CJSlNC; COMPUTERIZED IMAGINC TO EVALUATE THE VISUAL

PREFEHENCE EFFECTS OF DOWNTOWN SREETSCAPE ELEklENTS

A Thcsis

Presented to

The Faculty of Graduate Studies

of

The University of Guelph

BY

PETER DUARTE

In partial ful tillment of requiremcnts

For the drgrer of

Master of Landscape Arc hi tectiire

April. 7000

O Peter Duarte. 3000

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ABSTRACT

LWNG COWlPUTERIZED lMAGlNC TO EVALUATE THE VISCIAL PHEFERENCE EFFECTS OF DOWNTOWN STREETSCAPE ELEMENTS

Advisor: Prot'ttssor C'cct.li;i I1;iinc

-Tlierc hüs bern csic.nsi\ r. rcsearch reported on the visual impacts of various componcnis

of strertscüpes. 1 l o w x r . thrre üppeiirs to br relatively little data on ihr relative

impi)rtuiicr ol' those dcsigii cornponrnts on visual preferencrs. and u hstlier a corrclüiion

rsists beiwccn niimber o f di IYrrent components and prekrencrs. Thc purpose of this

rescürch is 10 C'Y;I[LILI~C the rdütiw effects of streetscapt. components on visuül

prrferenccs. I'he eftbcts of trces. Iümps. brnches. and garbaye rcceptaclcs w r c

inws:igated iising t\r o I)«u.ntown Guelph strect scenes. Estimates of these c.ffccts u crc

obiained iisiny modcrn compiiterized trchnology and a factorial rsperiniental drsign.

Résults s h m üI I liwr çomponcnts having a positive etléct on visual pretkrtrncss for

do\\ntotw strtxtsçüpcs. uith trees having a very large positit.r rffect. Iümps Iiaving a

large r t'kct. benches haviny a medium effeA. and garbage receptacles hriving ü smül l

positive efkct on visual prrferences. It was also Iound that a positive correlation csistcd

briwecn thc numbrr of tjrprs of components and r isual prrkrencrs.

Acknowledgements

Throuphout the course of this research project. 1 have received extensive encourayc.ii~r.nt and support rrom many pwple. First of all. 1 would like to thank my tàculty advisor Pro t: Crçelia Püinc fbr her onpoing support and advice. Her knowledge and energ), tor gctting tliings dont. \\as iruly hrlpful in guiding me through this dilvticult but rewrding projeci. I \tould also likc io tliank ni). cornmittee member Dr. John Fitzsimons tOr his ussistancc in Iielping rnc dc\dop rcliable rrsrarch mrthods and for his assistance with the data mril!.sis. r.rpeciülly \ \hm i t came tirne to mnning a Rrpeated Mcnsiircs anal. 4.; <II'

variance. I \could tilso likr to thank Steven Hawken frroni the Ashton Statisticd Consultiiig dcponniciit at the University of Guelph for his help in using the SAS data aniilyis üpplisiition to gzneratr: sonle of the results used in this studq.

I ~wiild dso l ike to tliank the four ( Promotion. LACAC. R e ~ i talization. and D tiSSC' ) Do\\ n t w n (iiielpli coiiiniiitees and their manbers for participatiny in this stud>. I'licir ~.nthusiasni and çoopcration wüs ven, supportive and welcorning. A special thünks gocs to David Paisle). blanager of the Downtown Board. and Paul Kraehling and Krircn Rollk froni the City of Guelph Planning Department for their help in idcnti t)hg and conixtiiig t hcse committws.

I u ~ u l d iilso like to thank I3rot'. Jim Taylor for chairing my drfence and Prut'. Nailiün Perkins h r his espcrtisr in visual preference evaluations. 1 am also gr~tetùl tu thc Füculty of l.ondsç:ipc. Architecture and rny kllow classmaies for bring prcscnt i i t ni! defence.

1 a m wry yratc1ii1 to ail rn) p o d fkiends and tàmiiy who have prcwided mc uitli ilir mental and rniotional support. as 1 required i t during this long voyage to coniplctins tliis thesis projrct.

Table of Contents

.......................................................................................... 1 . I Rcscr rch Problerns -3 1.2 Rcsciireh Goals and Objectives ...................................................................... 4 1.3 Purpose o f Study ............................................................................................. b

2.0 Litcruturc Hevicw ..................................................................................................... 7

......................................................................... 2 . I Duwntocvn Business Districts 7 ............................................................................................. . . 1 I 1 Drtinition 8

....................................................................................... 2.1.2 Rr\.italizatioii 2.1.3 CoiiirnittetrsandOrganimtions ......................................................... I O

2.2 Thcorcticul Framework ................................................................................ I I 2.2.1 bliislow's Theory of Motivalion ........................................................ I I

.................................................. 2.2.3 Iievin Lynch's Theory of Legibility 12 3 3 . X Kaplan and Kaplan's Preferencc Theory ........................................... I -

2 3 lssucs o f Validity ......................................................................................... 13 2.3.1 Vnlidityol'PhotographsasLandscapeSurrogatcs ............................. 14

................................... 2 . 3 . Vülidity of Computerized Visual Simulations 14 2.4 Prcïeücnt Rcsearch in Viaual Preference Studies ...................................... 16

2.4.1 Prefttrenctts on Natural Environments ................................................ 16 .................................................. 2.4.1 Prckrcnces of' Urban Environments 16

......................................................................... 2.5 Slcthudologicr l Framework 17

............................................................................ 3.1 Idcn tification o f Vuriables 20 3.2 Prcprr i t iun o f Stimulus Photographs ......................................................... 30

7 3 ....................................................................................... 3.3 Sunc' Develupment ................................................................................................... 3.4 Rcspunlcnts 14

3.5 Duta Collection .............................................................................................. 75 3.6 .4 nulysis Techniques ...................................................................................... 26

4.0 Results und Anuîysis ..~.~......e................................................................................... 28

4 . l Responsc Rate ................................................................................................ 28 ..................................................................... 4.2 Dernographies o f Respondents 29

4.3 Effccts of Strcetscape Components ....................................................... 31 -7 4.4 Strcetscrpc Composition and Preference ................................................... -3-

........................................................... 4 . 1 Merisures of Central Tendency 33 4.4.2 Frequency Analysis .......................................................................... 35 4.4.3 Spcarman's Rank Correlation Coefficients ....................................... 42

........................................................................................... 1.5 Content Anaiysis 45

5.O Discussion ..........................m...........m.....m.............mm.m..............m................................... 49

5.1 Results in Relation to Theories* .................................................................... 4 1 .......... 5.2 Rcsults in Relation to Precedent Research ....................................... 51 - 'I ............................................................. 5.3 Limitations and Recommendations IJ

5.4 Implicutions for Future Research ................................................................ 5 5 ................................................... 5.5 Implic~itionsforLandscapeArchitectur~ 56

.................................................................................................... 5.6 C'onclusions 5t(

Rcfcrenccs ............................................m....................................m...................................... 60

..........*........ .............m.........................*....... Appendis A: Phu togrrphs o f Stimuli .... 06

