeprivation,atnationallevel,inNorth

Fenland(Source:Author)

Figure2FlowchartsummarisingtheFoodAccessRadarconstructionprocess(Source:Author)

19

Toprojectthethreekeyvariables(Censusdata,foodoutletsandpublictransport

stops)inaGIS,IfirsthadtocollectappropriatedataforCambridgeshire(Step1

in Figure 2). The FoodAccessRadar construction guidelines provided a list of

datasets needed, but only limited information onwhere these could be found.

Dataonaccess-inhibitingcharacteristicsattheOutputArealevelwasextracted

froma governmentdatabaseof2011Censusdata (InfuseData).Theguidelines

suggestedusingsixsocio-economicanddemographic indicatorsofaccessibility

issues: lack of car ownership, lone pensioners, lone parents, being sick or

disabled, BME and income level. With no freely accessible sources of income

data,Iused‘highestlevelofqualifications’asaroughproxyforincome.Secondly,

I created a dataset containing the location of all 3461 bus stops in

Cambridgeshirebyeditinganationaldatasetofbusstops,downloadedfromthe

Government open database (data.gov.uk). The final dataset required was one

containingallfoodoutletssellingfoodforhomepreparationandconsumptionin

Cambridgeshire. The most comprehensive source of this data is the ‘Food

Premises Register’ held by local authority Environmental Health (EH)

departments.Dataprovedparticularlyhardtoobtain. Icontactedemployees in

EH departments at the five District Councils in Cambridgeshire, but was only

able to obtain three of the five datasets, despite filing Freedomof Information

requests.CambridgeCityCouncil,forexample,askedmetopay£800,whichwas

notfeasible.Instead,inneedofuniformdatasetsacrossthecounty,Idecidedto

usedatafromtheFoodStandardsAgencyFoodHygieneRatingScheme(FHRS).

TheFHRSisapubliclyaccessibleregisteroffoodpremisesandrepresentedthe

next best alternative to theEHFoodPremisesRegister.However,manyof the

addressesintheFHRSwereincompleteornon-existent,sobeforegeocodingthe

data,significantcleansingwasrequired.

Once the datasetswere cleansed and geocoded, Steps 2-5 involved visualising

dataintheGISsoftware.IusedESRI’sArcGISsoftware(ArcMap10.4),despiteit

beingless‘user-friendly’thanothersoftware,becauseitistheindustry-standard,

usedbymostotherfoodaccessstudies(Charreireetal.,2010).Inowexplainthe

constructionprocesswiththehelpofscreenshotsfromtheArcGISGUI.Asshown

20

bytheredsquareinFigure3,thescreenshotsrepresentaparticularsubsection

ofCambridgeshireasadataframeatthescaleof1:100,000.

After having visually represented the county, district and Output Area

boundaries,sourcedfromgovernmentopendata,Step2involvedprojectingthe

CensusdataintoGIS(Figure4).

Figure3MapoflocalauthorityboundarylinesinCambridgeshire.Theredsquareshowsthedataframeusedinthenext5screenshotstovisuallyexplainthe

constructionprocess(Source:Author)

21

Data for each Census characteristic likely to cause food access problems, as

suggested by the guidelines,was visualised in a choroplethmap. Each dataset

wasmappedproportionatelyinto10deciles,meaningeachcolourinthegradient

represented a 10th of Output Areas. So, for example, Figure 4 shows the

percentage of the population in each Output Area who do not own a car,

separatedintotendeciles,witheachdecilerepresentinga10thofOutputAreas.

Theareasof interest fortheFoodAccessRadarwerethose inthetopdecileas

they indicated areaswith high concentrations of peoplewith access-inhibiting

characteristics.

Toprojectandmanipulate the twoothervariables, foodoutletdataandpublic

transport data, into useful map layers, the Ordinance Survey (OS) Integrated

TransportNetwork(ITN)wasrequired(Figure5).

Figure4ArcGISscreenshotshowingCensusdatamappedattheOutputArealevel,classifiedintodeciles,witheachdecilerepresentinga10thofOAs.Inthiscase:

highesttolowestcarownershipfromyellowtoblue(Source:Author)

22

The ITN is anOSdataset containingdetails of the transportnetwork forGreat

Britain and, when projected, provides a virtual representation of the UK road

network,constructedasaseriesofpoints,andlinesbetweenpoints.Importantly

fortheFoodAccessRadar,theITNprovidesmeterlevelaccuracyforroadlength,

sodistancesandtimestakentotraveltofoodoutletandpublictransportstops

couldbecalculated.

The846foodoutletssellingfoodforhomepreparationwereprojectedontopof

the ITN in a point shapefile (Figure 6). Several technical adjustments to the

dataset were required in ArcMap to ensure that the food outlet data

superimposeddirectlyontopoftheITN.Todifferentiatebetweentypesoffood

stores,supermarkets(113points)wererepresentedbyabluepointwhilstgreen

pointsrepresentedallotherfoodoutlets.

Figure5ArcGISscreenshotshowingIntegratedTransportNetwork(ITN)shapefile.ITNdownloadedfromOSresourceindigimap.com(Source:Author)

23

Since the purpose of the Food Access Radar is to identify areas that are

geographically distant from food outlets, and are thus at risk of being food-

access-poor,walkingdistancesfromeachfoodstorewerecalculated.Asoutlined

bytheguidelines, ten-minutewalking-timepolygonswerecreatedaroundeach

foodstoreusingtheServiceAreafunctionoftheNetworkAnalystextensiontool

(shownbyredpolygonsinFigure6).Thesepolygonsshowhowfaranindividual

can travel along the road network in 10minutes walking at an average pace,

nationallydefinedas81metersperminute(Knoblaughetal.,1996).Fromthisit

is possible to locate pockets of population that live outside of these polygons,

who are therefore geographically distant from food outlets. The Food Access

Radarmethodthencombinesthisinformationongeographicaldistancewiththe

accessibilityinformationfromtheCensusandpublictransportdata.

Thesameprocessofwalking-timepolygoncreationwasemployedforbusstop

andtrainstationdata(Figure7).LearninghowtousetheNetworkAnalysttool

and creating an appropriate ‘network dataset’ from my data was the most

complexandtime-consumingpartoftheresearch.

Figure6ArcGISscreenshotshowingfoodoutletpointdatasetanditsassociatedwalking-timepolygondataset,overlayingtheITN(Source:Author)

24

With maps for all three variables projected in ArcGIS in accordance with the

construction guidelines, Step 6 involved overlaying them in the same GIS

document(Figure8).

Having completed the layer construction process, I subsequently carried out

analysis to locate Output Areas in Cambridgeshire that could be classified as

‘food-access-poor’ (Step 7 and 8 in Figure 2). The construction guidelines

Figure7ArcGISscreenshotshowingpublictransportpointdatasetanditsassociatedwalking-timepolygondataset,overlayingtheITN(Source:Author)

Figure8ArcGISscreenshotshowingfoodoutletpointdatasetandpublictransportpointdataset,andtheirassociatedwalking-timepolygondatasets,overlayingthe

ITNandCensusdatamappedatOAlevel(Source:Author)

25

suggestedthat‘food-access-poor’areasshouldbedefinedasOutputAreaswitha

high concentration of people with access-inhibiting characteristics, which are

alsonotwithinfoodstorepolygonsorbusstoppolygons.

Step 7 involved identifying the ‘high concentration’ Output Areas in

Cambridgeshire.TodothisIusedStructuredQueryLanguage(Figure9)toselect

the Output Areas that, as recommended by the guidelines, had at least two

Censusindicatorsintheworstperforming10%ofallOutputAreasinthecounty.

From this Iwas able to create a newdata layer of ‘high concentration’Output

Areas,reducingthenumberoftargetOutputAreasfrom1937to214.

I then (Step 8), manually, but systematically on the GUI, selected those ‘high

concentration’OutputAreasthatalsohadasignificantproportionofhouseholds

fallingoutside the foodoutletpolygons. I achieved thisbyprojectingOSraster

maps at the 1:25,000 and 1:50,000 levels as basemaps so I could distinguish

households. I selected those Output Areas meeting both criteria, as shown in

Figure 10, and from these made a new GIS layer: ‘food-access-poor’ areas

(Example in Figure 11). In total I identified 8OutputAreas in Cambridgeshire

thatwere‘food-access-poor’andtherefore,ingovernmentdiscourse,were‘food

deserts’.

Figure9ScreenshotofattributetableforthefoodoutletdatasetinArcGIS,showingequationinqueryboxusedtoidentify‘high-concentration’OAs(Source:Author)

26

Theconstructionofthefooddesertmodeldescribedabovewasanecessarystep

towards explaining the influence of GIS on the food desert debate. By

constructing the Food Access Radar I could get inside Sismondo’s ‘black box’

(2010:120):enablinganobservationoftheeffectofGISonfooddesertsthrough

an‘insidergaze’.Thepositionoftheresearcherrelativetothetechnologyisakey

Figure10ArcGISscreenshotshowing‘highconcentration’OAdatasetasbluepolygonsandfoodoutletwalking-timepolygonsinred,overlyinga1:50,000raster

OSmap(Source:Author)

Figure11ArcGISscreenshotshowingpurple‘food-access-poor’OAcircled,alongwithfoodoutletpointsandpolygons,overlyinga1:25,000rasterOSmap(Source:

Author)

27

focusofcriticalGIS(Elwoodetal.,2011).Positioningmyselfasan‘insider’tothe

technology, therefore, allows me to critique GIS on its own terms (Sheppard,

2001:549),usingthelanguageofthediscipline(Schuurman,2000:569).

