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Which ‘public'? Sampling effects inpublic participation GIS (PPGIS) andvolunteered geographic information(VGI) systems for public landsmanagementGregory Brown a , Maggi Kelly b & Debra Whitall ca Department of Environmental Planning, University ofQueensland, Brisbane, QLD, 4072, Australiab Cooperative Extension, Ecosystem Sciences Division, Departmentof Environmental Sciences, University of California Berkeley,Berkeley, CA, 94720-3114, USAc USDA Forest Service, Pacific Southwest Region, 1323 Club Drive,Vallejo, CA, 94592, USAVersion of record first published: 09 Jan 2013.

To cite this article: Gregory Brown , Maggi Kelly & Debra Whitall (2013): Which ‘public'?Sampling effects in public participation GIS (PPGIS) and volunteered geographic information(VGI) systems for public lands management, Journal of Environmental Planning and Management,DOI:10.1080/09640568.2012.741045

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Which ‘public’? Sampling effects in public participation GIS (PPGIS)

and volunteered geographic information (VGI) systems for public lands

management

Gregory Browna*, Maggi Kellyb and Debra Whitallc

aDepartment of Environmental Planning, University of Queensland, Brisbane QLD 4072,Australia; bCooperative Extension, Ecosystem Sciences Division, Department of EnvironmentalSciences, University of California Berkeley, Berkeley CA 94720-3114, USA; cUSDA Forest

Service, Pacific Southwest Region, 1323 Club Drive, Vallejo CA 94592, USA

(Received 3 July 2012; final version received 10 October 2012)

Web 2.0 technologies including Public Participation Geographic InformationSystems (PPGIS) and Volunteered Geographic Information (VGI) providemethods for engaging multiple publics in public lands management. We examinedthe effects of sampling in a PPGIS/VGI application for national forest planning inthe US. A random sample (RS) of households and a volunteer public (VP) wereinvited to participate in an internet-based PPGIS to identify national forest valuesand use preferences. Spatial and non-spatial group responses were analysed. TheVP group expressed stronger utilitarian values and consumptive use preferenceswhile the RS group preferred forest amenities. These results would lead todifferent planning decisions. PPGIS/VGI methods should include scientificsampling to ground-truth voluntary participation.

Keywords: public participation; PPGIS; volunteered geographic information;VGI, forest planning; public lands

1. Introduction

A comprehensive review of public participation for environmental assessment anddecision making concluded that when done well, it can improve the quality andlegitimacy of decisions, build capacity to engage in the policy process, enhance trustand understanding of participants, and lead to better results for environmentalquality and social objectives (Dietz and Stern 2008, Klinsky et al. 2010, Kelly et al.2012). In practice, public participation processes may not be effective because theyfail to

achieve genuine participation in planning or other decisions; they do not satisfymembers of the public that they are being heard; they seldom can be said to improve thedecisions that agencies and public officials make; and they do not incorporate a broadspectrum of the public. (Innes and Booher 2004, p. 419)

There is increased academic and government interest in the integration of technologyand spatial information into public participation methods in developed countries.

*Corresponding author. Email: greg.brown@uq.edu.au

Journal of Environmental Planning and Management, 2013

http://dx.doi.org/10.1080/09640568.2012.741045

� 2013 University of Newcastle upon Tyne

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Two approaches, public participation geographic information systems (PPGIS) andvolunteered geographic information (VGI) have featured prominently in the pastdecade. The term ‘PPGIS’ was conceived in 1996 at the meeting of the NationalCenter for Geographic Information and Analysis (NCGIA) to describe a field ofgeographic information systems (GIS) that seeks to enhance public participation andfoster the empowerment of non-governmental organisations, grassroots groups andlocal communities (Sheppard et al. 1999, Sieber 2006). Although the formaldefinition of PPGIS remains ‘nebulous’ and inconsistent across applications (Tulloch2007), PPGIS generally describes the practice of having non-experts or the lay publicidentify spatial information to augment expert geographic information systems(GIS) data. The use of the term ‘PPGIS’ emerged in the US and developed countrieswhile the term participatory GIS or ‘PGIS’ emerged from participatory planningapproaches in rural areas of developing countries, the merging of ParticipatoryLearning and Action (PLA) methods with geographic information technologies(Rambaldi et al. 2006). PGIS is often used to promote the goals of non-governmental organisations, grassroots groups and community-based organisationsthat may oppose official government policy, especially as pertaining to the rights ofindigenous peoples and the current distribution of wealth and political power. Incontrast, PPGIS systems may be sanctioned by government agencies, especially inWestern, democratic countries as a more effective means to expand publicparticipation and community consultation.

Volunteered geographic information (VGI) refers to using the Web to create,assemble and disseminate geographic information provided voluntarily by indivi-duals (Goodchild 2007, Elwood et al. 2012). VGI overlaps with PPGIS in that bothinvolve the investigation and identification of locations that are important toindividuals (Tulloch 2008). One potential difference between VGI and PPGIS relatesto the purpose or motivation for participation; PPGIS projects are oftenimplemented to inform planning and policy issues while VGI systems may haveno explicit purpose other than participant enjoyment. However, the distinctionbetween VGI and PPGIS in Web 2.0 geospatial applications (e.g. see Hall et al. 2010)appears more semantic than real.

Who participates and produces spatial information in the public participationprocess (i.e. the ‘public’) is at the core of this study. Schlossberg and Shuford (2005)argued that the meaning of ‘public’ and ‘participation’ are essential to understandingthe public participation dimensions of PPGIS. In their typology, the term ‘public’may include decision makers, implementers, affected individuals, interestedobservers or the random public. The ‘participation’ dimension can range on aspectrum from the public passively receiving information to increasingly complexmodes of engagement resulting in citizen control over a decision process. Thus,PPGIS refers to a broad range of participatory engagement methods with variouspotential publics involving spatial information.

PPGIS for public lands planning and management adds further complexitybecause the public has spatial as well as issue interest boundaries. For example, the‘public’ can include geographic populations proximate or distant from the publiclands. Local populations are presumptively more affected by public land decisionswhich can influence local economic conditions and the amenities found on publiclands. In the US, some federal laws recognise the impact of public land decisions bycodifying revenue sharing arrangements with county governments containingsignificant public lands. However, laws and regulations governing participation

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processes for public lands do not distinguish among the various categories of publicor their geographic distribution. For example, regulations for the US Forest Servicestate that the agency ‘‘must use a collaborative and participatory approach to landmanagement planning . . . by engaging . . . Forest Service staff, consultants,contractors, other Federal agencies, federally recognised Indian Tribes, State orlocal governments, or other interested or affected communities, groups, or persons’’(36 CFRx219.9). In providing opportunities for participation, the agency take‘‘. . . into account the discrete and diverse roles, jurisdictions, and responsibilities ofinterested and affected parties’’.

