URISA Journal Volume 19 No. 1 2007

Volume 19 • No. 1 • 2007

Journal of the Urban and Regional Information Systems Association

Contents

RefeReed

5 UsingGIStoMeasuretheEffectofOverlappingServiceAreasonPassengerBoardingsatBusStopsThomas J. Kimpel, Kenneth J. Dueker, and Ahmed M. El-Geneidy

13 TeachingbyDoing:PPGISandClassroom-BasedServiceLearningMarc Schlossberg and Darren Wyss

23 WorldwideImpactAssessmentofSpatialDataClearinghousesJoep Crompvoets, Floris de Bree, Pepijn van Oort, Arnold Bregt, Monica Wachowicz, Abbas Rajabifard, and Ian Williamson

33 FromTexttoGeographicCoordinates:TheCurrentStateofGeocodingDaniel W. Goldberg, John P. Wilson, and Craig A. Knoblock

47 AnalyzingtheUsabilityofanArgumentationMapasaParticipatorySpatialDecisionSupportToolChristopher L. Sidlar and Claus Rinner

On the Cover:Millions of people depend on buses for their professional and personal livelihood. For many urban and suburban residents they are the only option for those who sit outside the straining grasp of commuter rail transportation. Buses go where other forms of public transportation are unable to go. They carefully twist and wind their way through narrow streets and around unforgiving corners. Whatever the fee, their thankless goal remains the same: deliver human cargo safe, sound, and on schedule.

There are few worse feelings than missing the bus. A bus only waits so long before it must lurch back into traffic carrying another load of weary passengers on its back. Whoever is left at the stop must sit, wait, and contemplate where things went wrong. The bus then rejoins the commute only to frustrate motorists who

lack the patience for these vehicular beasts of burden. The life of a bus is lumber, stop, idle, start, all the while being brayed at by rush hour traffic. What if there was a better way?

An article by Thomas J. Kimpel, Kenneth J. Dueker, and Ahmed M. El-Geneidy entitled Using GIS to Measure the Effect of Overlapping Service Areas on Passenger Boardings at Bus Stops examines common route problems. The article examines the potential overlap in walking service areas on the demand for bus transit using a geographic information system. The results could lead to better stop placement and more reliable commutes in metropolitan areas. The days of sprinting to catch the bus could be over. Routes will be better structured and based on real, not perceived, population distribution. Technology will help usher in a new era of efficiency.

Hail to the bus driver.

� URISA Journal • Vol. 19, No. 1 • 2007

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Electronic Journal: http://www.urisa.org/journal.htm

URISA Journal • Vol. 19, No. 1 • 2007 �

URISA Journal EditorEditor-in-Chief

Jochen Albrecht, Department of Geography, Hunter College City University of New York

Thematic EditorsEditor-Urban and Regional Information Science

VacantEditor-Applications Research

Lyna Wiggins, Department of Planning, Rutgers University

Editor-Social, Organizational, Legal, and Economic Sciences

Ian Masser, Department of Urban Planning and Management, ITC (Netherlands)

Editor-Geographic Information ScienceMark Harrower, Department of Geography, University of Wisconsin Madison

Editor-Information and Media SciencesMichael Shiffer, Department of Planning, Massachusetts Institute of Technology

Editor-Spatial Data Acquisition and Integration

Gary Hunter, Department of Geomatics, University of Melbourne (Australia)

Editor-Geography, Cartography, and Cognitive Science

VacantEditor-Education

Karen Kemp, Director, International Masters Program in GIS, University of Redlands

Section Editors

Software Review Editor Jay Lee, Department of Geography, Kent State University

Book Review EditorDavid Tulloch, Department of Landscape Architecture, Rutgers University

Article Review Board

Peggy Agouris, Department of Spatial Information Science and Engineering, University of MaineGrenville Barnes, Geomatics Program, University of FloridaMichael Batty, Centre for Advanced Spatial Analysis, University College London (United Kingdom) Kate Beard, Department of Spatial Information Science and Engineering, University of Maine Yvan Bédard, Centre for Research in Geomatics, Laval University (Canada) Barbara P. Buttenfield, Department of Geography, University of ColoradoKeith C. Clarke, Department of Geography, University of California-Santa BarbaraDavid Coleman, Department of Geodesy and Geomatics Engineering, University of New Brunswick (Canada)David J. Cowen, Department of Geography, University of South CarolinaMassimo Craglia, Department of Town & Regional Planning, University of Sheffield (United Kingdom)William J. Craig, Center for Urban and Regional Affairs, University of MinnesotaRobert G. Cromley, Department of Geography, University of ConnecticutKenneth J. Dueker, Urban Studies and Planning, Portland State UniversityGeoffrey Dutton, Spatial Effects Max J. Egenhofer, Department of Spatial Information Science and Engineering, University of MaineManfred Ehlers, Research Center for Geoinformatics and Remote Sensing, University of Osnabrueck (Germany)Manfred M. Fischer, Economics, Geography & Geoinformatics, Vienna University of Economics and Business Administration (Austria)Myke Gluck, Department of Math and Computer Science, Virginia Military InstituteMichael Goodchild, Department of Geography, University of California-Santa BarbaraMichael Gould, Department of Information Systems Universitat Jaume I (Spain)Daniel A. Griffith, Department of Geography, Syracuse UniversityFrancis J. Harvey, Department of Geography, University of Minnesota

Kingsley E. Haynes, Public Policy and Geography, George Mason UniversityEric J. Heikkila, School of Policy, Planning, and Development, University of Southern CaliforniaStephen C. Hirtle, Department of Information Science and Telecommunications, University of PittsburghGary Jeffress, Department of Geographical Information Science, Texas A&M University-Corpus ChristiRichard E. Klosterman, Department of Geography and Planning, University of AkronRobert Laurini, Claude Bernard University of Lyon (France)Thomas M. Lillesand, Environmental Remote Sensing Center, University of Wisconsin-MadisonPaul Longley, Centre for Advanced Spatial Analysis, University College, London (United Kingdom)Xavier R. Lopez, Oracle CorporationDavid Maguire, Environmental Systems Research InstituteHarvey J. Miller, Department of Geography, University of UtahZorica Nedovic-Budic, Department of Urban and Regional Planning,University of Illinois-Champaign/Urbana Atsuyuki Okabe, Department of Urban Engineering, University of Tokyo (Japan)Harlan Onsrud, Spatial Information Science and Engineering, University of Maine Jeffrey K. Pinto, School of Business, Penn State ErieGerard Rushton, Department of Geography, University of IowaJie Shan, School of Civil Engineering, Purdue UniversityBruce D. Spear, Federal Highway AdministrationJonathan Sperling, Policy Development & Research, U.S. Department of Housing and Urban DevelopmentDavid J. Unwin, School of Geography, Birkbeck College, London (United Kingdom)Stephen J. Ventura, Department of Environmental Studies and Soil Science, University of Wisconsin-MadisonNancy von Meyer, Fairview IndustriesBarry Wellar, Department of Geography, University of Ottawa (Canada)Michael F. Worboys, Department of Computer Science, Keele University (United Kingdom)

F. Benjamin Zhan, Department of Geography, Texas State University-San Marcos

editoRs and Review BoaRd

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URISA Journal • Kimpel, Dueker, El-Geneidy �

INTRODUCTIONThis study examines the effects of overlapping bus stop service areas on the demand for transit at the bus stop level. Potential transit demand is measured at the most disaggregate level, in terms of the number of dwelling units per parcel of land. Transit supply is measured spatially at the individual bus stop, and temporally for the morning peak hour of service. A GIS-based approach is used to measure accessibility of dwellings at the parcel level to the nearest bus stop. The distance decay parameters of the acces-sibility function are empirically derived by varying intercept and slope values systematically using ordinary least-squares regression. Demand at the bus stop level, as measured by average morning peak hour boardings, is related by regression to a measure of ac-cessibility-weighted dwelling units that controls for competing bus stops.

Thisexaminationofwalkingdistancetobusstopsfocusesonpotentialtransitdemandfromaresidentialstandpointusingameasureofintegralaccessibility(MakriandFolkesson1999,Song1996).Thestudyfocusesoninboundradialroutesinthemorn-ingpeaktimeperiodservingclose-inurbanneighborhoods—aroutetype,servicedirection,andtimeperiodinwhichdemandisprimarilyassociatedwithresidentialboardings.

Aone-quarter-milewalkingdistanceisawell-knownruleofthumbintransitserviceplanning.Inmostinstances,busstopsarespacedcloserthanaquartermile,creatingoverlappingbusstopserviceareasonthesameroute.Inmanyareas,parallelbusroutesarespacedatdistances less thanone-halfmile,creatingoverlapping service areasbetween routes that oftenoperate atdifferent service frequencies.To control for these overlappingserviceareas,ageographicinformationsystem(GIS)isusedtomeasuretheaccessibilityofeachparceltobusstopswithinwalk-ing distance and the integral accessibility of each bus stop todwellingunitswithinwalkingdistancetothestop.Derivingandincludingdistancedecayparametersintheaccessibilitymeasureisanimprovementovertraditionalmethodsinwhichridership

isrelatedtopotential transitdemandby1)intersectingcensusblockgroupswithbusstopbuffersandusingarealinterpolationtocalculatepopulationor2)countingthenumberofhousingunitswithinstopbuffers.Thesemethodsarebasedonthequestionableassumptionofuniformdensityofdemandtoallocatepopulationorhousingunitstotransitserviceareas.Theapproachusedinthisstudydisaggregatespotentialtransitdemandtothestoplevelandrelatesittoactual morningpeakhourbusboardingsateachbusstopalthoughthedataareaggregatedtoaverageboardingspertripinthemorningpeakhour.

BACKGROUNDA review of the existing literature shows that stop-level transit demand is modeled from a spatial standpoint. Miller and Shaw (2003) stress the need for understanding the underlying spatial assumptions as they relate to GIS transportation analysis. A number of researchers have empirically analyzed walking dis-tance to transit stops (Neilson and Fowler 1972, Levinson and Brown-West 1984, Hsiao et al. 1997, Zhao et al. 2003) based on information derived from passenger surveys. These studies found that the relationship between transit demand and walk-ing distance is expressed as a negative exponential distance decay function. The findings from these studies suggest 1) that passenger demand decreases with respect to walking distance to stops and 2) that a one-quarter-mile bus stop service area will not capture all potential transit users while a larger service area will result in an overestimation of the number of potential riders if distance decay is not explicitly addressed.

GIStechniqueshavebeenusedtorelaxtheassumptionofuniformdensitytoproratepotential transitdemandtotransitserviceareabuffers(PengandDueker1998).Insteadofuniformdensity, O’Neill et al. (1992) used street density, while Zhao(1998)useddwellingunitsfromaparceldatabaseasthebasisforassignment.Also,Zhaoaddressedbarrierstowalkingandusednetworkdistanceratherthanstraight-linedistancetodefinetransit

Using GIS to Measure the Effect of Overlapping Service Areas on Passenger Boardings at Bus Stops

Thomas J. Kimpel, Kenneth J. Dueker, and Ahmed M. El-Geneidy

Abstract: This study examines the effects of overlapping walking service areas of bus stops on the demand for bus transit. This requires controlling for variation in potential transit demand as measured by the number of dwelling units and their loca-tions. A model of passenger boardings for the morning peak hour of service is estimated. Boardings are modeled as a function of potential transit demand at the level of the individual bus stop. To address overlapping bus stop service areas, a geographic information system is used to measure the accessibility of each parcel to each bus stop relative to other accessible stops. A distance decay function is empirically estimated and used to calculate walking accessibility from dwelling units to bus stops. This stop-level boarding model is an improvement over methods in which ridership is typically related to potential transit demand using one-quarter-mile service areas under the assumption of uniform density of demand, often with little or no consideration given to double counting.

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• ArcGIS Extension• ArcIMS Integration• ArcSDE Database Support

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• 3D Modeling

ArcGIS, ArcIMS, ArcMap, ArcSDE, and ESRI are trademarks, registered trademarks, or service marks of ESRI in the United States, the European Community, or certain other jurisdictions.


serviceareasaroundbusstops.WhiletheseGISapproachesservetomoreaccuratelymeasurepotentialtransitdemand,theyarenotrelatedtoactualtransitridership.

Ratherthanusingridershipdatabasedonpassengersurveys,econometricmodelstypicallyuseasamplingofactualpassengerboardings.Mostpreviousstudiesseekingtoexplainthedetermi-nantsoftransitdemandhavebeenconductedateithertheroute(Kemp1981,Horowitz1984,AzarandFerreira1994,HartgenandHorner1997)orroute-segment(PengandDueker1995,Kimpel2001)levels.Stop-leveltransitdemandhasbeendiscussedintheliteratureasbeingthemostappropriatelevelofanalysis(PengandDueker1995,Kimpel2001,Furthetal.2003),andimplementedinT-BEST(Chu,2004).Theuseofautomaticpas-sengercountersattransitagenciesincreasinglysupportsthistypeofmodelingbecauseanabundanceofhigh-qualityridershipdatacanbecollectedatrelativelylowcost.(seeFurthetal.[2003]foradiscussionoftransitdatacollectiontechnologies).

Busstopsaretypicallylocatedandspacedaccordingtoatransitagency’s service standards.Ammons (2001) looked atbus stopspacingstandardsforanumberoftransitpropertiesandfoundthatstopspacingtypicallyrangesfrom656to1,968feetinurbanareas.Suchsmalldistancesbetweenstopsleadstooverlappingbusstopserviceareasonthesamerouteaswellaswithstopsonadjacentroutesservingsimilardestinations.Inpriorresearch,competitionforchoiceriderswasaddressedattheroute-segmentlevelbyPengandDueker(1995)andKimpel(2001)throughdifferentmeans.Intheformerstudy,competitionwasaddressedinthemodelingstageusinganexplanatoryvariablebasedonthepercentareaofabuffersubjecttooverlap.Inthelatterstudy,competitionwasad-dressedduringthedata-processingstagebyproportionallyassign-ingpotentialdemandinoverlappingserviceareasusingsecondaryinformationderivedfromdisaggregatedata(taxparcelvalue)asthebasisforallocation.Oneoftheprimaryreasonsthatstop-leveldemandmodelsarelackingisbecauseoftheexceedinglycomplexdifficultiesassociatedwithallocatingpotentialtransitdemandinoverlappingtransitserviceareastospecificstops.AlthoughtheuseofaGIStosolveproblemsrelatedtotransitaccessibilityisnowfairlycommon,onlyafewresearchershaveadequatelyaddressedoverlappingserviceareasinamannerconsistentwiththeoryandonlyat spatial levelshigherthanthe levelof thebusstop.Alsonotableisthatnoneoftheeconometricstudieshaveaddressedtheissueofdistancedecaybutinsteadhavereliedontheassumptionofauniformdensityofdemandwithintransitserviceareas.Inthepresentanalysis,ratherthanusinganarbitraryone-quarter-mileserviceareabuffer,weuseaninitialdistanceofone-thirdmileandthenapplyadistancedecayfunctionthatispresentedinmoredetaillater.Weutilizeanetwork-basedmethodfordeterminingtransitserviceareasusingaGISandundertakeananalysisthataddressesoverlappingserviceareasthroughmeasurementofintegralacces-sibilityatthetaxparcellevel.

Accessibility is a measure of potential opportunities forinteraction(Hansen1959).Whileaccessibilitycanbecalculatedinvariousways,thegravity-basedmeasureofaccessibilityisthemostwidelyusedmeasureinplanningstudies(HandyandNie-

meier1997).Therelativeaccessibilitytotransitserviceusingagravity-basedmeasureisobtainedbyweightingopportunitiesofattractionfortransitusers(e.g.,servicefrequency)anddiscountingthisattractionbyanegativeexponentialoraGaussianimpedancemeasurebasedondistance.Inthisanalysis,weuseintegralacces-sibilitytotransittoaddresstheoverlapinserviceareas.Integralaccessibilityisthesumofrelativeaccessibilityoverallpossibledestinationsdividedbythetotalattractionofthebusstopbeingstudied(Song1996).

Inadditiontoissuesofoverlappingserviceareasanddistancedecayinstop-leveldemandmodeling,athirdissueconcernsser-vicequantity.Besidesspatialproximitytobusstops,passengersarealsoconcernedwiththeavailabilityofserviceacrossthetemporaldimension(KittelsonandAssociates2003)becauseitinfluenceswaittimesattransitstops.Ameasureofservicequantitysuchasthenumberofbusesperhourpassingagivenlocationisneededtocaptureanyvariationinthelevelofservicebetweenstopsonthesamerouteaswellasbetweenstopsoncompetingroutes.Intheformercase,certainbusstopswillhavehigherservicelevelscomparedtoothersbecauseofvaryingservicepatterns(e.g.,regu-lar,limited,andexpressservice).Inthecaseofoverlappingbusstopserviceareasondifferentroutesservingthesamedestination,choiceriderswouldmostlikelywalktothebusstopassociatedwiththegreaterservicefrequencycertis paribus.ThereviewoftheliteratureshowsthestrengthofGIS-basedmethods,theneedforadistancedecay-weightedmeasureofpotentialtransitdemandatthebusstoplevel,andtheneedtorelatedemandtoautomaticpassengercounter–generatedpassengerboardings.Thisresearchbuildsonthesedevelopmentsandestimatesadescriptivemodelatthedisaggregatelevel—passengerboardingsatbusstopsaveragedoveralltripsinthemorningpeakhour.Thisissimilartoplanningmodels suchasT-BEST,which is a stop-levelmodel thatalsoaggregatestripstotimeperiodsandidentifiespotentialdemandusingabufferingtechnique,butdoesnotaddressdistancedecay.Ourparcel-basedaccessibilitymeasureincorporatesthesizeeffect(numberofhousingunits),thelikelihoodofwaitingatabusstop(scheduledheadway),andadistancedecayfunction.

STUDY DESCRIPTIONThe study uses data from three sources. TriMet, the regional transit provider for the Portland metropolitan region has auto-matic vehicle location (AVL) and automatic passenger counter (APC) technologies on most of the fixed-route bus fleet collecting boarding and alighting information as well as service reliability information at each bus stop. Metro, the regional transportation and land-use planning organization, distributes GIS data for bus stops, bus routes, and tax parcels on a quarterly basis as part of the Regional Land Information System. The Multnomah County tax assessment database was used to obtain information on the number of units associated with multifamily parcels.

Boardingsassociatedwiththemorningpeakhourofservice(7:30A.M.to8:30A.M.)fortworoutesfor69stopswereob-tainedfromTriMet.Theroutesofinterestarethe14Hawthorneand the 15 Belmont, two radial routes connecting southeast


PortlandwiththeCentralBusinessDistrict.Thestudyareaen-compassesaninner-cityareathatiswellservedbybustransitthatiswellpatronized.Ninemonthsofdataassociatedwithweekdayserviceyieldedapproximately126,000datapoints.The studystopswerelimitedtothoselocatedbetweenI-205andS.E.12thAvenue.Stopsthatcouldattractpatronagefromothersourcessuchastransferandpark-and-ridelocationsratherthanthesur-roundingneighborhoodswereeliminatedfromconsideration.

Thestudyareaandthebusstopserviceareaswithinone-third-milewalkingdistancealongthestreetnetworkarepresentedinFigure1.Notetheprevalenceofoverlappingbusstopserviceareasonthesamerouteaswellasbetweenroutes.ThedistributionofdwellingunitsassociatedwithparcelsinrelationtothreebusstopsispresentedinFigure2.Thedifferentcoloredareasrepresentlocationswhereparcelshaveaccesstooneormorestops.

DISTANCE DECAY FUNCTIONZhao et al. (2003) fit a negative exponential function to survey data of walking distance to transit stops. Others use an arbitrary one-quarter-mile service area buffer, in which the probability of demand falls from one to zero at exactly a one-quarter-mile distance. Similar to Vuchic (2005), we posit something in between—that a negative logistic function of the form exp(a – bdij)/(1+exp(a – bdij)) is better suited for distance decay of transit demand to reflect a more gradual decline in transit demand at short distances, a steeper decline as distance approaches one-quarter mile, and a more gradual tail. We estimated the distance decay function by empirically analyzing multiple sets of intercept (a) and slope (b) parameters in a series of ordinary least-squares regression models of transit demand allowing us to identify the parameter set that maximizes goodness of fit. The estimation of the distance decay function utilized distance to the nearest stop and does not include accessibility to more than one stop. The following model specification was used to empirically derive the parameters:

ONSXj = f {DWDUj} (1)where: ONSXj = average passenger boardings per trip at stop j in the morning peak hour over all days;

DWDUj = ∑i (exp(a – bdij)/(1+exp(a – bdij)) * DUi) = the sum of distance-weighted dwelling units associated with stop j ex-pressed as a probability using a negative logistic distance decay function;

where:dij = on-street distance in miles from parcel i to stop j; and

DUi = dwelling units at parcel i.

Theestimatedprobabilitiesforseveralofthelogisticfunc-tionsexp(a–bd),Zhaoetal.’sexponentialfunctionexp(-6.864d),andtheuniformdensityofdemandassumption(UDD)wherep=1ford<=0.25milesandp=0ford>0.25milesareshowninTable1.Figure3showsthisinformationgraphically.

Parametersa=2andb=15wereselectedasthebestrepre-sentationofdistancedecayusingthenegativelogisticfunctionsincethisparticularmodelprovidedthebestfitofthedata.Thisparametersetdepictsasteepdistancedecaypriortoone-quartermile.Atshortdistancestheprobabilityoftakingthebusishigh,whileatdistancesapproachingone-quartermiletheprobabilityislow.

Ourapproachtoestimatingthewalkingdistancedecayfunc-tionisindirect.Thedirectapproachrequiresinformationaboutwhereeachtransitriderlivesandwhichparticularstopheorsheaccesses.Thisknowledgeisoftengainedbymeansofanonboardsurveyoftransitriders;however,thistechniquenormallyyieldssamplesizesthataretoosmallforsubsystemanalyses(e.g.,stop,corridor,orroutelevel).Instead,ourindirectapproachinvolves

Figure 1.Studyarea Figure 2.Overlappingbusstopserviceareas


estimatingthedistancedecayfunctionparametersbyrelatingac-tualboardingstodistance-weighteddwellingunitsbymeansofaniterativefittingprocessusingordinaryleast-squaresregression.

While the model with the exponential function had thehighestR2,theresultsacrossthemodelsdidnotvarythatmuchwithvaluesrangingfrom0.285to0.315.Theparametersa=2andb=15yieldedthebestR2valueofallthenegativelogisticfunctions;however,thelowinterceptvalueofa=2makesourfunctionsimilartotheexponentialfunctionestimatedbyZhaoetal.Ridershipisquitesensitivetodistance,butthevariousmeasuresofdistance-weighteddwellingunitswerenearlyindistinguishable,perhaps because of the simplifying assumption of distance toneareststop.Nevertheless,our“best”distance-decayfunctionisconsistentwithpriorresearchthatshowsdistancedecaythatstartsclose,issteep,andhasalongtail.Similarly,ourestimationdoesnotsupporttheuseofaquarter-milebufferthatiscommonlyused

inGIS-basedanalysisoftransitdemand.Althoughpeoplecanwalkthatdistance,mosttransitridersdonot.Thus,aquarter-miletransitbufferoverestimatesthepopulationthoughttobeservedbytransitandlendssupportforbusstopspacingstandardsthatcallforrelativelyshortdistancesbetweenstops.

ACCESSIBIlITY-WEIGhTED DEMAND MODEl With the empirically estimated parameters for distance decay, another demand model is estimated for the case of overlapping bus stop service areas using a measure of integral accessibility. The average number of passenger boardings per trip per bus stop during the morning peak hour is modeled as a function of potential transit demand at the level of the individual bus stop controlling for overlapping bus stop service areas. Our model controls for variation in potential transit demand as measured by the number of dwelling units and their location (by distance from all bus stops within walking access) as well as the amount of scheduled service provided at stops. The following specification was used for the model:

ONSXj = f {AWDUj} (2)

where:ONSXj = average passenger boardings per trip at stop j in the morning peak hour over all days;

AWDUj = ∑i ((Aij / ∑j Aij) * exp(a – bdij)/(1+exp(a – bdij)) * DUi = accessibility-weighted dwelling units around stop j;

where:Aij / ∑j Aij = integral accessibility or proportion of accessibility at parcel i attributable to stop j;

where:Aij = accessibility of parcel i to bus stop j = exp(a – bdij)/(1+exp(a – bdij)) * BUSHRj * DUi;

where:exp(a – bdij)/(1+exp(a – bdij)) = probability of taking transit based on the negative logistic distance decay function using the parameters a = 2 and b = 15;

Figure 3.Estimateddemandprobabilities

Table 1.EstimatedProbabilitiesforVariousDistanceDecayFunctions

NegativeLogistic

NegativeExponential

UniformDensity

Parameters/Distance

5-23d 4-21d 3-22d 2-22d 2-15d -6.864d UDD

d=0.10mile 0.9370 0.8699 0.6900 0.4502 0.6225 0.5034 1.0000d=0.20mile 0.5987 0.4502 0.1978 0.0832 0.2689 0.2534 1.0000d=0.25mile 0.3208 0.2227 0.0759 0.0293 0.1480 0.1798 1.0000d=0.30mile 0.1301 0.0911 0.0266 0.0100 0.0759 0.1276 0.0000d=0.40mile 0.0148 0.0121 0.0030 0.0011 0.0180 0.0642 0.0000


where:dij = on-street distance in miles from parcel i to stop j;

BUSHRj = scheduled service measured by buses per hour at stop j;

DUi = dwelling units at parcel i; and

∑j Aij = accessibility of parcel i to all stops j within 1/3 mile of parcel i.