Explanation of the process demonstrated for me that the Food Access Radar

construction is not such a linear, scientific process, as mainstream GIS would

have it (Elwood and Cope, 2009). The Food Access Radar was constructed

throughaseriesofinteractionsbetweentheGISsoftwareandmyself,astheuser,

wheresubjectivestepswerenecessarytomaketheGIS ‘work’(Warren,2011):

for example, Step 8 involved manual selection of ‘food-access-poor’ Output

Areas.Eventheaboveaccountissimplifiedandmakestheconstructionprocess

appearsequential,withaninitialaimandafinaloutputthatisworkedtowards

throughastep-by-stepprocess.Therealitywasverydifferentandquite‘messy’.

Manytimesthereweresetbacks,datahadtobereworkedto fit thepurposeof

the model and the process did not proceed in stages, but in an iterative and

piecemealfashiondependingonavailabilityofdata.Youcouldarguethiswould

beasmootherprocessifdonebyagovernmentbodyorlocalauthoritywithall

theinformationathand,buttherealityisthatdatagetsshapedandfilteredtofit

theprocess.

However,apartfromtheimperfectionsoftheprocess,thebiggerlessonIlearnt

from being ‘on the inside’ was how selective and simplified the inputs are.

Despite the impressive advances of digital cartography and the power of the

softwaretoassimilateandtransposedenseinformationintoengagingmaps,the

outputswere all basedon a very limited rangeof data inputs and some crude

assumptions. I followed the guidelines, but was left with a realisation of the

partialityofthedata,thearbitrarinessofquantitativecut-offsandthesimplistic

assumptionsunderlyingthealgorithmsthatdrivetheprocess,whichIdevelopin

thenextchapter.

Yet, in thecontextof theFoodAccessRadar’sapplication ingovernment, these

inconsistencies are not what the policy-makers see. In an interview with a

member of the GIS research team at the Staffordshire County Council, for

example, I was told that the research team that constructed the Food Access

28

Radarwerenotinvolvedinthesubsequentpolicyteamthatusedtheresults:“I

usedtogetstuckinadarkroomattheresearchunittodoallthisstuff,thatother

peopleusedtotakeon.Itmaybethatcolleaguesofminearemorewellplacedto

saywhatactuallyhappened[withtheresults]”.Thus,forthosepolicy-makersthat

used the GIS-created knowledge, the process remained within the ‘black box’.

Thesepolicy-makersseeonlyapowerful representationof fooddeserts,which

canmake the food desert concept seem like something quite real. In the next

section,Isuggestthat,inthecontextofUKfooddesertpolicyandresearch,food

desert GIS models such as the Food Access Radar had an influence on the

longevityandcredibilityofthefooddesertidea.

5.TheinfluenceofGISonthefooddesertdebate

29

AsCumminsandMacintyrehaveconvincinglyargued(CumminsandMacintyre,

2002a; Cummins et al., 2007; Cummins, 2007), the food desert concept lacks

nuance when portraying food access. It reduces the complexity of social

behaviour down to an assumption that humans act homogeneously and

rationallywiththeirfoodchoices.Prioritisinga‘conventionalview’ofspaceover

a ‘relational view’, it silences the socio-cultural factors that determine food

accessbyrepresentingonlyphysicalaccessoverEuclideandistance.Repeatedly,

CumminsandMacintyresuggestedthatthefooddesertconceptwasof“limited

further use” (Cummins et al., 2005: 2) and an “idea whose time had come”

(CumminsandMacintyre,2002a)

Despite the flaws identified in the concept, the food desert idea remained a

legitimatemeansofdirectingUKgovernmentsocialexclusionandhealthpolicy

foraroundadecade.In1995theLowIncomeProjectTeamoftheNutritionTask

Forcefirstcoinedtheterm(Wrigley,2002:2030),whilst, in2005, fooddeserts

were still considered significant enough an issue to recommend that all local

authoritiesshouldcarryoutaFoodAccessRadarstudy(ViningandDay,2005:

49).Betweentheseperiods,apolicyteamhadthespecificmandateofdeveloping

astrategyforimprovingaccesstoshoppinginpoorneighbourhoods(Wrigleyet

al.,2003)andtheFoodPovertyEradicationBill,withclauseson foodaccessat

the neighbourhood level, was passed through the Commons (Cummins and

Macintyre, 2002a). Even as late as2013, theBristol CityCouncil FoodPoverty

ReportsuggestedtheuseofFoodAccessRadarasaGIStool forassessing food

poverty(Malsenetal.,2013).Furthermore,duringthedecade-longperiodfrom

1995therewerefewacademicorpolicyresearchersthatquestionedthevalidity

of the fooddesertconceptand those thatdidrepresented “more theexception

thantherule”(Shannon,2014:256).

This section seeks to address why the misguided food desert idea gained

purchaseinUKgovernmentandacademiaforsolong.Althoughthefactthatfood

desert policies alignedwith the social exclusion interests of the 1990s Labour

government (Mohan, 2002; Cummins and Macintyre, 2002a) is significant, I

suggest that the ability to represent food deserts through GIS played a role. I

argue that the convenience of GIS representations for policy-makers in the

30

context of an evidence-based policy-making paradigm, in combination with

objectifyingpowerofGIS technology, gave the fooddesert conceptaveneerof

objectivity in policy-making spheres. Throughout this chapter, I use examples

fromtheFoodAccessRadarandevidencefrommykeyinformantinterviewsto

arguemycase. I suggest that the interplayof three factorsmake the fetishized

objectivity of GIS projections of food deserts convincing in UK government

policy,despitetheheterogeneityofthesocialworld,wherefoodaccessismuch

more complicated than physical distance alone. Ramsey’s quote succinctly

capturestheeffectofGISonUKfooddesertpolicydiscussedbelow:“GISisnot

merelyanobjectivetoolfortheunbiasedanalysisandrepresentationofspatial

phenomena.Rather, it isasystemoftechnologyandsocialpracticesthat isnot

onlyshapedby itssocial,cultural,political,and institutionalcontext,but italso

activelyshapesthatcontext”(Ramsey,2009:2348,myemphasis).

5.1Simplicityandrelevancetoevidence-basedpolicy-making

ThefirstreasonGISwasimportantinthefooddesertdebateisthatitfacilitated

theuseof thesimple fooddesertmechanismwithinan ‘evidence-basedpolicy-

making’ (EBPM) paradigm. EBPMwas a significant government reform in the

1990s. The 1997 New Labour government introduced EBPM in order to

‘modernise’thedecision-makingprocess(Wells,2015).Byextendingthealready

established concept of ‘evidence-basedmedicine’ to all policy areas, including

socialpolicy,decisionswere tobemadeon thebasisof rigorouslyestablished,

‘objective’evidenceandwerethereforeconsideredtobemoreaccurate(Fafard,

2015). However, critics have attacked the apolitical, ‘scientised’ way evidence

informs social policy. Sanderson has branded EBPM as a return to positivism

(2002:6).Nolan(2015)andothershavecritiquedtheassociationsofEBPMwith

neoliberalism, in that EBPMenables the government to cede responsibility for

theirdecisionsbymakingclaimstothelegitimacyofpositivistscience.

In the context of the simple food desertmechanism, EBPM created a space in

which theuseofGISas ‘evidence’of fooddesertexistence legitimised the food

desertconcept.Thesupposedcausalmechanismforresolvingfooddesertissues

31

wasalreadyattractiveasaresultofitssimplicity(AdaptedfromCumminsetal.,

2007:1833):

1. Lack of healthy, affordable food affects food consumption, which has

healthanddeprivationimpacts

2. Peoplelivetoofarfromhealthy,affordablefood

3. Therefore,increasingaccesstofoodcanreducefoodconsumptionissues

andsocialexclusion.

Within the context of an EBPM paradigm this mechanism is particularly

appealing.This isbecause, forpolicytobe implemented,onehasonlyto locate

theseareasofpoorfoodaccesstoprovideevidenceoftheirexistence,andthen

implementstrategieswithinthemtoincreaseaccess.Locatingtheseareaslends

itself to GIS. The Food Access Radar is a case in point. In my study of

Cambridgeshire, for example, through the process of creating the Food Access

Radarmodel I reduced the number of possible food-access-poor output areas

from 1937 to 8. From a government perspective, this is a highly attractive

prospect because it reduces the number of areas that policy makers need to

focus on and, resting on the assumption that the Food Access Radar is an

accurate representation of reality, provides evidence of food desert existence.