The regulatory admonishment for agencies to account for diverse publicsprovides little operational guidance for the agency. There is ongoing debate aboutwho participates (or should participate) in the PPGIS community (see e.g. Talen2000, McCall 2003, Schlossberg and Shuford 2005, Elwood 2006, Wright et al. 2009),but this question appears especially acute for participatory processes for public landswhere every citizen is potentially an affected party. For public land planning andmanagement decisions, how should an agency weigh individual versus collectiveinterests, direct versus indirect interests, and proximate versus distant interests?

The implementation of PPGIS for public land management does not resolvethese questions, but rather makes these questions more explicit. The operationalisa-tion of a PPGIS process necessarily involves sampling choices that identify or targetindividuals and groups for spatial information relevant to the public lands inquestion (Brown 2012b). Sampling strategies can be purposive or convenience-basedand both will expose assumptions about whose interests count – or count more. Thediversity of spatial information generated by participants can vary dramaticallybased on a variety of factors including personal values, knowledge and experience.Thus, the PPGIS target population as well as differential participation rates caninfluence and bias the spatial information collected in support of public landdecisions.

In this study, we sought to understand how PPGIS sampling might influencenational forest planning in the US if the Forest Service, the agency responsible fornational forest planning, were to use the data to inform their decisions. SufficientPPGIS data were collected to evaluate similarities and differences between a randomsample of California residents targeted in the study and volunteer public responses tothe PPGIS data collection. During the PPGIS process for three national forests inCalifornia, we observed the mobilisation and participation of individuals locatedoutside the sampling area to participate in the process. In the absence of regulatoryapproval to engage in systematic public data collection, a volunteer public approachwould be the default mode of public engagement. We wanted to understand how avoluntary participation process could potentially influence the results.

Our primary objective in this study is to compare the non-spatial characteristics(e.g. socio-demographics, forest policy preferences) and the spatial information(type, number and location of PPGIS markers) provided by randomly sampled (RS)residents and a volunteer public (VP) to understand forest planning implications.How do the results deviate from a representative public sample and how vulnerableis a volunteer approach to public participation to interest group mobilisation? In thefinal analysis, we show the PPGIS spatial results of preferences for fuels treatment(i.e. vegetation management such as thinning or controlled burns) from the twogroups to highlight the complexity of decisions presented by explicit, spatialpreferences in PPGIS. We close with a discussion of the challenges for implementing

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PPGIS in future forest national planning efforts, and for public lands management ingeneral.

1.1. Background information: PPGIS studies involving public lands

The potential effects of PPGIS participation and sampling have been explored in anumber of empirical studies involving public lands in the US, Canada, Australia andNew Zealand. However, the reporting of results is inconsistent due to the differentstudy contexts, objectives, data collection methods and analyses performed. Themajority of studies engaged participants in identifying landscape values on publiclands, but some studies also included development preferences, experiences andperceived impacts. The studies reviewed are listed in Table 1.

The majority of studies used a random household sampling approach within adesignated study region. Regional random sampling, consistent with most forms ofsurvey research, produces participation bias that deviates from regional populationcharacteristics on several socio-demographic variables. PPGIS participants tend tobe disproportionately male, older and with higher levels of formal education andincome (Brown and Reed 2000, Clement-Potter 2006, Nielsen-Pincus 2007,Raymond and Brown 2007, Beverly et al. 2008, Brown and Reed 2009, Brownand Brabyn 2012). PPGIS methods may also under-represent ethnic groups withinthe region (Brown and Reed 2000, Clement-Potter 2006). The socio-demographicvariables appear to have a small, but in some cases, statistically significant effect onthe type of mapped spatial information in PPGIS (Clement-Potter 2006, Brown andReed 2009).

There are few PPGIS studies that have examined similarities and differences inspatial information from multiple sampled publics. One study examined PPGISresponses from visitors compared to regional residents. Visitors to the Otways regionin Victoria, Australia mapped somewhat different locations of tourism developmentpreferences within the region compared to randomly sampled residents (Raymondand Brown 2007). However, the characteristics of visitors themselves also appear toinfluence the type of spatial information identified in PPGIS. In the Alpine region ofVictoria, lower-income visitors to national parks mapped somewhat differentactivities (e.g. more overnight stays) and experiences (more solitude and less socialinteraction) while higher-income visitors identified more trail-based activities (Brownand Weber 2011). A consistently important PPGIS variable is the participant’sfamiliarity with the study area, which is positively related to the amount of spatialinformation provided by the participant.

Multiple PPGIS studies have used Internet technology, providing the opportu-nity for a ‘volunteer public’ to participate in the study as a contrast to the randomlysampled target population. However, most of these studies did not generate enoughvolunteer public response for meaningful analysis. In one reported study, PPGISresponses from an Internet panel were compared to individuals that voluntarilyaccessed the PPGIS website. The Internet panellists spent less time and mappedfewer attributes than the volunteer public, resulting in lower quality spatial data(Brown et al. 2012).

Public lands often attract interest groups and non-governmental organisationsthat monitor management of the lands to promote specific uses and outcomes. Someagencies maintain mailing or email lists of individuals and organisations that wouldlike to be kept informed of proposed activities on public lands. In one study that

4 G. Brown et al.

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Table

1.

Areview

ofPPGIS

studieswithafocusonpubliclands.

Publiclands

studyarea

PPGIS

method&

spatialattributes

Targeted

‘public(s)’and

reported

participation

rates

Participationbias

(Censuscomparison)

Groupdifferences(ifmore

than

one‘public’

samplinggroup)

Source

Crownlands,

Alberta,

Canada

Internet

maps

Landscapevalues

Random

householdsin

fourcommunitiesin

studyarea(22%)

Male,more

form

al

education,higher

income

N/A

Beverly

etal.

(2008)

Chugach

National

Forest,Alaska,

U.S.

Paper

maps

Landscapevalues

Random

householdsin

12communities

proxim

ate

tonational

forest

(31%)

Male,older,more

form

aleducation,

less

minorities;

Response

rates

higher

for

communities

locatedcloserto

nationalforest

N/A

BrownandReed

(2000);Brown

(2005)

Otw

aysRegion,

Victoria,

Australia

Paper

maps

Landscapevalues

Development

preferences

1.Randomly

selected

voters

from

electoral

rolls

2.Visitors

toregion

(contacted

onsite)

Older,more

form

al

education

Visitors

less

familiarwithregionthan

residents.Differencesin

mapped

locationsoftourism

development

preferenceswithin

region.

Raymondand

Brown(2007)

AlpineRegion,

Victoria,

Australia

Internet

maps

Experiences

Impacts

1.Nationalpark

visitors

contacted

onsite

(51%)

2.Volunteer

public

(N/A

)3.Nationalpark

staff

(25%)

Individualswithgreaterknowledge

ofregionmapped

more

attributes.