Theintegralaccessibility(Aij/∑

jA

ij)ofparcelitostopjis

akeyconceptinthisresearch.Itmeasurestheshareofparcelidemandthatisallocatedtobusstopj,wherethedenominator(∑

jA

ij)measurestheaccessibilityofparcelitoallstopswithin

walkingdistance.Theaccessibilityofparcelstoallwalkingac-cessiblestopsisshownforparcelsassociatedwithbusstop2606inthefirstpanelofFigure4.Thesecondpanelshowswalkingaccessibilitytostopnumber2606withoutconsideringoverlap.More intense colors indicate a combination of nearness anddensity.ThethirdpanelofFigure4takesoverlappingbusstopserviceareasintoconsideration.Thethirdpanelshowstheeffectofapplyingintegralaccessibility(A

ij/∑

jA

ij)ofstop2606times

theaccessibility(Aij)ofstop2606,theresultofwhichwecall

accessibility-weighteddwellingunitsatparcel i attributable tobusstopj(AWDU

ij).

Thenumberofdistance-weighteddwellingunitsforthe69studystopsaccordingtotheuniformdensityofdemandassump-tion, thenegativeexponential functionderivedbyZhaoetal.(2003),thenegativelogisticfunctionusingtheparametersa=2andb=15,andthesamenegativelogisticfunctioncontrollingforintegralaccessibilityareshowninTable2.Byincorporatingdistance decay, potential transit demand is shown todecreasebyafactorofapproximately2xusingthenegativeexponentialfunctionandthetwonegativelogisticfunctionsrelativetothetraditionalone-quarter-milebuffermethod.Potentialdemandishigherrelativetothenegativeexponentialdecayfunctionforthenegative logistic functionusingnearest stop criterion andlowerbasedonthenotionofintegralaccessibility.Theseresults

areaggregatedoverall69studystopssoconsiderablevariationinpotentialdemandatanygivenstopmayexist,dependingonwhichparticulardistancedecayfunctionisused.

Table3containsthedescriptivestatistics forthevariablesusedintheaccessibility-weighteddwellingunitmodelandtheother comparativemodels.Table4 contains the results of theregressions.

TheresultsinTable4showthattheaccessibility-weighteddwelling unit (AWDU) model performs better than do thecomparisonmodels.

Theparameterforthenumberofdwellingunits,control-lingforintegralaccessibility,0.0147boardingsperaccessibility-weighteddwellingunit,isusedtoestimatemorningpeakhourboardings at stopsonaper-tripbasis for countsof accessibil-ity-weighteddwellingunits.The resultsof this simulationareshowninTable5.

CONClUSIONSThe research examined the determinants of transit boardings, taking advantage of automatically collected passenger data at bus stops. A tax parcel layer database was used as the basis for calculating potential transit demand at each stop using the

Figure 4.Measuresofparcelaccessibility

Table 2.Distance-WeightedDwellingUnits

Decay Function Assumption Distance (feet) UnitsUDD Nearest stop 1,320 10,854DWDU (Neg. Exponential) Nearest stop 1,760 4,937DWDU (Neg. Logistic) Nearest stop 1,760 5,601AWDU (Neg. Logistic) Integral accessibility 1,760 4,266

Table 3.DescriptiveStatisticsforAccessibility-WeightedDwellingUnit(AWDU)ModelandComparisonModels

Name Mean Std. Dev. Var. Min. MaxONSX 0.92 0.68 0.46 0.02 2.76UDD 157.36 85.87 7374.40 17.00 391.00DWDU (Neg. Exponential) 71.55 38.92 1515.00 10.19 194.53DWDU (Neg. Logistic) 81.18 44.38 1969.10 11.33 210.36AWDU (Neg. Logistic) 61.83 28.24 797.30 16.86 150.16

10 URISA Journal • Vol. 19, No. 1 • 2007

Table 4.ModelResultsforAccessibility-WeightedDwellingUnit(AWDU)ModelandComparisonModels

Coef. Std. Err. T-ratio Adj. R2 ConstantUDD 0.0044 0.0008 5.5714 0.3064 0.2167DWDU (Neg. Exponential) 0.0099 0.0018 5.6833 0.3152 0.2049DWDU (Neg. Logistic) 0.0086 0.0015 5.5939 0.3082 0.2159AWDU (Neg. Logistic) 0.0147 0.0069 6.3350 0.3652 0.0069

Table 5.SimulationofStop-LevelBoardingsUsingAccessibility-WeightedDwellingUnits

Accessibility-Weighted Dwelling Units Per Stop Estimated Stop-Level Boardings Per Trip During Morning Peak Hour 25 0.368 75 1.104 100 1.472 150 2.207

measure of integral accessibility that takes into consideration distance-weighted accessibility and competing stops. The analysis was confined to the morning peak hour, when transit demand is most directly related to dwelling units.

DatapreparationrequiredtheuseofaGIS,whichconsistedof snapping dwelling units from parcel centroids to abuttingstreets,computingdistanceonthestreetnetworktoallbusstopswithinone-third-miledistance,computingintegralaccessibilityofdwellingunitstothosestops,andsummingtheintegralacces-sibilityofdwellingsforeachbusstop.

Distancedecayparametersoftheaccessibilityfunctionwereempiricallyderivedfromordinaryleast-squaresregressionmodelsbyvaryinginterceptandslopevalues.Theseparameterswerethenusedtoestimateastop-levelbusboardingmodelusingaccessibil-ity-weighteddwellingunits.Thenumberofaccessibility-weighteddwellingunitsispositivelyrelatedtothenumberofboardingpas-sengers.Theparameteronthisvariablecanbeusedtoestimatemorningpeakhourtransitridershipatthebusstoplevel.

Thisresearchillustratesthepowerofanalysisusingdetaileddisaggregatedata,boardingsatthebusstoplevel,andforparcel-levelcountsofdwellingunits.AGISanalysiswasneededtorelatedwellingunitstothestreetnetworkandtocalculatedistancestobusstops.Adistancedecayfunctionwasderivedandusedtocomputeanaccessibilitymeasuretoaccountforoverlappingbusstopserviceareasforanimprovedestimationofstop-leveltransitdemand.

Itisimportanttonotethatdistancedecayparametersmaynotbeconstant;theymayvarybytrippurposeandaccessmode.In the future, it is recommended that more reliable distancedecayparametersbeestimatedfrompassengerinterceptsurveysconductedatbusstops.Thesesurveyscanasktransitusersabouttheirpointoforigin,trippurpose,destination,accessmode,andwhethertheywillundertakeatransfer.Itisexpectedthatdecaycurveparameterswillvarybasedonthesefactors.Accordingly,abettertransitdemandmodelcanbegenerated.

About The Authors

Thomas J. KimpelisaresearchassociateintheCenterforUr-banStudies,PortlandStateUniversity,wherehehasbeenemployed since 1996. His areas of interest include GISanalysis, transportation and land-use planning, and bustransitperformancemonitoring.

CorrespondingAddress:ThomasJ.KimpelCenterforUrbanStudiesPortlandStateUniversity506S.W.MillStreet,Room350Portland,OR97201Phone:(503)725-8207,Fax:(503)725-8480E-mail:[email protected]

KennethJ.Dueker,ProfessorEmeritusofUrbanStudiesandPlan-ning,PortlandStateUniversity,isanexperiencededucatorandresearcher intransportation. HedirectedtheCenterforUrbanStudiesatPSUfrom1979to1998.Hisareasofinterest include transportation and land-use interactions,travelandparkingbehavior,andGIStransportation.

CorrespondingAddress:KennethJ.DuekerCenterforUrbanStudiesPortlandStateUniversity506S.W.MillStreet,Room350Portland,OR97201Phone:(503)725-4040,Fax:(503)725-8480 E-mail:[email protected]

URISA Journal • Kimpel, Dueker, El-Geneidy 11

AhmedM.El-GeneidyisapostdoctoralresearchfellowintheDepartmentofCivilEngineeringandtheHumphreyInsti-tuteofPublicAffairs,UniversityofMinnesota.HeformerlyworkedundertheauspicesoftheCenterforUrbanStudiesasagraduateresearchassistant.HisareasofinterestincludeGIS analysis in transportation planning and bus transitperformancemonitoring.

CorrespondingAddress:AhmedM.El-GeneidyDepartmentofCivilEngineeringandHumphreyInstituteofPublicAffairsUniversityofMinnesota500PillsburyDriveS.E.Minneapolis,MN55455E-mail:[email protected]

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URISA Journal • Schlossberg,Wyss 1�

INTRODUCTIONGIS is much more than making static maps or representing com-plex data in simple map form; it is also a tool that can facilitate bottom-up participatory decision making. Many organizations, mainly nonprofit and advocacy groups, have begun to utilize GIS in this way, but more mainstream GIS users, such as municipal governments, continue to view GIS in the same top-down data synthesis and presentation model of the past. Part of this discon-nect in uses can be traced to the types of GIS education that most students receive that emphasize technical skills over the context within which those skills can be applied. Public participation and GIS (PPGIS) represents much more than a set of technical skills; it represents a suite of concepts that incorporates both the technical use of GIS and the larger contextual elements of par-ticipation, policy making, and social change. For these ideas to be successfully implemented in the workplace by knowledgeable practitioners who realize the potential of participatory decision making, this knowledge should be cultivated in students.

ThispaperdescribestheeffortsofonecoursethatstrivestoteachPPGIStostudentsfrommultipleperspectives.Theclass,“AppliedGISandSocialPlanning,”isafive-credit,mixedunder-graduate/graduatecoursethatcombinestraditional,intermediate-levelGISlabswithaneighborhood-basedservice-learningprojectand lectures on social change, PPGIS, and community-basedresearch.Moreover,theclassfocusesontheuseofnewmobileGIStechnologyasawaytofacilitatecommunity-basedparticipa-toryGIS,aswellastogivestudentsexperienceinanemergingGIStechnology.

Theremainderofthispaperisorganizedintothreeprimarycomponents:thedescriptionandrationalethatunderpinsthis

course,adiscussionabouttheclassandservice-learningproject,andtheevaluationoftheproject’simpactonstudentlearning.Mostservice-learningevaluationsfocusonthebenefitsthataccruetothecommunity,butgivenourinterestinteachingPPGISef-fectivelytostudents,wewerecuriousabouttherelativebenefitofincorporatingcommunity-basedGISworkaspartofthenormalcourserequirementsintermsofteachingPPGISconcepts.

CONTEXTMany professionals in the planning field have identified public participation as an important aspect of the planning process. This is particularly true at the local level where neighborhood residents need to be empowered to help develop ideas and plans that reflect the wishes of the community (Jones 1990). Many dif-ferent approaches to participation have been taken in the past, but recently there has been an interest in a bottom-up approach that puts more of the planning process in the hands of the residents. This bottom-up approach to planning has helped to generate an increase in research surrounding the topics of public participation GIS (PPGIS) and community-based research (CBR). An aspect of PPGIS seeks to make GIS technology and training accessible to local residents as an empowering tool to use in the decision-mak-ing process, while CBR emphasizes the inclusion of community members as research partners to improve the practicality and responsiveness to local needs.

Public Participation GISThe phrase public participation GIS (PPGIS) comes to the GIS community from the planning profession (Obermeyer 1998). The

Teaching by Doing: PPGIS and Classroom-Based Service learning

Marc Schlossberg and Darren Wyss

Abstract: As geographic information systems (GIS) continue to be used as tools for participatory decision making, it becomes increasingly important to teach the next generation of GIS users about public participation GIS (PPGIS) ideas, concepts, and skills. This paper describes an effort to teach a GIS course that utilizes PPGIS, community-based research notions, and ser-vice-learning ideas as core concepts in teaching intermediate-level technical skills in GIS. The class, “Applied GIS and Social Planning,” is a mixed undergraduate/graduate course that combines traditional, intermediate-level GIS labs with a neighbor-hood-based service-learning project and lectures on social change, PPGIS, and community-based research. Moreover, the class focuses on the use of new mobile GIS technology as a way to facilitate community-based participatory GIS, as well as to give students experience in an emerging GIS technology. This study utilized six different instruments to collect data from students to evaluate this applied approach toward learning GIS in general and PPGIS in particular. In general, students found that the community-based PPGIS project was an overall positive learning experience for both technical skill development and in ap-plying PPGIS theory to practice, that more community interaction and involvement with project planning would enhance the experience, and that learning and applying PPGIS in a course context gives students an insight into the long-term and complex approaches needed to help facilitate local community change.


phrase can be used to describe a “variety of approaches to making GIS and other spatial decision-making tools available and acces-sible to all those with a stake in official decisions” (Obermeyer 1998). PPGIS embodies the desire to utilize the capacity of GIS to engage and empower the public because planners realize the critical importance of community input in decision making. How-ever, PPGIS approaches often differ from project to project, from Internet-based map servers to field-based development methods. Because of this variability, Schlossberg and Shuford (2005) rec-ommend that “understanding how specific publics are linked to specific types of participation is an important effort to undertake so that users of PPGIS ideas can appropriately characterize, utilize, implement, and evaluate their PPGIS efforts” (15).

AlthoughnoconsensushasbeenreachedonacleardefinitionofPPGIS, thedebatehasprogressed intoamoreconstructiveresearchengagementinwhichcommunityempowermentthroughGISisastatedintention(HarrisandWeiner1998).Whatschol-arsandpractitionersseeincommonisthatGIScanfacilitateabroadersetofparticipantsintheplanningprocessbecauseofitsvisualorientationwhenaddressingspatial issues(Al-Kodmany2001).Thisprocessof spatially investigatingan issue throughPPGIScanproducepositivereturnsingroupdynamics,consensusbuilding,andjointplanning(SchlossbergandShuford2005),althoughparticipatoryGISitselfexistsinamurkyareabetweenfieldsandgoals,“oftenwithcontradictoryimplications,priorities,andoutputs”(Elwood2006a,197).

Toensure therealizationof thepositivereturnsofPPGIS,Leitneretal.(2002)formulatedsixmodelsforsuccessfullymak-ingGIS available to communityorganizations.The sixmodelsare: community-based (in-house) GIS, university-communitypartnerships,publiclyaccessibleGISfacilitiesatuniversitiesandlibraries,maprooms,Internetmapservers,andtheneighborhoodGIS center.Eachmodel inherently contains certain advantagesanddisadvantages,buttheuniversity-communitypartnershipisofparticularinterestbecauseofthepossibilityofaddingthecom-ponentofservicelearningtotheproject.ThisthreadofPPGISisoftenoverlookedandprovidesaninterestingmodelofbuildingcommunitycapacityandempowerment.SawickiandPeterman(2002)suggestthat“anidealPPGIScouldbewhereneighborhoodresidentscollecttheirownspatialdataandprocessitthemselvesusingGISsoftware.”Service-learningPPGIScouldbeasteptowardthat“ideal,”wheretheinitialuniversity-communitypartnershipmayleadtocommunityempowermentandself-sufficiency,ormayleadtoanongoingrelationshipbetweentheuniversityandcom-munity,butarelationshipbasedonsharedbenefit.

Whileconductingservice-learningGIScanbeimportantforthecommunity,evaluatingtheeffortcanbehelpfulforfutureinstructionforstudents.Jordan(2002),however,foundthatPP-GISevaluationisoftennotconductedwithenoughrigor,makingitdifficultforotherstoproperlylearnfrompastefforts.Barndt(2002),onobservingtheroleofGISasatoolforparticipation,developedasetofcriteriafortheevaluationofPPGIStoencour-ageamorerigorousevaluationprocess.Thefocusofthecriteriaisonthevalueoftheprojectresults,particularlyforthecommunity;

however,whenconsideringthemodelofuniversity-communitypartnershipsandtheroleofservicelearninginPPGIS,thisformofevaluationonlytoucheshalf(thecommunity)oftheparticipantsinvolved.Thestudentsareinvolvedtoprovideaservice,butalsotogaineducationalvaluefromtheprocess.Usingtheservice-learn-ingprincipleofreflectioncouldhelptounderstandthebenefitstothestudents(Leitneretal.2002,JoerinandNembrini2005).“Service learning involves faculty and students inproviding aservicetothecommunity,suchasdevelopingaGISapplicationbasedonacommunityrequest,andthenreflectingonthelessonslearnedfromtheexperience.Itsprimarygoalistoenhancelearn-ingthroughtheserviceexperiencewithlessemphasisonchang-ingsocialsystemsorgeneratingnewknowledgealthoughitcanprovidetheopportunityforbothtohappen”(Leitneretal.2002,XX).ThisprocessalsoincreasesthestudentsappreciationforthecommunityusageofGISthroughobservationandunderstandingofhowcommunitiesdeveloptheirownspatialnarrativeswithinaparticipatoryGISendeavor(Elwood2006b).

Community-Based ResearchThe concept of community-based research (CBR) is predicated on including the community members as research partners and active participants in a community-based project (Checkoway 1997). This emphasis on the participation and influence of nonacademic researchers in the process of creating knowledge is what Israel (1998) identifies as the fundamental characteristic of CBR. Viewing the community as a social entity instead of simply a place or setting in which community members are not actively involved is the critical distinction between CBR and other research processes (Hatch et al. 1993).

The more traditional “professional-expert” model, whereprojectdecision-makingpowerisconcentratedinthehandsofthe researcher, oftenproduces results that are impractical andunresponsivetolocalneeds(Whyte1989).CBR,byinvolvingthecommunityintheresearchprocesses,attemptstoovercomethe “professional-expert” shortfalls. For example, equitableparticipationandsharedcontroloverallphasesoftheresearchprocessisagoaltostrivetoachieveforbeneficialresults(Greenetal.1995).Aparticipatorybottom-upapproachinvolvesthecommunitythroughouttheprocess,fromidentifyingtheissuesexaminedtoparticipatingindatacollectiontoanalysisanddis-cussionoftheactionsteps(Heskin1991).Thisempowermentapproachcanleadtogreatercommunityownershipoftheproj-ectandsignificantlyincreasetheparticipationoflocalresidents(Reardon1998).Additionally,CBRcanconnectcommunitieswithuniversity knowledge, a potentially local resource that isoften difficult or confusing to access by the local community(Checkoway1997).CBRutilizedasaservice-learningactivityhelps improve communication with constituencies, increasesthe accessibilityof knowledge, andbuilds support foruniver-sity-communitypartnerships thathelphighereducation fulfillitsresponsibilitiestosociety(Checkoway1997).Theseareveryimportantaspectsofthescholarshipofintegration,application,andteaching(Boyer1994).


Service LearningThe term service learning has come to be applied to a wide range of activities, from tutoring programs across grade levels to commu-nity tree plantings, and with students from kindergarten through higher education (Waterman 1997). Although no agreed-on defi-nition of service learning exists, the basic requirement is a service experience that is both personally meaningful and beneficial to the community (Pritchard 2002). The Corporation on National and Community Service, an independent federal agency that supports volunteering and community service nationally, suggests four key components to service learning, which form the theoretical basis for this research. Service learning is a method:1. under which students learn and develop through active

participationinthoughtfullyorganizedserviceexperiencesthatmeetactualcommunityneedsandthatarecoordinatedincollaborationwiththeschoolandcommunity;

2. thatisintegratedintothestudents’academiccurriculumorprovidesstructuredtimeforthestudentstothink,talk,orwriteaboutwhatthestudentsdidandsawduringtheactualserviceactivity;

3. that provides students with opportunities to use newlyacquiredskillsandknowledgeinreal-lifesituationsintheirowncommunities;and

4. thatenhanceswhatistaughtinschoolbyextendingstudentlearningbeyondtheclassroomandintothecommunityandhelpstofosterthedevelopmentofasenseofcaringforothers(NationalandCommunityServiceActof1990,5).

Thebasicideabehindservicelearningistouseacommunityorpublicserviceexperiencetoenhancethemeaningandimpactof traditional course content (Sax and Astin 1997). Dewey(1916)viewedthecommunityasanintegralpartofeducationalexperiences,becausewhatislearnedintheschoolmustbetakenandutilizedbeyonditsbounds,bothfortheadvancementofthestudentandthebettermentoffuturesocieties.Dewey(1956)laterhelpedadvancetheviewthatactivestudentinvolvementinlearn-ingwasanessentialelementineffectiveeducation.Service-basedlearninghasbeenshowntobeaneffectiveeducationalapproachto improve student learning (Markus, Howard, and Peterson1993;Boss1994;CohenandKinsey1994)andcarefullydesignedservice-learningexperiencescanleadtoprofoundlearninganddevelopmentaloutcomesforstudents(McEwen1996).

Intermsofservice-learningoutcomes,SaxandAstin(1997)foundthatthereal-worldvalueofserviceparticipationrevealsitself in thepositive effects observed in three areas of studentsatisfaction:leadershipopportunities,relevanceofcourseworkto everyday life, and preparation for future career.They alsoidentified additional benefits in terms of a number of collegeoutcomes,includingstudents’commitmenttotheircommunities,skillsinconflictresolution,andunderstandingthecommunityproblems—allskillswewouldhopethatfuturePPGISpracti-tionerswouldhold.

BACKGROUND: ThE COURSE These skills are being taught in the course, “Applied GIS and So-cial Planning,” a mixed, five-credit, undergraduate/graduate class taught during a one-term quarter (ten weeks) with enrollment usually between 12 to 15 students. Offered each fall, the course is a regular intermediate-level GIS course for students across campus, with priority given to students in the home department of Plan-ning, Public Policy, and Management. Students are expected to have taken an Introduction to GIS course or have an equivalent level of knowledge prior to enrolling in this course, and such skill level is assessed during the first week of class. The class meets six hours per week, with two of those hours dedicated to lectures and discussions and the other four dedicated to GIS lab work. The class is essentially divided into four primary components, each of which is discussed more fully in the following sections: technical GIS skills, theory and practice of PPGIS, applied service-learning experience, and individual projects.

Technical GIS Skills This class is an intermediate-level GIS course and teaches a va-riety of technical skills, including network analyses, a variety of more advanced spatial analyses, analysis of census data, and an introduction to three-dimensional modeling. Moreover, there is a significant focus on mobile GIS technology, both on operating GIS on a personal digital assistant (PDA) and in creating custom-ized data-entry interfaces for field-based data collection.

Theory and Practice of PPGIS Unlike many GIS courses, the lecture component of this course does not cover the theoretical underpinnings of GIS science skills. Rather, discussion time focuses on the environment in which GIS can be applied, with a special emphasis on social and participatory applications. Students have an extensive reading list and in-class discussions based on those readings include social planning, community-based research, PPGIS, and social equity and empowerment. Short two-page thought papers are assigned to give students an opportunity to think about these more con-text-oriented issues and how they relate to the use of a technical tool such as GIS.

Applied Service-Learning ExperienceAll students are required to participate in a community map-ping service-learning project that is ongoing throughout the entire term. As mentioned in more detail in a following section, this component includes attending neighborhood meetings (in Eugene, Oregon) and partnering with a neighborhood resident to collect community data to train that community member in data collection, and to build goodwill between the university and the community.

Individual ProjectsFinally, each student is required to conduct an individual and original GIS analysis. Students may choose to use the community

1� URISA Journal • Vol. 19, No. 1 • 2007

project data as an input to their individual projects, or students can choose to work with other community organizations or com-munity issues for their projects. The project emphasizes that GIS is a tool in understanding some larger question or issue, and, ac-cordingly, students are required to write reports and make public presentations of these larger efforts.