Therefore, theFoodAccessRadar epitomiseshow the growthof democratized

GIS software facilitated the uptake of the food desert idea within a specific

political context. GIS provided a convenientmeans to collect evidence of food

deserts,makingthefooddesertideaparticularlyattractiveingovernmentwithin

anEBPMparadigm.

5.2.GIS,fooddesertsand‘conventionalspace’

Building on the convenience of GIS software for providing evidence of food

desertsinaUKpoliticalcontext,GIScanalsobeunderstoodasaconvenientway

ofportrayingfooddesertsatthesoftwarelevel,whichisthesecondreasonwhy

GISwasimportantforthefooddesertdebate.Thisisbecausebothfooddeserts

and GIS software are grounded in the same understanding of ‘conventional

space’.InmainstreamunderstandingsofGISsoftware(Pavlovskaya,2006),data

is plotted as Cartesian coordinates and the space between places is

conceptualised inEuclidianterms. Inotherwords,onamapwithmanypoints,

32

the two points that are ‘nearest’ each other will have the smallest physical

straight-linedistancebetweenthem.

The food desert concept starts from the same fundamental understanding of

space:termeda‘conventionalview’ofspaceandplacebyCumminsetal.(2007:

1827). In thisviewof space, theproximityof thehousehold toa foodoutlet is

determinedentirelybythephysicaldistancebetweenthetwopoints,measured

throughCartesiancoordinatesinEuclidianterms.IntheFoodAccessRadar,for

example, access is conceptualised through thewalking-time polygons: a direct

measurementofthedistancewalkedintenminutesfromashopalongtheroad

network. In themodel, a fundamental determinant of food-access-poor output

areas are that they fall outside ofwalking-time polygons. In so doing, through

quantification of food access in spatial terms, food deserts create a binary

betweenthosewhohaveaccessandthosewhodonot.IntheFoodAccessRadar,

forexample,thelimitofthefood-access-poorareaistheboundaryoftheoutput

area.Thiseffectivelysilencesmorecomplexunderstandingsof foodaccessthat

relyona ‘relational’understandingofspace(Cumminsetal.,2007:1833). Ina

relational understanding, the distance between places is understood in socio-

cultural terms, where the social power relations imbued in each place are as

muchadeterminantofaccessasphysicaldistance.Conceptualisingthisnatureof

foodaccessrelationshipreliesonamorecomplexandnuancedinterpretationof

GIS,discussedinChapter7.

By reducing food access to physical distance in such a way, the food desert

concept is clearlyvery compatiblewith traditional representation throughGIS,

asbothareunderlainby‘conventionalspace’.GISthereforeinherentlyfacilitates

the projection of physical distance between points as the determining

characteristicof foodaccess.TheappropriatenessofGISprojectionsmakesGIS

an even more appealing and legitimate tool in the eyes of policy-makers

representing food deserts. GIS being more appealing leads to more people

representing fooddeserts throughGIS.Asmorepeople represent fooddeserts

through GIS, themore the idea that food access can be simplified to the food

desertconceptisreproduced,givingthefooddesertcredibilitybysilencingother

interpretations of food access. Reproduction as a result of a common

33

understandingofconventionalspacethereforepresentsonemechanismthrough

whichaveneerofobjectivitycouldhavebeencastoverthefooddesertconcept.

ThepowerofmapsandthevalidationofGISasascientifictechnology,discussed

inthenextsection,cementthiscommonunderstandingconventionalofspace.

5.3Objectifyingpowerofmapsandvalidationasascientifictechnology

InthelasttwosectionsithasbeenarguedthatGISlegitimisedandincreasedthe

appeal of food deserts in UK government policy because of the appropriate

politicalcontextandsoftware-levelcompatibility.Thethirdmainreasonforthe

purchase of GIS in the food desert debate was to do with appearances and

perception; I argue the power of GIS representations and the software’s

validationastoolsofscientificprogressgavethealreadyconvenientfooddesert

concept a veneer of objectivity. This veneer of objectivity legitimises the food

desert concept, silencing other interpretations of food access and hiding the

‘messy’blackboxofGISfooddesertcreation.

GIS gets much of its seductive power from the power of representation

associated with maps (Crampton, 2001; Pavlovskaya, 2006). Elwood explains

succinctlythat,“asmapsarepresentedandperformed,theyshapethemeanings,

identities, and characteristics that individuals and groups may assign to

individualplaces,andevenproduce theplacesthataretheretoknow”(Elwood

andCope,2009:10,myemphasis).ElwoodandCopealludetothewaythatmaps

cancreatetherealitiesthattheyseektorepresent.Inthecontextoffooddeserts,

representingthemthroughGIScantherefore‘bringthemtolife’,asitwere.GIS

takesthe inherentlysubjectiveconceptof foodaccessand, throughsilencingof

the partiality and situatedness of data, creates a realitywhere the food desert

becomesanobjectiverepresentationoffoodaccess.TheFoodAccessRadarisa

caseinpoint.Thefinaloutputlayeroffood-access-poorareas(seeFigure11in

previouschapter)is,totheuninformedobserver,aconvincingrepresentationof

those areas that are likely to suffer from food access problems. The complex

knowledge production process involved in the Food Access Radar, including

subjective steps taken by the researcher, partial data, and the setting of

quantitativecut-offs,is“blackboxed”:lostinthefinalrepresentation.Therefore,

34

inanenvironmentwherethefooddesertconceptisalreadyattractivebecauseit

meetsparticulartargets,thefinalfooddesertmapproducedbytheGIScouldbe

interpretedasanentirelyobjectiverepresentationoffoodaccess.

To compound the power of maps, the widespread faith in GIS as a scientific

technology(Pavlovskaya,2009;StMartinandWing,2007)thickens theveneer

ofobjectivityoverfooddeserts.Theinitialquantitativedatathatisinputintothe

model “has enormous cachet amongst policy makers” (McGuirk and O’Neill,

2012)totheextentthatpolicymakersdonotconsiderwhatis insidetheblack

box.Theyassumethattheoutput, fooddesertknowledge, is legitimatebecause

the input is quantitative data from ‘reliable’ sources: Census data and

government databases. Indeed, in an interview with a representative of the

OxfordshireCountyCouncilIwastoldthatCensusdata“iswhatitis.It’snational

statistics.Youcan’targuewiththat…”.Additionally,theteamusingthedatainthe

OxfordshireFoodAccessRadar,asintheStaffordshirecase,wereseparatefrom

the team creating the GIS. The interviewee, part of the policy team using the

results, described theGISprocess as “wizzy” and “technical”, in almostmagical

terms.TheinterviewsvalidateSchuurman’sclaimthat“mostuserstreatdataas

ifittheywerethetruthabouttheworld”(Schuurman,2009:42),atleastinthe

context of the Food Access Radar. Furthermore, even at a semantic level, the

FoodAccessRadarappearstobeassociatedwithpositivistscience. ‘Radar’ isa

scientific technology used to survey the physical environment. Application of

radar toahumancontext appears to imply that the resultsof theFoodAccess

Radar will also be objective. Therefore, the association of GIS with scientific

technology, combined with trust in quantitative data, enhances the veneer of

objectivity that veils the subjectivityof the fooddesert concept, especially in a

policyenvironmentwherequantitativeevidenceisrequiredforEBPM.

In sum, the argument above, collating evidence from my Food Access Radar

studyandcriticalGIS literature,suggests thatGISmodelsof fooddesertscould

have plausibly given the food desert concept a veneer of objectivity in UK

government in the decade from 1995. Firstly, the conceptualisation of food

deserts through GIS fitted in with political context of EBPM, and food desert

conceptualisations were compatible with GIS at the software level. These two

35

characteristics of GIS suggest a possible explanation as to why food desert

eradication was such an attractive government strategy. To add to this, the

associationsofGISwithpositivistscienceandtheseductivepowerofmapsare

likely tohavecastaveneerofobjectivityover the fooddesertconcept. Indeed,

my interview with a former policy-team member from Oxfordshire County

Council suggests that, at least in her case, GIS representationswere treated as

‘real’. Following from this argument it is possible to suggest two speculative

conclusionsrelatingtoGIS’sroleinthefooddesertdebate.Firstly,Iproposefood

desert projections in GIS as a possible reason explaining why the food desert

idealastedsolonginUKgovernmentpolicy.ForoveradecadeUKgovernment

toyedwith theproblematic fooddesert concept.The legitimacyand credibility

that GIS gave the food desert concept made it a productive use of time and

resourcesforpolicymakerstryingtosolve‘real’issues.Secondly,followingfrom

its appeal in policy, I suggest that the veneer of objectivity cast by GIS is a

contributingfactortothelackoffooddesertcritiquesduringtheUKfooddesert

debate. Belief in the objectivity of the food desert, fostered throughGIS, could

have crowded out other explanations of food access issues. Indeed, in my

experience,onlybylookingattheFoodAccessRadarthroughacriticalGISlens

wasitpossibletoseethroughtheveneerofobjectivitycastbyfooddeserts.