Lower-incomeparticipants

mapped

more

overnightstaysand

solitudeandless

socialinteraction.

Higher-incomeparticipants

engaged

inmore

trail-based

activities.

BrownandWeber

(2011)

Publiclandsin

Idaho,

Washington,

US

Paper

maps

Landscapevalues

Development

preferences

Randomly

sampled

property

ownersin

threecounty

study

area(49–56%)

More

form

al

education

N/A

Nielsen-Pincus

(2007) (continued)

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Table

1.

(Continued)

Publiclands

studyarea

PPGIS

method&

spatialattributes

Targeted

‘public(s)’and

reported

participation

rates

Participationbias

(Censuscomparison)

Groupdifferences(ifmore

than

one‘public’

samplinggroup)

Source

Nationalforestsin

Oregon,

Arizona(U

S)

Internet

maps

Landscapevalues

1.Random

households

proxim

ate

tonational

forests(10–12%)

2.Forest

Servicemailing

list

(17–24%)

Male,older,more

form

aleducation,

higher

income

FSmailinglist

groupmore

familiar

andrecreate

more

onpubliclands;

identified

more

recreationvalues

andspecialplacesthanrandom

sample

BrownandReed

(2009)

Nationalforestsin

Colorado(U

S)

Paper

maps

Landscapevalues

Random

households

proxim

ate

tonational

forests(33%)

Male,older,more

form

aleducation,

higher

income

N/A

Clement-Potter

(2006)

Conservation

areasin

Otago,

Southland

regions,New

Zealand

Internet

maps

Landscapevalues

Experiences

Development

preferences

1.Random

households

inregion(3%

a)

2.Volunteer

public

(N/A

)3.Visitors

toregion

contacted

onsite

17%

a

Male,older,more

form

aleducation

Groupdifferencesnotassessed

Brownand

Brabyn(2012)

andDOC

(2011)a.

Nationalparks,

Southwest

Victoria,

Australia

Internet

maps

Experiences

Activities

Values

Impacts

1.Internet

survey

panel

(12%

overall,77%

after

screening)

2.Volunteer

public

Internet

panel

spentless

timeand

mapped

fewer

attributesthan

volunteer

publicresultingin

lower

quality

spatialdata;reported

less

income

Brownet

al.

(2012)

Publiclandsin

southern

Wyoming,US

Mixed:Internet

andpaper

maps

Landscapevalues

Development

preferences

Randomly

selected

householdsin

region

(17%

paper,7%

internet)

DifferencesbyPPGIS

mode:

internet

participants

wereyounger

with

more

form

aleducationthanpaper

participants;mapped

more

attributes

Pocewiczet

al.

(2012)

aReport

prepared

byNew

Zealand

DepartmentofConservation

(DOC)available

athttp://w

ww.doc.govt.nz/upload/documents/about-doc/role/policies-and-plans/cm

s/southland-values-survey-results.pdf[A

ccessed1July

2012].

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compared responses of US Forest Service list members with a random sample ofregional residents, Brown and Reed (2009) found the mailing list group were morefamiliar with national forest lands and participated in more recreation activitieswithin them than the random sample. In terms of spatial information, the mailing listgroup identified more recreation values and special places than the random sample.

After a decade of PPGIS research involving public lands, the accumulation ofknowledge about the potential effects of participation rates and the sampled publicsis patchy at best. For public land management agencies whose primary missioninvolves conservation and recreation (e.g. US National Park Service, Parks Victoria(Australia), and New Zealand Department of Conservation), the potential effects ofparticipation rates and sampling methods may not be critical in determiningappropriate management direction. However, for multiple use public lands (e.g. USForest Service and Bureau of Land Management) where management involvescompetitive and often conflicting uses, who participates in PPGIS may be crucial fordetermining the future management of these lands.

1.2. The study context: national forest planning

National forest planning in the US is a process marked by conflicting values andambiguous or contested goals. The US Forest Service, the agency responsible fordeveloping and implementing forest plans (called ‘Land and Resource ManagementPlans’), has historically lacked effective protocols to cope with ‘wicked’ value-relatedproblems and place-based planning issues. The requirements to develop forestmanagement plans under the National Forest Management Act (NFMA) of 1976and codified in 36 CFR 219 (CFR ¼ code of federal regulations) have not resulted inany practical advancement in the systematic mapping of place-specific values thepublic attaches to national forests. The public participation process for forestplanning that accompanies the development of forest plans has been unable tomoderate the conflict over the multiple values intrinsic to the national forest system(NFS). For example, every one of the 96 national forest plans completed throughout1996 had been appealed (Kaiser 2006).

PPGIS methods have been pilot tested by academics for national forests inAlaska, Arizona, Oregon and Colorado using both paper-map and Internet-basedmethods. A proposed PPGIS model for national forest planning was published in2009 following the pilot tests (Brown and Reed 2009). The Forest Servicesubsequently requested implementation of PPGIS methods in support of forestplanning for up to 15 national forests over three years (Federal Register, 15 April2009). However, the agency has thus far been blocked by the Office of Managementand Budget (OMB) that must approve all federal data collection efforts under thePaperwork Reduction Act.

In anticipation of eventual OMB approval for Forest Service PPGIS datacollection, researchers piloted the first Google1 maps PPGIS interface for threenational forests in California in 2012. These national forests were identified as threeof eight national forests that will be the first to revise their forest plans using a newNational Forest System Planning Rule after the rule is finalised (Forest Service PressRelease, 30 Jan. 2012).

The proposed planning rule (Federal Register, 9 April 2012) does not specificallyindicate that PPGIS methods should be used in public involvement andcollaboration, but does encourage the agency to be ‘‘proactive and use contemporary

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tools, such as the internet, to engage the public . . .’’ (x 219.4) while continuing torequire that the agency identify the presence and consider the importance of variousphysical, biological, social, cultural and historic resources on the plan area ((x 219.7).Thus, the 2012 national forest pilot study sought to evaluate the internet PPGISmethod and to potentially assist the three national forests in their plan revisionprocess by providing spatial data on national forest values and use preferences.

2. Methods

2.1. Study location

The PPGIS study location was three national forests located in Region 5 of theUSDA Forest Service. The Sierra National Forest (NF) is located on the westernslope of the central Sierra Nevada Mountains and is known for mountain scenery,oak-covered foothills, heavily forested middle elevation slopes and alpine land-scapes. The national forest encompasses more than 1.3 million acres ranging,between 900 and 13,986 feet in elevation (USDA Forest Service: http://www.fs.usda.gov/sierra). The Sequoia NF is located south of the Sierra NF and isknown for its 30 groves of Giant Sequoia trees (Sequoiadendron giganteum), granitemonoliths, glacier-formed canyons and whitewater rivers. The forest coversapproximately 1.2 million acres with elevations ranging from 1000 feet in thefoothill region to peaks over 12,000 feet in alpine areas (USDA Forest Service:www.fs.fed.us/r5/sequoia/). The Inyo NF is located east of the Sierra and SequoiaNational Forests and extends 165 miles near the California and Nevada border. Theforest covers approximately 2 million acres, mostly on the eastern slope of the SierraNevada with elevation ranging from 4000 feet in the Owens Valley to 14,495 feet atMt. Whitney, the highest peak in the contiguous United States (USDA ForestService). Figure 1 provides a location map with the forest boundaries that appearedin the Google1 maps PPGIS interface (see also http://www.landscapemap2.org/sierra).