PARTICIPATORY GIS IN PRACTICE: ThE WUN MAP PROJECTStudents in the class are able to translate the theory and discussions about PPGIS to practice in the classwide-applied service-learning project. Planning and carrying out this community-based project occurs throughout the term, some of which is in direct collabora-tion with the community and some on behalf of the community. The basic goals of this part of the course are:1. The project should be of immediate value to the

neighborhood.2. Theprojectshouldbesmallenoughinscopetoensuresuccess

attheend.3. Thestudentsmustbeabletogaintangibleskills.

TheWUN MAP (pronounced one map) project, whichstands for theWest University Neighbors Mapping Project,occurredduringthefall2004term.1TheWUNMAPprojectwasbornfromtwokeyeventsthattookplacealmostsimultane-ouslyduringthespringof2004,aboutfivemonthspriortotheclass.ThefirsteventwaswhenthechairoftheWestUniversityNeighbors(WUN),acity-sanctionedneighborhoodassociation,contactedtheUniversityofOregonseekingassistanceinvisual-izingtheneighborhoodinsomeway.Therequestsoughttocreateameansofincreasinginvolvementintheneighborhood,improv-ingtheneighborhoodforresidents,andatthesametimemakinguseofthevastresourcesattheuniversity.Eventually,theWUNchairwasplacedincontactwiththeDepartmentofPlanning,PublicPolicy,andManagement(PPPM)becauseofitsinterestinsocialplanning,empowerment,andGIS.Itisimportanttonotethattheinitiationoftheprojectcamefromtheneighborhooditselfandnotfromtheteacher/researcherofthecourse.Oftenincommunity-basedworksuchasthis,“experts”atuniversitiesseek to impose solutions on neighborhoods rather than workcollaborativelywithneighborhoods(Checkoway1997).Thattheprojectwasneighborhood-driveninthefirstplaceestablishedagoodfoundationforajointPPGISeffort,andonethatcanflipthe research university paradigm where community partnerswouldberegardedas“researchpartnersandactiveparticipantsinknowledgedevelopmentratherthanashumansubjectsandpassiverecipientsofinformation”(Checkoway1997,310).

Ataboutthesametimeasthecontactbytheneighborhood,PPPMwasawardedasmallclassroomtechnologygrantthatallowedthisintermediateGIScoursetodevelopanewteachingcurriculumaroundcommunity-basedGISandtheuseofmobile,PDA-basedGIS.Thisgrant,togetherwiththeinterestfromtheneighborhood,ledtotheformationofacourse-basedservice-learningproject.

ThE PlANNING OF WUN MAP The class is taught in the fall, and because the course is only ten weeks in length, considerable planning for the project occurred in the summer prior to class. As the instructor and teaching assistant for the course, we met with the chair of the WUN group several times to explore the type of joint project that would make sense for all involved and we established the following three points during our discussions:1. Controloverthebasicstructureandcontentoftheproject

wouldbeinthehandsofneighborhoodresidents.Theeffortwouldbebasedontheneighborhoodinvitingtheclasstoparticipate.

2. Asaclass-basedexercise,theeducationalvaluetothestudentswasessential.

3. Theprojectshouldbeviewedasanopportunitytoestablishpositiveuniversity-communityinteractionswhereeachcouldderivebenefitfromtheproject.

Oncetheneighborhoodformallyinvitedusin,thedevelop-mentandplanningoftheprojecthappenedoverthecourseofregularmonthlyWUNmeetings.Ofprimaryimportancewasthatresidentschosewhatdatawastobecollected.Aftertakingintoaccountthesizeandlayoutoftheneighborhood,theamountoftimethatwouldbeavailableforcollecting,andthenumberofpossible student-resident teams, theneighborhooddecidedonmappingthelocationofthreekeyassets.

Public Street Trees.Theneighborhoodwas interested inknowingwherethetreesinthepublicright-of-waywere,aswellassomebasicfactsaboutthem.Theirinterestinstreettreesstemsfrom their desire to protect trees in their neighborhood.Theprimaryattributeofinterest,therefore,wastreediameterbecausetreesgreaterthaneightinchesindiameterhaveadifferentandstrongerlegalstatus.

Streetlights.The neighborhood has a spatially unequaldistributionofstreetlights,whichcanimpactsafety.Ofequalinterest to the neighborhood was where “traditional” or old-fashionedpedestrian-orientedandstyledstreetlightswere.Onceresidentsknowwherethesecommunityassetsareclustered,theycanbeginthinkingaboutstrategiestousethemforadditionalcommunity-buildingactivities.

Visible Dumpsters. In addition to detached residentialhousing, the neighborhood has many multiunit apartmentbuildingsandsomebusinessesthatutilizeDumpstersforgarbagecollection.Insomeinstances,theseDumpstersarehighlyvisiblefromanywalkingpath,detractingfromtheviewshedthroughouttheneighborhood.Moreover,theDumpstersareoftenmisused,furtherimpactingthe“feel”ofthecommunity.

Wedecided thatoneweekendday,preferablyaSaturday,wouldbededicatedtobringingstudentsandresidentstogethertocollectneighborhooddata.Thefinalstepsweretopublicizethedata-collectiondayeventandtodevelopthedata-collectioninstrumentusingArcPad,amobileGISsoftwareprogram.2Theprocessofcreatingandusingthedatainstrumentwasdevelopedinto an in-class lab exercise for students to learn anddevelop


thenew skills andunderstandingof themobileGIS technol-ogy,anditalsoallowedstudentstoparticipateinatestruntofamiliarizethemselveswiththedata-collectioninstrumentsbeforeparticipatinginthedata-collectionday.Conceptsofinstrumentdevelopmentandpretestingwereincorporatedintothelearningconceptsoftheclass.

ActualdatacollectiontookplaceononeSaturdaythatbeganwithcomplimentarycoffeeandpastriesandincludedacompli-mentarypizzalunch,bothfromneighborhoodshops.Althoughthestudentswere required toattend,neighborhoodparticipa-tionreliedonvolunteers,andfreefoodisalwaysagoodwaytogethelp.Moreimportant,thesocialtimeaffordedduringthesemealsallowedstudentsandresidentstomeetoneanotheranddevelopaninitialtrustandbondthatwouldservethemwellfortheprojectandforalarger,althoughunspoken,goaloffosteringgoodcommunity-universityrelations.

Theneighborhoodwasdividedinto12sections,andstu-dent-residentteamswereresponsibleforcollectingthethreesetsofdatainonesectioneach.Ofthe12teams,sixusedPDAstocollectdataandsixteamsusedpen-and-paperdata-entryforms.Thetotal timecommitmentofthedata-gatheringdaywassixhoursandthestudentsspentanothertwohoursenteringdatathathadbeengatheredwiththepaperinstrument.

The final phase of the project included several studentscreatingmaps that represented thedata in variousways.Onestudentcreatedamaptemplatethatwasusedtocoordinatethelayoutofallthemaps.Intheend,approximately80mapsweregiven to theWUNgroupusingaconsistentandcartographi-callypleasingformat.Thesemaps,alongwiththerawdata(inspreadsheetandGISformats)wereuploadedtoaprojectWebsite,freetouseandmanipulateasanyoneseesfit.Thesemapsanddatahavesubsequentlybeenusedbytheneighborhoodtolobbyvariouscitydepartmentsonavarietyofdecisionsthataf-fecttheneighborhood.Inoneexample,thepresenceofthemapsandneighborhoodknowledgeputpressureonthecity’surbanforestertobeginaneffortofdatacollectiononthecity’streesthatwasmoredetailedandaccuratethanwhatthecommunityproject gathered.While there were other community benefitsthataccruedfromthisproject,theremainderofthispaperwillfocusonthevalueofthisPPGISeffortonstudentlearningandexperienceintheclassroom.

PPGIS AND STUDENT lEARNINGMany service-learning projects are evaluated based on the out-comes for the community, but we were interested in the outcomes for students. Specifically, we wanted to know whether this type of applied PPGIS project added to students’ GIS skill set, afforded students a beneficial learning opportunity, what the opportunity costs for including a service-learning component to class was in terms of time away from technical-skill building, and how the project could be improved, if indeed it is worthwhile. Using recommendations of Bradley (1997), we used six different instru-ments to collect project evaluations from students:

Preproject questionnaire.Thepreproject questionnairewasdistributedtostudentsthemorningofthedata-collectionday.Thequestionnaireintendedtogaugestudentexpectationsandfeelingsaboutparticipatingintheproject.

Postproject questionnaire.Thepostprojectquestionnairewasdistributed students on the completion of the field-collectionactivity.Thestudentstookthequestionnaireshomeandreturnedthem during class the following week.The questionnaire wasdesignedtoinducereflection,acriticalpartofservicelearning,fromthestudentsontheirparticipationintheproject.

Focus group. A focus group was organized approximatelyoneandahalfmonthsaftertheclassendedandwasledbytwoneutralfacilitators.Thediscussioncoveredawiderangeoftopics,fromtheeducationalbenefitsoftheprojecttosuggestionsforimprovement,andthefullparticipationbyallstudentscreatedalivelyandenergeticdialogue.

Outcome survey. A survey that addressed key elements ofservice-learningtheorywasthensenttostudentsbasedonthemesthat emerged from the focus group.Questions includedbothopen-endedandclosedLikert-scaledquestions.

One-on-one interview.These interviews were conductedabout threemonths followingtheendof thecourseandweredesigned to provide an additional means of reflection for thestudentparticipantsandtoallowformorein-depthdiscussionaboutthepersonaloutcomesforeachparticipant.Conductingtheinterviewsthreemonthsaftertheendoftheclasspermittedthestudentstohavesteppedaway,completedanothertermofcoursework,andhavetimetothinkabouttheexperience.

Participant observation.Wewere involved in allphasesoftheproject,fromplanningtoimplementationtoevaluation,andactedasparticipantobserversduringtheprocess.Thisconstantconnectionwiththeprojectallowedustoobservestudentinterac-tionsandreactionsandtoholdcandidconversationsabouttheirinvolvementintheprojectalongtheway.

Thesevariedapproachesproducedinformationpertainingto student expectations, learning, and recommendations, andbecauseofthemultiplemethods,weareconfidentinthereliabilityofthestudentassessments.

STUDENT REFlECTIONSAfter collecting and analyzing the data derived from the methods previously discussed, four primary findings of student outcomes emerged:1. The classroom-based PPGIS project provided a positive

learning environment that the students felt was worthwhile to their educational experience.Theopportunitiesto interact,communicate, and share ideas and knowledge was animportantcomponentoftheprojectforthestudents.Veryfewopportunitiesexistinmostclasses,especiallyGISclasses,forthestudentstoworkonareal-worldproject,particularlyinvolvingpersonal,hands-oninteractionwithacommunitygroup. One student commented, “I enjoyed the teambuildingaspectofit.LearningGISandsharingskillswith


others(wewerenotallexperts,buthelpedoneanothertobeefficient)wasawesome.”Thisexperienceoflearningandusinganewtechnology,notonlyforstudentbenefitbutalsoforthecommunity,wasempoweringforthestudentsandhelpedtocreateasenseofpurposefortheirwork.

Participantsexpressedsatisfactionwiththeopportunityto build communication skills outside of the university,whichincludedexplainingGIStotheresidents.Thisreal-world application of learning the software and applyingthose skills worked well for learning through action, notjust reading. One student commented, “The communityinvolvementaspect,listeningtotheneedsofthecommunity,wasagoodcomplementtotheplanningprogram.”Thosewhoattendedneighborhoodmeetingsexpressedtheaddedbenefit of witnessing the “cynic factor” of those opposedto the project and the work needed to compromise andaccommodate.

2. The classroom-based PPGIS project was of appropriate structure to learn ArcPad and practice the concepts of PPGIS and CBR.Theinherentpurposeofthesoftwareistocollectfielddata,sothehands-onaspectoflearningitwasverybeneficialtounderstandingtheworthandutilityofthetool.Participatingintheprojectalsoallowedtheclassroomconceptstobebetterunderstoodthroughimplementation.Studentparticipantswere able to witness and make connections between thereadings and the project. It provided the opportunity toexperiencetheimportanceofplanning,collaborating,andcompromisingwhendevelopingandimplementingapublic-participationendeavor.Onestudentcommented,“Iwasabletoseethedividebetweenlettingthepublicchoosesubjectsversus the researcher seeing things that shouldhavebeendone,butthatcouldhavejustbeenthelimitsofresidentsnotunderstandingwhatcouldhavetakenplaceandtheneedtoeducatethem.”Andanotherstudentobserved,“IfeltliketheexplanationsoftheprojectwereOK,althoughIdidn’tbuyintohelpingthecommunitywithwhatwascollecteduntilafterwardswhenintroducedtoaportionofthegroupwhowereskepticalaboutGISandsawthatitwasacontroversialissue.”

StudentsspokeonthebenefitsoflearningArcPadandthenewskillsetitprovidedthem.Theyalsoreiteratedthatthewayitwastaught,throughthehands-onexperienceoffield collection, was a valuable learning experience.Theyviewedtheprojectasateameffortthatallowedforpositiveinteractionandanexchangeofideas,whileworkingtowardsthe goal of helping the neighborhood address its needs(“Reallyfeltthatworkingwithothersintheclassonarealproject was beneficial.”).The students saw the value inintroducingthetoolofGIStothecommunityandhelpingtocreatearelationshipbetweentheuniversityandcommunitythat could lead to future projects. (“The experience ofworkingwithfolksoutsideofacademiawasgreatforlearningtocommunicate ideasbetter, throughexplaining theusesof GIS and what it can/cannot do.”) Student responses

alsoindicatedtheprojectwasagoodeffortatconnectingtheoryandpractice.TheprojectprovidedinsightintohowtoengagethecommunitywithGISandinvolveresidentsintheprojectplanningthatotherwisewouldhavebeenmissed.Onestudentcommented,“Theprojecthelpedmegainreal-lifeexperiencethatIcouldreflectuponandthencomparetothelearningintheclassroom,whichwasdifferent.Afterward,Icouldseetheconnectionbetweenthem.”

3.The classroom-based PPGIS project could have had more of an impact on student learning by increasing interaction with the community, more participation in the planning process, and a greater transfer of knowledge to the community.Studentsspokeofwishingformorecommunityinteraction(before,during,andafterdata-collectionday)thatwouldhaveenhancedthelearning experience. As mentioned previously, because oftheshorttimeframeofthecourse(tenweeks),someoftheprojectplanninghappenedpriortothestartoftheacademicterm.Beinginvolvedintheplanningoftheprojectwouldhaveallowedformoreinteractionandexposedthestudentsto the intricacies of developing a PPGIS/CBR project.Students felt that more involvement in these preproject-planningstagescouldhavedevelopedskillsforformulatingsuch a project in the future. Students also expressed theneedformoretimetofullyappreciatetheproject,perhapsbyextendingtheclassovertwoorthreeterms.

Studentsalso felt that increasing interactionbetweenstudents and community members could have helped toenhancecommunication,collaboration,andanalyticalskills.Interactions between students and community residentswerelimitedtooneactiveengagement(jointdatagathering)andtwomorepassiveinteractions(projectpresentationsatmonthlymeetings)over the courseof the term.Studentswere not given an opportunity to more formally transferGISskillstoresidents,althoughtheyprovidedtrainingonthePDAonthedata-collectionday.Studentssuggestedthataddingseveralopportunitiestointeractwiththecommunity,whether on the project planning or on direct GIS skilltransfer,wouldhavebeenofvalue.

4. The classroom-based PPGIS project was restricted by time in meeting the goals of community empowerment and building a relationship with the community, but the value of working towards those longer-term goals was understood and evident in the student reflections.The student participants weretrulyinterestedinachievingthegoalsofPPGISandCBR,as evidencedby suggestions fordevelopingaprojectovermultiple ten-week classes to experience the communityoutcomes.This lackof completenessor ability to see theproject through on a longer-term basis was discouragingforthestudents,buttheabilitytostandbackandreflectonlonger-termcommunitychangegoals, and thepiece theyplayedintheprocess,allowedthestudentstoappreciatetheireffortsandenvisiontheworthofaclassroom-basedPPGISproject.


Priortothedata-gatheringday,studentswereasked,“Whatareyourexpectationsforthedayfromaneducationalstandpoint?”Threekeythemesemerged:1)togainskill-buildingexperience,2)toshareskillsandinteractwiththecommunitymembers,and3)toexperiencethevalueofapublic-participation/collaborativeprocessforstudentsandresidents.Thekeyfindingspreviouslymentionedsuggestthatallthreeoftheseexpectationsweremetatvaryingdegrees.Thestudentsgainedtangibleskills,whiletheinherentnatureoftheprojectwastointeractwiththecommu-nityinaparticipatoryenvironment.Increasedinteractionandagreatertransferofknowledgecouldhavehelpedtobettersolidifytheseexpectations.

Whenstudentswereasked,“Whatareyourideaswherethisprojectcouldlead?”thelonger-termgoalsofbuildingrelationshipsandempoweringtheresidentswerethekeythemesthatemerged.Theseexpectationswerenotcompletelymetbytheproject,butthekeyfindingssuggestthatthestudentswereabletoexperienceandvaluethecontributionsthatweremadeinworkingtowardthoselonger-termgoals.TheseoutcomesprovidedthestudentsaviewintothepositiveattributesoftheconceptsofPPGISandCBR.

When students were asked, “What are your feelings inparticipatingwith thecommunity?” they respondedwith it isimportant tobuild relationships that include the community,itgivescontextandvalue to studentwork throughreal-worldexperience, and it is enjoyable to transfer knowledge/skills tobenefitthoseoutsideoftheuniversity.Thekeyfindingssuggestthattheseexpectationsweremetforthestudentsfoundtheprojectapositivelearningexperienceandtheywereabletoexperienceallofthelistedprocesses.Again,improvementscouldhavebeenmade,buttheintroductiontotheexpectationswasvaluabletotheparticipants.

RECOMMENDATIONS FOR FUTURE WORKBased on the experiences of the students, as well the instructors, we offer the following recommendations on future classroom-based PPGIS activities:

Project Continuation The creation and implementation of classroom-based PPGIS projects are effective ways to teach the application of new GIS skills in addition to the technical know-how of GIS software. Moreover, for those interested in understanding how GIS can play a role in fostering community change, bottom-up planning, or participatory decision making, a service-learning model of class-room learning can be an invaluable tool to link theory, practice, and experience. Students in general have limited opportunities to participate in service-learning endeavors where they can practice concepts and utilize skills learned in the classroom—especially in GIS classes where the focus is predominantly on technical skills and the GIS science that informs its use. This real-world appli-cation of knowledge, hands-on experience, and communication

that takes place provides numerous educational benefits for the students, including insight into community skepticism about data, maps, and the motivation of university students to “help” their community neighbors. One student reflected, “[the] com-munity meetings seemed disruptive, but introduced [us] to the element of ‘conspiracy’and distrust that is inherent in projects working with the public.” Additionally, understanding what it takes to work toward the larger goals of community empower-ment, building relationships, and increasing participation in the decision-making process is difficult to achieve without directly participating in such a project.

Increased Interaction Classroom-based PPGIS projects need to include multiple, re-quired activities and meetings for the students to interact with community members. The project described earlier required only one interaction between students and the community, with two additional opportunities for interaction highly recommended. Students who participated in these recommended opportunities strongly believed that they significantly enhanced their PPGIS experience and helped them better understand the complexities in conducting a community-based, collaborative GIS project. Students who did not attend these optional meetings felt that they missed out on something important—“I didn’t go to any com-munity meetings, but wish I could have and maybe it should have been required.” The expectations of student participants and the benefits to student learning are directly tied to the communication and collaboration with the community. Participation in goal-setting sessions, conducting GIS workshops for the community, working together in analyzing data, and attending neighborhood association meetings are a few examples of activities that could be required to increase interaction. One student reflected, “A meeting before the data-collection day would have been helpful in having a dialogue about why things were being done and to understand a more comprehensive reason to do things.”

That said,abalancebetweenstudentdesire tobepartofthe entire project-planning process and the requirements forsufficiently organizing a project prior to the beginning of anacademictermneedstobemet.Especiallyininstitutionsonaten-weekquartersystemwheremultiplecoursetermsdedicatedtotheservice-learningprojectareunrealistic,someworkpriortothebeginningofthetermmustbeperformed.Ataminimum,thecommunitygroupwithwhichtoworkshouldbeidentifiedandsomeinitialconversationsaboutthetypesofprojectthatmightworkshouldbehadpriortothestartofclass.Also,thecommu-nitymustagreetotheproject(evenifonlylooselydefined)priortotheclassbeginningsothatproperplanningfortheacademictermcanproceed.

Evenwiththoseconstraints,itisfeasibletodelaymuchofthe substantive planning decisions until the course begins, aslongastheprojectisdefinedinsuchawaythatitcanbeviablycompletedduringtheterm.Finalcommunitydeliberationsanddecisionsonpreciselywhatdata tocollectandanalyzecanbedelayeduntilthefirstweekortwoofclass,leavingenoughtime

�0 URISA Journal • Vol. 19, No. 1 • 2007

forthecommunityandtheclasstofinalizedata-collectionideasandprotocolsbeforeembarkingonthecollaborativedata-collec-tioneffort.Clearly,planningaproject,decidingonwhatGISdatatocollect,collectingthedata,analyzingdata,andpreparingdataandmapswithavolunteercommunityeffortisalottodowithinaten-weekquarter(itisevenalotfora20-weeksemester),butthevalueforstudentstobeinvolvedinallphasesoftheprojectprovidesimportantinsightintoandexperienceinthecontextofGISprojects.AndunderstandingthatcontextiswhatwillhelpstudentslearnhowtousetheirnewtechnicalGISskillsappro-priatelywhenworkingwithinaPPGISenvironment.

Scalability and TransferabilityThis type of approach to learning PPGIS by doing PPGIS can be carried out in at least two ways. First, the basic approach outlined previously could be scaled up to classes with larger enrollments without much effort if the class is to focus on only a single project. Nothing in this approach becomes more difficult with more participants; rather, the greater the participation (of students and community members), the larger geographical area could be covered through the community-based mapping or the more depth of data that could be captured within a smaller geographical extent. Second, if a course such as this were to take on multiple projects, a separate staff person would need to handle project-management activities. It is not realistic for a single instructor to manage a normal set of responsibilities with the addition of managing several community projects and to do that project management in a way that adheres to the principles of community GIS work discussed previously.

Despitethepositiveexperiencesandrichlearningopportunityaffordedtostudentswithinthisservice-learningmodel,anextraor-dinaryamountoftimewasrequiredbytheinstructortomanagetheprojects.Inadditiontopreparinglabsandlecturesthatwouldnormallybepartofthecourse,theinclusionofaservice-learningprojectrequiredmanyout-of-classmeetingswiththecommunityinbothsmallworkinggroupsandlargerneighborhoodmeetings,arranginglogisticsforthedata-gatheringdaymeetingplace,reserva-tionpaperwork,food,trainingmaterials,etc.Fosteringacollabora-tiveapproachtotheprojectalsomeansspendingextratimeworkingwithcommunityskepticstobuildthetrustingrelationshipthatiscriticaltoshort-termandlong-termsuccessesforthecommunityandapositiveexperienceforstudents.

Giventhesetimeconstraints,ifmultipleprojectsareneededbecause of high course enrollments or because of a range ofcommunityinterests,thenwewouldsuggestpullingtheprojectcomponentoutof theGIS class and insteadoffer theprojectcomponentasaparallelcoursetothePPGIScourse.Thisparallelcoursemaybefocusedonservicelearningitself,withinwhichPP-GISoffersonesetoftoolsthatmaybeappropriatefortheprojectathand.AnddependingontheskillortimeoftheGISinstructor,theservice-learningandproject-managementcomponentmaybebetterhandledbyfacultywhospecializeinthesetypesofappliedexperiences.ThePPGIScourse,then,actsalmostlikearesourcefortheservice-learningsequence,whichitself isaresourcefor

bothstudentlearningandcommunityempowerment.ThisapproachofmakingPPGISavailableasacommunity

andstudentresourceisbeingexploredattheUniversityofOregonwheretheinstitution’sCommunityServiceCenter(CSC)profes-sionalstaffalreadyactivelymanagesfourtosixservice-learningprojectsperyear.ThereisoftenadesiretohavecommunityGISasacomponentintheseprojects,butnotenoughPPGISexpertisewithintheCSCstaffhasbeenavailabletoadequatelyofferitasaresourcetothecommunityandtostudents.Thecurrentexplora-tionistodevelopathree-prongedapproachtomakingPPGISopportunities: 1) continue to offer the PPGIS class discussedpreviously,butpulltheprojectoutofthecourserequirements;2)developcommunityprojectsthroughtheCSCthatincorpo-ratePPGISandcoordinatetheseeffortswiththePPGIScourseasmuchaspossible,eithertoruninparallelortousestudentswhohavecompletedthePPGIScourseascoreorganizersoflaterPPGISprojects;and3)developanongoingPPGISlaboncampusthatcontinuallytrainsandengagesstudentsincommunityworkindependentofanyparticularcourse.