However,theaboveargumentconcerningGISwouldbeinsignificantifthefood

desertconceptwasitselfconcreteandreal.Yet,ascriticshaveshown(Cummins

and Macintyre, 2002a; Cummins et al., 2005; Cummins et al., 2007) the food

desert is an oversimplified and politically constructed explanation for food

access issues.Theclear implication, then, is thatprojecting fooddeserts inGIS

may have led to misguided policy initiatives, which could have failed to

incorporate important ‘relational’ aspects of food access. McGuirk and O’Neill

(2012),resonatingRamsey(2009),capturetheeffectofconventionalGISonthe

fooddesertconceptwell:“GISmakesrealitiesasmuchasitrepresentsthem:by

naming,coding,andfixingtheidentitiesandimaginariesofplacesandpeople,all

fromanimaginedpoliticallyneutralknowledgecreationposition”(McGuirkand

O’Neill,2012:1385).InthenextsectionIaddresstheissueofconventionalGIS

representations of food deserts by proposing qualitative GIS as a means re-

representingtheFoodAccessRadarinrelationalterms.

36

6.QualitativeGIStore-envisiontheFoodAccessRadar

InthepreviouschapterIhavesuggestedthattheconventionalviewofthefood

desertduringtheUKfooddesertdebatewasreinforcedandreproducedbythe

representation of food deserts in GIS. The Food Access Radar presents a

particularly salient example of reproducing the fooddesert idea because of its

political context (Monmonier, 1998). When food deserts are understood as

oversimplified social and political constructs (Cummins and Macintyre, 2002;

Cummins et al., 2007), their representation and reproduction through

37

conventional formsofGISbecomesproblematic.TheFoodAccessRadarmodel

conceptualisesfooddesertsinthisconventionalway,wherefoodaccessbecomes

a function of only physical accessibility from fixed locations within a distinct

neighbourhood, in ‘absolute’, Cartesian and Euclidean terms (Cummins et al.,

2007).

Yet, despite the deficiencies of conventional GIS as a means of understanding

food access, GIS can still be an effective tool if alternative representation

methodologiesareadopted.QualitativeGIS,departingfromanunderstandingof

GISashavingalwaysbeen ‘non-quantitative’ (Pavlovskaya,2009:13),presents

anopportunitytoincorporatethequalitativeandsituatedaspectsoffoodaccess

thatconventionalGISisunabletocapture.InthissectionIattempttore-envision

theFoodAccessRadarbyadoptingmethodsfromqualitativeGISandbuildingon

Cumminsetal.’s theorisationofa“relationalviewofcontextandspace”(2007:

1826).

‘Relational space’, a concept developed in human geography (Massey, 1999;

2005),isamorenuancedframeworkthroughwhichtoconceptualisefoodaccess

because it isdirectly focussedon the socio-cultural aspectsof foodaccess that

conventionalunderstandingsoffooddesertssilence.Placesareviewedasnodes

innetworksratherthanasdiscreteandautonomousboundedspatialunits,and

areimbuedwithsocialpowerrelationsandculturalmeaningsthatchangewith

timeandbetween individuals (Cumminset al., 2007:1827).Rather thanbeing

separatedbyphysicaldistance,placesareseparatedbysocio-relationaldistance.

Interactions are not bounded within a fixed locality; they take place within a

relativelyfluidanddynamicallydefinedarea.Thus,inadoptingarelationalview,

accesstohealthyandaffordablefoodcanbeunderstoodasdependenton“social

networks and social power, interventions of various ‘actors’ and degrees of

regulationwhichproduce‘layers’ofresourcesaccessibletodifferentmembersof

localpopulationsindifferentways”(Cumminsetal.,2007:1827).Itispossibleto

integrate this relational understanding of food access into GIS by adopting

methodsfromtheemergingfieldofqualitativeGISresearch.

38

Researchers in the sub-field of qualitative GIS have proposed methods for

integrating qualitative data into GIS software (Elwood and Cope, 2009). For

them, integration isnecessarybecause conventionalGIS representations, using

quantitative data, lack reflexivity and silence the situated, partial and socially

constructed nature of GIS knowledge (Pavlovskaya, 2009; Elwood, 2006).

Integrating qualitative data to expose silences is possible because “geographic

phenomena,theirrelationships,andtheirmeaningsareproducedandnegotiated

atmany differentmoments in GIS development and application” (Elwood and

Cope, 2009: 2). Qualitative GIS engages with these different moments of

production and negotiation of meaning to both qualify contexts and

epistemologies and to integrate new forms of qualitative data. Schuurman

(2009), for example, engages with spatial databases, whilst Kwan (2004)

engageswithspatialanalysistechniques.

Here I draw on several attempts to integrate qualitative data into GIS and

speculate how they couldbeuseful as ameansof re-representing food access,

thereby re-envisioning a Food Access Radar to incorporate situatedmeanings

andsilences.Inthisprocess,Itrytorepresentlivedexperienceandhowpeople

differentiallyengagewithandmakeuseof their foodenvironment,ratherthan

simply mapping spatial distance from locations where food is available, as

traditional food desert studies have done (Shannon, 2015). I propose using

Schuurman’s ‘ontology-based metadata’ technique (2009: 42) to enrich GIS

analysis of food access, and Pavlovskaya (2002, 2009),Matthews et al. (2005)

and Pain et al.’s (2006) techniques for incorporating qualitative data on lived

experience intoGIS. I thenpropose integrating theseperspectives intoGISata

software level through Jung’s Computer-assisted Qualitative GIS (CAQ-GIS)

(2009:115)andKwan’sSpace-TimeAquaria(2004:267).

One way that qualitative data can be incorporated into GIS is by expanding

existing metadata structures used in spatial databases (Schuurman, 2009).

Metadata(informationaboutadataset)canbecomearepositoryforqualitative

information, enablingGIS users to understand the distinct ontology associated

withaspecificdataset.SchuurmancallsthisqualitativeGIStechnique‘ontology-

basedmetadata’ (2009:42). ‘Ontology’ in critical/qualitativeGIS isunderstood

39

throughitsinformationsciencedefinitionratherthanthatofsocialtheory,inline

withcallsfromcriticalGIStoengagewiththesoftwareonitsownterms(Kwan,

2002;Sheppard,2005).Ininformationscience,eachdatasetandeachdatafield

inalegendorattributetablehasitsownseparateontology,reflectingadistinct

way of seeing the world (or epistemology). By including more thorough

metadata,adeeper,contextualisedunderstandingofeachdistinctontologycan

be obtained. Ontology-based metadata is therefore a way of reintegrating the

socialcontextofadatasetbyincludingnon-spatialattributesofthedataintoGIS.

In the caseof theFoodAccessRadar, for example, themetadata for thedata I

usedwasoriginally limitedtospatialattributes(Figure12showing foodoutlet

dataset). To turn this into ontology-based metadata, more information was

added using ArcCatalog, ESRI’s programme formanaging and organizing data.

This included the process of collecting, cleansing and representing food outlet

dataaswellasidentifyingsilencescreatedbythedataset(seewithinblackboxin

Figure 13). In my application of the Food Access Radar, food outlet data was

takenfromtheFSA’sFoodHygieneRatingSchemeandusedforpurposesbeyond

theoriginalintention.However,whilstexpandingmetadatastructuresisauseful

wayofre-contextualisingtheFoodAccessRadar,onitsownitisonlyoflimited

value for integrating the lived experience andnuancedunderstandingsof food

access that a ‘relational’ food access model would require. For this, other

qualitativeGIStechniquescouldbeused.

Figure12ScreenshotshowingtheoriginalfoodoutletshapefileinArcCatalogwithnometadataatall(Source:Author)

40

Painetal.(2006),Pavlovskaya(2002,2009)andMatthewsetal.(2005)provide

accountsofhowtousequalitativedataonlivedexperience,primarilyintheform

ofkeyinformantinterviews,withGIS.Qualitativedataisintegratedtodifferent

extentsintotheGIS.Painetal.useinterviewdataasseparatefromGIS,toqualify

theoutputofGISmapping.PavlovskayaandMatthewsetal.inputthequalitative

datatheyobtainintotheirGISrepresentations.Painetal.(2006),analysingthe

effect of street lighting on crime rates through GIS, use interviews with key

informants to compare their GIS results with lived experience. This technique

hasclearapplicationtotheFoodAccessRadar.Uponcreationandanalysisofthe

fooddesertmodel, interviews couldbe soughtwith individuals living in ‘food-

access-poor’areas,aswellaswithindividualsinareasnotclassifiedassuchfor

comparison.Questionssuchas‘doyouconsideryourselftoliveinafooddesert?’

or‘doyoufeellikeyournearestsupermarkettoofaraway?’couldrevealsocio-

cultural characteristics silenced by the Food Access Radar’s focus on physical

access. Following Pain et al. (2006), spatial analysis of food access can be

qualified by qualitative data on lived experience. In the process, interview

evidencewouldalsotestthevalidityofthefooddesertmodel itself.Yet, inthis

applicationofqualitativeGIS,analysisisclosertomixed-methodanalysis.