............................................................................... Appendis B: Suncy Questionnaire 76

Appcndix C': Du t;i Sets .................................................................................................... H l

Appcndir D: S.AS Cudc and Output ....................................*........................................ 80

Appendix E: Ddïnitiuos ........ .. .................mm..........m..............m.mmmm................................. 92

List of Tables

Table 1

Table 2

Tuhlc 3

Tiihle 4

Table 5

Table 6

Table 7

Table 8

. ..................................*.... The Prckrenctt Matris (Kaplan and Kaplan 1989) 13

................................................................................ The Analusis of Variance -31

. . . Contr;ist Ml ithin each Factor ............................................................................ 32

....................................... bleasiires of Central Trndrncy For Streetscape One 33

...................................... >~Icüsiircs LI 1'Ccntral Tendency for Strertscape Two 34

.................... Inirr-pliotograph Spciarman's Correlations for Streetscapc One 43

.................... Inter-photogrnph Spcam~lin's Comlations hr Streetscape ' f w 44

....................................... C'untcni .-\ nül)sis for Theme 1 : Scenr Composition 45

.................................... Table 9 Ciintent Analysis Ior Theme 2: Emotional Responsrs JO

List of Figures

Figurc 1

Figure 2

Figurc 3

Figure 4

Figurc 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 1 1

Figure 12

Figure 13

hletliods Summary ...................................................................................... 19

............................................................................. Crosscd F:ictorial Design 21

3' ............................................................................... I>hotoprüphs of Stimuli -J

I)eniîrprnpliics of Popdation ....................................................................... 2')

............................................................. Siihgroup Anulysis of Respondents -30

...... I~rcqiicnc~ Cliarts for O or I Type of Element Presrnt-Strccsçiipc Onc 36

..... I ..r eqiicncy Chans for O or I Type ol' Elrment Prrsent-Strerscapc I I ' w 37

.......... I~'rcqiicnçy Cliorts for 2 Types of Elements Prcsent-Strwscüpr Onc -38

......... I..rcqiicnc! . Clians Ior 2 Types of Elemmis Prttsent-Strecscapc Tu 11 3')

i:rt.yur.nç) Chans for 3 or 4 Types of Elemrnts Present-Strecscep One .. -+O

Frequcncy Chans t'or 3 or 4 Types of Elrmcnts Present-Sirccsclip c. Tu o .. 4 l

Content ;\nalysis of Respondents' "Like" Descriptors ............................... 47

Coiitcnt ; \ n a l y h ol' Rrspondents' "Disli ktt" Descriptors ........................... 48

1.0 Introduction

For ceniurics ci tics and small towns alike have originated and expanded aroiind

Downto\r n B~isinrss districts-areas sustaining various commercial. institutional. and

rcsidcntid uscs. ~ ~ C S C centra! rireas tend to bear hisiorka1 and c u l t u ~ i ! 4gnitiç;ince.

brinping t'orih stxzrd orgünizations and committres io manage their Ltppearancc.

sticwssion. ~ind liitrire growth.

Man!, of tlicsc «Idcr ci)mniunitirs have felt the neptivi: impacts of tirne and urbün

s p i 1. I'licir agc is ollen e*pressrd in their decaying buildings and streetsclipes. u hilc

thc ncu large-sçalc commercial centres and residential communities are drawing i ~ i t tlic

local rrsidcnts. niercliants. and general visitors. Although these dcvclo pmen ts ha\ c hccn

incrcasing dl uround Nonh America. a city's cultural and commercial «rigin is

continuousl~ heinp prcsenrd and revitalized to mainiain its. sociologicül. üesthctic. and

historicril \ ;ilucs.

Silbrrbcrg et al ( 1 976). Frmcaviglia ( 1996). Tiesdell et a1 ( 19%). and Evans ( 1 W 7 ) have

al1 documttnted the revitalization and regneration of townscapes. whi le othrrs

(Robcrtson. lW8) have reponed the importance of these revitalization efforts on ü

downtown's eçonomic and arsthetic quality: A downtown's aesthetic quality is not only

recopnized i t its buildings but also in its streets. An important element within a

downtown centre is its streetscape network that lines the edges of its buildings and open

spaces. Dountown streetscapes have a great influence on the city's image as a w holc.

sinçr pcoplr tcnd to associnte poor quality downtowns with poor quality streetscapes

(Tiesdrll. 1 906. Evans. 1997). Along with re-emphasizing an area's identity. strectsc~ipc.

~iirniture çan he iisrd to visually entice users and visitors to the area by providing ü

c m tiirttihlc. cleiin and plcamt rnvironrnrnt. These strertscaps contain various

pliysical çomponcnts ( e.g. t rets. lampposts. benches. etc.) that provide aesthet ic and

tiinctional ornenitics for the people who travel dong them.

Man! citiss lin\ r. iaken the approüch to revitalizr and improve their downtown

strcctsciiprs on an ongoiny basis. reguiarly introducing new strret trecs. lampposts. oncl

0 t h ~ ' ~ streel Iiirniiiirc. Sinçr the intent of implementing streetscape improvcmc.nts is io

üdd to the \isiiril quality and tiinctionality of the urban spacr. it is important io iindersinnd

the visual impacts that these panicular elements have on people's perceptions.

1.1 Rcseiirch Problems

This brings Iorth the problem that there appears to be relatively little data on the efft'cts

of downtown strertscapc: cornponents on visual preferences (Stamps. 1997). Thrre ülso

scttms to hc littlt. inli~rrnation regnrdiny downtown streetscape composition and

prelarence. i.c.. ir hethrr the number ofdifferent streetscape components afkcts \.isiiiil

preti.rc.iiccs. I t is clcar that di ffrrrnt streetscape components could enhünçe. maintain. or

di min isli the ovcrd l Iippeürnnce of the street (Stamps. 1 997). 1-lowewr. excepi ibr

gcncrül reports thiit the prrsence of some natural rlements increases the visual nmcnitj of

strect scencs. rhr rcsults srrm to br sornewhat ambiguous and difficult to transiair. into

iirhün dcsi y puidclines or policy recommendations (Stamps. 1997).

Therc arc a t h possible reasons b r why it is çurrently ditliçult to translate sciriiii tic dütn

into poliq. One rcason is that rach study makes use of its own methods and

nicasiircnicnts. niiikiny i t di tlicult to compare tindings across studies (Stcimps. 1 097 1.

Ior esample. Eaplüii and Kaplan ( 1989) used nonmctric scaling. Namr ( 1988) riscd

corrclations. Orliind et ü1 ( 1992) used a IO-point xmantic difkrentiül scale. Ndrssen

( I W j ) iisrd ÜII 8-point srmantic scale and Stamps ( 1997) hüs usually uscd an X-point

srrnüntic dil'l'rrrntial scale. Stamps (1997) feels that a possible solution to this prohlcn~

could br cichirwd if researchers reported îïndinp in tems of standardized metin

di fferences.