CONClUSIONPlanners have long recognized the importance of public participa-tion in the planning process and this has led to an interest in the concept of PPGIS. This introduction of GIS tools to community organizations for furthering participation has also empowered communities through access to the technology. The act of giving community access to the technology can follow several models, but the university-community model is particularly interesting because of the service aspect possibilities for students. By allowing students to transfer their “expert” knowledge of GIS to the com-munity, the students are gaining educational value as residents gain tools that are intended to empower.

Theuniversity-communitymodelofPPGISalsocorrespondsnicelywiththeconceptofcommunity-basedresearch(CBR).CBRemphasizesrecognizingthecommunityasaresearchpartnerandusingabottom-upapproachtoprojectdevelopmentbyinvolvingthecommunityinallphases.Inaservice-learningenvironment,the partnership would benefit everyone with the communitygainingempowerment,ownership,andneededassistance,whiletheuniversityfurtherstheeducationofthestudent.

The service-learning model is a widely used approach toenhancingstudenteducationthroughapplyingclassroomideastoreal-worldprojects.Assessingthevalueofaservice-learningproject through an evaluation strategy isdifficult, but recom-mended.Avarietyofassessmentscanbemade,butevaluatingstudentoutcomesiscrucialtothecontinuousimprovementoftheprocess.

Inthecasepresentedhere,thefourprimaryfindingsfromstudentreflectionsshowthatthestudentparticipantsappreciatedtheopportunitytoapplytheirdevelopingskillstoacommunityprojectandthattheoutcomesfromthatparticipationwereben-eficialtotheirlearningexperience.Ingeneral,theirexpectationsfortheprojectweremet,andstudentsdidnotfeelthatthePP-GISprojectundulytooktimeawayfromlearningtechnicalGIS

URISA Journal • Schlossberg,Wyss �1

skills.Thestudentsfelttheprojectcouldhavebeenimprovedbyincludingstudentsmoreintheproject-planningphaseandbyrequiringmultipleeventswherestudentsandcommunitymem-berscouldinteractaroundtheproject.Overall,studentsfoundvalueincombiningtechnicallearningwithappliedexperiencesinPPGIS:“IgotagoodsenseofusingGISasatooltoengagethecommunityandreallycouldn’ttrulylearntheconceptofPPGISwithoutexperiencingareal-worldproject.”

About The Authors

Marc SchlossbergisanassistantprofessorofPlanning,PublicPolicy,andManagementattheUniversityofOregon.HisresearchandteachinginterestsfocusontheuseofmobileGIStechnologytofostercommunityempowermentandtounderstandlocalwalkability.

CorrespondingAddress:Planning,PublicPolicy,andManagementUniversityofOregon128HendricksHallEugene,OR97403Phone:(541)346-2046E-mail:[email protected]

Darren Wysshasamaster’sdegreeinCityandRegionalPlanningfromtheUniversityofOregonandiscurrentlyalong-rangeplannerforthecityofTigardinOregon.

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Footnotes

(Endnotes)1 Additionalinformationcanbefoundbyvisitinghttp://www.

uoregon.edu/~wunmap/.2 ArcPadiscreatedanddistributedbyEnvironmentalSystems

ResearchInstitute.

URISA Journal • Crompvoets, de Bree, van Oort, Bregt, Wachowicz, Rajabifard, Williamson ��

INTRODUCTIONMany international regions, countries, states, and counties throughout the world have spent considerable resources over the past few years implementing and managing Spatial Data Clear-inghouses (SDCs). These SDCs are prominent features of Spatial Data Infrastructures (SDIs) (Clinton 1994, Federal Geographic Data Committee 1997, Onsrud 1998, Crompvoets et al. 2004), because they are the facilities for making spatial data accessible to the general public and promoting data sharing. SDCs facilitate the searching, viewing, transferring, ordering, publishing, and/or disseminating of spatial data and services from numerous sources via a Web site (interface) on the Internet, and, as appropriate, providing complementary services. These SDCs contain data catalogs, which are access systems that use metadata (INSPIRE Architecture and Standards working group 2002, Maguire and Longley 2005, Tait 2005).

The access service for spatial data on theWeb is knownvariouslywithinthespatialcommunityasclearinghouse,catalogservice,spatialdatadirectory,geoportalandgeospatialone-stopportal.Althoughdifferentnamesareused,obviouslythegoalsofaccessingspatialdatathroughthemetadataremainthesame(Crompvoetsetal.2004,Beaumontetal.2005).Theenhance-mentofdata/serviceaccessibilityandthesharingofspatialdataand related services between suppliers andusers are themainreasonstobuildtheseelectronicfacilities(Bernardetal.2005,Beaumontetal.2005,MaguireandLongley2005).

Basedonanoverallassessment,theaveragecostofanSDCisapproximately€1,500,000ayear(SouthernCaliforniaAs-sociation of Governments 1998, INSPIRE Architecture andStandardsworkinggroup2002,Pascaetal.2004).Thismoneyis

spentonmanagementandcoordinationcosts,GISandInternetapplicationdevelopment, training,hardware, standardizationactivities, legal environmentcreation, andmetadataprepara-tion. Currently, about 500 (noncorporate) SDCs have beenestablishedandmanymoreSDCsprobablywillbesetup inthefuture.Onaglobalscale,hundredsofmillionsofdollarsarespentyearlyonSDCactivities.Uptonowthislargeinvest-menthasrarelybeenauditedorevaluated.AstudyconductedbytheUrbanandRegionalInformationSystemsAssociation(Gillespie2000)citedthatwhilethecostsofSDCprojectsmayberelativelyeasytoassessandhighly“front-loaded,”thebenefitsareoftendifficulttomeasureandmaynotemergeuntilwellintothelifeoftheSDCanddependonotherfactorscomingintoplay(FederalGeographicDataCommittee2002,CommissionoftheEuropeanCommunities2004).

SDCscouldbedevelopedatdifferentadministrativelevels,rangingfromlocaltostate/provincial,national,andinternationallevelstoagloballevel,tobetteraccessandsharespatialdataandrelatedservices.Thereisaneedtoaddresspoliticiansanddeci-sionmakerstodemonstratethebenefitsofsuchasystem.Oneofthedifficultiesofsellingthebenefitstodecisionmakershasbeen thepaucityof systematic evidenceof the full economic,social, and environmental impacts.This was highlighted inthecontextofGeospatialOne-Stop(FederalGeographicDataCommittee2002)andtheExtendedImpactAssessmentoftheINSPIRE-initiative(CommissionoftheEuropeanCommission2004).However,ithasbeendifficulttoextrapolateimpactsfromtheseindividualcasestoreachmoregeneralizedconclusions.Inaddition,itiscriticaltomoveawayfromanarrowfocusonthetechnicalconsiderationsofSDCstotheirpotentialcontribution

Worldwide Impact Assessment of Spatial Data Clearinghouses

Joep Crompvoets, Floris de Bree, Pepijn van Oort, Arnold Bregt, Monica Wachowicz, Abbas Rajabifard, and Ian Williamson

Abstract: This paper provides results from a worldwide impact assessment of spatial data clearinghouses. Its aim is to assist policy makers in their task of evaluating whether or not investment in setting up and maintaining these establishments is justified. To achieve this objective a procedure was devised for the comprehensive and systematic evaluation of sustainable development within the worldwide clearinghouse population. The assessment procedure entailed a survey undertaken by clearinghouse coordinators. A range of economic, social, and environmental indica-tors was chosen to evaluate the relevance, efficiency, and effectiveness of clearinghouses. This paper also presents the results of complementary analyses that were carried out to assess the significance of the impacts recorded. They were also used to assess the objectivity of the responses of the coordinators. The results of these assessments reveal that clearinghouses provide mainly positive impacts. In addition, the results also indicate the significance of clearinghouses as relevant facilities for enhancing spatial data accessibility, providing efficient means of accessing spatial data, and the effective promotion of data use and distribution. Finally, the results could be used to justify present investments and to support future investments in the clearinghouse system.


toareacompetitiveness,innovation,productivity,jobcreation,etc.(Cragliaetal.2003).

Thefocusofthispaperisontheworldwideimpactassess-mentofthecurrentSDCswiththemainobjectiveofprovidingthis informationtopolicymakers toassist theminevaluatingwhetherornotinvestmentinsettingupandmaintainingtheseSDCsisjustified.Inthiscontext,thetermimpactisdescribedasthe(positiveornegative)effectthatSDCscouldhaveonsociety.Fewstudiesexistabouttheworldwideimpactofthesefacilities.Tothebestof theauthors’knowledge,nocomprehensiveandsystematic impactassessmenthas takenplace.Thepurposeofthepresentpaperistofillthisgap.

ThispaperpresentsandassessestheimpactsofcurrentSDCsthroughouttheworldwithreferencetotheeconomic,social,andenvironmentaldimensions.ThisimpactassessmentisbasedonasurveyundertakenamongcoordinatorsofknownSDCsoftheworldusingindicatorstoassesstherelevance,efficiency,andef-fectiveness.Complementaryanalysesareimplementedtointerpretthesignificanceoftheimpacts.

INTRODUCTION TO IMPACT ASSESSMENTImpact assessment is a key tool for improving policy making and implementation, and promoting sustainable development (Long and Alastair 1997, Commission of the European Com-munities 2002, Bråthen 2003). Many techniques can be used to assess the impacts (Jorgenson 1998, Environmental Protection Agency 2000), but whatever method is used, the results need to be transparent, reproducible, and robust. To make comparisons as accurate as possible, impacts are expressed in quantitative and monetary terms (e.g., cost-benefit analysis) in addition to a qualitative appraisal.

Impactassessmentidentifiesandassessesproblemsarisingfrompursuingtheobjectivesandtheoptionsavailabletoachievethose objectives. It also highlights the positive and negativeimpactswiththeirrespectiveadvantagesanddisadvantages,in-cludingsynergiesandtrade-offs(CommissionoftheEuropeanCommunities2002,Bråthen2003).Anyassessmentshouldbebasedonthefollowingcriteria:• Relevanceforsolvingtheproblem,• Efficiencyintheuseofhumanandfinancialresources,• Effectivenessinachievingthedefinedobjectives.

These assessments of impact are difficult mainly becauseofthedegreeofuncertaintyinthereliabilityofthedata,theas-sessmentsoftheproportionoftheimpacts,therangeofaffectedstakeholders,theshort-termandlong-termdevelopments,andtheefficacyoftheassessmentmethod.

Systematicassessmentofimpactsshouldalsoconsidersus-tainabledevelopment.Sustainabledevelopmentisbasedontheideathatinthelongerrun,economicgrowth,socialinclusion,andenvironmentalprotectionshouldgohandinhand.Atthismoment,manygovernmentsregardtheseeconomic,social,andenvironmentaldimensionsasthemaindrivingforcebehindtheir

policies(Williamsonetal.2003).Theeconomic,social,andenvi-ronmentalimpactsshouldbeidentifiedandcoverallpositiveandnegativeeffects,includingcostsandbenefits.Economic,social,andenvironmentalimpactshavebeenidentifiedbythereportoftheEuropeanCommunities(2002).

EXISTING IMPACT ASSESSMENT STUDIES Several studies assess the impact of SDIs including SDCs (Renong Berhad 1995, PriceWaterhouse 1995, Canadian Council of Land Surveyors, Canadian Institute of Geomatics, Geomatics Industry Association of Canada 2000, Berends and Weesie 2001, Fornefeld and Oefinger 2001, Federal Geographic Data Committee 2002, Pasca et al. 2004, Commission of the European Communities 2004). These studies encountered difficulties in estimating the costs, while the estimation of benefits appeared to be even more difficult.

Previousassessmentresearchfocusedmainlyontheimpactof one SDC and was neither comprehensive nor systematic(PriceWaterhouse Nederland 1996, Federal Geographic DataCommittee2002,CommissionoftheEuropeanCommunities2004,Pascaetal.2004,Tait2005,Walther2005).AswithmanySDIinitiatives,themajorityofimpactswerequalitativeinterms.ThemainfindingsofthesesixstudiesarethatSDCs:• Improve the availability, accessibility, usability, and

“downloadability”ofdatasupplied.• Arecost-effectiveandefficient.Forexample,thebenefit-cost

ratio,relatedonlytothereductionoftimetoaccessdata,rangesfrom1.1to4.

• Widentherangeofuserswithdifferentlevelsofeducationandtechnicalskills.

• Increase the awareness of spatial data among the generalpublic.

• Enhance the performance and productivity of (publiclyfunded)organizations.

• Improvemetadataquality.• Increasegovernmentparticipation.• Supportbetterdecisionmaking.• Serveascatalyststoinnovationandnewwaysofworking.• Improvepartnerships.

These initialassessment resultsand literature (e.g.,GrootandSharifi1994,Askewetal.2005,MaguireandLongley2005,Beaumontetal.2005)suggestthatSDCsarearelevantmeanstoenhancedataaccessibilityaswellasdatasharing,botheffectiveandefficientintheuseofhumanandfinancialresources.

Incontrastwiththepreviousassessmentresearch,thispaperfocusesontheworldwideclearinghousepopulationandiscom-prehensiveandsystematic.

METhODOlOGYThis paper focuses on the development and implementation of a procedure to assess the impacts of currently existing interna-


tional, national, federal, interstate, state, county, and local SDCs of the world. The “preclearinghouse situation” was considered the baseline against which to assess the current impact of SDC development. The preclearinghouse situation refers to when no electronic facility existed on the Internet to access spatial data using metadata. To undertake the assessment, it was important to take into account developments over time, to use existing knowledge and experience, to consult interested parties and rel-evant experts, to be transparent, and to compare negative impacts with positive impacts.

Assessmentdifficultieshavecircumscribedtheveryfewstud-iescontainingquantitativeandqualitative informationontheimpactsofSDCs.Therefore,theapproachchoseninthestudywastodetermineimpactsbyreferringtotheexpertknowledgeandexperiencesofSDCcoordinatorsastheirperceptionsaresensi-tiveindicatorsforchangesaswellasimpacts.Thesecoordinatorsorganizeactivitiesasmanagement,marketing,technicalandlegalenvironmentcreation,andhumanresourcessothattheirSDCsoperatewell.OtherreasonstofocusonSDCcoordinatorsweretheirintermediaterolesbetweendata/servicesuppliersandusers,their awareness of the historical, institutional, cultural, legal,economic,andtechnologicalcontext,andtheirabilitytoprovideaccuratedataaboutthedevelopment,use,management,content,andtechnologyoftheirSDCs.Moreover,theywererelativelyeasytocontact.ThiswasnotthecasewiththedatausersaswellasthesuppliersofSDCs.Inaddition,theexpertiseandexperiencesofaselectednumberofEuropeanSDCpractitioners(usersanddata/service suppliers)wereused toevaluate theobjectivityofcoordinators’perceptions.Theavailabilityofthisexpertisemeantthattheimpactintermsofeconomic,social,andenvironmentalcontextcouldbedescribedfairlycomprehensively.

Theprocedureused in thisassessment studyconsistedofthefollowingsteps:• Undertakingextensiveliteratureresearch(seetheprevious

sectiononexistingimpactassessmentstudies);• Determiningassessmentindicatorstoevaluatetherelevance,

efficiency,andeffectiveness;• Designingandconductingthesurveytocollectinformation

abouttheperceptionsofcoordinators;• AnalyzingresultsbycategorizationoftheSDCstofacilitate

theinterpretationoftheseresults;and• Assessingtheobjectivityofcoordinators’responses.

DETERMINING ASSESSMENT INDICATORSThe assessment was confined to using a number of economic, social, and environmental impact assessment indicators, because a full implementation of a quantitative assessment study was proscribed by cost considerations. These indicators were mea-surable and illustrative (Taylor et al. 1990). They could measure the relevance, efficiency, and effectiveness of SDCs and provide insight into how economic and social structure and environment alter when SDCs are implemented. The selection of indicators

was based on expert knowledge, literature, and direct relevance for SDCs.

The economic indicators used were:• Consumptionofdata/services,• Datamarkettransparency,• Duplicationofdatacollection.

The social indicators were:• Spatialdata/serviceawarenessand• Socialcohesionbetweencitizens.

The only environmental indicator was:• Datadeliveryforenvironmentalpolicyformulation.

DESIGNING AND CONDUCTING SURvEYThe survey was undertaken (November 2004 to April 2005) to collect information about the perceptions of coordinators. A questionnaire was distributed to all known coordinators of SDCs. This survey was strongly supported by the INSPIRE expert group (a group composed of representatives of the European Commis-sion and environmental and GI communities of member states) and the Executive Board of the Permanent Committee of GIS Infrastructure for Asia and Pacific (PCGIAP).

AsmanySDCcoordinatorsaspossiblecompletedthesurveytoprovideafullandreliableimpactassessment.ForthisreasonaninventoryofidentifiedSDCswascompiledbyextensivebrowsingontheInternet(usingseveralsearchengines),readingliterature,contactingexpertsandSDCcoordinators.Wherepossible,thee-mailaddress(andname)oftheSDCcoordinatorwascollected.

Aquestionnairewasused tocollect the relevant informa-tion.Thequestionswerebasedoncurrentliteratureaswellasonexpertknowledge,sothatthecoordinators’perceptionsoftheirSDCscouldbeanalyzed.Mostquestionscouldbeansweredbyselectingtheappropriateoptionboxes;noneofthequestionswereopen.Thequestionswereframedinawaythattheydescribedthe impactsofSDCsaswell as the futuredevelopments.Thequestionswere:1) Onwhichadministrative level listed isyourSDCmainly

operating? (In the next section, the administrative levelslistedarepresented).

2) For which of the countries listed does your SDC cover(partly)metadata(193countrieswerelisted)?

3) WhichoftheoptionslistedarethemainbenefitsofyourSDC?(Figure3presentsthebenefitslisted.)

4) WhichoftheoptionslistedarethemaindrawbacksofyourSDC?(Figure4presentsthedrawbackslisted.)

5) WhichoftheoptionslistedislikelytotakeplacewithyourSDCwithinthenextfiveyears?(Inthefollowing“FutureDevelopments” section, the future options are partiallypresented.)


Moreover, 14 statements were formulated to assess whatSDCcoordinatorsconsideredthe impactsof theirSDCsonascalefromstronglyagreetostronglydisagree.Examplesofthesestatementsinclude:a) YourSDC increases the consumptionof spatialdata and

services.b) YourSDCimprovesdatamarkettransparency.c) YourSDCreducesdataduplication.d) YourSDCimprovestheawarenessofspatialdata.e) YourSDCstrengthensthesocialcohesionamongcitizens.This

statementreferstothesolidarityandsocialbondingbetweenpeoplewithinstate,country,orinternationalregion.

f ) Your SDC improves the appropriate data delivery forenvironmentalpolicyformulation.

g) EstablishmentandmaintenanceofyourSDCiseconomicallybeneficial.

In addition, supplementary statements were included tocheckthefacevalidityoftheresponses.

Thequestionnairewasdistributedviae-mailandwasad-dressedpersonallytothecoordinators.Themainadvantagesofusinge-mailarethatitisfast,easy,andinexpensivefordistribu-tion.Intotal,428coordinatorswerecontacted.

ANAlYzING RESUlTSThe worldwide answers were aggregated. However, because the world is so diverse in historical, institutional, legal, cultural, technological, and economic respects, and different geographical

information (GI) processes take place at various administrative levels, the variability of the answers between regions and admin-istrative levels was categorically analyzed. The classification by region was based on the division of Dorling Kindersley (2002). Eight administrative levels were identified: worldwide, conti-nental, international, national (federal), interstate, state, county, and local. The chi-square and Fisher exact tests (Agresti 1990) were used to test whether respondents at different regional areas and administrative levels reacted differently to the questions and statements of the questionnaire. Throughout, test results with a (one-sided) P value of less than 0.1 were considered significant.

ASSESSING ThE OBJECTIvITY OF COORDINATORS’ RESPONSES Because the results of the questionnaire were based on the re-sponses from the SDC coordinators, it was expected that their views could be biased. To mitigate this, a comparison of responses from the European SDC coordinators with those of the European user community was made, assuming that the objectivity of Eu-ropean coordinators’ responses represent well the objectivity of all SDC coordinators’ responses. To facilitate this procedure, a short version of the questionnaire was distributed to 75 European representatives of the GI user community (June to August 2005). These practitioners were members of the INSPIRE Expert Group and were considered important stakeholders who could use SDCs to access or supply spatial data (e.g., ministries, municipalities, mapping agencies, cadastres, universities, public/private institu-tions, utilities, etc.). The chi-square and Fisher exact tests were

Figure 1.Worldwidedistributionofspatialdataclearinghouses(456)bycountry


also used to test the differences of the views between the European SDC coordinators and these practitioners.

RESUlTS AND DISCUSSIONThe inventory resulted in a list of 456 SDCs (of 80 countries) of which 428 had personal e-mail addresses of their SDC coordina-tors. Figure 1 indicates the worldwide distribution of all identified SDCs by country. Apparently, the establishment of SDCs has become a global activity as recorded by Crompvoets and Bregt (2003) and Crompvoets et al. (2004). Most SDCs are established in Europe, Southeast Asia, North America and South America. The countries with the highest number of SDCs are the United States and Canada. The areas with few implementations are Africa and the Middle East.

Atotalof105coordinators from31countriescompletedthesurvey(25percentofthepopulationofcoordinators).Thispercentageisinlinewiththeresponsestosimilartypesofsurveys(Hamilton2003).ThissamplesizewasadequateinrespecttotheSDCpopulationinthedevelopedworldfortherespondentsweremainlycoordinatingSDCsinNorthAmerica(theUnitedStates/Canada)(41percent),Europe(32percent),andAustralia(8percent)(only19percentintotalwereAfrican,SouthAmerican,andAsian(seeFigure2)).Toobtainreliableresults,theregionalanalysisincludedonlytheNorthAmerican,European,andAus-traliancoordinators.Theotherregionswereexcludedfromtheregionalanalysisbecauseofthelimitednumberofresponses.

Asmentionedpreviously,thesurveyidentifiedeightadmin-istrativelevels(question1).Toachievereliablestatisticalanalysis,

severallevelswerereclassified.Finally,threeclasseswereconsid-ered:(inter)state,national(includingfederal),andinternational.Interstateandstateclasseswerereclassifiedinto(inter)state(41percent);nationalclasswasunchanged(31percent);worldwide,continental, and internationalclasseswere reclassified into in-ternational(20percent);countyandlocalclasseswereexcludedfromtheadministrativelevelanalysis(8percent).

BENEFITS AND DRAWBACKSThe enhanced access to spatial data and the improved data sharing and distribution are regarded as the main benefits (question 3) of the current SDCs (see Figure 3). This confirms the results derived from the previous studies and literature (see the previous section on existing impact assessment studies). On the basis of this result, overall SDCs are relevant facilities to access data/services and to promote sharing. However, many SDCs still lack integration among suppliers and users. This could result in inefficient use of resources, potential duplication, inconsistency, incompatibility, and the inability to maximize the value of data and services. The main benefits appear to be economic in nature. Minor benefits are the more effective use of available data, the improved spatial data awareness, and the reduction of spatial data duplication. Cost savings are not really seen as a benefit, which could indicate that SDC coordinators are not very cost-conscious.

CoordinatorsofNorthAmericanSDCsregardthereduc-tion of data duplication and the improved data sharing anddistributionsignificantlymoreasbenefits(thisisincontrastwithEuropeanSDCs).

Figure 2.Worldwidedistributionofsurveyresponses(105)bycountry


In addition, coordinators of international SDCs see thereductionofdataduplicationsignificantlylessasabenefit.Thisisincontrastwith(inter)statecoordinatorswhoalsolookoncostsavingssignificantlymoreasabenefit.

Besides costs and funding (80 percent), not one singledrawback(question4)couldbeidentifiedasanotherimportantobstacleforSDCimplementationsandmaintenance(seeFigure4).Institutionalproblems(33percent),lackofspecializeddatamanagers(25percent),anddatastandardization(23percent)canbeconsideredassignificantdrawbacks.Thelackofharmonizedreferencesystems(3percent),liabilityproblems(12percent)andinadequateInternetbandwidth(16percent)arelesssignificantasdrawbacksforSDCimplementation.Thisresultisinlinewithliterature(INSPIREArchitectureandStandardsworkinggroup2002, Federal Geographic Data Committee 2002,Wehn deMontalvo2004,Askewetal.2005).Noneofthemainobstaclesaredirectlytechnology-related.Itseemsthatthechallengesaremorelikelytobeorganizationalthantechnical.