Figure13ScreenshotshowingtheeditedfoodoutletshapefileinArcCatalogwithmetadata.Textwithintheblackboxshowsanexampleofontology-basedmetadata

usingtechniquessuggestedbySchuurman(2009)(Source:Author)

41

Pavlovskaya (2002), inherstudymappingurban transformation inpost-Soviet

Moscow, and Matthews et al. (2005), mapping neighbourhood mobility of US

welfare recipients, use qualitative research to uncover silences in traditional

information sources before including their data in GIS. Pavlovskaya uses 45

interviewstorevealthemultipleeconomiesinMoscowhouseholds:formaland

informal, monetary and non-monetary, and includes these in her GIS

representations. Similarly Matthews et al. create GIS layers visualising the

community resources that key informants visit (Figure 14). Matthew’s

conclusions are highly relevant to the Food Access Radar because, by

representingdataonlivedexperience,theyrevealthatindividualsdonotalways

gototheirnearestcommunityresource.Livedexperiencethereforerevealsthat

socialandculturalvaluesareimportant.Bycontrast,theunderlyingassumption

of the Food Access Radar, and other food desertmodels (Donkin et al., 2000;

Pearceetal.,2006),isthatrationalconsumersvisitthenearestshopwithintheir

neighbourhood. Mapping lived experience, garnered through qualitative

research, therefore complicates conventional GIS food desert models by

revealing silences, and creates a space in which a ‘relational’ model of food

access (Cummins et al., 2007) might be a more accurate representation.

Qualitative GIS, in this respect, by plotting data garnered through interviews,

provides amechanism tomap food accessmore accurately. However, plotting

information collected from interviews is only innovative in a content sense:

traditionalGISmechanismsare stillused to representdata,whichcanobscure

thesocialcontextofdata.

42

AsawayofintegratingqualitativedatadirectlyintoGISwithoutlosingthedata’s

richness or social context, Matthews et al. (2005) also propose ‘geo-

ethnography’. They suggest integrating audio, video, image and text files,

collectedthroughethnographicresearchmethods,bycreating“hotlinks”(2005:

76)within the GIS software to external databases. This way the ethnographic

material isnotquantified,so is integratedwhilstmaintaining itssocialcontext.

Applying this technique to food access models could be an important way of

integrating written and photographic accounts of lived experience. After

establishing an appropriate sample of theoretically food-access-rich and food-

access-poorindividuals,eitherfromtheFoodAccessRadarresultsorresearcher

knowledge, individuals’ lived experience of their food environment could be

garnered through qualitative methods. Participants could be asked, through

ethnographicinterviews,aboutthesocio-culturalmeaningsimbuedinfoodretail

infrastructure and about access as a determinant of their food consumption

patterns.Tocomplimentinterviews,theycouldbeaskedtodrawsketchmapsof

their foodenvironment, therebyaccounting fortheirsituatedknowledgeof the

Figure14ThediagramproducedfromtheethnographyresearchconductedbyMatthewsetal.(2005)revealsthatmanyofthecommunityresourcesusedbythe

samplefamilylieoutsidetheirhouseholdcensustract

43

foodenvironment,highlightingtheoutletstheyusemostfrequently.Theycould

alsorecordthefoodstuffspurchasedfromparticularshopsthroughphotographs,

andalsophotographtheseshopstocaptureaground-levelviewofthesesites.All

thesequalitativedatasourcescouldbe“hot-linked”(Matthewsetal.,2005:76)

into the GIS, building a rich picture of food access patterns, and generating a

‘relational’understandingoffoodaccessthroughqualitativeGIS(Cumminsetal.,

2007).Throughthismethod,individuals,otherwisesilencedinconventionalGIS

accessmodels,aregivenagencyintheGISrepresentations.

Taking ‘geo-ethnography’ (Matthews et al., 2005) techniques to the limit, Jung

(2009) proposes integrating qualitative data directly into GIS data structures

through ‘Computer-aided Qualitative GIS’ (CAQ-GIS) (2009: 115). Directly

encodedqualitativedataismoreusefulbecauseitcanbeanalysedinGISthrough

‘queries’and‘selections’,forexample.Jungproposestointegratequalitativedata

intoarastergrid,superimposedasanewlayerinGIS.Eachlineintherastergrid

isdefinedbygeographicalcoordinates.Qualitativedata,havinggonethroughan

encodingprocessinComputerAssistedQualitativeDataAnalysissoftwaresuch

as ATLAS.ti, can be given a geographical coordinate in the raster layer

corresponding to the location forwhich itwascollected.Therastergridstores

theencodedqualitativedata in anattribute table compatiblewithGIS analysis

(Figure15providesa summaryof theCAQ-GISprocess).Theprocess couldbe

appliedoveraprojectionofCambridgeshire,andethnographicaldataproduced

aboutfoodaccesscouldbeintegrated.Thisabilitytointegratemultipleformsof

evidence and multiple ways of knowing into GIS in this way resonates with

Knigge and Cope’s use of “grounded visualisation” (2006: 2021) to analyse

iterativelybothquantitativeandqualitativedatainGIS.BytakingqualitativeGIS

to its limit, by integrating qualitative data into GIS at a software level, well-

grounded,richdescriptionsandexplanationsoffoodaccessprocesseswouldbe

availabletoresearchersandpolicymakers(Jung,2009:116).

44

One final application of critical GIS, whilst not qualitative GIS per se, can

integratefurtherdetailsoflivedexperiencedirectlyintoGISsoftware.Kwanhas

created a ‘space-time aquaria’ (Kwan, 2004: 267) to conceptualise how the

interactionofspaceandtimecanstructurehumanactivitypatternsinparticular

localities.Space-timeaquariacanbedirectlyappliedtofoodaccessibilityissues,

addingfurthercomplexityandnuancetofoodaccess.A‘space-timeaquaria’isa

three-dimensional representation in GIS of an individual’smovement over the

course of a day, between different nodes (Figure 16). It therefore represents

movement as a function of space-time, directly advancing an understanding of

therelationalgeographiesoffoodaccess.Thesignificanceofthisforfoodaccess

isthatitaddsgreaterflexibilitytothelocationfromwhichpeoplepurchasefood

thanconventional foodaccessmodels.Conventionalaccessmodels,suchas the

Figure15ThisimagedescribesthequalitativedataintegrationprocessinCAQ-GIS.Inthetopleft-handcornertherastergridisoverlainontothedesiredspatial

area.Thebottomtwotablesshow(1)thespatialdatabasefortherastergridcontainingqualitativeinformation(animageinthiscase),and(2)thesocialdatabasecontainingfurtherqualitativedataassociatedwiththatimage(Source:

Jung,2009)

45

Food Access Radar, consider the home as the only basing point from which

people access food, and thus consider food-access-poverty as a function of

distance from food stores to households. This does not consider that the

workplace,school,church,socialfacilities,oranypointalongtheroutebackfrom

these locations to home, are also places where an individual can make food

purchases(Burgoineetal.,2014).WhilstNetworkAnalyst,asused in theFood

AccessRadar,isoneofthefewinstrumentsinGISthancananalysespace-time,it

doesnotgofarenough.Constructinga3Dspace-timeaquariumfromindividual

mobility accounts (the qualitative element of the aquariawhen garnered from

ethnographic interviews) offers a richer perspective on food access

opportunities across a range of sites. Space-time aquaria can be created using

complex algorithms, as detailed by Kwan (2004), in ArcGIS using ARC Macro

Language (AML). Space-time aquaria have additional value for food access

studiesbecause foodoutletopeninghours,which restrictaccess in space-time,

could also be integrated into 3Dmodels. Space-time aquaria therefore offer a

richer perspective into relational food access conceptualisation, integrating

mobilityintoqualitativeGISandnewparametersforaccessthroughspace-time.

46

AsIhavesuggestedwiththeabovestrategiesforintegratingqualitativedatainto

GIS analysis, significant potential exists within qualitative GIS to respond to

Cummins et al.’s (2007) call for a relational approach tomapping food access.

Unlike conventional approaches to spatial analysis of food deserts such as the

Food Access Radar, that explore only physical, Euclidean proximity as a

determinant of food access, qualitative GIS offers potential to integrate lived

experience,situatedknowledges,andreflexivityintoGISanalysis.

Figure16AdiagramofKwan’sspace-timeaquaria.Thediagramshowsadailyspace-timepathforanindividualbetweentheworkplace,churchandhomeacross

FranklinCounty,Ohio.Thisindividualpathwaycanbeinsertedintospace-time

aquariain3DGISalongwithotherspace-timepathways(Source:Kwan,2004)

47

7.Conclusion

Inthisdissertation,theFoodAccessRadarhasbeenusedintwoprincipalways.

First,ithasprovidedanavenuethroughwhichto‘unpack’theintricaciesofGIS-

projectedfooddeserts.CriticalGISnecessitatesan‘insiderview’(Elwood,2006),

andonlybyconstructingtheFoodAccessRadarcouldIpeerinsidetheGIS‘black

box’(Sismondo,2010).TheseconduseoftheFoodAccessRadarhasbeenasan

evidence base. Together with a review of relevant literature and qualitative

information garnered from interviews, I have assembled a critique of the food

desertconcept,whichfocusesontheinfluenceofGISinthefooddesertdebate.