A scçond possible reason is that few studies on environmental preferrnces use fornial

cxperirnentiil designs on the stimulus factors under study. in tum. making i t diftiçiilt t»

scparatr the speci tic tactors atTecting prekrence (Stamps. 1 997). Foni~ül txpcrinirntnl

drsigns. sucli as baliinced crosscd factorials. permit allocations of prekrence c'tkcts ro

the spwi tic tictors iindcr investigation (Stamps. 1997). A bolanced crossed îiicti~riül is (1

rcsccircli nicthod t h groups al1 combinations of the visual hctors and üllows one to

compare ihc prekrcncr rcsults o f the scrnes with and without the individual ~àçtors.

lnformitl espcrimtmts ~,here environmental scenes are chosen tbr convenienct. do not

tend to üllaçritc prct2rcncc eI1Fcts to specitïc factors. and thrrefore. iisuallp resiilts in

rcpimitig unihiguoiis lindinys (Stümps. 1997).

Ciinsidering ttic issiics stated abovr. the goal of this research is to provide cülciil;itcd

rstiniatrs of itir cltkcts ofdowntown streetscape components on visual preti.rcnçc.s.

Sincr thrrc is siibstantiül conccm regarding the effécts of physical design componcnts on

prcfrircnces o t'scrnic beacity (Nasar. 1988; Rideout. 1988: Mayill. 1990). and as thc

technology is now available to perform formal experimental designs using phoiognphs. i t

seems tinwly for investigation.

The research objective is to test the effects of tour cornmonly u s 4 strretscüpc

components on Jowntown streetscapes. According to litenture (Malt. Ic)70: Pushkarev

et al. 1975: Arnold. 1980: Gibbons and Bernard. 1992). trees. lamps. benches. and

garbag rrcrptiiçlrs have commonly been described as signiticant streetscape

componr nts. Considering t hat these elements are commonl y used in strretscape

rnvironmrnts. tliere is a yeneral tendency to assume that these componrnts irnprove an

environment's visual quality . Some of these components have already undergone

~s iw inwstigütion. tiw rxample. trees in urban environments have hem shuu-n to

tic:iiit l!. i m p r i w people's perceptions of streetscapes (Arnold. 1980: Kaplan iind

an. 1 W): Orlünd et al. 1992: Stamps. 1997).

'i'liere fim. assuining that these tour components should have positive visual prrfcrcncc

etlkcts on strcctsciipt.~. the îiillowing five hypotheses were invest igntcd:

( 1 ) Strccts \ ~ i t h trcss will be preferred ovrr streets without trees.

( 2 ) Strcris \vit11 lomps will be prrferred over streets without lamps.

( 3 ) Strects \ i i t l i bcnçhcs will be preferred over streets without benches.

(4) Strerts u ith yiirhage recrptacles will be preferred ovrr strttets wiihout yarbügz

rrcrptnclcs.

( 5 ) A positiw corrrlntion cxists between the nurnber of difierrnt strertscapr: componcnis

and \risiici1 prcfercnces.

1.3 Purpuse of Study

The purpose ol'this stiidy i s threrfold. Firstly. the results of this reseürch will

substmtiüir. ilic rttrctiveness of usine an experîmental design to evaluate rnviroiimcnt:il

prefireiices. as w l l cis add to the tindings reported by other similar stiidirs (Nüsar. 1988:

Stanips. I '147 ),

Szçondl). tliis siiid) is intendrd to provide decision-makrrs (design professionals.

miiniçipal o tliçids and staff. local organizations and cornmittees. etc. ) wi th ün

üssesstiirni ol'the rl'fect of prrfrrences of streetscapr components on downtoun strccts.

scn ing ss ;i h.lsis tiw tlic implemrntütion of urban design guidelines.

I.ast l y. the intcnt is also to provide various downtown intrrest groups u ith suhstantiiii

r videncc or jiisti k i i ion Ior the integration of environmental prekrençe üssessmcnt

tccliniques inio tiitiirc downtown planning and design procrsxs.

2.0 Literaturc Rcview -

It \\as important ici gain a sound understanding of the various related topics prior to

undenaking this study. The follocving sections discuss literature related to Jowntov, II

busiiicss disiricis iiiid tlirir i ied lbr rer italiüitiw. ilièoretical aiirl iii~tliodulogicid

ti-mit.\\ orks riscd to giiidc this study. val idi ty of visuül pre t'rrencr methods. and prcccdciii

rcscarcli Ibçusing un cnvironrnental preferences.

2.1 Downtown Business Districts

Since cul! civil izüiion beyan. downtown business districts were reçognized fi>r

sustaining the econornic viability. public social lire. and various institutional units daii!

smiill t w n or cil!,. AS stated rarlier in this paper. downtown business disiricis are Iiigid~

v n l i i d 10r rhcir ciiltiir;il, social. historical. and aesthetic char;icteristics. This is sctn in

thc nunimus private and public organizations and interest groups thnt urrc çrcütsd t i )

presrnx and y uide tlisse central areas for future sustainabi 1 iiy. blany o 1- thesc y ri)iips ürc

rcsponsi blt. b r manüy in p. maintaininp. preserving. and revitalizing thrir local urban

centre. This task becornes somrwhat diflkult considering al1 the various Il~nctions and

ümenitirs pruvidcd in ii downtown district.

2.11 Definition

Due to their dynümic nature and cornplex function. these areas have been detinrd cis

Dou ntonn Busintx Districts. Main Streets. Central Business Districts (CBD). and -l'ou n

Centres ( Evans. 1997: Tiesdell et al. 1996). Thrre are ditkrent perspectives on the

nature ol'd»unto\i n business districts. making them somewhat difficult to detint..

Proprn!. viiliir.~. rt.i;iil tiirnciver. pedrtstrinn tlows. spatial ciincrntraticin of tloor spiicc.

s i x of urbiin ;irea. have d l been used to detine the central business district and tti

establish the relritiw commercial importance of town centres (Evans. 1997).

Evans ( 1997) describrs the diftrrent ways in which people see or drlinc tiiun ccntrts

Whilr sonic prrict itioners have adopted physical and mechanistic modcs ol'dist iiigiiishiny

the stat 11s 0 1' di t'fircnt centres and their constituent tiinctions. theorists have t;~cuscd nwrc

on the iindcrlying processes and the institutional units shaping the built cnviri)nincnt in

thesr centrd loctitions (Evans. 1997). Four distinctive views of town centrcs hucc hccn

identi tied. caçh plücing a diffc'rent emphasis on the physical. çconomic. culturtil. and

institiirional !iinctions of thest. centres.

For the purposrs of this paprr. d l four characteristics are equally important and

undivided t'rom sach othrr. and therefore. are al1 deemed necessary in detining downtoun

business districts. tlowever. it is the physical characteristics of the downtown centre ihot

are usually considercd in nred of improvements and revitalization.

2.1.2 Rcvitalization

Dealing üppropriütr1~- with the valued legacy of the past is a challenying problein for

man! t«u ns iind cities. Sincr the 1970's. historic areas of towns have undergone n rc-

rvüluütion o f tlieir importance (Tiesdrli et al. 1996). The first efforts of historic

prcscnation pdicics protrçted individual buildings. often h r iheir national or rclipioiis

vsliic. Thr. wcond \ w v e ~it'presrrvation or conservation policirs was conçrrncd ~r itli

groiips o f Iiistoric buildings. townscape. and the urban spacrs b r t w r n the buildings.