NorthAmericancoordinatorsconsider lackof specializedmanagerssignificantlymoreasadrawbackandproblemswithdatapricingasless.Ontheotherhand,theEuropeanSDCcoordina-torslookonproblemswithdatapricingandcommercializationofdatasignificantlymoreasdrawbacks.

Thehighdegreeofcorrespondenceincoordinators’viewswithrespecttotheperceivedbenefitsanddrawbacksissignificantinsofarasitgivesaclearindicationthatSDCsworldwidefunctionwithinabroadlysimilaroperatingenvironment.

ECONOMIC, SOCIAl, AND ENvIRONMENTAl IMPACTSEconomic Impact. The economic impact is primarily assessed by using economic indicators. Several statements in the ques-tionnaire refer to these economic indicators. The survey results show the likelihood of higher consumption of spatial data and services as well as the reduction of data duplication as the main

economic impacts. This impact result is illustrated in Figure 5, which presents the responses of SDC coordinators to three eco-nomic indicators: consumption of data and services (statement a), data market transparency (statement b), and duplication of data collection (statement c). On the basis of these results, it is apparent that the vast majority of respondents agree with the statement that their SDCs increase the consumption of spatial data and services. This implies that this increase of consumption could be regarded as the most important economic impact. Ad-ditionally, a majority also agrees with the statement that their SDCs reduce duplication of spatial data. The result related to the statement that an SDC improves data market transparency is not clear (the majority neither agrees nor disagrees). On the basis of the responses related to these three economic indicators, it could be deduced that SDCs have a significant (positive) impact on the economic dimension.

Fromaregionalperspective,evidencecanbefoundthatmoreNorthAmericancoordinatorsagreewiththestatementsthattheirSDCsincreasetheconsumptionofspatialdataandservicesandreduceduplicationofspatialdata.

Evidence exists that national SDCs agree less that theirSDCsincreasetheconsumptionofspatialdataandserviceswhile(inter)stateSDCsagreemorethattheirSDCsreduceduplicationofdata.

Besidesthestatementsdirectlyrelatedtotheindicators,thecoordinatorscouldalsorespondtothestatementthatestablish-mentandmaintenanceoftheirSDCsareeconomicallybeneficial(statementg).Some70percentof thecoordinatorsagreeandonly11percentdisagreewiththisstatement.Becausethemainbenefitsanddrawbacksarelikelytobeeconomicinnature,thisresultindicatesthatSDCcoordinatorsperceivethatthepositiveimpactsmorethancounterbalancethenegativeimpacts.

BothdatausersandsupplierscouldgaineconomicallybytheimplementationofSDCs.Datausersbenefitfromtheimprovedefficiencytoaccessspatialdata,anddatasuppliersfromthein-

Figure 3.WorldwidedistributionofSDCcoordinators’responses(percentage)relatingtothebenefitsofspatialdataclearinghouses

Figure 4.WorldwidedistributionofSDCcoordinators’responses(percentage)relatingdrawbacksofspatialdataclearinghouses


creasedeffectivenesstodistributetheirspatialdataandtheimprovedefficiencytocollectdatabyreducingdataduplication.It seemsthat the establishment andmaintenance costsof these facilitiesareeconomicallyjustified,althoughthecostsavingsfortheSDCcoordinationorganizationsappeartobealessimportantimpact.

Social Impact. Thesocial impactisprimarilyassessedbyusingsocialindicators.Twostatementsinthequestionnairerefertotheseindicators:spatialdata/serviceawareness(statementd)andsocialcohesionbetweencitizens(statemente).Theseimpactresultsare illustrated inFigure6.FromtheresponsesofSDCcoordinators,thevastmajorityagreesthattheirSDCsimprovespatialdataawareness.Thus, this improvementof spatialdataawarenesscouldberegardedasthemostimportantsocialimpact.ItappearsthatSDCscouldchangethewaysocietyisusingthisspatialdata.Inmanydecision-makingprocesses,theroleofspa-tialdataisincreasing.SDCsimprove(indirectly)theseprocessesinawaythatenablesstakeholderstobecomebetterinformed.Additionally,amajorityalsoagreesthattheirSDCsstrengthenthesocialcohesion.ItappearsthatSDCsare,forexample,abletoprovideequalspatialinformationaccesstorural,urban,andremotecommunities,whichwillsupportlocaldecision-makingcapacitydevelopmentandnewsocioeconomicactivitiesinthesecommunities. In view of these social results, it is reasonableto deduce that SDCs exert a significant impact on the socialdimension.

From a regional perspective, evidence exists that NorthAmericancoordinatorsagreemorewiththestatementthattheirSDCsimprovetheawarenessofspatialdata.Fromanadministra-tive-levelperspective,nodifferencesinagreementexist.

Environmental Impact. Theenvironmentalimpactisas-sessedbyusingoneenvironmental indicator:datadeliveryforenvironmentalpolicyformulation(statementf ).Thecoordina-torsexpectlittleimpactontheenvironment.Fromtheresponseitappearsthatthemajorityofthecoordinatorsneitheragreenordisagree (60percent)with statement f.SDCsdonot seem todeliverthedataappropriatelyforenvironmentalpolicyformula-

tion.Nevertheless,someenvironmentalpolicymakersmakeuseofSDCstoaccessneededspatialdataandservices(Williamson2004).

From a regional perspective, the evidence indicates thatNorth American coordinators do not consider this impact asimportant.Fromanadministrative-levelperspective,nodiffer-encesinagreementexist.

Examiningassessmentindicatorsincombinationwiththebenefits,itappearsthatthemainpositiveimpactofimplementingSDCsiseconomic.Thehighdegreeofcorrespondenceincoordi-nators’viewswithrespecttotheeconomic,social,andenviron-mentalimpactsissignificant,confirmingthatSDCsworldwidefunctionwithinbroadlysimilaroperatingenvironments

FUTURE DEvElOPMENTSThe coordinators were asked to select what they expect will hap-pen with their SDCs in the next five years (question 5). A subset of their response was that:• Theuseofspatialdatawillincrease(89percent).• More(new)serviceswillbeprovided(55percent).• Thedataqualitywillimprove(50percent).• Theusebygovernmentswillincrease(49percent).• Moredatasetswillbeprovided(35percent).• Morespecificdatasetswillbeneeded(34percent).• The metadata standards applied will be changed (31

percent).• Newexpertisewillbeneeded(26percent).

Thecoordinatorsexpectmainlythatthespatialdatacon-sumptionaswellastherangeofserviceprovisionoftheirSDCswill increase.These developments are in line with literature(Maguire andLongley2005,Beaumont et al.2005) and linkstronglytothegradualshiftinfocusofSDCdevelopment:fromdata-centrictouser-centric.Inthe1990s,dataandtechnologywerethemaindrivingforcesforSDCs.Atthepresentmoment,

Figure 5.WorldwidedistributionofSDCcoordinators’responses(percentage)tostatementsrelatingtoeconomicindicators

Figure 6.WorldwidedistributionofSDCcoordinators’responses(percentage)tostatementsrelatingtosocialindicators


theuseofdata(andservices)andtheneedsoftheusersarebe-comingthemainforcesforSDCdevelopment(ReeveandPetch1999,Williamsonetal.2003,Crompvoetsetal.2004).

Thesimilarityindevelopmentviewsofthecoordinatorsissignificant,showingthatthecoordinatorspossessthesamefutureobjectives probably created by such external developments asexpandingtechnologies,marketdemand,changingbusinessmod-els, sustainabledevelopment, e-government, andparticipatorydemocracy.ThefewdifferencesarethatmoreNorthAmericancoordinatorsexpectthatadditionaldatasetswillbeprovidedandnewexpertisewillbeneeded.

ASSESSMENT OF ThE OBJECTIvITY OF COORDINATORS’ RESPONSES A total of 41 European practitioners completed a short version of the questionnaire. The high degree of correspondence between the responses of these European practitioners and the European SDC coordinators (34) with respect to the questions and statements is significant. This result implies that the coordinators’ perceptions are not unduly biased (at least the European coordinators’ perceptions) and justifies the choice to focus on SDC coordinators as reliable sources of information to assess the impacts. Furthermore, the practitioners look on cost savings as a more significant benefit and consider the improved awareness of spatial data as a less important impact. This indicates that the coordinators underestimate the ef-ficiency of SDCs and overestimate the improved awareness.

METhODOlOGY USEDThe implementation of the assessment procedure was appropriate to measure the impact of SDCs on a worldwide scale to assist policy makers to decide whether investments in the establishment and maintenance of SDCs are justified. When compared to pre-vious studies, the strength of this impact assessment was that it was comprehensive and systematic, reproducible, robust, based on expert knowledge, and that it identified significant economic and social impacts. Through the survey it was possible to gather the perceptions of the coordinators in a fast, inexpensive, and easy way. The complementary analyses were needed to interpret the results of the survey. The main limitation of this study was that only qualitative impacts could be assessed and it was not possible to determine quantitative measures such as financial impacts. The current experiences of the SDC operations are limited by the fact that they are still at an early stage of their development. There is a need to refine methodology so that more precise records of numerical and financial data can be recorded. In this way, a bet-ter and more accurate grasp of financial and operational impacts could be delivered. Nevertheless, the usage of indicators gave some insight into how economic, social structure, and environment alter when SDCs are implemented.

CONClUSIONSThe main conclusions of this comprehensive and systematic impact assessment referring primarily to SDCs of the developed world are:• SDCsarelikelytoexertapositiveimpactonsociety.The

main(positive)impactsareofaneconomicnature,butsocialimpactsareobviouslyimportantaswell.Ontheotherhand,SDCslikelyhavelittleimpactontheenvironment.

• SDCscouldbeconsideredasrelevantfacilitiestoenhancespatialdata/serviceaccessibilityandtopromotethesharingoftheseresources.

• SDCscouldbeconsideredasefficientfacilitiestoenhancedata/serviceaccessibilityandtoreducedataduplication.

• SDCscouldbeconsideredaseffectivefacilitiestoincreasetheuseanddistributionofspatialdata/services,toimprovetheawarenessof spatialdata/services, to strengthensocialcohesionbetweencitizens,andtoimprovepotentiallybetter-informeddecisionmaking.

• CostsandfundingcouldberegardedasthemainobstacleforSDCimplementation.

• Inthenearfuture,theuseofspatialdataresourcesofSDCswillincreaseaswellastherangeofserviceprovisions.

• Coordinators have similar views toward the benefits,drawbacks,andimpactsaswellasthefuturedevelopmentsofSDCs.Thesesimilaritiescouldformaperfectbasistoensureinteroperability between datasets and access mechanisms,andtocreateacultureofsharingaswellasasharedlanguageamongcoordinators.

North American SDCs are considered the most efficientandeffectivefacilities,andaresubstantiallyacceptedwithinthecommunity.ThisisinlinewithMaguireandLongley(2005),whomentionthatmanyAmericanaswellasCanadianSDCsalreadyinthe1990swereabletopromoteawarenessofspatialdata,createcommunityinvolvement,andbuildcapacitytoaccessthisdata(MaguireandLongley2005).TheAustralianSDCsformtheintermediateinefficiencyandeffectivenessbetweenNorthAmericanandEuropeanSDCs.

Thediversity inbenefits, drawbacks, impacts, and futuredevelopmentsbetweenthedifferentadministrativelevelsappearto be low.This could imply that theGIprocesses relating tospatialdata/service accessibilitydonotvarymuchatdifferentadministrativelevels.

Theresultsobtainedcouldbeusedtojustifypresentinvest-ments and to support future investments in SDCs. However,theauthorsobservethatdespitethesepositiveresults intermsofrelevance,efficiency,andeffectiveness, theSDCconcepttoshareresourcescontinuestoberesisted,whichleadstounneces-saryinefficiencies,resultinginduplicationofdatacollectionandstorageandconsequentcosts(Nedovic-BudicandPinto2000,FederalGeographicDataCommittee2002,Askewetal.2005).ToutilizetheseSDCseffectively,theremustbeaclearunderstand-ingofhowtheyinfluenceandjustifytheircosts,andovercome

URISA Journal • Crompvoets, de Bree, van Oort, Bregt, Wachowicz, Rajabifard, Williamson �1

institutionalproblems.Itappearsthatmoreimpactassessmentresearchisneeded(e.g.,casestudies).

Acknowledgments

The authors would like to thank Hugh Buchanan, Watse Castelein, Michael Gould, Louis Hecht, Günther Pichler, Gerda Schennach, Paul Smits, and Marc Vanderhaegen for their contributions to the generation of the questions of the questionnaire, and the members of the INSPIRE Expert Group and the Permanent Com-mittee on GIS Infrastructure for Asia and Pacific (PCGIAP) for disseminating the questionnaire to potential SDC coordinators. Moreover, the authors gratefully wish to acknowledge the support of all the SDC coordinators and the European practitioners in the preparation of this paper. Their contributions by answering the questions and statements were crucial for the results presented in this paper. Finally, the authors thank Gert Jan Hofstede, Brian Goodsell, and Rini Kools for comments and suggestions on earlier versions of this paper.

TheauthorsalsoacknowledgethefinancialsupportbytheDutchinnovationprogram,“SpaceforGeo-Information.”

About The Authors

Joep Crompvoets, Floris de Bree, Pepijn van Oort, Arnold Bregt,andMonica Wachowicz arewiththeCentreforGeo-InformationatWageningenUniversity,TheNetherlands.

CorrespondingAddress:JoepCrompvoetsWageningenUniversityCentreforGeo-InformationP.O.Box47,6700AAWageningen,TheNetherlandsPhone:(+31)317474399;Fax(+31)317419000E-mail:[email protected]

Abbas RajabifardandIan WilliamsonarewiththeCentreforSpatial Data Infrastructures and Land Administration inthe Department of Geomatics at Melbourne University,Australia.

CorrespondingAddress:MelbourneUniversityCentreforSpatialDataInfrastructuresandLandAdministrationDepartmentofGeomaticsVictoria3010,Australia

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URISA Journal • Goldberg,Wilson, Knoblock ��

INTRODUCTIONThe process of geocoding forms a basic fundamental compo-nent of spatial analysis in a wide variety of research disciplines and application domains (e.g., health [Vine et al. 1998, Boulos 2004, Rushton et al. 2006]; crime analysis [Olligschlaeger 1998, Ratcliffe 2001]; political science [Haspel and Knotts 2005]; computer science [Hutchinson and Veenendall 2005b, Bakshi et al. 2004]). This act of turning descriptive locational data such as a postal address or a named place into an absolute geographic reference has become a critical piece of the scientific workflow. However, the geocoding of today is a far cry from the geocoding of the past. Geocoding data that used to cost $4.50 per 1,000 records as recently as the mid-1980s (Krieger 1992) quickly moved to $1.00 per record in 2003 (McElroy et al. 2003), and can now be done for free with online services (e.g., Yahoo! Inc. [2006], Locative Technologies [2006]), with far greater spatial accuracy and match rates.

Astheavailabilityandaccuracyofreferencedatasetshaveincreasedover thepast severaldecades (Dueker1974,Werner1974,Griffinetal.1990,HiggsandMartin1995,MartinandHiggs1996,Johnson1998a,Martin1999,Boscoeetal.2004),geocodinghasundergonemarkedtransitionstoaccommodateandexploitchangesinbothdataformatanduserexpectations.Thesetransitionscanclearlybeseenintheinput,output,andinternalprocessingofthegeocodingprocess.Theinputdatasuit-ableforgeocodinghaveexpandedfromsimplepostaladdresses(O’Reagan and Saalfeld 1987) to include textual descriptionsofrelativelocations(LevineandKim1998,Davisetal.2003,Hutchinson andVeenendall 2005b).The output capabilitiesof the geocoding process have moved from simple nominalgeographic codes (Tobler 1972, Dueker 1974,Werner 1974,O’ReaganandSaalfeld1987)tofull-fledgedthree-dimensional(3-D)geospatialentities (Beal2003,Lee2004).Likewise, theinternalprocessingmechanismsthatproducethegeographicout-puthavemovedfromsimplefeatureassignment(O’Reaganand

Saalfeld1987)tocomplexinterpolationalgorithmsusingavarietyofheterogeneousdatasources(Bakshietal.2004,HutchinsonandVeenendall2005a,b).

Whilesignificantlyimprovingtheusability,reliability,andac-curacyofthegeocodingprocess,thesedevelopmentshavebroughtwiththemahostofissuesthatapotentialusermustrecognizeandbepreparedtocontendwith.Specificissuesincludetheas-sumptionsmadeduringtheinterpolationprocess(Dearwentetal.2001,Karimietal.2004),theunderlyingaccuracyofthereferencedataset(Gatrell1989,Block1995,Drummond1995,MartinandHiggs1996,Chungetal.2004),theuncertaintyinthematch-ingalgorithm(O’ReaganandSaalfeld1987,Jaro1984),andthechoiceofarealunitgeocodedto(Krieger1992,Geronimusetal.1995,GeronimusandBound1998,Kriegeretal.2002a,2003).Thesetopicshavereceivedconsiderableresearchinrecenttimes,andagreatdealofliteratureisavailable.Thisarticlewillsurveythefieldofgeocodingthroughacross-disciplinarystudyofthegeocodingliteraturefocusingforemostonthetechnicalaspectsoftheprocess.Thechangingconceptofgeocodingwillbedescribed,andthefundamentalcomponentsofthegeocoderwillbeout-lined.Potentialsourcesoferrorinthegeocodingprocesswillbeexplored,andparticularlydifficultgeocodingscenariosrequiringfurtherresearchwillbehighlighted.Theprimarycontributionsofthisarticlewillbetoinformthereaderofthestateoftheartin geocoding through a discussion of its evolution over timeandtowarnofpotentiallystickysituationsthatcanariseinthegeocodingprocessifoneisnotawareofhowone’sdecisionsandassumptionscanaffectthegeocodedresults.ThisworkshouldbeseenasdistinctfromtherecentworkpublishedbyRushtonetal.(2006),whichalsooffersareviewofthegeocodingprocess,butisfocusedonitsapplicationtohealthresearch,inparticularcancerstudies.Theirworktakesanarrowandlimitedviewofgeocod-inganddoesnotdelvesodeeplyintotheevolutionortechnicalaspectsofthegeocodingprocessasdoesthatpresentedhere.Assuch,thispapercanbeseenasamorecomprehensive,technically

From Text to Geographic Coordinates: The Current State of Geocoding

Daniel W. Goldberg, John P. Wilson, and Craig A. Knoblock

Abstract: This article presents a survey of the state of the art in geocoding practices through a cross-disciplinary historical review of existing literature. We explore the evolving concept of geocoding and the fundamental components of the process. Frequently encountered sources of error and uncertainty are discussed as well as existing measures used to quantify them. An examination of common pitfalls and persistent challenges in the geocoding process is presented, and the traditional methods for overcoming them are described.


targeted,broadlyvisionedjourneythroughthegeocodingprocessandshouldbeusedasacompanionarticletofield-specificreviewssuchasthatofRushtonetal.(2006).

ThE CONCEPT OF GEOCODINGOver the years, the changing availability of geographic data has forced the concept of geocoding to remain flexible and adaptive in terms of its requirements and capabilities. The increasing avail-ability, accuracy, and reliability of digital geographic reference datasets has meant that the geocoding process has continually evolved to keep pace with the underlying datasets that facilitate its use. As such, practitioners have been pushing the boundaries of what types of information can be geocoded using different information sources from the very beginning. Early geocoding systems used by the U.S. Census in the 1960s simply turned postal addresses and named buildings into geographical zones delineated by numerical codes (O’Reagan and Saalfeld 1987), not the valid geographic objects such as points, lines, areas, or surfaces with which consumers of geocoded data are accustomed to today. More modern attempts at geocoding have tackled the problems of assigning valid geographic codes to far more types of locational descriptions such as street intersections (Levine and Kim 1998), enumeration districts (census delineations) (Sheehan et al. 2000), postal codes (zip codes) (Gatrell 1989, Collins et al. 1998, Sheehan et al. 2000, Krieger et al. 2002b, Hurley et al. 2003), named geographic features (Davis et al. 2003, United Nations Economic Commission 2005), and even freeform textual descriptions of locations (Wieczorek et al. 2004, Hutchinson and Veenendall 2005a, b).

Thesefundamentalshiftsingeocodingattitudesandoppor-tunitiescanbetraceddirectlytothetechnologicaladvancesmadetotheunderlyingreferencedatasetsonwhichtheyarebased.Theearlyattemptsatgeocodingwerehinderedbythelackofdigitalgeographiestouseintheassignmentofcodes,andwerelimitedbytheiruseofflattext-basedfiles.Thisresultedinlow-resolutionnongeographicoutput,turningaddressesandbuildingnamesintothecensusblocktowhichtheybelonged.ThedevelopmentoftruedigitalgeographiesintheformofproductssuchastheU.S.Cen-susBureau’sDualIndependentMapEncoding(DIME)filesen-abledtheassignmentoftruegeographiccodes,buttheirstructurelimitedtheprocessingthatcouldbeappliedtoderivetheoutput.Theintroductionofthevector-basedgeographicdatasetssuchastheU.S.CensusBureau’sTopographicallyIntegratedGeographicEncodingandReferencing(TIGER)(U.S.CensusBureau2006)databasehaveenablednewgenerationsofgeocodingalgorithmstoapproximaterepresentationsforthegeographicoutputusinginterpolation-basedapproaches,greatlyincreasingtheresolutionofthegeographicoutput(Dueker1974,O’ReaganandSaalfeld1987,Martin1998,Ratcliffe2001,Nicoara2005).Takingthisastepfurther,thecreationofprecompiledgeocodednationalad-dressregisterssuchastheADDRESS-POINT(OrdnanceSurvey2006)andGeocodedNationalAddressFile(G-NAF)(Paull2003)databases in the United Kingdom and Australia, respectively,havefacilitatedhighlyprecisegeocodingcapabilitiesatnational

scales (Higgs andMartin1995,Martin1998,Ratcliffe2001,Churchesetal.2002,HiggsandRichards2002,Christenetal.2004,ChristenandChurches2005,MurphyandArmitage2005).Furthermore,theemergenceofhigh-resolutiondigitalparcelandpropertyboundaryfilesmayenableevenmoreaccuratedigitalgeographicresultstobereturned(Dueker1974,Olligschlaeger1998,Dearwentetal.2001,Ratcliffe2001,Rushtonetal.2006),butthesedevelopmentsarepushingthelimitsofwhatformtheoutput of geocoding should take. Likewise, the developmentofmultiresolutiongazetteersdefininggeographicfootprintsfornamedgeographicplacessuchastheAlexandriaDigitalLibraryGazetteer (Frewetal.1998,Hill andZheng1999,Hill etal.1999,Hill2000)arepushingthelimitsofwhattypeofgeographicfeaturescanhavegeographiccodesassignedtothem(Davisetal.2003,UnitedNationsEconomicCommission2005),aswellastheroleofthegeocoderinthelargergeospatialinformation-processingcontext.Theproliferationofavarietyofdiversetypesoflocationaladdressingsystemsthroughouttheworldprecludesa“onesizefitsall”geocodingstrategythatwillworkinallcases(Fonda-Bonardi1994,Lind2001,Davisetal.2003,Walls2003,UnitedNationsEconomicCommission2005).