TheresearchinthisdissertationchallengestheobjectivityofGISprojectionsin

thecontextofUKfooddeserts.Previouscritiqueshaveunderstoodfooddeserts

asoversimplified,partialexplanationsof foodaccess (CumminsandMacintyre,

2002a; Cummins, 2005), which do not take into account the relational

geographies of resource access (Cummins et al., 2007). This dissertation has

extended such arguments, by suggesting that the use of GIS, specifically,

transformedthepartialfooddesertideaintoatotalisingandseeminglyobjective

concept. Combining knowledge garnered from the Food Access Radar

constructionprocesswithinterviewevidence,informedbyCriticalGISliterature,

IhavearguedthattheprojectionoffooddesertsinGISatleastpartiallyexplains

thepoliticalpurchaseheldby the fooddesert idea inUKgovernmentpolicy in

the late 1990s and early 2000s. GIS both facilitated the identification of food

deserts within an evidence-based policy-making paradigm and reinforced the

concept.GISsoftwarestartsfromthesameunderstandingofconventionalspace

as the food desert concept, framing a more complex and nuanced issue as a

simplified geographical reality based on a limited number of quantitative

variablesmapped in space.GIS also casts a veneerof objectivityover the food

desert concept through the power of maps and its association with positivist

science. From the argument that GIS was both convincing and convenient,

tentativeconclusionscanbedrawn.Firstly,IwouldarguethatGISprojectionsat

leastcontributedtothepersistenceofthefooddesertideaforalmostadecadein

UK government policy. Secondly, and running in parallel, the convincing and

convenient nature of GIS food desert projections possibly explains why there

48

were so few critiques of the food desert concept in policy and academic

literature.

With the Food Access Radar, and other ‘conventional’ food desert GISmodels,

problematizedastotalisingandobjectifyingconstructions,thisdissertationthen

proposes an alternative way of conceptualising food access through GIS.

RespondingtoCumminsetal.’scalltoengagewitha‘relational’approachtofood

access(2007),IinvestigatethepotentialitiesofqualitativeGIS,afieldofresearch

that has been growing in recent years, for projecting food access in GIS.

QualitativeGISisappropriatebecauseitenablesaresearchertoincorporatethe

subtletiesoffoodaccess,silencedbytraditionalfooddesertmodels,directlyinto

theGISsoftware.SeveralqualitativeGISmethodshavebeenproposedandmade

relevant to food access modelling, such as geo-ethnography and CAQ-GIS. To

implement these qualitative GIS techniques would require greater scope and

resourcesthanthisdissertationpermits;however,ithasshowntheneedfor,and

potentialityof,aqualitativeGISapproachtofoodaccessmodelling.Insodoing,it

hasopeneduparesearchavenueforfuturefoodaccessstudiestobuilduponthe

qualitative GIS techniques suggested here. As the field of qualitative GIS

continuestogrow,doubtlessothertechniquesapplicabletofoodaccessresearch

willalsoemerge.

In UK food accessibility research today, there is growing interest in the

proliferation in other aspects of food availability, such as the proliferation of

takeawayoutlets (Burgoine et al., 2014).Perhaps it is in this bodyof research

that qualitative GIS may be applicable. Alternatively, in the on-going US food

desert context, selected critiquesof the fooddesert concept arenowemerging

(notably Shannon, 2014). In these debates too, this dissertation couldmake a

number of contributions. Firstly, American academics and policy-makers alike

shouldbemadeawareofthepowerfuleffectsthatvisualisingfooddesertsinGIS

canhave,inordertobetterunderstandthepartialityofthefooddesertconcept.

Second,theuseofqualitativeGISasameansofrepresentingfoodaccesscouldbe

further advanced in the US context. A qualitative GIS approach to food access

may destabilise current understandings of food deserts and, importantly,

uncoverhiddengeographiesoffoodaccess.

49

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9.Appendix:interviewtranscripts

TelephoneinterviewwithJonathanVining,StaffordshireCountyCouncil,GIS

practitionerfortheFoodAccessRadarProjectinStaffordshire,byLouis

Rawlings(L),05/09/16

56

[19:32mins]

Louis:Hello?

Jonathan:HiisthatLouis

L:ThatisLouishello!

J:Hiit’sJonViningfromStaffordshireCountyCouncil

L:HiJon,thankssomuchforagreeingtospeaktome

J:SorryI’vejustbeenonareallylongphonecall

L:Noproblematall,I’lltrynottokeepyoufortoolongthen

J:HowcanIhelpthenLouis?

L:SoifIgiveyouabriefoverviewofwhatmyprojectconsists.EssentiallyI’ma

thirdyeargeographystudentanddecidedthatIwantedtodoadissertationon

socialinequalityandGIS,andIcameacrossthefoodaccessradar,thatwasabout

threemonthsago.SincethenI’vecreatedthefoodaccessradarfor

CambridgeshireandthenI’mmakingitintoanacademicdissertationby

analysingthecontributionofthefoodaccessradartopolicydebatesintheUK

andthewideracademicdebateaboutfooddesserts.Aswellaslookingatits

applicationinAmericatodaywherethefooddeserthastakenoff.Socouldyou

describetomeyourroleinStaffordshire?

J:Yeasure.InmycurrentroleI’minvolvedineconomicregeneration.Lookingat

businessparksandinfrastructure,employmentland.ButrewindingtheclockI

wasintheresearchunit.Thiswasverymuchfocussedonprovidingresearchfor

ourplanningandeconomicdevelopmentteams.Butalsotothecorporate

functionsasaCountyCouncil.Sooneofourserviceareasisinconsumerhealth,

socolleaguesinthatdepartmentapproachedustoaskforhelpwiththeirfood

accesspieceofwork.Myroleintheprojectpredominantlywasinthedatasideof

it.Soassemblingthedata,doingtheGISworkanddoingallthemagicwiththe

datasets.

L:Sointermsoftheproject,youweren’tinvolvedincomingupwiththefood

accessprojectthen?

J:Asin,asanidea?

L:Yeahthedirection

J:Theideaofproducingthefoodaccessradarwasinitiatedbythehealthteam

whosatwithintradingstandardsatthetime.Theywereawarethatweworked

withGISandcouldhelp.Obviouslythetechnologyhasmovedonsignificantly

sincethen.WhatIwasdoingwaslotsofdrivetimeanalysis.Particularlythings

57

like,populationslivingwithinacertaindrivetimeofabusinessparkora

motorwayjunction.Sotranslatedthatreallyintoawalkingdistancefromvarious

shopsandfoodoutlets.That’swheremyrolecameinbecausetheywereaware

thatIdidthat.AndIcameupwiththemethodologyandhowtomakeitwork

withthedatareally.

L:Sodoyouknow,intermsofthewholefoodaccessidea,doyouknowwhere

thepressurescamefrom?

J:TherewasanorganisationinvolvedcalledtheNationalConsumerCouncil.

Theywereleadingonitatanationallevel.Somefundingwasmadeavailableto

helpStaffordshiretakethispieceofworkforward.Bythesoundsofitthey’ve

longgoneunfortunately.Butintermsofmyrole,Icantellyouthatwehadthe

FLAREdatabase,whichessentiallyhadallthedetailsofthepremisesthatwere

licensedtosellfoodacrossStaffordshire.Iessentiallygotallthepostcodesand

mappedthoseandgroupedthemintodifferentcategoriesofstress.Off-license,

supermarkets,cornershops,thattypeofthing,andstratifiedthatontheGISfile,

andthencreatedthewalkingtimeisochrones.

L:yeahI’vehadfundoingthat

J:Areyoudoingthatatthemoment?

L:I’vefinisheditnow.IwaslearningGISatthesametimeasworkingonthis

thing.Usingthenetworkanalyst

J:Yeahthatwastheoldfavourite.ArcGIS

L:WasthismodeldesignedspecificallyforStaffordshire?

J:Iwouldn’tsayitcouldonlyworkinStaffordshire.Ithinkitcouldbereplicated

elsewhere.Imeanthehardestpartformeinmanywayswasgettingaclean

databaseoffoodstores.Idon’tknowifyou’vehadthisissue?

L:YeahIhave,it’sclearedupalittlebitsince.TheEHteamskeeparecordof

theirfoodstoressoit’sabitmorestandardised.

J:Sothatwasthebiggestchallengetostartoffwith,ittookmequiteawhileto

produceadatasetthatwasworthyofdoingapieceofworkwith.Andthenwe

werequiteluckybecausewereabletopullonthe2001census,sowegotdown

tooutputareageography.Wecoulddoallthepopulationdemographictrends

withthat.Andalsowedidabitofworkoverlayingpublictransportroutes.We

hadcolleaguesinourpassengertransportteamwhowereabletoprovideus

withbusroutesandsoon,andtheygaveussomeisochrones.Sowecould

interchangetimesonvariousareas.Soifyougotabusfromyourhouseyou

couldgettothesupermarketinxminutes.Butthatwasabitfurtherdownthe

linereally.