Si~icr thcn. nian! do~~ntoum business districts have undergone various physical and

tùnçtionül çhangcs. ]:or thrsr üreas to cope with this continuous chanpc. orgüiiizniions

liai t. rcspondtd h l providin y them with guidelines on hou. to manage the rc\ iiol iïat ioii

process ( Si1 hcrbcrg et al. 1976). as well as rvaluations of the successi'uliiess 01' this

pruçcss ( Projtxt !or Public Places. 1982: Whitman. 1997: Unterman ct al. 1098: 13rokni.

1 '19') ).

Along \r itli gcncrül ürchiteçtural repairs and rcnovations. adjacent strwtscapcs l i u c

rxprrirnçed coniinuous irnprovrmrnts and replacement of componrnts. Thesc

components cün çuniribute to the idrntity of a place and to the makiny or improwncnt O!'

iirbün spaces (Gihbons and Oberholzer. 1992). With respect to thrse spiiczs bstwrn

biii ldi ngs. mue h lias bern wri tten about the Fundamentals of urban strcetscapes

( Pushkart.\. and Ziipün. 1 975: Hmdman and .Jaszewski. 1984: ( i i bbons and O brrholzrr.

1 W ) and their importance to a city's image (Lynch. 1960). Hurand ( 1988) also dr;iuSs

this conclusion in his rvaluation of community design aestheiics and how cornmunit?

pridr is 1 inked uith appearance and how appearance becomes a symbol of the toi\ nos

herilth.

Gosling ( 1998) stütes thüt "at the heart of successful livr/work neighbourhoods is tlic

strcctscapc. uliich tùnctions as the community living room". Authors have iilso uiitien

ahoiii the \ ürious coniponents that makr up the streetscap (Malt. 1970: Arnold. IWO )

and how t t i q cari ht. LIS^ to revitalize citiss (Correalr. 1990: Robertson. 1998 ).

2.1.3 C'omrnittces and Orgvnhations

~\ticitlir.r intcyrd part o f downtown business districts is the V ~ ~ O U S socid and pu1 i ticül

intcrnctions tliai oçciir within it to help manage and maintain its viability. rTliis is

rci1ecit.d in [lit. numcrous orgünizations and cornmittees whiçh werc initiaicd b! tlic locd

residrnis. business ou-ners. and merchants who have wiitchsd their cornmunitics Jccip

bctorc k i r el cs (.Uxls«n. 1991 ). I t is important for local govrmments. planning to

improve tlicir ccritriil business districts. to work collaborütively with the local rcsiclents

and mercliants. One of the simplest and most cost-effective ways local govcrnnisnts u n

participatc in streeiscape improvemrnts is through a cooperaiive program with locul

mrrcliants and raidents (Abelson. 1991). Today. many o f these programs or committccs

involve nunierous lrvcls of interest groups-municipal officiais and staff. eleçtcd

ofticials. business ownrn. retailrrs and merchants. professionals. local residents. etc.

Theretore. the most successful revitalization projrcts are those that involve al l thc

decision-nirikers,

2.2 Theoretical Framework

The fol louing sections drscribe threr areas of thcory (hienrchy of needs. lrgibility and

rnvir«nnient. and environnientai preference), which providr a contrst for undrrstündin y

thc oiitcunics of' this rcsearch.

2.2.1 hliislow's Theory o f Motivation

Abr~hüm h l d o u ( 1962) provides a basis for understanding human h d t h in rcliition io

thc rn\~iroiinicnt. Tliat is. "the authentic or heaithy pcrson ma\: be delincd not ii i Iiis on n

ri plil. noi ab di llkrcnt h m thc cnvironrnrnt. independent of i t or opposed t« it. hiit citlicr

in cm ironn~entiilstmterrd terms. rg.. of abitity to master the rnvironmsnt. tu hc çüpühlc.

efkctiw. cunipcirnt in reliition to it. to perceive it well. to be in good relations to it. und

tri bc siicccss hl in i ts terms" ( Maslow. 1962). Maslow ( f 9 5 4 also dcscri bes Iiiiniiin

hctiltli in rclation to Iiiinion niotiution. He proposes theop to explain h w himion

motil atiim ça11 hc ocliiet cd ihrough one's satisfaction of basic nrrds. These Iiicrürcliicol

needs Lire pli' sidopicni. safety and security. belongingnrss. rstecni. sel S-üctiiül imiion.

tindtmiandiny. üiid ücsthrtic (Maslow. 1954). The basic human neds model çoiild hc

applird to ttiis stud) to hrlp explain the results. Certain strertscape çoniponcnts. likc

thosc proposcd IUr testiny (trers and lampposts), tend to provide sütislàction io tlic niore

important nceds (saftty and srcurity). while other components (benches. and garbagc

receptacles ) tend t« salis. the lower human needs (corn fort and aesthetic funçtion ).

2.2.2 lievin Lynch's Theory of Legibility

Kmin Lynch ( 1 %O) described a visual framework that urban centres rquire in ordcr ro

he prrceiwd üs bciiiitiful and plrasant. He defines this frmeworli as .-lcpibiliip-(lis

case with u hich a city's parts can be recognized and can be organized into a colicrcnt

piititxn" ( 1.1 iicli. 1 %O). *fliis visual quality of legibil ity encompasses the broadcr

s i r i ~ t ~ r c O!' L I ~ I I b m i (strcct pattern. positioning of buildings and open spact.. etc. 1. :is

\rd1 as the ni«rc dctail-spcrcitic areas like streetscapes and the componcnis tliat muks iip

tlicir coniposiiion.

- . 1 Iicsr spüçcs ;ire i iiiportant Iàctors in determinine people's visual percrptions. iliiis

rnakiny i t imponnnt fbr designers to shape fom and helping the percciwr crcütc o

colicrcni. tiic.oiiinytiil. and moving image (Lynch and 1-lack. 1984). Whüt peopls tsiid i i )

Itiok tbr arc iirhan londscüpes. which are technically organized so that its pans w r k

iopcther but arc also perceiveci to bc Iogically connectrd to one another ( L j nch and I lock.

1984).

2.2.3 Kaplan and Kaplan's Preference Tbeory

Kaplan and Kaplan ( 1989) provide a more recent framework fur rescarch theor'. rlieir

preferencc theory heips rxplain why certain elements of the rnvironmrnt. naturd \ ~ r j u s

bui l t . might have a grrater positive impact on people's perceptions. The' devised ri

preferencr matris thüt relates human needs of exploration and discovery io visual

preferences. The prekrence matrix. shown in Table 1. has been concerned with two

basic informational needs. understanding and exploration. which yield four combinations.

or patterns ( Kaplan and Kaplan. 1989).

Table 1. The Prcference Matrix (Kaplan and Kaplan, 1989).