2.2. Data collection process

From February to April 2012, we randomly sampled 2343 households located withinapproximately 20 km of the three national forests. We attempted to distribute thehouseholds roughly proportional to the three national forests. The names andaddressed were provided by a commercial vendor based on zip codes proximate tothe national forests. We mailed letters of invitations to the households inviting themto the PPGIS study website. Each letter of invitation contained a unique access codeto be entered by the participant on the website allowing us to track responses bysampling group.

The study also allowed any volunteer member of the public to request a dynamicaccess code to participate in the study. We specifically invited two key groupsrepresenting environmental interests (Sierra Forest Legacy – a conservation NGO) andforest management industries (California Forestry Association – the central voicerepresenting California’s forest-resource companies) to contact their members forparticipation, but we received no response. We did send notice of the website to a list ofindividuals who previously participated in the Sierra Cascades Dialog sessions, a forumin northern California designed to ‘‘bring together public and private land managers tograpple with an ‘all lands’ approach to planning and conservation, beginning with fire

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Figure

1.

Google1

mapsPPGIS

interface

forcollectingnationalforest

values

andpreferencesforthreenationalforestsin

California.

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protection issues’’ (see http://www.cce.csus.edu/conferences/ccp/scds10/index.htm).The list included individuals, interest group members and government agencyrepresentatives.

The study website consisted of an opening screen for the participant to enter theiraccess code, followed by an informed consent screen for participation, and then aGoogle1 maps interface that allowed the participant to drag and drop differentdigital markers onto a web map of the three national forests (see Figure 1). Themarkers described 14 landscape values, 13 acceptable uses and 13 parallelunacceptable uses. The markers and their operational definitions appear in Tables2 and 3. The instructions requested the participant to ‘‘use the map markers on theleft to identify the places you value and your forest use preferences . . . with yourmouse, click on a marker and drag it to the relevant map location . . .’’. The differenttypes of markers placed and their spatial locations were recorded for eachparticipant on the web server in a database, along with other information includinga timestamp of when the marker was placed, the Google1 map view at time ofmarker placement, and the Google1 map zoom level (scale) at which the marker wasplaced. Participants could place as few or as many markers as they deemed necessaryto express their values and use preferences.

Following completion of the mapping activity (placing markers), participantswere directed to a new screen and provided with a set of text-based survey questionsto assess general, non-spatial forest management preferences and to measure

Table 2. Landscape value definitions.

Forest values Operational definition

Aesthetic These areas are valuable because they contain attractivescenery including sights, smells, and sounds.

Recreation (day use) These areas are valuable because they provide day-use orpicnicking recreation opportunities.

Recreation (camping) These areas are valuable because they provide overnightcamping and recreation opportunities.

Economic(timber/biomass)

These are valuable because of their timber and biomassresources.

Economic (tourism) These areas are valuable because of their benefit to the tourismindustry.

Economic (energy) These areas are valuable because of their energy resources.Life sustaining These areas are valuable because they help produce, preserve,

clean and renew air, soil and water.Discovery/learning/

scientificThese areas are valuable because we can use them to learn

about the environment.Biological/habitat These areas are valuable because they provide for a variety of

wildlife and plants.Spiritual These places are valuable because they are spiritually special.Intrinsic/existence These places are valuable because they exist, no matter what I

or others think about them or how we use them.Historic/cultural These areas are valuable because they represent history,

cultural identity, or provide places where people cancontinue to pass down memories, wisdom, traditions or away of life.

Therapeutic These places are valuable because they make people feel better,physically and/or mentally.

Wilderness These areas are valuable because they are wild, uninhabited orrelatively untouched by human activity.

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Table

3.

Operationaldefinitionsforacceptable

andnon-acceptable

forest

uses.

Acceptable

use

Operationaldefinition

Non-acceptable

use

Operationaldefinition

Tim

ber

harvest

Thisareaisacceptable

fortimber

harvest.

Notimber

harvest

ThisareaisNOT

acceptable

fortimber

harvest.

Fuelstreatm

ent

Thisareaisacceptable

forfuelstreatm

ents.

Nofuelstreatm

ent

ThisareaisNOTacceptable

forfuelstreatm

ents.

Grazing

Thisareaisacceptable

forgrazinglivestock.

Nograzing

ThisareaisNOTacceptableforgrazinglivestock.

Trails

Thisareaisacceptable

forinstallingnew

trails.

Notrails

ThisareaisNOT

acceptable

forinstallingnew

trails.

Roads

Thisareaisacceptable

forinstallingnew

roads.

Noroads

ThisareaisNOT

acceptable

forinstallingnew

roads.

Motoriseduse

Thisareaisacceptable

formotorisedrecreational

use

(e.g.motorcycling,ATVing,

motorboating).

Nomotoriseduse

ThisareaisNOT

acceptable

formotorised

recreationaluse

(e.g.motorcycling,ATVing,

motorboats).

Non-m

otoriseduse

Thisareaisacceptable

fornon-m

otorised

recreationaluse

(e.g.hiking,canoeing,

picnicking).

Nonon-m

otorised

use

ThisareaisNOT

acceptable

fornon-m

otorised

recreationaluse

(e.g.hiking,canoeing,

picnicking).

Wildernessorwild/

scenic

river

Thisareaisacceptable

fordesignationas

WildernessoraWildandScenic

River.

Nowildernessor

wild/scenic

river

ThisareaisNOT

acceptable

fordesignationas

WildernessoraWildandScenic

River.

Urban

development

Thisareaisacceptable

fornew

urban

development(residentialandcommercial).

Nourban

development

ThisareaisNOT

acceptable

fornew

urban

development(residentialandcommercial).

Ruralresidential

development

Thisareaisacceptable

forruralresidenceswith

acreage.

Norural

residential

development

ThisareaisNOT

acceptable

forruralresidences

withacreage.

Industrial

development

Thisareaisacceptableforindustrialdevelopment

includingmanufacturing,processing,mining

(e.g.gravel),oroilandgasdevelopment.

Noindustrial

development

ThisareaisNOT

acceptable

forindustrial

developmentincludingmanufacturing,

processing,mining(e.g.gravel),oroilandgas

development.