L:ItsinterestingthatI’veseensofar,alotofthemorerecentstudieshave

measureddistancefromthehouseholdtotheshop.TheFoodAccessRadarwas

58

fromtheshoptothehouseholds.Didyoudothatdeliberatelyorwasit

somethingmoreconvenienttodo?

J:Ithinkitwaseasiertodoitthatway.Becauseinsomewhereyou’vegot

somethinglike400,000addresspoints,whereasthenumberofpremisesis

gettingonsomethinglinkathousand,oracoupleofthousand.SoIcan’t

rememberoffthetopofmyhead,butthecomputinginvolvedinthecoupleof

thousandpremiseswasabitmorestraightforward.Weworkedonlooking

wherethegapswere.Wecouldseethatthecoveragewasgoodbutitwasabout

identifyingwherethegapswere.Soobviouslyyouhadthatinruralareas,

becausewe’vegotsomefairlysubstantialrurallocalities.Butalsoinpartsof

someofourlargertowns,wherebythetenminuteshowedusthatyoucould

correlatequitecloselywithareasofdeprivation.Ithinkthatweoverlaiditwith

theIndexofMultipleDeprivation2004andwithotherhealthindicatorse.g.

debilitatinglong-termillness,theusualsuspectsthatyou’renodoubtawareof.

L:DidthatrevealanythingsurprisingaboutStaffordshirewhenyouplottedit?

J:Can’trememberI’msorry.I’velivedalifeofmapssinceIdidthis.Ididhavea

documentonitbutIhaven’tbeenabletofindit.

L:Doyourememberhowyouselectedtheproblemareas?I’velookedatthe

documentthatyouhad.Downnearthebottomittellsyouabouthowtoidentify

thesepotentialproblemareas.Doyourememberselectingthempurelybecause

ofwhatthemapshowedorwereyouselectingareasalsofromyourknowledge

ofStaffordshireanddeprivation?

J:Ithinkitwasprobablyalittlebitofboth.Themapwillhaveshownthe

geographicareaswherethereweregapsfromtheisochrones.Butwedidfocusin

onthedeprivedcommunitiestoseeifthefoodaccessreinforcedorconflicted

againstthoseideas.Inthelargesenseitwasthosedeprivedareasthattendedto

exhibitafoodaccessissuethanthemoreaffluentareas.

L:I’mawarethatI’mkeepingyoualittlebitlongerthanIsaid,butcouldIjustask

youafewmorequestions?

J:Ofcourseyoucan

L:I’vebeenlookingtocritiquethemodel,lookingattheassumptionsthatlimit

thefoodaccessradarasatool.Doyourememberatthetimewhetheryouwere

awareoftheassumptionsandlimitationsatthetime?

J:Yeatherewasprobablyoneortwo.Thefirstonewasthequalityofthedata

thatwewereworkingwith.Iwasn’ttotallyconvincedthatitwasafullyvalid

dataset.Butwehadtolivewiththat,becauseitwasthebestwehadatthetime.I

thinktheGISsoftwarewasfairlygood,butbackinthedayweusedthenetwork

analyst,andIthinktheunderlyingnetworkdatasetwasn’tnecessarilyasgreatat

pedestrianconnectivityasitcouldhavebeen.Sotherewaspossiblysomeareas

whereyouhavecutthroughthatwouldn’thavebeenidentifiedinthenetwork,

socouldhavebeenoverexaggeratingaprobleminthoselocations.

59

L:Finalareaofquestioning.Doyourememberwhattheresultswereinthe

Staffordshirestudy?DidtheGIShaveanymaterialresultsonStaffordshire?

Policychanges?

J:It’sabitofashame,Iusedtogetstuckinadarkroomattheresearchunittodo

allthisstuff,thatotherpeopleusedtotakeon.Itmaybethatcolleaguesofmine

aremorewellplacestosaywhatactuallyhappened.There’saladycalledNicola

DaywhowasmoreintothisthanI.

L:That’severythingIreallywantedtoknow.ThanksJon.OnafinalnotecanI

justaskifIcanusethisconversationrecording?

J:Yeahofcourse,you’rewelcometousemynameifyouwant,Idon’tmindthat.

Ifthere’sanythingyoucanthinkofjustletmeknow.

L:ThankssomuchforthatJon.Andthankssomuchforyourtime,that’sbeen

reallyuseful.

J:Okay,noproblem.Goodluckwithyourdissertation!Byefornow

L:Bye!

TelephoneinterviewwithKateAustin(K),OxfordshireCountyCouncil,directly

involvedintheFoodAccessRadarProjectinOxfordshire,byLouisRawlings(L),

08/09/16

[26:58mins]

Louis:Hello?

Kate:HiLouis,itsKate

L:HelloKate,thanksforagreeingtospeaktome.

60

Kate:Noproblem,whatcanIhelpyouwith.Itsfoodaccessisn’tit?

L:Itisyeah.InthelastfewmonthsIhavebeenconstructingthefoodacessradar,

puttingtogetherthedatasetsthatI’vefoundandthenI’veputthemintotheGIS.

I’vecreatedtheGIStoolandamnowanalysingitforCambridgeshire.Itlookslike

therecouldhavebesomesortoffoodpoverty/foodaccessibilityissue

K:Okayyeah

L:ButonthewholeinCambridgeIfoundthat…maybeitsbecausestudieslike

thishavebeengoingonforthelastdecade,thatactuallyonthewholefood

accessibilityinCambridgedidappeartobequitegood

K:BecauseactuallyifIremember,oneofthefirstfunctionsoffoodbankswasin

Cambridge.Ithinkthat’swherealotofitstarted.SoIthinkyou’rerightthere’s

probablybeenloadsofactivityinCambridge

L:Essentiallyformydissertation,asmuchasIneedtoconstructthefoodaccess

radar,it’sgoingtobequiteusefultohaveabitaboutthecontext,ofhowthisidea

offooddesertswasdrivenandhowitcameaboutintheOxfordCountyCouncil.

AndthenIhadafewfinalquestionsaboutGISingeneralinthecouncil.

Qualitativevs.quantitativedata.I’dbereallygratefulifyoucouldanswerafew

ofthesequestionsforme.CouldIrecordthisanduseitbytheway?

K:Yeahnoproblem,I’lldomybest.ItwasalittlewhileagoandIwasoneof

manypartnersinvolvedintheproject.TheGISstuff,Iwasn’tsoinvolvedinatthe

time.Butintermsofthefirstquestion,whereitcamefromforOxford:therehad

beenalotteryfundedproject,thatstartedoffastheEastOxfordhealthyliving

initiative,andthenitbecameknowasthehealthylivingpartnership,anditwasa

lotteryfundedprojectthatlookedathealthandwellbeingwithinareasthathad

beenhighlightedasdeprivedwithinthecity,wheretherewerehealth

inequalities.MainlyfocusedonhealthinequalitiesinBlackbirdLeysandEast

Oxford.Oneofthemainpartsoftheprojectwasaboutfoodworkandabout

healthyeating.Andsotherewereawholeseriesofprogrammesandworkshops

withinthecommunityaroundhealthyeating.Sotherewasquitealotofcontact

withdifferentgroups,goingoninthebackground.

Andthenwealsohadvariouspartnershipssetup.Thereusedtoexistan

Oxfordshirehealthalliance,andthereusedtobetheOxfordcommunitycaterers

network,wherecommunityorganisationssuchasthecouncilandtheNHS

representativeswouldgoto,butitwasmainlyintermsofsoupkitchens,and

providefoodforhomelessnessorganisations.Sotherewasthatelementaswell.

SoawarenessthatfoodwasquiteanissueinOxford

Andthen,coincidently,someonenewjoinedtradingstandardswithintheCounty

CouncilwhowasfromStaffordshire.

L:Okaythatwouldmakesense.

61

K:AndtheyhaddonesomeworkinStaffordshireandwantedtoseewhether

theycouldreplicateitinOxfordshiretodevelopitfurther.Soitallthencombined

togetherreally.Andthenwesetupapartnership.TheCityCouncilhadaccessto

lotsofgroupsandunderstoodtheactualontheground,qualitativesituation.We

thenhadtheNHSguyswhohadthatstatutoryviewonhealthyeatingand

pickingupthingsthroughthecriticalsideofthings.Therewassomeoneelse

fromtheCityCouncilinvolvedwitholderpeoplethatalotofthequalitativestuff

wascomingoutof:oldpeopleandyoungfamilies.ThenTradingStandards

joinedinandtheyhavealltheexpertisewiththeGISandthemapping.They

knewhowithadbeendoneinStaffordshire.Andthenwedevisedabitofaplan

abouthowwewoulddoit.E.g.foodbasket,groups,andthenTradingStandards

cameupwithallthetechnical,wizzystuff,andpulleditalltogetherifyouget

whatImean.Andthenwewentouttovisitoutgroups.AndthenTrading

Standardslayeredupwhereallthebusroutesmightbe,andwheretheshops

are.Probablysimilartowhatyou’vedoneforCambridge.