Understanding Exploration

Irnrnedhte Coherence

I nfcrrcd. predicted Legibility

Of thesr lbiir pitterns. complc.uity is the on& of greatest interest in relation to the stuJ> ot

hünd. Kaplan and Kaplan ( 1 989). de fine tliis characteristic "in ternis of the nuni bcr u i-

dit't'crent \isi~aI clt.mt.nts in a scene: how intricate the scenc. is: and its richness".

l'hcrehre. ihis iliwry of c«mplrxity and visual preferencr suggests that thc strrct scciics

\r i th trccs shuiild bc more prekrrcd than the scrnrs without tncs diic to the ph' siçul

cornplexit) of irees. This thcory should also assist in predicting highcr prrlèrençcs l i ~ r

the strectsçapes showing more types of components (more complrx) wrsus those u i t h

t k w r types ol'components (lm con1ple.u).

2.3 Issues of Validity

The following scctions relate to the different issues of validity concerning the methuds

and applications used for this study.

2.3.1 Vdidity of Photographs as Landscape Surrogates

Photograplis Iiaw been used as surrogates for field experiments in landscape üsscssnicnt

rescarcli ibr man) y r s . Ovrr the past two decndes. there has been a çonsidcrahlc

amount of rrscrircli on this issue. The relevant litenture tends to sugpesl ihat prckrences

obiüined lkoiii slidss or colour photographs comlate highly with preferenccs obiained on

sitc ( Ilcardm 1090: Xrisswer. 1983: Kaplan. 1988: Yasar. 1988: Strimps. 1993 1.

l 'he ilse o f plioiogriiphs to cvaluate visual prefercncrs hos bcen estensivcly doçiimcnicd

( Nüssü~ier. I W: Yusnr. 1 988: Pomrroy. 1989: Kaplan. 1989: Magill. I W O : O r l d .

1001: Sianips. 1W3: Jutla. 1997: Law. 1997: Stamps. 1997: Hands. 1 W)). Alilio~~gli II IC

ont! küsi blc nict hod to o btüi n cnvironmental prefrrences given practicül. rcononi ic. aiitl

iimc çonstr~iiits ( l'oiiicroy et al. 1 983 ).

2.3.2 Validity of Computerized Visual Simulations

C'ornputeri~rd 1 isiial simulation is a technique by which full-colour pliotographic imuycs

arc digiiizrd and ediied by corn puter to represent photo-realistic stimul i ( Hands. l gC)') ).

This elcçtronic manip~ilation is otien undetrctable in the final image (Orland. IW4). .A

computerized visual simulation (CVS) is a technique that is growiny popularit) in

preference rcscarch. I t ûllows the user careful control of landscape characteristics as ~ c l l

as photo-redistiçüll> portray patent ial design outcornes.

Photo-realisrn swms to be extremrly useful in the comprehrnsion of a proposed

landscript: cliangc b!, the average layperson (Sheppard. 1989: Orland. 19'14). Studies

l i ü w s h o ~ i n that non-designers tind CVS to bc more redistic than the traditional

renderin ys or photomontages used by designers (Orland. 1994). Many other inlbrmiitivc

and ussful books on \r hy cornputerizcd visual simulations art: effective in rrseurcli Iinvc

been writtcn (%m;irtlon et al. 1986: Mngill. 1990: SanotT. 199 1 1.

Thcrc arc L w nitiin principles surrounding the validity otùsiny cornpiitsrized visuid

siniiilütions tbr rcscarch ( Adapted from Sheppard. 1989 and klands. 1 9OC)). Visiiiil

simti1;itions slioiild hc:

( I Heprcscntutiw - ihr degrec to which a simulation drpicts importani \ iw s O t' tlic

prc).jcçt. md show the projttct in typical views and conditions:

(1) Accurute - tlie siniuiation shows a view that is as close as possible to tlic rcol

viru \r hm secn tioin the smr viewpoint:

(3 ) Cleur - ilic degrec to which the visual content and drtail of the image cün hc

cleûrl>. presentcd and distinguishable: and

(4) Defensible - the a i e n t to which a simulation can be identitkd as being üccumtc

and represrntative.

2.4 Prccedcn t Reseli rch in Visual Preference Studies

Man! reseürchm Iiavtt used the above techniques to gather useful inbrniation rrgnrding

visual prekrenccs. t'vülunting both natural and huilt rnvironments.

2.4.1 Prcferenccs o f Natural Environments

U'i tliin tIic en\ in>nnicntnl ps! chology communi ty. researchrrs brgün investi yating

nütiirül clcnicnts and whcther the! improved the visual quality of the urban cnvironnicnt

(Stamps. 1 W7 ). Kaplan and Kaplan ( 1989) produced an in tluential book. j&

Esperience ot' Nüiurc.. sumrnariziny various preferencr studirs for naturül en\ iro~inic~iis.

0tht.r rt.sr:irclicrs hr iw providrd ernpiricai research substantiüting the Iiighcr prctixcnccs

tbr iiatiirül Iündscapcs wrsiis man-made ( Pomeroy. 1983 ). Wohlxill ( 1 W) and I Ilrich

( 1985 ) u w t c \diiable revirws on how people rrspond to the natural snvironmcnt.

Rrçently. rescürch in natural environments has been reportrd usina more controllcd

en\ ironinrntül prrikrencr e\.duations. For example Hands ( 1999) used ~ i s u n l

çompiitrrizd siniiilütions to evaluatr preferences of rehübilitation scenes drpiciiny

vürying colour Irvels and varying signs of human care and intent.

2.42 Preferenccs o f Urban Environmen ts

Over two drcadrs ago. it was suggested that ideals have evolved h m the old Ch!.

Beautiful niovrmrnt to idrals that are based on how one exprriences urban tounscüpe

16

(Jacobs and Appltyrd. 1987: Sutla. 1997). The rmphasis is on how one's exprrience

depcnds on "tlir sights. sounds. feels. and smells of the city. its materials and tcstiircs.

Iücadcs. st~les. signs. liyhts. szatiny. trees. sun and shade" (Jacobs and Appleyrd. I LM7 ).

Sincr thm. \wioiis pr«fessionals have investiptrd the arnenities of urban strtxtscapcs.

lising \ arioiis mcihods of investigation. Lynch ( 1960: 198 1 : 1984). Anderson ( 197X 1.

.+\pplq 3rd r 1 '179: 198 1 1. Cooper and Francis ( 1990). and Whitman ( 1 997) tint c :il1

repunnl tindinys regiirding strrrtscapes.

2.5 %lethudulogicrl Frumework

Dus to modern isuül imÿginy techniques. resrarch has becn able to inove iiuaj tRmi itic

gcncrül c\.alitütions that certain components enhance the visuül amrni t! o t' urbiin sircet

sccnrs to xcns rcgürding sprcitic forma1 or non-formai design componrnts (Orlund ci :il.