Theresultofthisevolutionisasomewhat“fuzzy”conceptofgeocoding,tailoredtothespecificrequirementsanddataavail-ability of thepersonperforming the geocoding.For example,almost everyone involved in or using geocoding today wouldagreethatturningapostaladdressintoageographicpointismostcertainlyincludedinthesetofgeocodingoperations.Likewise,theywouldprobablyagreethatturningaportionofthepostaladdresssuchasthepostcode(zipcode)intoageographicpointorpolygonisalsopartofthegeocodingprocess.However,con-tinuingthislineofreasoningpresentsaslipperyslopebecauseaseriesoffundamentalquestionsarise.Whatshouldthepointreturnedasrepresentativeofthepostalcodebe?Shoulditbethecenterofmass(centroid)?Shoulditbeweightedbythepopula-tiondistribution?Furthermore, if thedigital boundaryof thepostalcodeisavailable,whynotreturnitinsteadofjustasinglepoint? Questions such as these are just the beginning. If thepostalcodecanbegeocoded,canthecitybeaswell?Ifso,whatis thedifferencebetween thegeocoder returningageographicrepresentationofthecityandthegazetteerdoingthesame?Andif they are, in fact, performing the same operation, why is itcommonlyunderstoodthatagazetteercanprovidegeographicrepresentationsforawidevarietyofgeographicfeaturessuchasrivers,mountains,andshorelines,whiletheseareseldomthoughtofascandidatesforthegeocodingprocess?Wecanseethroughthisdiscussionthatthetermgeocodingcanmeandifferentthingstodifferentpeople,andtheirperceptionwillbebasedontheirprimary experienceorusagewithaparticulargeocoding tool.Tosome,“geocoding”issynonymouswith“addressmatching”(e.g.,Drummond1995,Vineetal.1998,Bonneretal.2003),highlightingitsprevalentuseoftransformingpostaladdressesintogeographicrepresentations(Drummond1995,250).Forothers,“geocoding”isunderstoodtoproduceavalidgeographicoutput,butitsinputisnotnecessarilylimitedtosimplepostaladdresses


(e.g.,LevineandKim1998),andstillfurtherdistinctionscanbedrawnbetweenthetwoterms(Johnson1998a,25).Takenliterally, geocodingmeans “to assignageographic code.”Thisdefinitionstemsfromthetworootwords:geo, fromtheLatinforearth,andcoding,definedas“applyingaruleforconvertingapieceofinformationintoanother”(similartothatdefinedearlyoninthegeocodingliterature[Dueker1974,320]).Noticethatthisliteraldefinitiondoesnotimplynorconstraininanywaytheinputtothegeocodingsystem,theprocessesordatasourcesusedtoassignthegeographiccode,orevenwhatthegeographiccodereturnedasoutputmustbe.Itispreciselythisrelaxationofformalconstraintsonthegeocodingprocessthathasallowedittomatureandprospertothemanyformsthatweusetoday,andthatwillinturndrivethetechnologicaladvancesoftomorrow.

GEOCODING FUNDAMENTAlSEven with this varied notion of geocoding, it is still possible to characterize it in terms of its fundamental components: the in-put, output, processing algorithm, and reference dataset (Levine and Kim 1998, Karimi et al. 2004, Yang et al. 2004, Nicoara 2005). The input is the locational reference the user wishes to have geographically referenced that contains attributes capable of being matched to some datum that has been previously geo-graphically coded. The most common data to be geocoded are postal addresses. In fact, there are very few geocoding services that geocode anything other than postal address data. The simple reason for this is that postal address data are among the most prevalent forms of information (Eichelberger 1993), and address geocoding is cited often throughout the literature as a national health goal that will “be the basis for data linkage and analysis in the 21st century” (U.S. Department of Health and Human Services 2000, goal 23-3). Address data are how people locate, situate, and navigate themselves, and are presently the easiest method by which to describe one’s location (Walls 2003). In the future when all cellular phones come equipped with reliable global positioning system (GPS) units and all homes and businesses are geographically referenced with coordinates available via wireless location-based services, the postal address may, in fact, become obsolete. But for the foreseeable future, the postal address will remain the critical and ubiquitous data throughout most forms of information processing.

Aspreviouslynoted,however,addressdataarenottheonlytypeoflocationaldatathatcanorshouldbegeocoded.EventheearliestgeocodingsystemsoftheU.S.Censusaccountedforthegeocodingofnamedbuildings(O’ReaganandSaalfeld1987),but the task of associating geocodes with geographic featuresother than addresses is most commonly associated with theservicesprovidedbyagazetteer(Hill2000).Theproblemwiththis, though, is thatagazetteer typicallydoesnotcontain thefunctionality to generate the geocodes that it returns, insteadactingasastoragemechanismafter thegeocodeshavealreadybeendeterminedusingothermethods.Assuch,thegeocoderiscommonlyemployedtoproducethegeocodesforfeaturesinthegazetteerthatareaddress-based,emphasizingthecrucialconnec-

tionbetweenthetwocomponentsaspartofalargerspatialqueryandanalysisframework.ThissituationisdisplayedinFigure1,wherethegeocoderisshowntobeoneofmanypossiblesourcesoffootprintdataforagazetteer,withitselfbeingcomposedofseveraldatasources.

Theoutputisthegeographicallyreferencedcodedeterminedby the processing algorithm to represent the input. In mostsituations,theoutputisasimplegeographicpoint,butnothingforbidsitfrombeinganyvalidtypeofgeographicobject.Thedevelopmentofdetailed spatialdatasets enables theoutputofincreasinglydetailedmultidimensionalgeographicfeatures,in-cludingtheemergenceof3-Dindoorgeocodingsolutions(Beal2003,Lee2004).

Theprocessingalgorithmdeterminestheappropriategeo-graphiccodetoreturnforaparticularinputbasedonthevaluesofitsattributesandthevaluesofattributesinthereferencedataset.This isby far themostcomplicatedportionof thegeocodingprocessinwhichthemostresearchhasbeeninvested.Thekeytopics involved intheprocess includethestandardizationandnormalizationoftheinputintoaformatandsyntaxcompatiblewiththatofthereferencedataset(Johnson1998b,Churchesetal.2002,Laenderetal.2005,Nicoara2005),thematchingalgorithm

Figure 1.Relationshipbetweenthegazetteerandgeocoder


thatpicksthebestfeatureinthereferencedataset(Drummond1995,Vineetal.1998,Davisetal.2003,Bakshietal.2004),and the final geocode generation mechanism that determineswhat to return based on the reference feature selected as thebestmatch(Drummond1995,LevineandKim1998,Ratcliffe2001,CayoandTalbot2003,Davisetal.2003).Figure2showsaschematicdiagramofhowasimpledeterministicprocessingalgorithmcouldproceedusingstandardization,normalization,and attribute relaxation.The standardization and normaliza-

tionprocesscanvaryincomplexityfromsimpletokenparsingwithlookuptablesforstandardizingabbreviationstoadvancedprobabilisticmethodsusingmachine learningtechniquessuchashiddenMarkovmodelsthatcanhandleattributemisspellingsandmisplacements(O’ReaganandSaalfeld1987,Fulcomeretal.1998,Churchesetal.2002,Christenetal.2004,Yangetal.2004,ChristenandChurches2005,Nicoara2005).Ingeneral,thekey roleperformed in this step is todeterminewhateachpieceofthe input isandtoturneachintoversionsconsistentwiththoseinthereferencedataset.

Oncetheinputhasbeensufficientlymassagedtobecompat-iblewiththereferencedataset,thematchingprocesspicksthebestcandidatetobeusedtoderivethefinaloutput.Trickssuchaswordstemming,usingSoundex,andrelaxingtherequirementofmatchingallattributescanbeusedtoimprovetheprobabilityoffindingamatchinthereferencedataset(O’ReaganandSaa-lfeld1987,Drummond1995,Fulcomeret al.1998, Johnson1998a,LevineandKim1998,Gregorioetal.1999,Boscoeetal.2002,Churchesetal.2002,Beal2003,Christenetal.2004,Yangetal.2004,ChristenandChurches2005,Nicoara2005).Heretheissuemayarisethatzero,one,ormorethanonerefer-encefeaturescanbethebestpossiblematch.Inthecaseofonematch,thealgorithmwilluseittodetermineageocode.Inthecaseofzero,thematchingalgorithmmayprompttheuserformoreinformation,attempttogeocodeatalowerresolutionwithadditionaldatasets,ortrytofindadditionalinformationinotherdatasetstoenableamatch(Laenderetal.2005).Likewise,inthecaseofmultiplematches,thealgorithmmayprompttheusertodeterminetheappropriateoneorconsultadditionaldatasetsformore informationtouse inbreaking the tie (HutchinsonandVeenendall2005b,a).

Inanycase,oncetheappropriatereferencefeaturehasbeenselected,thealgorithmmustdeterminetheappropriategeocodeforoutputbasedontheinputandthereferencefeature.InthecaseofaprecompiledgeocodeddatasetsuchastheADDRESS-POINT (Ordnance Survey 2006) and G-NAF (Paull 2003),thealgorithmcansimplyreturntheexistinggeographicrepre-sentation.However,inthecaseofTIGER(U.S.CensusBureau2006),theoutputgeographymustbederivedbasedonthelinesegmentdeterminedtobeamatch.Hereinterpolationalgorithmsdeducetheappropriateoutputgeographybasedonattributesofthestreetsegmentsuchasaddressrangesandpolarity(Drum-mond1995,LevineandKim1998,Ratcliffe2001,CayoandTalbot2003,Davisetal.2003).Ingeneral,theseinterpolationalgorithmsworkbyfirstidentifyingthecorrectstreetsegmentinthereferencedatasourcebasedontheattributesoftheaddresstobegeocodedandtheattributesofthestreetsegment(addressrangesassociatedwithbothsidesofthesegment,streetname,streetsuffix,etc.).Oncefound,theappropriatesideofthestreetsegmentisascertainedusingthepolarity(even/odd)ofthead-dressandeachofthestreetsegmentsides.Thecorrectlocationalongthestreetsegmentisthendeterminedbycomputingwheretheaddressesinquestionwouldfallasaproportionofthetotaladdressrangeassociatedwiththeappropriatesideofthestreet

Figure 2.Schematicofdeterministicaddressmatchingwithattributerelaxation


segment.Thisproportionisthenappliedtothetotallengthofthestreetsegmenttoobtainalocationalongthecenterlineofthestreet,andadditionalparameterssuchasdistanceanddirectionfromthestreetcenterandoffsetfromtheendpointsofthestreetcan be introduced to further improve the accuracy (Ratcliffe2001,CayoandTalbot2003).Additionaldatasourcescanbeconsultedtoobtainknowledgeaboutthenumberofparcelsonthestreetandtheirgeographicdistribution(Bakshietal.2004)toovercometheparcelhomogeneityassumption(Dearwentetal.2001)thatallparcelswithinanaddressrangetrulyexistandhavethesamedimensions.InFigures3through6thesepointsareillustrated.

Figure3showstheparameters for the interpolationalgo-rithm, d and,thestreetcenterlineoffsetdistanceandangle,q ,thecorneroffsetdistance,and v ,theinterpolateddistance

tothecenteroftheparcel.Alsoshownaretheaddressrangesforeachsideofthesegment,601through649ontheoddparityside,and600through648ontheevenparityside.Figure4showsasampleblocksegmentwiththegeocodedpositionof631MainStreet displayed. Figure 5displays how theparcel homogene-ityassumptiondivides thesegment intoequalportions foralladdresses within the range of the street segment, placing thegeocodedpoint foraddress631at thewrong location(shownasring)comparedtothetruelocation(shownasshadedring).Figure6alsodisplaystheparcelhomogeneityassumption,butinthiscasethetruenumberofparcelsonthestreet isknownandtheresultinggeocodedpointforaddress631isatacloserlocation(shownasring)tothatofthetruelocation(shownasshadedring).Whenusingarea-basedreferencefeaturessuchaspostalcodeandparcelpolygonstocomputepointgeographies

Figure 3.Sampleblockshowingparametersofthegeocodingalgorithm

Figure 4.Sampleaddressblockwithtrueparcelarrangementshowingtruegeocodedpointasring


toreturnasoutput,thealgorithmmustcalculateanappropriatecentroid(Stevensonetal.2000,Dearwentetal.2001,Ratcliffe2001).Itmaysimplyreturnthecenterofmassoftheobject,oritmayperformmorecomplexcalculationsinconjunctionwithotherinformationsuchaspopulationdistributionsacrossanareatodetermineamorerepresentativeweightedcentroid(Gatrell1989,DurrandFroggatt2002).

Thereferencedatasetconsistsofthegeographicallycodedinformationthatcanbeusedtoderivetheappropriategeographiccodeforaninput.Asnotedearlier,thedatasetsusedasgeocodingreferencefileshavechangedrapidlyover timeandarerespon-siblefordrivingnewtechnologicalbreakthroughsingeocodingmethodologies.Theearlydatasetsoftext-basedlistshavegivenwaytotruedigitalgeographicdatasets,andarerapidlymovingtowardadvanced3-Drepresentations.Theunderlyingadvancesintermsofefficientstorage,retrieval,andindexinghaveallowedthesedatasetstogrowexpansivelyinsize,detailofresolution,andspeedofaccess.Theonlyconstraintonthesedatasetsisthattheyneedtomaintainattributesinaconsistentfashionthroughout,so that the standardization and normalization algorithms canworktowardtransformingtheinputdatatobeappropriateforfindingamatch.

GEOCODING ERRORThis broad definition of geocoding also brings with it a significant burden in the form of anticipating and/or quantifying geocod-ing error. Even simply defining what the error of the geocoding process is presents an arduous task. When speaking of geocoding error, is reference made to the positional accuracy of the returned

geographic object, the probability that the feature returned is the one that was desired, or the validity of one or more assumptions used by the geocoding algorithm? Further definitions could include the error caused by the match rate, the weighting and relaxation techniques used in the standardization process, or the confidence cutoffs used during probabilistic matching. Common causes and effects of errors in each stage of the geocoding process are listed in Table 1.

Table 1.CommonCausesandEffectsofErrorsinStagesoftheGeocodingProcess

Stage Cause of error Effect of error

Matching

Attribute relaxation Incorrect feature

Probabilistic confidence level

Incorrect feature

Derivation

Parcel homogeneity assumption

Wrong distribution

Address range existence assumption

Wrong number

Reference Data

Spatial accuracy Results inaccurate

Temporal accuracy Results inaccurate

Figure 5.Sampleaddressblockwithparcelhomogeneityassumptionusingaddressrangeshowingerroneousgeocodedpointasringandtruegeocodedpointasshadedring


Itbecomesobviousfromthis(notevenclosetoexhaustive)listofcommonlydescribederrormetricsthatevaluatingtheerrorassociatedwithageocodedresultisdifficultatbest,andatworstnoteventakenintoconsideration.Itisanunfortunaterealitythateventhoughabroadrangeofliteratureexistsspecificallygearedtoexposinghowminorerroringeocodingaccuracycanaffectresultsbased on detailed spatial models (e.g., Gatrell 1989, Ratcliffe2001,HiggsandRichards2002,Bonneretal.2003,CayoandTalbot2003,Krieger2003,Kriegeretal.2005),recentresearchinitiativescontinuetoemploygeocodeddatawithoutregardforhowtheaccuracycanintroducepossibleinconsistenciesorbiasintotheresults(Diez-Rouxetal.2001,Brodyetal.2002,HaspelandKnotts2005).

Severalstudieshaveattemptedtoquantifytheerrorassoci-atedwiththegeocodingprocess,highlightingerrorintroductionfromspecificaspectsofthegeocodingprocess(e.g.,Davisetal.2003,Karimietal.2004).Onevaluatingapotentialgeocodingstrategy,oneshouldconsiderseveralkeyfactorstodetermineiftheoutcomewillmeettheirneeds.First,whatarealunitwillthedatabegeocodedto?Willtheoutputbetothegranularityofin-dividualpostaladdresses,orwillitbetoalargerdelineationsuchasacensusblockorzipcode,andwilltheimplicitaggregationofusingalargerunithaveaneffectontheresults?Thisdecisionisadivisivetopicinthegeocodingliteratureandseveralstudieshavedemonstratedthatarealunitchoicesbothhaveaneffectanddonothaveaneffectontheoutcomesoftheresults(Geronimusetal.1995,GeronimusandBound1998,1999a,b,KriegerandGordon1999,Smithetal.1999,Soobaderetal.2001,Kriegeretal.2002a,2003,Gregorioetal.2005).Evaluatingone’sconfi-

denceintheavailablescholarshipwillrequirepersonaljudgmenttodetermineifthiscouldbeanissuegivenaparticulardatasetandresearchobjective.

Second,howaccurateistheunderlyingdatausedastherefer-encedataset?Includedinthisdiscussionshouldbetheconceptsofspatialaccuracy(howclosearethefeaturesinthedatasettowhatisfoundontheground[Karimietal.2004,Wuetal.2005]?),temporalaccuracy(howclosearethefeaturesinthisdatasettohowtheywereatthetimeperiodofinteresttome[McElroyetal.2003,Hanetal.2005]?),originalcollectionpurpose(whatwerethesedataoriginallycollectedfor[Boulos2004]?),andlineage(whatprocesseshavebeenappliedtothisdata[Veregin1999]?).Theseaspectsmaybedifficulttoquantifybecausetheaccuracymeasurementsassociatedwithdatasetsareestimatesovertheentiredataset,notonaper-featurebasis.Forexample,whileachievinganacceptableaccuracyforshortstreetsegmentsinurbanareas,theTIGER(U.S.CensusBureau2006)datasetsmostcommonlyusedforlinearinterpolationgeocodingintheUnitedStatesareknowntobefarlessaccurateforgeocodinginruralareaswithlongerstreetsegments(Drummond1995,Vineetal.1998,CayoandTalbot2003,Bonneretal.2003,Wuetal.2005).Assumingaconsistentaccuracyvalueforadatasetthroughouttheentireareaofcoverageisrarelydiscussedornotedasapointofcontentioninthedeterminationofgeocodingaccuracy.

Athirdrelatedissueariseswhenoneconsidersmultitieredgeocodingapproachesusingmultipledatasources.Forexample,innumerousinstances,geocodingmatchratesinruralareasarefarlessthaninurbanareas(e.g.,Gregorioetal.1999,KwokandYankaskas2001,Boscoeetal.2002,Bonneretal.2003,Cayo

Figure 6.Sampleaddressblockwithparcelhomogeneityassumptionusingactualnumberofparcelsshowingerroneousgeocodedpointasringandtruegeocodedpointasshadedring


andTalbot2003).Thetypicalapproachtosolvingthisprobleminvolvesadecisionofwhethertogeocodetoalesspreciselevelortoincludeadditionaldetailfromothersourcestodeterminethecorrectgeocode.Choosingeithercasecreatesaresultingdatasetwith varying degrees of accuracy as a function of location, aconditionrecentlydefinedas“cartographicconfounding”(Oliveretal.2005)thathasbeenalludedtomanytimes,yetremainedundefinedthroughoutthehistoryofgeocodingresearch(Block1995,Ratcliffe2001,CayoandTalbot2003,Nuckolsetal.2004,Ratcliffe2004,Gregorioetal.2005).Aper-geocodeaccuracyisrarelymaintainedasaresultof thegeocodingprocessotherthanthelevelofgeographymatchedto(i.e.,censustractversusblockgroup),andrarelydospatialmodelsincludevariablestomodelthisphenomena,althoughsomeresearchers(Openshaw1989,Arbia et al. 1998,Cressie andKornak2003,GabrosekandCressie2002)havebegundevelopingmodelstoaccountforit.Despite this, informationdescribing thevaryingdegreesofaccuracyofeachindividualgeocodeisnottypicallyrepresentedduringsubsequentspatialanalysis.

Fourth,oneneeds todetermine if the assumptionsmadeby the geocoding algorithm are applicable to one’s needs. Aspreviously mentioned, the most common form of geocodin(linearinterpolation–based)makesseveralkeyassumptionsthatcanaffect the levelofaccuracyof theresults.First, itassumesthatalladdresseswithinanaddressrangeexist.Thus,whenitdeterminesthecorrectlocationforaparticularaddressalongastreetsegmentbyidentifyingtheproportionalongthesegmentwhereanaddressshouldfall,itwilloverestimatethenumberofaddressesplacingitatthewronglocation.Second,itassumesahomogeneousdistributionofaddressesintermsoflotplacementandsize,knownastheparcelhomogeneityassumption(Dearwentetal.2001,332).Thismeansthateachlotonthestreetisassumedtohavethesamedimensions,andbeorientedinthesamedirec-tion,whichistypicallynotarealisticassumption.Furthermore,itdoesnottakeintoaccountthatthecornerlotonasegmentmaybelongtothesegmentinquestion,ortothesegmentthatformsthecorner(Bakshietal.2004).Whilethemagnitudeoferrorintroducedbytheseassumptionsissmall(ontheorderofhalfthelengthofthestreetsegment[Wuetal.2005,596]),itcanhavedramaticeffectswhenthevariableand/orrelationshipsofinterest(e.g.,environmentalexposuredosestopesticide[Brodyetal.2002,Kennedyetal.2003],airpollution[Wuetal.2005],orproximitytovotingprecincts[HaspelandKnotts2005])varyovertensorhundredsofmeters,andbecomesamplifiedasthelandscapebecomesmorerural.Additionally,ithasbeenshownthat when geocodes are used for point-in-polygon operationstoderiveattributes fromotherdatasets, small spatialerrors ingeocodesthatliealongbordersbetweenthelargerlevelfeaturescancauseseriousmisclassificationsincombineddata(Ratcliffe2001,Schootmanetal.2004).

Fifth,oneneedstoconsidertheuncertaintycreatedbytheaggregationorrandomizationperformedontheresultingpointtoprotecttheidentityofthegeocodedobject.Thisismostoftenthecaseinthegeocodingofhealthdata,whereconfidentiality

requirementsnecessitatethegeocodeforanindividual’slocationtobenonidentifying.Researchhasshownthattherearewaystotradeoffbetweentheusefulnessofdatareturnedforspatialanaly-sisversusspecificconfidentialityrequirements,butfurtherworkisrequiredtoquantifytheeffectofthisinageocodingcontext(Armstrongetal.1999).Foramorethoroughdescriptionoftheissuesinvolvedspecificallygearedtowardhealthresearch,refertoBoscoeetal.(2004)andRushtonetal.(2006).

Finally,oneneedstodetermineiftheintendedspatialanalysiscandealwithuncertaingeographicvaluesornot.Hereafunda-mentaldecisionmustbemadewhetherprobabilisticmatchingmethodscanbeusedor strictlydeterministicones (O’Reaganand Saalfeld 1987). When interpreting an input query, thegeocodingsystemmustgothroughseveralstepstodeterminethe“best”matchinthereferencedataset(LevineandKim1998).Iftheinputcanbematcheddirectlytoanexistinggeography, itcanbereturnedimmediately.However,itismoreoftenthecasethatoneneedstomassagetheinputdataandtransformitintoaformatconsistentforfindingthebestmatch.Locationaldata,andinparticularpostaladdressdata,arenotoriously“noisy”;veryoften,extraneousinformation,missinginformation,orconfusingnonstandardization is contained in the input (Fulcomer et al.1998,Ratcliffe2001,2004,MurphyandArmitage2005,Nico-ara2005).Inthesecases,thegeocodingalgorithmisforcedtoeitherattempttocorrecttheinputsothatamatchcanbefoundorreturnanonmatch.Ithasbeenshownthatwithdeterministicapproachessuchasrelaxingtheconstraintthatallattributesmustmatchexactlyandallowingpartialmatcheswithavarietyofat-tributeweightingschemes,ahighermatchratecanbeachieved,but at thepriceof accuracy. Inparticular, studieshave foundthatrelaxingthestreetnameportionofanaddresswillgreatlyreducetheaccuracyofthegeocodedresults(Lixin1996,Bonneretal.2003,CayoandTalbot2003,Krieger2003,Rushtonetal.2006).Incontrast,probabilisticapproachestostandardization(Jaro1984)havebeenusedsinceveryearlyoninthegeocodingliteraturewithmuchsuccess(O’ReaganandSaalfeld1987)andcontinuetoimprove(Churchesetal.2002,Christenetal.2004,ChristenandChurches2005),butonemustrecognizetheriskthattheseresultsmaynotbeaccurate,astheyarerelyingontheconfidenceleveloftheiruncertaintymeasures,andtheywillinsomecasesproduceerroneousresults.

PERSISTENT GEOCODING DIFFICUlTIESFor all the technological advances and improvements that have been made to the geocoding process and the underlying reference datasets, the geocoding difficulties identified early on still exist. In developing countries with little GIS data infrastructure, the main roadblock to accurate geocoding is the simple nonexistence of reference datasets or GIS data infrastructure (Croner 2003, United Nations Economic Commission 2005). The development of basic GIS reference datasets is hindered by the existence of slum-like areas that change frequently, contain geographic features

URISA Journal • Goldberg,Wilson, Knoblock 41

that are not street addressable, and where many areas lack a con-sistent addressing scheme (Davis 1993, Oppong 1999, Davis et al. 2003, United Nations Economic Commission 2005). Efforts are under way to remedy these situations by developing standard-ized addressing systems that include facets for encouraging public participation aimed at promoting acceptance and eventual adop-tion, but these are costly endeavors being undertaken in areas with few economic resources to dedicate to the task (United Nations Economic Commission 2005).