L:andthenIguessthiswholeprojectwasspecificallyaboutfoodaccess,andI

guessthat’swhatI’vebeenlookingataswell,butattheCouncilwasthereany

distinctiondrawnbetweenfoodaccessandsocialvaluesthatinfluencedfood

eating?

K:That’swherethethinkingaroundfoodaccessandfoodpovertyhasreally

movedonnow.Weonlyreallygottospeaktoacoupleofgroups,onlyreallythe

oldpeople’sgroups.ButcertainlytheworkthatGoodFoodOxforddidrecently

haspickeduptheculturalissues.Wenowhaveinroadsine.g.Asianwomen’s

group.E.g.intheseyouwouldhearaboutpeopleonlywantingtoshopincertain

placesandthewholesortofhalalelementsaswell.Theperceptionofwhatwas

healthyandwhatwasnotvaried.Healthyforsomegroupsmeantagoodportion

offoodandhavingmeatinit.Orquiteabitoffatinit,becausethatwasgood.The

womenwereexpectedbythementoservethemeatwiththefatonit.Those

sortsofthingswereveryinterestingbuttherewasnoscopetoincludeitinthe

accessproject.

L:Soatthetimeaccesswasthemajordrive?

K:Itwas,butbecausewewereworkingwitholderpeopleinparticulartherewas

acertainamountofsocialelementaroundreducingisolation.Forexamplethe

socialgroupthatweworkedwithupinCuttselowe,wherewemanagedtoget

theRedCrossbustotakethemshopping,actuallyaccesswasthedriverinitially

butitbecameveryapparentthatpeoplewantedenoughtimetograbacoffee,at

theSainsbury’s,andsomeofthebenefitswereenjoyed,Theythenbuildupa

relationshipwiththecaféstaff,forexample.Peoplegottoknoweachotheron

thebusbutitwasseenasasocialeventaswellasforfoodaccess.Buttheinitial

intentionwasabouttheaccessandthesocialpartofitwasabonus.

L:Yousaidatthestartthattheemphasishasshifted.Whendidtheshiftaway

fromfoodaccesshappen?

K:Idon’tknowbecauseIhadabitofagap.Istoppedworkingatthecounciland

thencameback.

62

NowIamnoticingthatthereisadifference.E.g.thepaperbyGoodFoodOxford

showsthattheyhadamuchmoreindepthprocessinthefocusgroups.Theyhad

awiderrangeofdemographicstheywereworkingwithanditpulledoutmore

stuff.

SoinmymindI’mthinkinggoshtherehasactuallybeenashift,cultural

preferencesnowaswell.Ofcoursethey’vealwaysbeenthere,butwiththe

limitedscopeoftheveryearlyprojectitwasn’twideenoughtopickupthatsort

ofstuff.

L:That’sveryinteresting.Partofthisdissertationinvolvesavastliteratureofthe

academicworkonfoodaccessandI’vefoundthatthereseemstobeashift

aroundabout2006/2007towardssayingthatthefoodaccessideaisnotgoingto

causecausation.Todothisyouhavetoconsiderawiderrangeoffactors.

Cultural,socialeconomic…Soit’sveryinterestingtoseethatfromyourside.

K:Andwhatyou’resayingwouldabsolutelymakesense.Itwasaroundthattime

withtheCuttselowegroupwhenwehadfinishedthemapping,thatyouknow

thiswasamechanismfor…Actuallywefoundthatpeoplemightnothavegoton

abusattheendoftheroadtogosomewhere.People’selementsofchoicewe

noticedwereimportant.Theychosetokeeptheircaranddrivemilesfor

example.Peoplewerechoosingwheretheywereaccessingthingsforcultural

reasons.Theycouldhaveaskedforleanmeatatthebutchersbuttheychosefatty

meatbecauseofwhatitrepresented.

L:Iwasgoingtothenaskyouabouttheresultsbutwe’vediscussedthatalready.

Itbeingtheminibus.Elderlypeopledirectlytargeted.Onequestionmightbe:

obviouslyyoutargetedelderlypeopleasatargetgroup,butwherethereany

solutionsthatyouhadinmindbeforeyoustartedite.g.wecouldputanewshop

inplaceorcreateanewbusroute?

K:Ithinkwecameupwithafewsolutionsasweweregoingthroughtheproject.

Werealisedthatoneoftheissuesthatpeoplecouldn’tgettoashopthatsolda

goodrangeofproductsatareasonableprice,Andiftheycouldgetthereitwasn’t

veryeasy.Sotheyweregettingtaxisyouknow.Sotheycouldn’tactuallyget

thereindependently.Sowethoughtthroughafewoptions.Theshopwas

considered,butactuallywethoughtthatwouldbelimitedbecauseitwouldstill

notstocktherangeofgoodsneeded.Andyouknowthewholesocialaspectwas

comingout.Anditwasaroundthetimethatonlineshoppingcameout.Wewent

backtothegroupaboutonlineshopping.Buttheydidn’tlikethisideabecause

theythoughttheycouldn’tgooutandchoosee.g.whatapplestheycouldget.

Theywantedthatwholeelementofchoiceandthatelementofgoingout.Soit

wasatthistimethatwerealisedthatthesocialelementwascomingout.

L:Thanksfortalkingabouttheresults.Iguessmylastminisectionintermsof

questioningisonGISinhealthpolicymoregenerally,andqualitativevs.

quantitativeinhealthpolicy.Inthatstudyspecifically,didyouseetheGISasa

complimenttothequalitativestuffordidyouhaveconcernsaboutit?

63

K:IfIrememberitdidcomplimentquitewell.InCuttesloweforexampleitdid

showthattherewasalimitationintermsofwherefoodoutletswerepositioned.

Transportwasabitdifficult.Ithinktogetabustoamainshoppingplace

involvedwalkingagoodhalfmileatleasttogettothebusstop.Soitdid

compliment.InoneofthegroupsdownCowleyroaditshowedthatfoodpoverty

wasn’tasmuchofanissueasitmightbe.Yep,there’sasupermarketonthathigh

street.Therearealsoexcellentbusservices.Soactually,whatpeopleweresaying

wasmatchedbytheGIS.ButthenagaintheGISwasn’tasadvancedasitistoday.

L:andintermsofmoregenerally,intermsofwantingtocreateapolicyand

convincingsomeoneatahighleveltodosomething,whereisthebalance

betweenquantitativeandqualitativedata?

K:Ithinkthatifyouaretryingtogetfundingandbuyin,theshifthastogo

towardsthequantitative.Withfundingseriouslyrestrictednow,reducedtoa

fewspheres,itdoescomedowntofigurestoprovethatthereisanissue.Prove

thereisanissue.Okaytenpeoplearetellingyouthereis,butneedingthat

analysis.Butthequalitativeisnecessarybecauseitbringsitalive.Iwouldsay

thatthatthebalanceshouldbeshiftedtowardsthequantitative,sothereisthat

strongevidenceofwhatmightbecausingtheissues,thenbackingthatupwith

whatpeoplearesayingtobringittolife.SoyeahIthinkthathavingthat

evidencethereisabsolutelynecessary,gettingitasrobustaspossible.Case

studiesarealsouseful;Mrxdoesthisandthisandtheimpactuponthisisthis

andthis.Thatkindofthingsbringsittolifeaswell.

L:InmygeographydegreeIamtaughttotreatquantitativeevidencewithabitof

scepticism.IsittreatedasobjectiveintheCouncil?

K:Ithinkitdependonthesource.Ifyouhavedataontransportroutes,itiswhat

itis.Ifyouarelayeringupthingslikeindicesofmultipledeprivation,itiswhatit

is.It’snationalstatistics,youcan’targuewiththat…Youknowwhatthough,any

dataneedstobetreatedwithcaution.SoIwouldn’ttreatanydatadifferentlyto

anyotherdata.Alldatahasthecaveatthatit’sonlyasgoodastheperson

recordingandpublishingit.Andyouknowsometimesitdoesn’tgodownto

smallenoughlevelsandallthatkindofstatisticalstuff.Butifyouweregoingto

chooseyourdata,certainlytherearesomethingsthatyoucan’targuewith.Bus

routes,indicesofdeprivationandstufflikethat,therobuststuff.Thereissome

stufffromtheONSaboutpeoplebuyinghabitsandthingslikethatthatyouhave

totreatwithcautionbecausethat’speoplereportingit.Andpeoplearen’t

necessarilycorrectinwhattheywritedown.Theirperceptionofrealitymight

notberight,theymightthinkthattheyonlybuythreechocolatebarsaweekbut

actuallytheybuyoneaday.Sotreatthingswithcautionandbereallyselective

abouttheevidencethatyouarecollecting.

L:Wellthankssomuchforthat,you’veansweredthequestionsIhad.Justone

quickquestion:canIuseourconversationformydissertation?

K:Yesnoproblem,gladIcouldhelp!

L:WouldyouminditifIsentyouanemailaboutanythingthatcomesup?

64

K:Yeahnoproblemofcourse.Wellgoodluck.Byebye.

L:Thanks!Bye

Documents

An exploration of knowledge production through GIS in the ... · 09.05.2016 · An exploration of knowledge production through GIS in the context of the UK food desert debate: Constructing