Energy

development

Thisareaisacceptable

forinstallingnew

commercialwindturbines,photovoltaic

panels

orthermalsolarenergytechnology,or

hydroelectric

facilities.

Noenergy

development

ThisareaisNOT

acceptable

forinstallingnew

commercialwindturbines,photovoltaic

panels

orthermalsolarenergytechnology,or

hydroelectric

facilities.

Other

development

ThisareaisacceptableforANY

new

development

ofanytype.

Nodevelopment

anytype

ThisareaisNOT

acceptable

forANY

new

developmentofanytype.

Journal of Environmental Planning and Management 11

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respondent socio-demographic characteristics. PPGIS data collection ended withcompletion of the survey questions. Study participants had the option to return tothe PPGIS website later to use their access code to add new markers or to adjustpreviously placed markers.

To increase participation, two additional mail reminders were sent to non-respondents following the initial mail invitation. On the final reminder, an incentiveof a $10 electronic gift card was offered to participants that participated in the study.

2.3. Analyses

2.3.1. Participant characteristics

To assess the similarity of the random sample (RS) with the volunteer public (VP),we compared the groups on responses to questions on participant age, gender, levelof formal education, average number of visits to the three national forests, self-identified knowledge of places in the national forests, and satisfaction and level oftrust with the Forest Service.

2.3.2. Participant mapping and behaviour

We calculated the number, percentage and rank of all PPGIS markers placed by thetwo groups. To determine if there were proportional differences in the number andtype of markers placed by group, we generated cross-tabs, performed a chi-squaretest of independence and examined the standardised residuals. The chi-squarestatistic provides overall measure of whether two variables can be consideredindependent, or alternatively stated, whether there is potential association betweenthe two variables. With a statistically significant chi-square finding, standardisedresiduals identify the categorical source(s) of the significant association. Residualsgreater than 2.0 or less than -2.0 indicate that a given response category issignificantly over or under-represented.

We also compared respondent mapping effort and use of the PPGIS interfacebetween the two groups. Variables analysed include the percentage of markersplaced inside the national forest study area, mapping precision as indicated by theGoogle1 zoom level selected by the participant, the Google1 map type used whenplacing markers, and overall mapping effort as indicated by the mean number ofvalue, acceptable use, and non-acceptable use markers placed.

2.3.3. Non-spatial national forest use preferences

The survey contained a set of 11 items asking about needed changes in forest uses forfuture forest plans (see Table 6). The responses to the forest uses were coded on a 5-point Likert scale ranging from 1 ¼ Strongly increase to 5 ¼ Strongly decrease. Weran t-tests on the RS and VP group responses to identify overall group preferencesand significant differences.

2.3.4. Spatial preferences for fuels treatment

We first plotted the domicile locations of respondents by group (RS and VP) basedon their zip code to show their proximity to the three national forests. We then

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plotted spatial preferences for fuels treatment (both acceptable and unacceptable) onthe Sierra NF as an illustrative example. Finally, we generated connecting linesbetween domicile locations and fuels treatment preferences. This final map isintended to show the spatial variability in responses between the RS and VPsampling groups and to assist the reader in visualising the complexity of interpretingfuels treatment preferences.

3. Results

3.1. Response rate and respondent characteristics

Of the 2343 letters of invitation mailed to the random sample (RS), there were 144full or partial responses. A full response is an individual that maps one or morelocations and completes the survey questions at the end; a partial response is anindividual that maps one or more locations but does not answer the survey questionsfollowing the mapping activity. After accounting for known non-deliverable letters,the RS response rate was 7%. For volunteer public (VP), there were 84 full or partialsurvey completions.

The RS and VP groups were similar in average age (52 and 53 years) withdisproportionately more male participation (73% and 63%), while the VP had asignificantly higher level of formal education (74% vs. 57% with bachelor’s degree orhigher). There was no statistically significant difference in the number of self-reported visits to the three national forests by the two groups, but the VP group self-identified a higher level of knowledge of the national forests (61% reporting‘Excellent’ or ‘Good’) compared to the RS group (49%). There was no statisticallysignificant difference in the groups’ overall level of trust with the Forest Service, buta higher proportion of the VP group expressed ‘low’ or ‘very low’ trust in the agency(37%) compared to the RS group (25%).

One important, statistically significant difference between the two groups wasthe level of satisfaction with the Forest Service. Only 20% of the RS group were‘unsatisfied’ or ‘very unsatisfied’ with the way the Forest Service is managing thethree national forests compared to 43% for the VP group. This difference,discussed further below, suggests one potential motivation for volunteerparticipation.

3.2. Participant mapping of markers

We compared the number, percentage, and rank of all PPGIS markers placed bythe two groups and the results appear in Table 4. To assess the overall similarityin mapping, we calculated Spearman’s rank correlation (Spearman 1904) for allmarkers and the subsets of values and use preference markers. The two groupsdiffered much more in the placement of use preferences than values markers.Spearman’s rank correlation for values was 0.87 compared to 0.61 for forest usemarkers. The largest differences between the expected number of markers placedand the actual number of markers placed, as indicated by chi-square residuals, wason the acceptable use of the national forests for grazing, fuels treatment (bothacceptable and not acceptable use), trails use and timber use. The VP groupplaced disproportionately more markers than the RS group for trails use, grazinguse and timber use, and disproportionately fewer markers for no fuels treatmentand no wilderness designation. The proportions of markers placed by the RS

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Table

4.

Number,percentageandrankofallPPGIS

markersplacedbysamplinggroups.

(Bold

indicatesthatresponse

category

issignificantlyover-

representedoritalicmeansunder-represented,basedonchi-square

standardised

residuals).

Marker

category

Random

sample

Ranka

Std.

Residual

Volunteer

public

Rank

Std.

Residual

Total

markers

Volunteer

public

proportionssameinside

NFboundaries?b

Recreationvalue(camping)

542(9.5%)

272.1

311(12.5%)

13.2

853(10.4%)

YES

Recreationvalue(dayuse)

550(9.7%)

17.4

255(10.2%)

3.6

805(9.8%)

–Aesthetic

value

512(9.0%)

32.7

142(5.7%)

674.0

654(8.0%)

YES

Fuelstreatm

entuse

356(6.3%)

57.3

165(6.6%)

5.5

521(6.4%)

–Trailsuse

256(4.5%)

675.3

257(10.3%)

28.1

513(6.3%)

YES

Nofuelstreatm

entuse

486(8.5%)

47.8

6(0.2%)

34

711.7

492(6.0%)

YES

Grazinguse

121(2.1%)

15

76.8

198(8.0%)

410.2

319(3.9%)

YES

Motoriseduse

220(3.9%)

9.1

95(3.8%)

97.1

315(3.9%)

–Biologicalvalue

230(4.0%)

81.2

76(3.1%)

11

71.8

306(3.7%)

–Lifesustainingvalue

195(3.4%)