EvenindevelopedcountriessuchastheUnitedStates,theexistenceofruraladdressesandP.O.boxesimposeacontinualheadache for geocoding practitioners (Gregorio et al. 1999,Boscoe et al. 2002, Hurley et al. 2003, McElroy et al. 2003,Schootmanetal.2004,Gaffneyetal.2005,Oliveretal.2005).IntheP.O.boxcase,itisnotpossibletodetermineanaccurategeocodebecausetheinformationavailableabouttheaddressisjustnotspecificenough.Thebestthatonecandoistogeocodetoalowerresolutionsuchasapostalcodecentroid,butseveralstudieshaveexploredhowthiscanintroducebiasintotheresultsproducedwiththegeocodeddata(Sheehanetal.2000,Kriegeretal.2002b,Hurleyetal.2003).ResearchinitiativeshaverecentlyundertakencreativewaystoobtainenoughspecificinformationtoproduceamoreaccurategeocodebyusingsecondarysourcesincludingobtainingtheP.O.boxrenter’saddressfromthepostalservice,utilitycompanyrecords,andadministrativerecordsfromgovernment agencies.These tasks requirehuman interventionand arequite expensive (Levine andKim1998,Hurley et al.2003,McElroyetal.2003,Hanetal.2005).Whilecapableofproducing highly accurate results to within a few meters, thepracticeofusingaglobalpositioningsystem(GPS)technologytorecordpointlocationsforaddressesisanoptionforproducinggeocodedresults,butthishasitslimitations(e.g.,time-consum-ing,expensive,andlabor-intensive)(Wardetal.2005,Bonneretal2003).TheincreasingprevalenceofparceldataanditsusewhenGPSdataareunavailableisanalternativeoptionthathasbeen proposed throughout the history of the literature (e.g.,Dueke1974,Rushtonetal.2006).ArecentU.S.governmentreportfoundthatthereisanincreasingsurgeintheamountofsurveyqualitydigitalparcelboundarydatabecomingavailable(StageandvonMeyer2005),withsomestatesactuallypassinglegislationrequiringitsrelease(Lockyer2005),fromwhichac-curatecentroidscouldbederivedandusedassubstituteswhereGPSdataarenotavailable(Ratcliffe2001).

Likewise, the mandatory introduction of the Enhanced911(E911)systemintheUnitedStatesforallstructureswithtelephones is improving geocoding by increasing the numberof rural addresses reported as address data and creating moreaccurate reference datasets (Johnson 1998a, Cayo andTalbot2003,Levesque2003,Roseetal.2004,Oliveretal.2005),buthistoricaldatafrequentlyusedinresearcharenotbeingupdated,sotheproblemstillremains.Againinthiscase,thegeocodingpractitionerisforcedtoobtainsecondaryinformationtoidentifywhatanappropriatecity-styleaddresswouldbeforthelocationsoitcansuccessfullybegeocoded.E911geocodingtypicallyresults

inan“absolute”geocode,asopposedtoa“relative”geocode,asintraditionalinterpolation-basedgeocoding.“Absolute”geocoding,asusedhere,referstothefactthattheresultinggeocodeisbasedonalinearaddressingsystem,describingaknownpoint(e.g.,amilepost)andthedistanceonewouldhavetotraveltofindtheactuallocationfromthatpoint.“Relative”geocoding,incontrast,resultsinageocodedresultthatisaninterpolationalongorwithinageographicfeature(e.g.,apercentageofthedistancealongastreetsegmentorthecenterofmassofaparcel).

Aspeoplemoveawayfromtraditionalland-linephoneswiththeadoptionofcellphonetechnology,somemayarguethatthepromiseofE911solvingaddressingissueswillbegintodisappear.However,whileitistruethatinthefuturemorecallswillundoubt-edlybemadefromcellphones,thisisirrelevantformostmunicipali-tiesstillassumethatstructureswillhavephonesandlegislationisofteninplacethatrequirestheE911systemtobekeptup-to-dateandaccurate.Assuch,whenofficialaddressesarerequestedfornewconstruction,thedepartmentresponsibleformaintainingtheE911systemwillmostlikelyberequiredtovisitthepropertyandassigntheE911-basedgeocodefortheaddress.

A further problem, which the evolution of reference da-tasetsmayhelpsolve,isthatofsubparcelgeocoding.Thiscaseoccurswhenmultiplestructuresareresidingonthesamelandparcelsuchasinapartment/condominium-typepropertiesandlargecampusessuchasuniversitiesandbusinessparksorinthecaseoflargefarmswhereasinglesmallstructuremaybelocatedsomewherewithinamuchlargerparcel.Heregeocodingtothecentroidofthepropertymaynotpresentsufficientaccuracyforthe detailed applications previously described (Gaffney et al.2005).However,includingsecondarydatasourcesandoperationssuchashigh-resolutionimageryinconjunctionwithcomputervision techniques to identify and separatebuildingsmayhelpleadthewayinthisarena(HutchinsonandVeenendall2005b).Likeallreferencedatasourcesthough,whenemployingimagerydatainageocodingsolution,onemustbeawarethattheaccuracyultimatelyachievedcanbegreatlyaffectedbythepreprocessingapplied(orlackthereof ),typicallytherectificationandregistra-tionprocesses.Forin-depthhistoricalandstate-of-the-artreviews,consultGottesfeldBrown1992,PohlandVanGenderen1998,Toutin2004.Additionally, integrating andconflating existingdetailedmapsofcampuses(Chenetal.2003,2004)mayenabletheextractionofhighlyaccuratepolygonsforbuildingfootprints,butautomatingthistaskisstillanopenresearchproblem.Ofcourse,therelianceontwo-dimensional(2-D)GISdatasourcesofthetraditionalandcommonlyusedGISplatformsprecludestheabilityforhighlyprecisegeocodingof3-Dstructureswithmultipleaddressessuchasmultistorybuildings.

CONClUSIONThis article has explored the state of the art in geocoding through a discussion of the path geocoding and its reference datasets have taken over the years. This work should serve as a starting point from which potential geocoding projects can be undertaken with regard to identifying the potential pitfalls and challenges that are


commonly encountered. Each particular geocoding project will have its own requirements in terms of input and output data structure and format, confidentiality, cost, available tools, and technical know-how, but the survey presented here should allow a more thorough understanding of the ramifications of particular choices made during the process.

Acknowledgments

This research is based on work supported in part by the National Science Foundation under Award Number IIS-0324955 and in part by the University of Southern California Libraries. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of any of these organizations or any person connected with them.

About The Authors

Dan W. Goldberg is a third-year computer science Ph.D.student working in the GIS Research Laboratory at theUniversityofSouthernCalifornia.Heisarecentrecipientofthe2005–2006U.S.GeospatialIntelligenceFoundation’sGraduate Student Scholarship, and his research interestsincludegeographicinformationextractionandintegration,automatedapproachestobuildinghighlydetailedandac-curategazetteers,anddevelopingnewmethodsforgeocodingtextuallocationaldescriptions.

CorrespondingAddress:GISResearchLaboratoryUniversityofSouthernCaliforniaLosAngeles,CA90089-0255Phone:(213)740-8263E-mail:[email protected]

John P. WilsonisaprofessorofgeographyandDirectoroftheGISResearchLaboratoryattheUniversityofSouthernCali-fornia.HeisthefoundingeditorofTransactions in GIS,anactiveparticipantintheUNIGISInternationalNetwork,andPast-PresidentoftheUniversityConsortiumforGeographicInformationScience.Hismajorpublications include twobooks(Terrain Analysis: Principles and Applications; Handbook of Geographic Information Science) alongwithnumerousbookchaptersandjournalarticlesontopicsrangingfromsoilero-sionandgroundwaterpollutionproblemstourbangrowthmodelingandtheenvironmentalandsocialcharacteristicsofplaceandtheirimpactsonselectedhealthoutcomes.

CorrespondingAddress:DepartmentofGeographyUniversityofSouthernCaliforniaLosAngeles,CA90089-0255Phone:(213)740-1908E-mail:[email protected].

Craig A. KnoblockisaseniorprojectleaderattheInformationSciencesInstituteandaresearchprofessorincomputersci-enceattheUniversityofSouthernCalifornia.HereceivedhisPh.D.incomputersciencefromCarnegieMellonUni-versity.His current research interests include informationintegration, automated planning, machine learning, con-straintreasoning,andtheapplicationofthesetechnologiestogeospatialdataintegration.HeiscurrentlyPresidentoftheInternationalConferenceonAutomatedPlanningandScheduling and a fellow of the American Association ofArtificialIntelligence.

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INTRODUCTIONThe Argumentation Map (Argumap) concept was proposed by Rinner (1999, 2001) to support planning processes by facilitat-ing distributed, asynchronous discussions. Argumaps are based on the combination of an online discussion forum and an online geographic information system (GIS) component. Argumaps were conceived as a method to formalize debates that have geo-spatial elements in the discussion. Because of their distributed nature, Argumaps benefit from a number of characteristics of the Internet, for example the ability to share information with many stakeholders (Laurini 2004) and the anonymity provided in online discussions (Kingston et al. 1999).

Keßler(2004)implementedanArgumentationMapproto-typeasaproofofconcept.ThisWeb-basedprototypeintegratesadiscussionforumandasimplemappingtool.TechnologyusedintheimplementationincludestheGeoToolsLitemappingtoolkit,acustom-builtJavaappletforthediscussionforum,theMySQLdatabaseforstorageofgeographicallyreferenceddiscussioncon-tributions,andtheUniversityofMinnesotaMapServerforthesupplyofbackgroundmaplayers.Keßlerchosetheseopen-sourcesoftwaretoolsonthegroundsthattheyfulfilledtherequirementsfortheArgumapconceptsetoutbyRinner(1999)andthattheyminimizeddevelopmentcosts.

The functionality of the prototype includes map naviga-tion (zoom in/out, pan, zoom to full extent), layer manage-ment(switching layersonandoff ),anddisplayofmaplabels(e.g.,buildingnames). In thediscussion forum,contributionsaredisplayedbytheirsubjects,authors,anddatesinlistswithindentationsbydiscussionthreads,andthebodyofaselectedcontributionisdisplayedinatextwindow.Whenacontributionisselectedintheforum,itsgeographicreferenceswillbehighlightedonthemap.Likewise,whenamapobjectisselected,alldiscussioncontributionsreferringtothisobjectwillbehighlightedintheforum.TheArgumapprototypealsoprovidesafull-textsearchtoolforthediscussionforumandsummarystatisticswhenbrows-

ingthemap(numberofcontributionspermapobject).Finally,intermsofparticipationingeoreferenceddebates,thetooloffersalog-infeaturethatenablestheusertostartanewdiscussionthreadorrespondtoexistingcontributions.Wheneditingamessage,asetofgeographicreferencescanbespecifiedinthemapandisstoredtogetherwiththetextofthemessage.ThefunctionalityandarchitectureoftheprototypeissummarizedinfurtherdetailbyKeßleretal.(2005).

Thestakeholdersinplanningprocessesusuallyareheteroge-neousgroupswithavarietyofknowledgeandskilllevels(Healey1997,SimãoandDensham2004).Becauseofthewiderangeofpossibleusers,anyplanningsupport systemmustbedesignedinsuchawaythatallareabletolearntousethemajorityofitsfunctions.ThisintroducesamotivationforausabilityanalysisfortheArgumapprototype.

Thispaperprovidesaframeworkforusabilityanalysisforparticipatory spatial decision support tools such as Argumapsanddescribesacasestudy.WeinvestigatedhowKeßler’s(2004)prototype was understood and used by a heterogeneous par-ticipantpopulation.Thefollowingsectionsdescribetheresearchbackground,methodology,aswellasthepreparationandresultsof the case study.Conclusions are thendrawn in the formofrecommendationsforimprovingtheArgumapprototype.Whilethese recommendationsare specific to the software toolbeinganalyzed,thisresearchalsoprovidesanexampleforconductingusabilityanalysesforparticipatoryGIStoolsingeneral.

APPROAChES TO SOFTWARE USABIlITY ANAlYSIS“Human-Computer Interaction (HCI) is concerned with the de-sign of computer systems that are safe, efficient, easy and enjoyable to use as well as functional” (Preece 1993, 11). As long as there have been computers, their developers have been concerned with how the machine and its software will be used. The interaction between computers and humans is outlined by Licklider (1960)

Analyzing the Usability of an Argumentation Map as a Participatory Spatial Decision Support Tool

Christopher L. Sidlar and Claus Rinner

Abstract: Argumentation Maps support participants in geographically referenced debates as they occur, for example, as part of urban planning processes. In a quasi-naturalistic case study, 11 student participants discussed planning issues on the University of Toronto downtown campus. The analysis of this case study focuses on general usability aspects of an Argumentation Map prototype, such as cost of entry, efficiency, interactivity, and connectivity. By applying usability analysis methods from the field of human-computer interaction, we evaluate the learnability, memorability, and user satisfaction with this tool’s functionality. Our findings indicate that the participants were generally satisfied, but we include specific suggestions for improving the func-tionality of Argumentation Maps, e.g., with respect to map navigation, display of discussion contributions, and online status of participants. On a more general level, this case study contributes to the methods spectrum of research into participatory spatial decision support systems as an example of user testing in a realistic decision-making context.


when he viewed the role of the human as formulating hypotheses for problems, setting standards for the evaluation to follow, and finally evaluating the output, while the role of the computer was to facilitate the “routinizable work” to free time for analysis and evaluation.

HCIevolvedfurtherthroughtheworkofWilfredHansenwithEMILY,atext-editingsystemforprogrammers,inthe1970s(Pew2003,8).Hansenisaccreditedwithpioneeringtheuseofthetermuser engineering principles(1971).Hansen’sprinciplesincludedknowingtheuser,minimizingmemorization,optimiz-ingoperations,andengineering forerrors.Hansen’sworkwaslaterfollowedbythatofEngleandGranda(1975)atIBMthatsetoutguidelinesforvariousaspectsincludingthedisplay,re-coveryprocedures,userentry,andresponsetime.TheevolutionofguidelinespeakedwithSmithandMosier(1986)whensuchacomprehensivesetwasreleasedthattheguidelinesthemselveswerecontradictory.

Bythelate1980s,theconceptofuserengineeringprincipleshadnotonlybecomeaconsiderationforprogrammersbuthadalsoevolvedintoaniterativeprocessthatincludedtheproductionofprototypes,subsequenttesting,andultimatelytheproductionofmodifiedversions(RossonandCarroll2002).Thisspurredtheformationofusabilityengineering.“Initially,usabilityengineeringfocusedonthedesignoftheuserinterface”(RossonandCarroll2002, 14). Since the personal computer revolution, usabilityengineeringhasalsomadeitswaytosoftwareengineeringwiththedevelopers’mainconcernbeinghowtheuserinteractswiththesoftware.

Study TypesThis leads into the discussion on how best to study the use of a software system. Systems can be evaluated using different levels of controls. Kirkakowski and Corbett (1990) categorized evaluation procedures into three types: 1. Naturalisticstudy,2. Quasi-naturalisticstudy,and3. Experimentalstudy.

Studiesthatareobservational,takingadvantageofalreadyexistingsituationalcontexts,areconsiderednaturalisticstudies.Naturalisticstudiesproviderealisticallyapplicableresultsbuttocompletesuchastudytheinvestigatormustplayabackgroundrole,and,therefore,collectingtherequiredinformationtopro-ducethedesiredresultsprovestaxing.Quasi-naturalisticstudiesuseareal-worldcontextbutareusedwithsuchcontrolssothatboth evaluation and collecting of information are easier, andthereforeadeeperinvestigationcanbeachieved.Finally,experi-mentalstudiesusecontrolstofocusontheindependentvariablesthattheinvestigatorwishestostudy,whilemitigatingvariablesthatwouldcauseerrorsin,orcloud,theresults,butoccurintheleast“realistic”context.

ThisclassificationschemecanbecomparedtothatdescribedinPreece(1993).Preeceoutlinesfivecategoriesofevaluationforthepurposeofusabilityevaluation:analytic,expert,observational,

survey,andexperimental.Ananalyticalevaluationisdescribedas using “interface descriptions to predict user performance”(Preece1993,109).Anexpertevaluationusesidentifiedexpertsinthefieldrelatedtotheprototypetoanalyzeandevaluateit.Anobservationalevaluationconsistsofevaluationofthebehaviorandreactionsofusersinusingtheprototype.Asurveyevaluationutilizesaquestionnaire tosolicitusers’opinionsontheuseoftheprototype.And,finally,anexperimentalevaluation,similartothatofKirkakowskiandCorbett(1990),utilizesthescientificpracticeofcontrolstoanalyzetheprototype.

Evaluating Software UsabilityMeister and Rabideau (1965) outline a seven-step procedure for usability evaluation: 1. Determiningwhatasuccessfulapplicationoftheprototype

wouldbe;2. Identifyingtheultimategoalofusingtheprototype;3. Segmenting the goal of the prototype, so that it may be

analyzedashomogeneousfunctions;4. Identifyinganddescribingthefunctionsoftheprototype;5. Decidingoncriteriauponwhichtheuseofthefunctionsis

tobeassessed;6. Allocatingfunctionsonthebasisofwhethertheyareuser

functionsorprototypefunctions;and7. Performingtheexperimentandobservingonthebasisofthe

identifiedcriteria.

Shackel(1991)describesthreetypesofvariablesthatshouldbe investigated when considering the usability of a product:dimensional, performance, and attitude criteria. Dimensionalcriteria refer to the sizeof theproductor itsergonomics,anddonotaloneprovideamarkofusabilitybutmustbeconsid-eredinconjunctionwiththeperformanceandattitudecriteria.Performance criteria refer to how well the product facilitatesits function,andattitudecriteria refer to the feelings theuserhaswhenusingthespecificproduct.SimilarrepresentationsofthesecriteriacanbefoundintheevaluationproceduresusedbyChapanis (1965, 1981, 1991),Meister andRabideau (1965),andParsons(1972).

WongandChua(2001)investigatefourbeneficialaspectsoftheWebthatarelikelytoaidpublicparticipationGIS(PP-GIS):lowcostofentry,efficientdatatransfer,interactivity,andconnectivity.WongandChuaalsodescribefourbarriersthatareparticularlypresentinWeb-basedPPGIS:costofinteractivity,userdiversity,dataandcopyrightcosts,andtrustandlegitimacy.TheyadaptthismethodologytoinvestigatetheapplicationoftheInfoResourcesprojectcreatedbytheCenterforCommunityPart-nershipattheUniversityofPennsylvaniawhileotherresearchers(HarrisonandHaklay2002,Carver2001,Andrienkoetal.2002)employamoreclassicalmethodofusabilityanalysisasdefinedbyhuman-computerinteractionandusabilityengineers.

CASE STUDY METhODOlOGYFollowing Kirkakowski and Corbett’s (1990) classification, this

URISA Journal • Sidlar, Rinner 4�

experiment was designed as a quasi-naturalistic study. This study type was chosen on the basis that it allowed for Keßler’s (2004) Argumentation Map prototype to be analyzed in a real-world ap-plication while ensuring the case study was manageable enough so that a substantial investigation could be achieved.

Thisstudyusedtheprototypeinanearlyidentification/ex-plorationstageasanexampleofbottom-upplanning.Participantsinthediscussionwereinvitedtoidentifyplanningideas/concernswiththeUniversityofTorontoSt.Georgecampus.AmapoftheSt.GeorgecampuswasthereforeusedinthemapcomponentoftheArgumapapplication.Figure1showsthejuxtapositionoftheUniversityofTorontocampusmapwiththediscussionforumofthiscasestudy.Figure2showsthemapandthelistoflayers.Theapplicationprovidedtheparticipantswiththeabilitytoshapetheplanningprocessandexpresstheconcernsthatwereprevalenttothem.Participantswerefreetoparticipateinthethreadsofthediscussionthatmostinterestedthem,andwerealsogiventheabil-

itytostartnewthreadsinthediscussionforum.Thediscussionwasmonitoredforoffensivepostsbytheinvestigators.

Theparticipantswerecontactedusingasnowballsamplingprocedure.Theobjectiveinutilizingthisprocedureistoisolatestakeholdersbytargetingcampususersbeginningfromtheinvesti-gatorsandsnowballingoutwardthroughcontacts.Thecreationofsuchaparticipantgroupisreferredtoasa“dutchstudygroup”byJankowskiandNyerges(2001)intheirdescriptionoftheEAST2methodforGIS-supportedparticipatorydecisionmaking.Invita-tionsweresentoutviae-mail.Initially,39invitatione-mailsweresentout.Asetbackoccurredwhenthee-mailwasfilteredasjunkmailbye-mailproviders,suchashotmailandgmail.Afollow-upe-mailwasreleasedandinsomeinstances,directcontactwasmadetotheseparticipantsinformingthemthatthee-mailmayhavebeendirectedtotheirjunk-mailbox.Fromthe39e-mails,11peoplerepliedwithinoneweekshowinginterestinthecasestudy,a28percentresponserate.Fromthese11,theinvestigatorsreceivedtwoadditionalcontacts,oneofwhichshowedinterestinthecasestudy.Intotal,therewere12participantsinthestudy.ThisresponseratecanbecomparedtotheresponserateachievedinasimilarstudybyHarrisonandHaklay(2002)whoachieveda23percent(19of82)responseandan11percent(9of82)partici-pationrateintheirsecondstudy,whichusedasimilarsamplingprocedure.Asa resultof thecomparable samplingproceduresandsamplesizes,weareabletomakethesameconclusionthattheparticipantsare“‘typical’,ratherthanrepresentative”oftheirpublics(HarrisonandHaklay2002,845).

Meetings were then set up with the participants.Thereweretwotypesofmeetings:agroupworkshopandindividualmeetings,butallparticipantswereexposedtothesamepresen-tation.Ofthe12participants,fourattendedthegroupsession.Theintroductoryworkshopslastedabout30minutes,andtheparticipantswerebriefedabouttheconceptandthecasestudyandshownhowtoaccess,login,andmakeacontribution.Attheworkshops,participantsstatedthattheyunderstoodthefunctionsoftheprototypeandwhatwasexpectedfromthem.Attheendofthesession,participantswereaskedtofilloutaprediscussionquestionnaireandaninformedconsentform.Ofthosewhoat-tendedtheworkshops,allfilledouttherequiredformstobecomeparticipants.

We noticed that participants attending the individualmeetingstendedtoaskmorequestionsthanwereaskedinthegroupsession.Ingeneral,somepeoplewillshyawayfromask-ingquestionsingroupsandaremorelikelytoaskquestionsinan individual setting. Another aspect of consideration is howwellaparticipantretainsthe instruction.Thiswillbeassessedon thebasisof theirpreexistingknowledgeof skills related tothe case study (Internet forums, GIS, computers, geography,andplanning).

The method chosen for investigating the usability of theArgumentationMapprototype involves a combinationof thepreviouslyexplainedusabilitymethodologies.Usabilityof thisprototype, we feel, needs to be considered on two levels: thegeneralaspectsofthetoolandthespecificfunctionsoffered.The

Figure 1.ArgumapprototypewithUniversityofTorontocampusmaptotheleftanddiscussionforumtotheright(datasource:DMTISpatialandUniversityofToronto,CartographyOffice)

Figure 2.Argumapprototypewithlayersforpersonalgeographicreferences(red),otherusers’geographicreferences(orange),andaerialbackgroundimage(datasource:DMTISpatial,UniversityofToronto,CartographyOffice,andJ.D.BarnesFirstBaseSolutions)


generalusabilityaspectsoftheprototypewerestudiedthroughinvestigating• costofentry,• efficiency,• interactivity,• connectivity,and• intendedusers,

followingacondensedversionofthemethodemployedbyWongandChua(2001).Thespecificfunctionsoftheprototypeareanalyzedbyinvestigatingthe• learnability,• memorability,and• satisfactionofcase-studyparticipants.

Thisinvestigationfocusesonthedimensional,performance,andattitudecriteria(Shackel1991)expressedbytheusers.

Thefunctionalityofthetoolwasevaluatedthroughsurveysthattheparticipantswererequiredtofilloutatthebeginningandtheendofthetrialperiod.TheprediscussionquestionnaireaskedparticipantsabouttheirparticipationinInternetforums,famil-iaritywithGIS,geographyandcomputerknowledge,previousinvolvementinlocalplanningdecisions,aswellasdemographicvariablessuchassex,yearofstudy/tenure/occupation,age,andhometown.Theobjectiveoftheprediscussionquestionnairewasto identify the participant characteristics as well as their abil-itytounderstandandcontributeto.geographicallyreferenceddiscussions. A postdiscussion questionnaire considered topicsthatreferredtotheprototypesuchasitsgraphicaluserinterface,clarityofitsfunctions,designandlayoutoftheprototype,andsuitabilityoftheprototypeforthepurposeofspatialplanning.Thesecondquestionnairewasgearedtowardanalyzingtheus-abilityof theprototypeandwhether itwouldbebeneficial toplanningprocesses.

ANAlYzING ThE USABIlITY OF ThE ARGUMAP PROTOTYPE

General UsabilityOn the general level, the prototype must be evaluated with respect to the • costofentry,• efficiency,• interactivity,• connectivity,and• itsintendedusers.