1 W: Sianips. 1097). In uilier words. researchers are now capable ot'ideniil) ing spcci tic

design cornpoiieiiis and rclating thrm to unambiguous or calculated prcdictions ut' thcir

e tliiçts on \ isiilil prclkrenccs. For esample. Stamps ( 1997) invrstiptcd kiir çoiiiponcnis

ofneiylib~)iirlio«d strcets. Usine a forma1 factorial experirntintal design and digital

technolog>. lie tcsted tlie rtfects of trees. wires. cars. and block housing typcs on \ isiiril

prefirencrs. This samr rnudrl was applied b this study to furthrr produce tindinps

rttlatrd to tlie rfîkct sizrs of othcr streetscape components on visual prefrrenccs.

3.0 Research Meihodology --

The tbl Io\+ iny iniiormütion describes the general mrthods used for investigating the

rfkçts of four strectsciipe compcments on visual preferences (refer io Figure 1 ). blurc

sprci ticall>. t l i r idcntiticürion of the srudy variables is discussed. aiony with an

csplanntion describiny the preparation of the stimulus factors (design componcnts) und

tiot+ thcsc WI-c di tcd iising coniputer soRware to obtain preferrncc. ratines. :\II

esplünütion of how the survcy uas drveloped and how the respondents were sclcctcd and

e~aluütr.d tii col tcct ciilta is dso discussed.

Thc final scgiiient \\ itliin this section describes the analytiçül techniqties thüt w r c iiscd tu

esnniinc the rcsults ( rckr to Figure 1 ). The analysis of variance and standardizcd

contmsts nerc iisrd to ewluatr the etfeci sizrs of the design components on \ isual

pretérence. Spcürman's rank correlation coefficient and &quency anal) sis w r e iiscd to

esaniinc rlic çorrclat ion hetwen the numbcr of di fferent componrnts aiid pre krcncc.

u-hi le content and !sis was used to evaluate respondents' back yround information.

Figure 1. Mcthuds Summary.

- Problem Identification

Literature Review I Identification of Study Variables

Photo Manipulations of Stimuli

Spearman's Rho & Frequency Analysis

Obtain Preference Scores of Stimuli

ANOVA & Standardized

Contrasts

Obtain Demographics and Background Information

of Respondents

Frequency Content Analysis Analysis

Analysis of Elements & Preference 1

3.1 ldcntification o f Variables

Thrrc arc miin! design components or clements that are traditionally placcd dong ths

cdgcs ol'do\rnto\\n streets. Becausr the task ofrvaluating al1 possible cornponrnts is noi

praciiçül br a stiid! o 1' this type. it was required to run an investigation to namon tlic

di ffercnt t! pcs o 1' components to a more managrable number. '1'0 obtnin eficicnt und

c.hpc~liciii iJc.iiii liciit ion o t' tlicsc: componcnts landscnpe archi trcture stiidents tii tlic

Ssliool oî'I.ündscnpc .-\rchiiccturr. in Guelph (ON) werr used to idrntitj four sircctscüpc

çoniponriiis. :\ccortliny io t his group. trers. lamps. benches. and garbiigr recrptuçlcs

w r c ideniiiicd ns k i n g tlis most valuable strctctscape componenis. 'fhcsc li~iir siinitiliis

tbciors hccüiiic i lic liscd or independent variables, V isual prckrcncr \ras seicctcd üs ilic

non- fiscd tir dcpsnrlrnt \.ilriahle to evaluatt. the rtfrcts of t hese ph! sicd Iüctors oii

d«u ntou n sircctscqxs.

3.2 Prcpimatiun o f Stimulus Photographs

ro prcpiirc ;l hiiclground for the stimuli. two colour photographs. tükcn iiom t\\o

Douiitinrn (iiiclpli strceis. wrrr digitized iit 300 Dots Fer Inch (DPI). Photogrüphs ot'

racli streetscnpr componrnt were selected and also digitized ai 300 (DPI) b r ci~nsistcnc! .

The strcet ircc image \vas scanncd from a tree nursery guide. selrcted Ior its fom und

liabit. \s hiçli hest suited ü strcrtscapr envimnmrnt. The othrr three elrments (Iünips.

bcnches. and garbüye reccptacles) were scanned h m street fumiture brochures and w r c

srlrctcd hr their hisiorical features. According to a study completrd by Untemian

McPhail Curning Associates ( 1 998). klain Street improvements that implrmrnt an

historiçd tlienir tend to hc more successful. Therefore. streetscape components u-crr

seleçted on tlic hiisis of complemcntinp and encompassing an historical contest.

Retkrriiig tu Stenips' ( 1997) mctliodoloyy. this study uses the similar experinientül

design-ii Iàctorial rsptxinicnt on the physical factors of trecs. lamps. hcnçhcs. and

cnrhiigr. rcccpt:iclcs on two downtown streetscaprs. The two strcrtscapcs wrrc crcütcd. C

iisiiis digitiil soRunrt.. with and without irees. with and witliout lamps. with and \\-iitioiii

benc hes. and \\ itli üiid without parbaye recrptacles. The design rc.sultcd in a sinipic..

balüncrd 7 s 1 x 1 s 1 crusscd factorid (refer to Figure I and Figure 3 ).

Figu rc 2. Crussd Factorial Design.

T - Trees

None L - Lamps 0 - Benches G - Garbage receptacle

T L B G

T-t T-B T-G L-G B-G

T-L-6 T-L-G T-B-G L-B-G J

L

AH >

t\ total of sistern strret scenes were created for both Streetscape One and Streetscape

Tu,». One ssent. Iiüd no coinponrnts prrseni. four scenes rrprescnted the individud

coiiiponents. sis sccnes depicted the various two-cornponent combinütions. four sccnss

hiid the tliree-coiiipont.nt combinat ions. and one scenr depicted al1 four cornponents.

Sepmtc I : i y - s I;v eüch coinponent werc created and irnposed ovzr tlie two Dot\ ntou 11

(;iidpii strcctsciipss. Sisteen photographs. of sach strrrtscape. werc produccd h> turning

on and uîl'ilic t ;iri)iis coniponent layers. resulting in images çorrcsponding to ilic

balünccd fiictorid design (rrkr to Figure 2 on page 23 ). In order to provide ddiiionnl

dütü w strt.tiytlicn tlic abilit) to analyze the findinps two random sets ot'thr sisiscii

photos ol'c.ricli sireet M ere copird into presentation software to br projected tliroiigii LI

çonipiitcr pn!jccior l i ~ r victving. Copies of the images arc shown in Figure 3 and

Appcndis .\.

L!ücli doirmonn scrcciscapr was to br viewrd twice. The tint viewing i~btaincd

prckrcncr ratinys. \i hile the second virwing providrd additional nt inys along \i itli

respondcnts' \\ ord üssociiitions to better explain their prefcrençes.

3.3 Survey Dcvclopment

A \-isual prckrenct: questionnaire for evaluating prekrenccs and collecting background

information of the respondents was prepared using Hands ( 1999) and Pomrroy ci al

( 1 989) as rct'erttnce models.

- Figure 3. Photographs of Stimuli

.. TREES YES ' RECEPTACLES

TREES 1

BENCHES

LAMPS SNO Y S s I

RECEPTACLES. YES

STREET

SCENE 1

BENCHES \ ' .YES LAMPS t-

' YES 1-

'f.