10

.674(3.0%)

12

7.9

269(3.3%)

–Notimber

use

239(4.2%)

74.7

15(0.6%)

27

77.1

254(3.1%)

YES

Wildernessvalue

183(3.2%)

11

.963(2.5%)

14

71.4

246(3.0%)

–Economic

value(tim

ber)

117(2.1%)

17

73.2

108(4.3%)

84.8

225(2.8%)

NO

Tim

ber

use

95(1.7%)

19

74.9

129(5.2%)

77.4

224(2.7%)

YES

Historicvalue

133(2.3%)

13

71.7

88(3.5%)

10

2.5

221(2.7%)

YES

Economic

value(tourism

)162(2.8%)

12

1.8

40(1.6%)

16

72.7

202(2.5%)

YES

Nodevelopment(any)use

131(2.3%)

14

2.7

18(0.7%)

25

74.1

149(1.8%)

YES

Wildernessuse

88(1.5%)

21

71.1

54(2.2%)

15

1.6

142(1.7%)

–Non-m

otoriseduse

68(1.2%)

25

73.0

73(2.9%)

13

4.6

141(1.7%)

NO

Noindustrialdevelopment

use

121(2.1%)

15

2.9

13(0.5%)

29

74.4

134(1.6%)

YES

Discovery/learningvalue

91(1.6%)

20

.139(1.6%)

17

7.1

130(1.6%)

–Nourbandevelopmentuse

96(1.7%)

18

2.2

14(0.6%)

28

73.4

110(1.3%)

YES

Roadsuse

73(1.3%)

23

.131(1.2%)

18

7.1

104(1.3%)

–Ruraldevelopmentuse

77(1.4%)

22

1.3

19(0.8%)

24

71.9

96(1.2%)

–Therapeuticvalue

73(1.3%)

23

.922(0.9%)

22

71.3

95(1.2%)

–Spiritualvalue

42(0.7%)

30

7.6

24(1.0%)

21

.966(0.8%)

–

(continued)

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Table

4.

(Continued)

Marker

category

Random

sample

Ranka

Std.

Residual

Volunteer

public

Rank

Std.

Residual

Total

markers

Volunteer

public

proportionssameinside

NFboundaries?b

Noruraldevelopmentuse

53(0.9%)

27

1.0

13(0.5%)

29

71.6

66(0.8%)

–Economic

value(energy)

39(0.7%)

31

7.8

25(1.0%)

20

1.3

64(0.8%)

–Nograzinguse

51(0.9%)

28

1.0

13(0.5%)

29

71.5

64(0.8%)

–Nomotoriseduse

48(0.8%)

29

.516(0.6%)

26

7.8

64(0.8%)

–Noroadsuse

56(1.0%)

26

2.1

5(0.2%)

36

73.1

61(0.7%)

YES

Intrinsicvalue

38(0.7%)

32

7.4

20(0.8%)

23

.658(0.7%)

–Other

value

33(0.6%)

34

.88(0.3%)

33

71.3

41(0.5%)

–Energydevelopmentuse

30(0.5%)

36

.69(0.4%)

32

7.8

39(0.5%)

–Urbandevelopmentuse

34(0.6%)

33

1.5

4(0.2%)

38

72.2

38(0.5%)

NO

Noenergydevelopmentuse

31(0.5%)

35

1.3

4(0.2%)

38

72.0

35(0.4%)

NO

Nowildernessuse

0(0.0%)

40

74.5

29(1.2%)

19

6.8

29(0.4%)

YES

Industrialdevelopmentuse

10(0.2%)

36

7.3

6(0.2%)

34

.516(0.2%)

–Other

developmentuse

5(0.1%)

37

7.7

5(0.2%)

36

1.1

10(0.1%)

–Nonon-m

otoriseduse

2(0.0%)

39

7.8

3(0.1%)

39

1.2

5(0.1%)

–Notrailsuse

4(0.1%)

38

.31(0.0%)

40

7.4

5(0.1%)

–Total

5691

2490

8181

100.0%

100.0%

100.0%

Notes:

aSpearm

an’srankcorrelationbetweentw

ogroups–allmarkers(r¼

0.71)values

only

(r¼

0.87),usesonly

(r¼

0.61).

bMeasureswhether

chi-square

standardised

residualsforVolunteer

Publicmarker

proportionsremain

statisticallysignificantforonly

markerslocatedinsideNFboundary.

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group reflect opposite positions on these prospective uses and non-uses of nationalforest lands.

Opposing positions were also found on the proportion of markers placed forusing national forest lands for various types of development: the VP placedproportionately fewer markers identifying the non-acceptability of roads, urbandevelopment, industrial development or development of any type. Conversely, theRS group placed proportionately more markers in each of these non-acceptableforest use categories.

There were several significant differences in the number of value markers placedby the two groups. The VP group placed proportionately fewer aesthetic value andtourism-based economic markers, and proportionately more camping-based recrea-tion values and timber-based economic values. The RS group placed proportionatelymore aesthetic values, and proportionately fewer camping-based recreation valuesand timber-based economic values.

Because the VP placed a lower percentage of markers inside the national forestboundary (discussed further below), it is possible that the proportional differences inmarker placement was the result of the VP placing more markers outside thenational forest boundaries. We examined whether the significant proportionaldifferences in VP marker placement held for markers placed inside the national forestboundaries. We ran chi-square analysis for markers only appearing inside the forestboundaries. All VP proportional differences in marker placement remainedstatistically significant with the exception of four markers: timber-based economicvalue, non-motorised use, urban development and no energy development. Thechange in statistical significance for non-motorised use, urban development and no-energy development is probably an artefact of the relatively small number of markersplaced in these categories. However, the change in the significance for timber-basedeconomic value for the VP group reflects a higher proportion of these markers beingplaced outside the national forest boundaries.

3.3. Mapping behaviour using the website

The RS group placed more markers than the VP group on average, but the meandifferences were not statistically different (p � 0.05) owing to the high degree ofvariability in the number of markers placed by participants. The largest differencewas in placing non-acceptable use markers (RS mean ¼ 9.2, VP mean ¼ 1.8,significant at p � 0.10) (see Table 5). When placing markers, the VP group placed ahigher proportion of markers at a larger map scale (higher zoom level) than the RSgroup. There was also a small, but significant proportional difference in the map typeused when placing markers. The RS group placed approximately 11% of markersusing the Google ‘hybrid’ map which contains satellite imagery while approximately6% of the VP placed markers using the Google ‘map’ mode. The ‘map’ mode isprobably easiest for quick navigation while the ‘hybrid’ map view provides greaterlandscape feature detail.