The cost of entryreferstotheexpenditureimposedontheintended users and administrators when using the prototype.Thecostoftheprototypeincludesthepriceoftheprototype,thetoolsneededtorunitoraccessit,aswellasthetimeittakestoset ituporuse it.Efficiencyreferstotheprototype’sabilityto fulfill its functions and objectives while taking a minimal

amount of resources, albeit time or hardware. Interactivity ismeasuredthroughtheusers’feedbackontheresponsivenessoftheprototype.Connectivityreferstohoweasyitisforausertoaccesstheprototype.Thefinalcriterion,intended users,includesvariousaspectssuchastheinvolvementinusingsimilarsoftwareandprocessesaswellaspositioninsocietyandfinancialsituation.Ofparticularinterestistherelationshipbetweenparticipantsandinvestigatorsandtheleveloftrustthatispresent,andhowthisisreflectedintheparticipation.

Cost of Entry. Keßler’s(2004)ArgumentationMapproto-typeisbasedonopen-sourcesoftwarecomponents,namelytheMySQLdatabaseandtheGeoToolsLitemappingtoolkit,andwas published under an open-source license itself. Like manyopen-sourceprojects,thissoftwarethusisavailableatnocost.TheprototypefurtheradherestoGISinteroperabilitystandardsasdefinedbytheOpenGeospatialConsortium(OGC2005).

AJavaappletisaprogramwrittenintheJavalanguagethatcanbedownloadedandexecutedaspartofaWebpage,providedtheuserhasaJavaRuntimeEnvironment(JRE)installedinaWebbrowser.TheprototypeusesanapplettocapitalizeonfunctionsthatcannotbeimplementedwithHTMLorJavaScriptaswellasdisplayingfiletypesnotsupportedbyWebbrowsers,e.g.,ESRIShapefiles(Keßler,2004).TheArgumappackageisdownloadedfreeofcharge,andtherequiredJREisalsofreelyavailableasadownload.Consequently,byusingaclient-sideapplet,theusermustendurethedownloadingoftheapplet.Thedownloadingtimedependsontheuser’sInternetconnectionandcomputerspeedandwillimplyconnectioncostsforsomeusers.

Ontheserverside,theprototypeusesopen-sourcesoftwareincluding the ApacheWeb server, the tomcat Servlet engine,the UMN MapServer, and the MySQL database. All thesecomponentscanbedownloadedfreeofcharge.ThecosttotheadministratorisincurredthroughtherequirementofWebserverhardware andWeb space.The processing speed of the serverwillaffecttheperformanceoftheprototype;dependingontheloadthatwillbereceived,anappropriateserverisneeded.Thiscasestudyuseda3GHzIntelPentium4withHyper-Threadingtechnologywith1GBofDDRRAM.

Thecostofentryforthisprototypemustbeexaminedfromtwoangles—fromtheclientandfromtheserveroradministrator.Fromtheclientsidethecostofentryisminimal;itdependsonhavingacomputerwithatypicalconfigurationandahigh-speedInternetconnection(thiswillbeexplainedinthefollowingcon-nectivitysection).Thisneedcanbecircumvented,fortheusercouldusepublicterminalsinInternetcafésorlibrariestoaccesstheprototype,ultimatelyeliminatingthecostofentry for theuser/client.Asfortheadministrator,theonlycostincurredisthatofaWebserverandWebspace,fortheprogramsandadministra-tiontoolsareallavailableonlineasfreedownloads.

Efficiency. Theefficiencyofthisprototypecanbeunderstoodintwoways.Efficiencycanbemeasuredviaaqualitativeanalysisofthediscussion,aswellasfromexplicitfeedbackfromtheusers.Thecontextofthiscasestudywastoexpressanddiscussproblemsorconcernsabouttheuniversitycampus.Threadswithmoreposts,

URISA Journal • Sidlar, Rinner �1

byanumberofdifferentofusers,couldbethoughtofasbeingmorepopularthanotherswithfewerornoreplies.Therefore,therateofrepliescanindicatetheimportanceofthetopicbeingdiscussed.Inthecasestudytherewere20threads.Ofthese,onlythreethreadshadthreeormorereplies,whilethemajorityofthreadshadonlyoneortworeplies.Thethreemostpopularthreadsconsistof25contributionsor42percentofallcontributions.Becauseoftheimportancethatthesethreethreadshadtotheoveralldiscussion,theywillbeanalyzedin-depth.Oneindicationwhythesewerethemostimportantthreadswasthelengthofthecasestudy.Ifthecasestudyhadbeenlonger,thenthethreadsstartedlaterinthediscus-sionmayhavegarneredmoreattention,butthesethreethreadsbeganwithin thefirst twodaysof thecase studyand thereforeexperiencedmoreexposuretodiscussion.

Oneaspectofefficiencyiswhetherthediscussionwaskeptconciseandtothepoint.Thein-depthanalysisindicatesaneffi-cientdiscussion.OneofthethreeimportantdiscussiontopicswasconcernedwiththecrossingfromonesideofcampustotheotheracrossQueen’sPark.Startingoffasasuggestion,withrepliesintheformofothersuggestions,thethreadstayedontopicdiscuss-ingdifferentmethodsthatwouldmakeitsafertocrossQueen’sParkCrescent.ThesecondoftheimportantthreadsrelatedtotheaestheticsoftheArchitectureBuildingatthecornerofHuronandCollegeStreets.Thisthreadwasmoreofanopinionthreadasuserswithconflictingviewsmet.Althoughtheconclusionwasthatthebuildingneededrepairs,someparticipantsthoughtthatthearchitectureprogramcouldbenefitevenfromabuildinginneedofrenovations.Thethirdimportantthreadfocusedonparkingonthecampus.Thisthreadwasstartedwithageneralquestion,followedupbymorespecificquestionsandsuggestionsforpark-ingaswellasalternativestodriving.Withthisdiscussiontherewasanobviousseparationbetweentheparticipantswhodrivetocampusandthosewhotakepublictransportation,involvingback-and-forthreplies.

Ananalysisofuserfeedback,acquiredwiththepostdiscus-sionquestionnaire,showsadifferentunderstanding.Participantsexpressedconcernsthatthediscussionwastoogeneralandthatit would have been better if the ability to start new threadswouldberestrictedorlefttotheadministrator.Forexample,oneparticipantwrotethat“OnemainissueIwouldidentifyisthedifficultyinmaintainingasenseofcontinuityonatopic,”whileanotherstated,“…Theprototypewouldhavebeenenhancedbyorganizingthethreadsbytopicinthediscussionlistsothatthethreadsrelatingtoonetopicaregroupedandseenaltogether.”Othersfeltthatitwastedioustomanuallyclicktoexpandeachthread,needingtoselectthecontributiontoreaditandlookfornewposts.Contrarily,someparticipantslikedthenesteddiscus-sionforumandfeltthatitwaseasyforthemtofindrepliestothreadsthattheyhadstartedthemselves.

Interactivity. Participantswereaskedavarietyofquestionsinthepostdiscussionquestionnairewithreferencetohowtheyinteractedwiththeprototype.Overall,participantsweresatisfiedwithhowtheprototypefacilitatedandhandledthediscussion.Participantsfoundthattheprototype“diditwell”and“Itwas

helpfultobeabletoseeamapofthecampusandvisualizetherelationbetweenallthebuildings.Also,ithelpedthatwhentherewasapostingaboutabuilding,thatbuildingwashighlighted.”Anotherparticipantnotedthat“Havingamap,andbeingabletointeractwiththemapandpostcommentsthatwaymakestheprototypeveryuser-friendly,andwouldprobablymakeitmorelikelythatpeoplewillparticipateintheplanningprocess.”Con-trarily,aparticipantnotedthatthetopicfailedtofullyengagetheparticipants,whilealsosayingthat“Thegeographicaspectwasnicebutinthiscaseitdidn’tseemtoouseful,asmostpar-ticipantsknewtheinvolvedbuildingsverywellalready.Ifonlythemessageboardwasthere...thatwouldhaveworkedjustaswellwiththeseusers.”Butthissameparticipantwentontosaythat“Combiningamapwithamessageboardisthemainpointoftheprototype,whichitsucceedsindoing.”Suchcommentsleadtoageneralindicationthattheprototypeprovidessufficientinteractiontofulfilltheobjectiveithasbeensetoutfor.

Connectivity. BecausetheprototypeisaWeb-basedapplica-tion,thepotentialusersoftheArgumentationMapprototypeultimatelyincludeanyonewhohasanInternetconnection.Tosupportinteroperability,Keßler(2004)designedtheappletus-ingtheplatform-independentJavaprogramminglanguage.Theparticipantshouldhaveahigh-speedInternetaccesstoensureareasonableconnectiontimeforthewholeappletmustbedown-loadedatthebeginningofeachsession.Evenunderthiscondi-tion,ittook1.5to2minutestoloadtheprototype.Normally,HTMLdevelopersintendtokeeptheirpagesloadinginundersevensecondsasaruleofthumb.OncetheappletisloadedrefreshtimesforthemapdependonthecomplexityoftheshapefilesandimagestheapplethastoloadfromtheWebMapServer.

Intended Users. Keßlerdevelopedthisprototypetoincreasepublicparticipationintheplanningprocess.Therefore,thepro-totype“facilitatesparticipationforcitizensandstakeholdersandgivestheplannersanopportunitytoretrieve,storeandorganizelocalknowledge.Itmustbestatedthatitisnotgoingtobeanexperttool,butrathertheopposite—itshouldbeusablebyasmanypeopleaspossible,especiallylaypersons”(Keßler2004,9).Intheformationofthisusabilitystudy,theintendedaudiencewaskeptinmindforourparticipantsvariedwidelyinbackgroundsandskilllevels.

Thecasestudyhadameanageof22.6andamedianageof22.Thelargestoccupationgroupwasundergraduatestudents,36percentofthegroupor4of11participants.Ofthosewhoweregraduates,noonehadgraduatedmorethantwoyearsago,makingthegroupidealfordiscussingtheuniversitycampus.Oftheparticipants,73percent(8of11)ofthemhadexpressedthattheydidhaveexperiencewithInternetdiscussionforumsonawiderangeoftopics(notrendswithinforumusageemerged).Fur-thermore,onlytwohadstatedeverparticipatingintheplanningprocess,themajoritycitingthereasonthattheyhadneverhadtheopportunity.Lastly,64percent(7of11)oftheparticipantsexpressedexperiencewithGIS.Theuserswerethereforequalifiedtoparticipateindiscussionsrelevanttothecampus,whilebeingdiverseenoughtoobtaindifferentiatedviewsontheprototype.


Functional UsabilityUsability of the specific functions of the prototype builds upon three main pillars: • learnability,• memorability,and• satisfaction.

Learnabilityfocusesonhoweasyitisforausertounderstandand recognize the usefulness of the prototype or tools in theprototype.Thisfactormustbeanalyzedinconjunctionwiththeparticipants’generaleducationlevelandknowledgeofspecifictopicsthatdirectlydealwiththeuseoftheprototype.Memora-bilitydenoteshowwellusersareabletoretainwhattheyhavelearnedaboutusingtheprototypeandhowtheycanreapplythisknowledgeonanotheruseoftheprototype.Andsatisfactionisabroadcategorythatencompassesbothhowtheusersfeltwhileusingtheprototype, thusrelatingbacktothe learnabilityandmemorability of the functions of theprototype, andhow theusersfelttheprototypefacilitateditsfunctions.

Learnability. The participant group included only twopeople(or18percent)whohadexperiencewiththeplanningprocess.Furthermore,themajorityofparticipants(8of11)cat-egorizedthemselvesasbeingeitherexpertsoradvancedusersofcomputers.Also,7of11participantshadGISexperience,rang-ingwidelyfromGISbeginnerstoGISexperts.Toanalyzehowdifferenttypesofusersreceivetheprototype,theparticipantsarecategorizedonthebasisofplanningexperience,GISexperience,andlevelofcomputerknowledge.Thosewhoarewellversedinallthesefieldswillbeconsideredexperts;participantswhoeitherlackoneofthebasesbutareskilledintheothertwowillbeconsideredasintermediateusers;whilelowrepresentationintwoorthreefieldswillbeconsideredaslayusersorbeginners.Typicalplanningmeetingsattractafewexperts,alargernumberofinvolvedcitizens,andafewcitizenswhoarenewtotheprocess.Reasonsforthelownumberofbeginnersisoftenattributedtoanoverwhelmingunfamiliaritywith,orintimidationby,theprocess,whichleavesthemajorityofissue-championingto“activepublics”(HarrisonandHaklay2002)or,inthiscase,intermediateusers.

Becauseofthelimitednumberofparticipants inthecasestudy,thenumbersinthecategoriesarealsolow,butsimilartothedistributionpresentintheactualplanningprocess.Inthisstudyoneofourparticipantscanbecategorizedasanexpert,nineasintermediateandoneasabeginner.Itisimportanttonotethatthetimetolearntheprototypeisestimated,basedontheuser’sself-ratedlearningtime.Theparticipantswerealsoaskedwhethertheyfeltthatthelearningtimewastoolong,whichworksasabetterindicationofpatienceforlearningthetool.Harroweretal. (2000)note an interestingfindingon the learnability of ageographicvisualizationtoolinthat“understandingthepurposeofatoolandrecognizingwhenitisusefultosolveaproblemaretwoquitedifferentissues”(298).Therefore,wemustbecautiouswhenwesaythatauserhaslearnedtheArgumentationMaptool,andwhethertheuseractuallyusesthetoolasRinner(1999,2001)hadconceptualizedit.

Fromtheperspectiveoftheexpert,ittooklessthantenmin-utesto“gettotallyfamiliarwiththeprototype,”asheputit.Thisparticipantfeltthatthelearningtimewasexcellentandthewaythisparticipantusedtheprototypeindicatesafullunderstandingofthetool,asavarietyofitsfunctionswereused.

Forthenine intermediateusers, the learningtimerangedfrom10to30minuteswithamedianof10minutes.Thegen-eralindicationreceivedfromtheusersisthatthelearningtimewas not too long, while only a couple had noted differently.One participant stated that “Good software should be usableinonetothreeminutes”whilelearningtookthisuser10to15minutes.Anotherintermediateuserfoundthetimetolearnthebasic functions acceptable but took longer to learn the moreadvancedfunctions.

Fromtheperspectiveoftheparticipantwhowascategorizedasabeginner,ittooka“few”minutestolearnhowtousetheprototype.Thisechoesthepointbroughtupbeforeofhowmuchofthetoolwasunderstoodbythisuser,asitwashypothesizedthatexpertswouldfinditeasiertolearnthetoolthanbeginnerswere.Theparticipantnotedthatthe“few”minutesittooktolearnthetoolwerenottoolong,whichispotentiallythebetterindicationofhoweasytheuserfeltthetoolwastolearn.Oninvestigationintotheactualcontributionsofthisuser,onlythebasicfunctionsofselectingbuildingsandmakingpostswereused.Also,thepostshadnomorethanonegeographicreferenceselected,withonlytheinitialmaplayersbeingvisible.Therefore,theuserdidnottakeadvantageofthemoreadvancedfunctionssuchaszooming,multiplegeoreferences,andlayermanagement.

Memorability. Toproperlytestmemorability,thepartici-pantsneedtoundergoasignificanttimeawayfromtheprototype.Becauseofrestrictionsonthecasestudyandstudyperiod,suchabreakinthemiddleofthecasestudywasimpossibletoorganize.Thisis,therefore,anaspectthatshouldbeinvestigatedfurtheratanother time ineithera longercase studyorbyasking theparticipantsofthiscasestudytousethesameprototypeagain,butthistimewithoutanintroductorysession.

Satisfaction. Tostudysatisfactionwiththetool,weaskedtheparticipantstoratetheiroverallexperiencewiththeprototypeonascalefrom1to5with1beingthelowestscoreand5beingthehighestscore.Theresponsesvariedonlyslightly,fortheoverallmean was 3.41.This indicates that the participants did see abenefitintheprototypebutalsonotedsomeaspectsthatshouldbeaddressedtoincreasethesatisfactionlevel.

DISCUSSION AND CONClUSIONUsing a quasi-naturalistic case study, we have analyzed the us-ability of an Argumentation Map prototype from two perspec-tives: on a general overview of the tool and on a functional level. When considering the general aspects of the tool, its usability is high. It costs nothing to prepare or use the tool; its loading time is acceptable for the amount of information being loaded, and was not a complaint of the participants; and the audience had little to no problems using it. While quantitatively the discus-sion looked to be efficient, participants had expressed feelings

URISA Journal • Sidlar, Rinner ��

of confusion with reference to thread organization, in particular concerning who could start new threads. Also, the tool generally engaged the users as indicated by participants’ suggestions for further applications of the tool.

Inaspecificreviewofitsfunctionality,thetoolalsofareswell.Itdidnottaketheparticipantsalongtimetolearnhowtousethetool.Butthereseemedtobeadiscrepancybetweenlearningthefunctionalityandapplyingthisknowledge,fortherateofadvancedfunctionswaslimited.Thiswouldindicatethatasimplificationofthetoolisinorder.Becauseofitsshortduration,thecasestudycouldnotbeusedtomeasuretheretentionofwhattheparticipantslearnedwhileusingthetooloveralongerperiodoftime,thereforenotfullyevaluatingthememorabilityofthetool.Withrespecttothesatisfactionlevel,theratingof3.4outof5indicatesthattheparticipantswerenotoverlyenthusiasticbytheuseofthetoolwhiletheywerestillrelativelysatisfiedwithitsfunctions.

Participantswerealsoaskedquestionsreferringtoconfusingeventswhileusingthetool,aswellasaboutthemostusefulandanymissingfunctions.Fromtheresponsestothesequestions,andthecomments/questionsthatweree-mailedtotheinvestigatorsorbroughtupintheorientationsessions,avarietyofadditionsandalterationstotheprototypecanbesuggested.

The largest concern among participants was that once aselectionofreferenceobjectsonthemaphadbeenmade,therewasnowayofdeselectingobjects.Giventhepresentdesignofthestudy,wecouldnotdeducewhetherthelackofadeselectionbutton reduced thenumberof contributionsor increased thenumberofselectedgeographicelements.

Another concern expressed by participants was that thediscussionwascomplicatedortedioustoread,foritrequiredtheusertoselecteachcomment.Participantsrequestedabuttonthatwouldexpandallnestedelementsatonceorawaytoreadallthecommentsasonecontribution,similartofunctionspresentinnewsreadersandInternetforums.Also,participantsexpressedaconcernwiththelackoforderinthediscussion.Becauseourcasestudywasconductedinanexploratoryplanningstage,this

critiqueisdifficulttoavoid.Currently,everyonecanstartanewthread on the main level.This option could be removed andtheadministratorleftwiththeabilitytoaddgeneralroottopics(e.g.,parking,noise,safety,construction).Userscouldalsoasktheadministratortoaddaroottopic.Alongsimilarlines,par-ticipantsshouldbegivenameanstocontacttheadministrator(e.g.,viae-mail),whethertoobtainhelpwithanissue,toreportoffensive material, or to provide comments to make the toolmoreuser-friendly.

Theparticipantsalsoexpressedawishtobeabletoseeallthecommentsthattheymadethemselves.Oneparticipantfeltthattheplanningprocesswouldbeenhancedwithdrawingsandpicturesattachedtodiscussioncontributions.Thustheabilitytouploadimagesandembedtheminthecontributionsshouldbedeveloped.Usersalsorequestedawayofformattingcontributions.Forthis,investigationtowardsadatabasestructurethatwillstoreuserformattingissuggested.

Otherusabilityconcernsdealtwiththeuserinterfaceele-ments themselves, particularly regarding nested comments inthediscussion forum.Participants suggested that this elementbechangedtostandarduserinterfacedesign(i.e.,aplusdenot-ingnestedelements).Alsotheystatedthat thezoomfunctionwasdifficulttomanage.AnalternativetoatraditionalGIS-typezoomfunctioncouldbepredefinedzoomlevelssimilartopopu-larWebmappingsites.Also,theparticipantswereconfusedbythescalebarforitdisplayedincorrectunitsthroughoutthecasestudy.Aminorissuewasthelabelonthe“answer”button;userscommentedthatthelabelshouldratherread“reply.”Moreofaconcernwasthelimitedamountofinformationshowninthetooltipsforbuildings.Whencontributionsaremade,thenameofthebuildingshouldbepreservedinadditiontocontributiontitles.Otheruserswantedtoseeinformationsuchastheoccupantsofthebuilding andbuilt characteristics (e.g.,numberoffloors).Theusersalsohadwantedtobeabletoselect/drawareas,thussupportingtheexpansionofthedrawingtoolsbeyondjustpoints,withemphasisonpolygonsoverlines/polylines.

Table 1. RecommendationsDerivedfromParticipantFeedbackonArgumapPrototype

Function Group Function DescriptionMAP NAVIGATION Zoom tool Provide separate zoom out button and/or predefined zoom levels

Scale bar Show correct units and scaleMap tool tips Provide both feature label and number/title of contributions in tool tips

for reference objectsFORUM NAVIGATION Message display Filter messages by author and keep track of unread/read statusDISCUSSION PARTICIPATION

Discussion moderation Require moderator approval for new threads and provide general e-mail contact option

Message formatting Offer HTML formatting when editing messagesMultimedia content Enable upload and inclusion of images in discussion messagesUser communication Identify users currently online

GEOREFERENCING OF MESSAGES

Reference feature types Allow for different feature types in reference object layerDeselection Reference objects can be deselected

GENERAL SYSTEM PROPERTIES

Help menu Provide a help system


Althoughthetoolwasdevelopedtobeusedinanasynchro-nous and distributed manner, participants wished to visualizewhoandhowmanypeoplewereloggedintothediscussionatanytime.Participantsalsoexpressedconcernsaboutthenumberof tabsandfelt that thethree tabscouldbeconsolidated intoonescreen.Mostparticipantsgenerally likedthelayoutoftheuserinterface.

TherecommendationsforimprovingtheArgumapprototypethatwerederivedfromuserfeedbackaresummarizedinTable1.ByapplyingusabilityanalysismethodsfromHCItotheevaluationofanArgumentationMapprototype,wealsohopetoprovideanexampleforuser-centereddevelopmentofparticipatoryGIS.AdditionalArgumapcasestudiescouldhelptobridgethegapbetweenexistingparticipatoryGIStechnologyanduserneeds.Wearespecificallyinterestedinreal-world(naturalistic)casestudiesdealingwithcurrenturbanplanningissues,inthecomparisonoftheusabilityofdifferentparticipatoryspatialdecisionsupporttools,andinunderstandingtheutilityofArgumentationMapsasageneralinformationsystemconcept.

Acknowledgments

We would like to thank the participants in the case study for their contribution to this research project. We are indebted to Carsten Keßler for his support in adapting and setting up the Argumap application for the case study. Lauren Beharry and Amrita Hari provided valuable help with preparing the manuscript. Partial funding of this case study from the Natural Sciences and Engi-neering Research Council of Canada (NSERC) and the GEOIDE Network of Centres of Excellence in Geomatics is gratefully acknowledged.

About The Authors

Christopher Sidlarisamaster’sofscienceinplanningstudentintheDepartmentofGeographyandPrograminPlanningattheUniversityofToronto(Canada).HiscurrentresearchinterestsincludeincorporatingGISintotheplanningprocessasatooltoassistpublicparticipation.Heisalsointerestedin the economic and social impacts of transportation in-frastructureprojectswithaparticularfocusondevelopingcountries.

CorrespondingAddress:DepartmentofGeographyUniversityofToronto100St.GeorgeStreetTorontoONM5S3G3,CanadaE-mail:chris.sidlaratutoronto.ca

Claus RinnerisanassistantprofessorintheDepartmentofGe-ography atRyersonUniversity (Toronto,Canada).WithintheareaofparticipatoryGeographicInformationScience,hedevelopedtheconceptofArgumentationMapsinhisPh.D.

thesisandsubsequentlyimplementedandrefinedit.Hesu-pervisedseveralArgumentationMapcasestudiesaspartofhiscontributiontoaGEOIDEnetworkprojecton“PromotingSustainableCommunitiesthroughParticipatorySpatialDeci-sionSupport.”Healsoworksongeographicvisualizationandmulticriteriaevaluationmethodstosupportdecisionmakinginurbanapplicationsandpublichealthplanning.

CorrespondingAddress:DepartmentofGeographyRyersonUniversity350VictoriaStreetTorontoONM5B2K3,CanadaE-mail:crinneratryerson.ca

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URISA Journal Volume 19 No. 1 2007

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