4 NO YES l NO

3.1 Rcsponden ts

.-Iccording to :\ bclson ( I VC) I ). Correale ( 1990). and Whitman ( 1 907) it is veq hcnc licinl

to an!. rwiirilizaiion projtrct to involve local oqanizations and interest yroups tliroiiglioiil

tlic revitui i h o n proccss. Considering that downtown conimittees tend to bc. i lit. maji)r

rlcçision-niokcrs conceniing revitalization. aesthctics. management. and otlier dori iitori n

issiics. these gsoi~ps hcçiinie the fbcus îbr input and trsting.

Donntorin Ciilclpli. in the City of Guelph. was selcctd as the study site. 'I'liis arco is

çi~rrcntl! plnnniiig b r rcvitiilization efforts to begin in the >car 2000. I:mr çi)itiiriittccs

w r c idcntitiltd t;w tlieir interest and involvement in downtown issues: I'ronwti~~n

C'oniniittcc. 1 .ucul :\ rchi tcciural Conservation Advisory Cornmittee. R w ital izaiioii

C«nimittcc. iinJ Ilou ntown Economic Strategy L Steering Committcc.

Witli the Iiclp ot'ihc Downtown Guelph Board of Management stüRand thc Cii! ot'

Ciiiclph siiil'î'. cxli o f the four cornmittees wrrc contactrd and invited io partakc in tliis

siin-ey. :\II the wordinators wlcomrd the survey and expresxd thcir iiitercst hy

schediiling tlic surwy presentation to take place at the start of one of tlieir reg iilor

rntxiinys. Al tbur cornmittees were surveyed during the month of Frbruarj- 2000.

3.5 Data Collection

;Ill cornniittct. nicnihers present at that meeting were asked to vitw the randoni sets oI'

sistren photos i i t tiw-sccond intervals and at ten-second intervals for eüçli of' the iwo

do\\ iiton n strcctscapcs.

During tlic li\~s-sccund-init.r~~aî slide presrntation rach of the rrspondriiis w s nskrcl IO

prwidc ii \ isunl prckrcncc ratiny hr rach of the sixtren images. Eacli respondcnt \\as

üskccl to rtitc ctich slide iising a seniantic ditferential scale. codcd froni I ( l u s & prcii.rrcil)

ro 7 (iiiost prcti.rrcd ).

Diiriiip ihc tcn-sccund-inien~d slide presrntation each respondeni was ügain askcd ti)

prwidc ri \ k i ; i l prckrence rriting f i ~ r rach of the sixteen images. ratine rach siidc using

the sü111c sc~iiil~iii~ di 1li.rcntiül sctlle. Along with each prckrencr rüting. thc rcspondciits

sistccn slidrs. fhis procedure was completed for each of the two downtown strcctscripcs.

Eacli rcsponiicnt uiis iilso üsked to completr a background informatiim srciion i)l'thc

3.6 .4nrlysis Techniques

Since previuiis rcsrürch indicated that many forms of analyzing preferencr data yicld

\ irtiially tlir sanic rcsults (Stamps. 199 1 : 1997) the data was ünalyzrd usiny the

respondrnts' prekrencr ntinys. Standardizrd contrasts (6).

bct\wen thc Ic\ cls ~I'cüch hctor (trtxs vrrsus no trees. Iûmps versus no lanips. hençiics

\,ersus no benchcs. and grirbayc receptaclrs versus no garbage rectiptüclrs) indicütc thc

strc~~gtli 111- i~iipiw~;~nce CI 1' cüch r tfrct on visual preferrnces. Tlis reüsun t'or using

stündiirdizcd riic;iii diî'lkrcncrs is i t allows for comparing resuits ücross siudics ( Roscntli;il

and [<osni)\\. 1 W. St;imps. 1997).

.-\ prï~hlrni ~ i i l i iranslritiny scientitic tindings is that each srudy uses difkreni

rneiwrcmsnts ur scniantic scülcs. makiny i t difticult to compare resulis. III otlicr \wrds.

;i reportcd contrnst o f 3 .O might be considered a strony etTeci if the range of

iiieasurenients w r e 7 points. but a 3.0 contrast Crom a range of70 points rnight be

considercd mai l (Stiinips. 1997). Stamps ( 1997) Feels that a possible solution to this

probltim is to analyze data using standardized mean differences. The analysis ot'vüriiinçs

and m a n çontrüsts were i w d to calculate the standardized contrast of eüch strcctsçape

component.

Spearman's rank correlation coeflicient analysis was usrd to evaluate the relatioiisliip

ht. tu een t hc nuni ber o l' types of componrnts and visual prelèrencrs. This nonparamctriç

nieüsure ot'tissociation is çonsidered a useftil and effective analysis teclinique to idcntil'j.

drsçriptivc associations hctween two variables (Gibbons. 1993). Meÿsures of ccntrül

teiidenc) iiiid Iieqiicncy ünalysis werc also applied to furthrr identi fy u hcther 3

corrclrition cxistcd b r ~ w e n the numbrr o f difkrent t y p s of strertsciipe çi~mpiincnis ;ilid

visunl prclkrciiccs.

Content aniilysis \vus usrd 10 cvnluntr the background infomintion cornplctd b! tlic

rcspondtmis. 1 king q~iantitcitivc content analysis in social and brliavioral restxirch stiidics

cün hc an clli.cti\ c. technique to express communicative content in a qiimtitüti\ c ri1;iiint.r

( Ritlk ct 31. f W S 1.

Tliis seçt ion bcpins b ~ . out lining the descriptive results. which includc the rcsponsc ratc

ubtainrd in his stiidy and the dcmographics of the respondents. An analysis 11 ts rlic dain

i i i iduiiuii i ~ ) ilid d k c r s i m of thc strcctscapc components (hypothesss cine i h r i ~ ~ i g h !iwr)

is aiso pro\ idcd. /i)lloirt.d by the analysis of ihr number of design çoinponcnts and i isuül

prcti.rcncc (tlic t iRIi hypothesis). The final segment within this section drscribcs tIic

yiiüntitntiw conicnt nnalysis iised to simpli- the descriptive information pro1 idcd h! tiic

rcsponclcnts in rclntion to tlieir visual prefrrences.