3.4. Non-spatial national forest use preferences

There were four statistically significant differences in responses measuringpreferences for national forest uses, with three of the items representing anopposite preference on the scale (see Table 6). The RS group indicated preferences

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to decrease commercial logging and livestock grazing on the national forests, whilepreferring increases in vegetation management and wilderness areas. In contrast,the VP group indicated preferences to increase commercial logging and livestockgrazing while decreasing wilderness areas. Both groups said they would prefer to

Table 6. Responses to survey questions by PPGIS participant group. ‘‘What changes inforest use are needed, if any, in future forest plans for these national forests?’’ Scale(1 ¼ Strongly increase, 2 ¼ Increase, 3 ¼ No change, 4 ¼ Decrease, 5 ¼ Strongly decrease).

Randomsample

(n ¼ 123) Position

Volunteerpublic

(n ¼ 51) Position

Differencesig.

(p � 0.05)

Commercial logging 3.1 Decrease 2.5 Increase YESLivestock grazing 3.1 Decrease 2.7 Increase YESProtection of fish & wildlife

and their habitat2.4 Increase 2.7 Increase NO

Vegetation management(e.g. thinning, burning)to reduce fire risk

2.4 Increase 1.7 Increase YES

Commercial mining 3.5 Decrease 3.4 Decrease NOMotorised recreation

opportunities (ATV/ORVs or snowmobiles)

3.3 Decrease 3.1 Decrease NO

Recreation facilities such ascampgrounds, picnicareas, trails

2.4 Increase 2.1 Increase NO

Energy development(oil, gas, coal)

3.4 Decrease 3.3 Decrease NO

Commercial recreation(e.g. ski areas)

2.8 Increase 2.8 Increase NO

Wilderness areas 2.6 Increase 3.1 Decrease YESResidential development 3.5 Decrease 3.5 Decrease NO

Table 5. A comparison of PPGIS mapping between random sample and volunteer public.

Random sample(n ¼ 144)

Volunteer public(n ¼ 84)

Percentage of markersinside NF study area

75.3% 63.8%

Zoom levela (12–13)Greater than 13

82.8%17.2%

69.3%30.7%

Map typea Terrain (default) 87.7% Terrain (default) 90.5%Satellite 0.3% Satellite 0.2%Hybrid 10.5% Hybrid 3.0%Map 1.5% Map 6.4%

Mapping effort:Mean number oflandscape values

20.4 15.4

Mean number ofacceptable use

10.0 12.4

Mean number of non-acceptable useb

9.2 1.8

Notes: aStatistically significant relationship/association (chi-square p � 0.05).bStatistically significant difference in means (t-test, p � 0.10).

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see an increase in vegetation management, but the preferences of the VP groupwere stronger.

3.5. Spatial preferences for fuels treatment

Plots of the domicile locations of respondents by group (RS and VP) indicated theirproximity to the three national forests. As shown in Figure 2, the RS participantslive proximate to the boundaries of the national forest, which is consistent with thesampling method implemented. Participants from the VP group, however, are farmore dispersed and located at a greater distance from the three national forests. Themajority of VP participants are located north of the three national forests, whichsuggests that these participants learned of the PPGIS study from the Sierra CascadesDialog email distribution.

We plotted responses from the RS and VP groups on the Sierra NF to showwhere fuels treatment is an acceptable or non-acceptable use (Figure 3a) and theorigin of the participant to the mapped location (Figure 3b). The northwest section

Figure 2. Map showing zip code origins of PPGIS/VGI participants by group(blue ¼ random sample, red ¼ volunteer public). See online colour version for fullinterpretation.

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Figure

3.

Mapshowing(a)spatialpreferencesforrandom

sample(R

S)andvolunteer

public(V

P)fortreatingforestfuelsin

SierraNationalForest;and

(b)lines

connectingthelocationoftheparticipants

andthemarkersplaced.See

onlinecolourversionforfullinterpretation.

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of the Sierra NF is dominated by ‘no fuels treatment’ markers placed mostly insidethe national forest boundary by two RS participants living just outside the nationalforest boundary. If one were to simply focus on the visual marker counts and theirdistribution, a logical inference would be to conclude that there is strong oppositionto fuels treatment among residents living proximate to the national forest. However,there are three participants from approximately the same origin that expressedpreferences for fuels treatment in the same area of the national forest. Theseparticipants only placed single markers.

Further south, the majority of markers indicate respondent support for fuelstreatment in the foothill regions just outside the national forest boundaries. Most ofthese markers were placed by RS participants, presumably to manage the risk ofwildfire near their residence outside the national forest boundary. When fuelstreatment preferences are viewed across the entire Sierra NF, the overall inferencewould be support for fuels treatment, both proximate and within the national forest,with the exception of several RS participants that mapped strong opposition to fuelstreatment.

The connecting lines between participant origin and the location of fuelstreatment preferences highlight the complexity of the PPGIS sampling issue. Thereare multiple fuels treatment preferences identified by VP individuals that do not liveproximate the national forest. In particular, one VP participant distant to thenational forest mapped multiple preferences in the Mono Lake region. How credibleare these fuel treatment preferences and how should these preferences be weighedagainst residents that live closer to the national forest, especially in the event of aconflict in preferences? In this particular case, there was no obvious conflict as RSparticipants did not express fuels treatment preferences in this area. However, largersampling efforts will undoubtedly expose differences in spatial preferences.

4. Discussion

Our results provide some insight into the future challenges of implementing PPGISor VGI participatory methods for public lands planning and management.Individuals with stronger forest utilisation preferences (VP group) mobilised forvolunteer participation in the study and their values and preferences significantlydiffered from the RS group. Further, these individuals lived much further from thenational forests engaged in the planning process. If the participatory results wereused as the basis for planning decisions, the resulting national forest plans woulddiffer based on whose participation was given greater weight in the process.

Previous PPGIS research has established that random sampling does not result ina truly representative sample of the general public; there is bias toward older, moreformally educated male participants with higher incomes, and under-representationof ethnic groups (Brown 2012b). However, deviation from socio-demographicrepresentativeness in PPGIS participants is of less concern than the potentialideological bias resulting from interest group mobilisation observed in this study.Our results indicate that volunteered geographic information should not bepresumed to represent the prevailing values and preferences of the general publicproximate to national forest lands.

The low random sample participation rate (7%) using the Internet PPGISmethod is a concern shared by all survey researchers, with response rates showingdeclines across all modes of delivery (de Leeuw and de Heer 2002, Curtin et al. 2005,

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Hansen 2006). However, the response rate is similar to other Internet-based PPGISmethods involving general public random samples that averaged 13% across fivestudies (Pocewicz et al. 2012). Participation rates can be increased by offeringsampled individuals multiple modes of participation (e.g. the option of paper maps),as suggested by Brown and Reed (2009) to reduce the digital divide effect. In thisstudy, multiple modes were not implemented due to a limited research budget.

While this study was conducted by university researchers, if the Forest Servicesponsored and fully implemented the PPGIS protocol as outlined by Brown andReed (2009), the forest planning implications would be far more acute. That nationalforest PPGIS protocol calls for proximate geographic random sampling asimplemented in this study, but also calls for national notification of the PPGISprocess in the Federal Register, consistent with public ownership of the nationalforests and the NEPA legal requirements for public notification of major federalactivities affecting the environment. While national forest interest groups failed tomobilise en masse for this particular process, formal agency adoption of PPGISwould encourage interests groups into the PPGIS arena. With few exceptions,industry, environmental NGOs and stakeholder groups in developed countries haveshunned PPGIS thus far, preferring to limit the number of actors in the planningprocess so they can better control and orchestrate public support for particular planoutcomes (Brown 2012b). Advocacy groups do not appear to trust a ‘wildcard’participatory process that could potentially result in outcomes unfavourable to theirparticular interests.

The planning for public lands, particularly those with a multiple-use mandatesuch as national forests, will continue to be a contentious and polarising process(Kelly et al. 2012). PPGIS has the potential to translate ideology-based naturalresource preferences (i.e. ultilisation vs. preservation) into place-specific discussionsabout potential management activities where compromise and accommodation canbe achieved. However, in practice, PPGIS may have the opposite effect of exposinggeographic conflict that is intrinsic to the concept of ‘public’. The mapped results,taken as a whole, suggest that the RS public proximate to the national forests weremost interested in maintaining forest amenities – protecting and preserving thepositive qualities of the national forests proximate to their homes. Resourceextraction and land development of all types were disproportionately identified asunacceptable uses of national forest land. Pejoratively labelled the NIMBY effect,but more accurately labelled as spatial or geographic discounting (Norton andHannon 1997, Brown et al. 2002), the geographically proximate RS group did notsupport land uses preferred by the more distant ‘public’. For example, logging andgrazing activities were supported by the more distant VP but not the proximate RSpublic. These results highlight the potential for differences between local andnational interests in public lands management, especially in the western US (seeKrannich and Smith 1998).

Previous PPGIS methods for public lands identified participant values but didnot include participant land use preferences. In our results, the differences betweenthe RS and VP groups were much less pronounced with mapped values than withmapped (un)acceptable uses. Agencies may be able to avoid extreme polarisation inthe PPGIS process by focusing on values rather than prospective uses, allowinginterdisciplinary planning teams to work out the compatibility of various uses withthe mapped values. This is the concept behind values compatibility analysis (Brownand Reed 2012), a decision support tool. However, even a more limited focus on

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values will probably not be acceptable to those individuals with strong political viewsabout how public lands should be managed. The response below from an electednorthern California county supervisor, who received our invitation for participationthrough the Sierra Cascades Dialog list, describes the challenge of using PPGIS forpublic lands planning:

I wish to protest the highly biased nature of your mapping exercise. It does notprovide the ability for the people I represent to reflect their ‘values’ – as you put it.The people I represent actually live here. They depend upon access to the NationalForest for their living, to fight wild fires, to retrieve lost tourists, to hunt and fish tosupplement their food supply for their families, to get firewood to heat their homes.They depend on water yield from managed forests for their farms and residential use.They recreate on the lands on a regular basis. They use snowmobles [sic] and OHVs,and are back country horsemen. Some ride mountain bikes. They depend upon activeforest service management to keep cartels from conducting large marijuana grows onthe federal forests that endanger their children, increase criminality and cause waterpollution. They have traditional family cattle drives like their ancestors. They gatherbasket making materials for Indian baskets, herbs for medicines, mushrooms forcooking or regalia for dances. They gold mine like the pioneers who settled here.They harvest wood and make them into wood products at our local mills – usingbiomass for heat and energy. They are small family businesses. This exercise isdesigned for a city person who knows about forests from what they read in the LATimes/SF Chronicle supplement, PBS or Sierra Club magazine. Or it is written for theGen Xers or younger who backpack and come out to the forest for a weekend. Thisthing is completely unethical and manipulative and I will not complete it because itexcludes my point of view and the values of the people I represent as an electedofficial.

The supervisor’s comments reinforce the challenge of managing public lands to meetboth local and national needs. The engagement of participatory, democratic ideals inshared decision making for public lands has not been successful (Moote et al. 1997)while collaborative methods using local partnerships also confront formidableinstitutional barriers (Carr et al. 1998). A central point about public landmanagement and national forest management in particular is that the majority ofthe ‘public’ actually never uses the public lands directly, but nonetheless derivesignificant non-material benefits from these lands. Non-users of public lands arestakeholders whose values and preferences ought to count in planning but arefrequently discounted because they are not specifically elicited and measured in theagency’s participatory process. The reality is that the general public favours non-material values over material values from national forests (Manning et al. 1999) suchas the aesthetic, recreation, biological and life sustaining values measured in thisstudy; these values may be incompatible with material-based, local economicdevelopment.

In the politically charged context of public lands management, it is natural foragencies to seek technical solutions to problems that are, in reality, ‘wicked’ socialvalue problems. Technology is a compelling and irresistible force in society. Thedevelopment of geospatial and Web 2.0 tools for public lands planning andmanagement may appear attractive to management agencies because they suggest atechnological solution to these wicked problems. However, in the absence of a clearand defensible sampling strategy that includes a random sampling as a baselinecomponent, the use of PPGIS, and especially VGI, will simply translate existingparticipatory process problems into the digital and cartographic world.

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In the present, non-spatial system of public participation in the US, required bythe NEPA process, land management agencies must often sift through thousands of‘volunteered’ comments about prospective agency actions wherein it is virtuallyimpossible to know how representative and informed these views are. Volunteermapping through an Internet PPGIS is the geospatial equivalent of this process. Thebarriers to systematic public data collection through random sampling provideagencies with few options but to implement voluntary participation systems. TheForest Service proposal to use PPGIS for up to 15 national forests in a three-yearpilot project was denied by the Federal Office of Management and Budget. If theresults of this study are a bellwether, the default VGI approach without systematicsampling will simply amplify existing deficiencies in the public participation process.

If PPGIS implementation for public lands planning and management were toimprove the quality of public participation, what would it look like? It would beexpected that the public would be better informed and more trustful of themanagement agencies, and the substantive quality of decisions would be improvedby incorporating public values into decision making, thus reducing overall conflict(Beierle 1999). Arguably, these participation goals cannot be achieved by layeringgeospatial technology on top of a limited and flawed model of public participation.Although limited in scope, our results strongly suggest that PPGIS without baselinerandom public sampling will not improve the quality of public participation. Simplystated, random sampling is an essential addition to voluntary participation. Further,there should be opportunities for both local and more distant publics to participatein the planning process. Expanding the participatory process to include the silentmajority of public land stakeholders through random sampling is consistent with theempowerment philosophy of PPGIS, and appears fundamental to reforming andimproving public participation for public lands management.

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