4 . Rcspunsc Hate

~~~~~~~~~~ciylit siincys \wrc ronipletrd using four Downtown Gurlpii Coitiniittcss as a

popi~liition. I'lic ioid population of the hur cornmittees cornbined is 53. Tl~crcti~rc. [IIC

rcsponsc. rare \vas npprosimatrl y 73%. According to Babbit: ( 1 995). ü responsc nilc O 1'

ovrr hO% is considcrcd gwd.

Other rcse:ircht.rs collrcted data using similar samplr sizzs. For example. Nüsür ( IW8 )

iised 33 respondrnts. membrrs of various neighbourhood groups. to tlvaluütr dillércni

rrsidentiül strert sccnrs. Stamps ( 1997) also had a sirnilar population sarnplr of 42

respondrnts to e\.üluütr the elfects of trees. cars. wires. and block housiny on iisual

prekrencrs. This indicatcs that this is a typical sarnple size for this type of stiidy.

4.2 Dcmogrrphics of Respondents

Rekrriiig tu Fig~irc 4. approsimately 55% of the respondrnts wrre mde and 4 2 n ~ ~ w r c

iknialr. l'lit. niiijorit! of tlic respondents were between the ago o t' 36-50 (45Uii 1. Ir liik

Y u uere hci\twn the üye 51-65. 13% were between 3 - 3 5 . and 13% o\er 65 y r s i)1'

qr. 0 1' ilic h i r w n i n i i ttrcs sur\-eyrd. approximatrly 74% of the rcspondcnts rcsidc

otitsidc ilic D«~wto\vn Guelph m a but within the City of Guelph liniits. IY"' rcsidc

\i ithin l h t nit)\\ II (iiiclpli. and 1 1 % rrsidr outside the city boundürics.

4. Dcmogrrphics of Population.

The breükdon n of the sarnpk yroup according to xven subgroups is identi tied in Figiirc

- - . 1-hc tint niain siihgroup is "Professionals" (A). which encornpasscs design

prokssionnls and Citl of Guelph membrrs (incliidiny staff. elected ollicials. ço~inçillors.

board membcrs. and public service employees). The second subgroup i s ~~Rusincsscs"

( R 1. u-hicii cnconipüsses Downtown Guelph financial serviccs and business nicm bers

( includiii;. iiicrcliüiits and retailers. business ownen. building owners. : i d hiisiiicss

Figure 5. Su hproup Anrlysis o f Respondcnts.

A & B & C 3% No Response

Users/Customers (C)

employes). fhc third main subgroup is "UserNisitoF' ( C ) . wliich includes Dinsnicnr n

Ciurlpli iism. custon~ers. citizrns. and visitors. The Iourth subgroup is a cornbinütion o f

( A ) and (B 1- the tiftli groiip is a combination of (A) and ( C ) . the sisth is a conibinlition ot'

(B 1 and ( C 1. and the final subgroup is a combination of (A) and ( B ) and ( C ) .

4.3 Effeets of Streetscrpc Components

Tlir cinalysis of wriiinçe is shown in Table i. The analysis of variance \vas genmtrd

iisinp the SAS data ünülyis cornputer application. It çan be swn that arnong tlir stinitilus

Iàctors. trcrs had tIic Irirgest s k c t on visual preference. fo1lowt.d by lamps. hcnchcs. nntl

garhage rcccpiüçlcs. The contnsts are sliown in Table 3. The mran preferencc score IOr

ilic >ii6cct:, ii i th ti.ccb LUS 4.88 and 2.58 h r thc strccts withoiit trccs. The t'i)ntr:ist ('4 \ \r is

2-20. I'lic standürdizrd contrüst was 2.36. This contrast corresponds with tlie Iirst

h!.pothesis rliür stutrd strwtscapes uith trees will be preferrrd over strrrtscüpcs \\ iihiuii

trws. ünd tlic si/c 0f'tliis cl'kct supports this hypothesis.

Tablc 2. Thc An;llysis of Variance.

Source of Sum of Degrees of Mean F Ratio Variation Squares Freedom Squares

Model 451 8.41 163 27.72 29.1 1

Receptacles 1 3.85 1 13.85 14.54

Benches 129.51 1 129.51 136.02

Lamps 293.05 1 293.05 307.79

Trees 3124.20 1 3 124.20 3281.25

Residual 2086.1 3 21 91 0.952

TOTAL 10,165.15 2358

Table 3. Contrasts Within evch Factor.

Factor Preference Contrast, T-Test Standardized Rating, p pl-p2 Contrasts (8)

C l Receptacles

No Recept.

C2 Benches

No Benches

C3 Lamps

No Larnps

C4 Trees

No Trees

t lypothrsis ( 2 ). Iiimps wiild have a positive effect on prekrences. is suppond by tlic

siündürdizcd çonirüst o10.73. A positive ét'kct of benches (hypothctsis 3 ) iind gnrbugc

recr.ptaçlcs ( liyotlicsis 4) &as also sern in the reponed standardizrd contrasts 01' 0.47

4.4 Strectscape Composition und Preference

CI!.pothesis ( 5 ) siiitcd that a positive correlation would be seen brtwecn the nuniber 01'

types of streetscapr components and visual preferences. In other words. as the numher ut'

di fferent strestscape components increased so should visual preferences. Mrtisurcs O i'

central tendent!.. frcqurncy analysis. and Speman's rank correlation coefticiriits iw rc

usrd to üntilyzr. the results in relation to this'hypothesis.

4.4. I Measurcs o f Central Tendoncy

-rüFlc 4 displü!~ the niode. mcdiün. and the nngr of the prrlérrncc rütings for horh tlic

tiw-second and icn-second intrwal sets for Strretscapr: One.

Table 4. Xlcrsurcs of Central Tendency for Streetscvpc One.

1 Photo - None 1 1 1

- -A-------

-.- -

I Photo - B / 3 2 : Photo - L 13 2

Mode 5 s 10-S

1 Pho

J

Median Range 5-S 104 1 5-S 1 O-S

:O - T

:O - G-8-T 5 5 :O - G-L-T 6 5 :O - 8-L-T 6 6 :O - Ali 5 6

5-S - 5 second interval 10-S - IO second intenul

L - Lamps T - T r w

t\nalysis of the mode and median of the respondenis' preference ntinys for eüch image.

of Strertscüpc One indicatrs a gcnrral trend for visual prefkrenccs to increasr as tlic

nunibcr of di t'î'ercnt coinponrnts increasr.

'I'üblt. F displiil s the mode. median. and the range of the prefrrence ratinys for both tlic

tk-sccond and tm-second intenal sets for Streetscape Two.

Table 5. ~lrrisures o f Cent r i l Tendcncy for Streetscap ' h o .

Mode Median Range 5-S 10-S 5-S 10-S 5-S

l 1 O-S

: Photo - None 1 1 1 1 4 3 Photo - G 2 2 2 2 ! 6

/ Photo - 0 - 2 2 2 2 ' Photo - L IL --- 2 2 2.5 3 4 i Photo - T 5 5 4 5 4 j Photo - G-0 , 2 2 2 2 3

Photo - G-L - 2 3 2.5 3 4 Photo - G-T 4 5 4 5 4 Photo - 6-L 3 3 - 3 3 3

Again. the analjsis of the mode and median of the respondents' preference ratings liw

each image of Streetsçape Two indicates a sirniliv tendency for visual preferençcs to

incrrasr as the nimber of difkrent components increase. For both Streetscapes Onc and

,

i i 1

5 5 6 6 7 7

-

Photo - 8-T Photo - L-T Photo - G-B-L Photo - G-6-T

' 5 5 5 5 3 3 5 5

5 5 ' 4 5 5 5

3 3 5 5 5 5 5 6

6 5 1

6 5 1

5 4 1

5 4 i

5 4 t i

6 6 , 5 3 i t

Tuu. ihe scrnes \\ i th kwrr number of different components were clrilrly not as prekrred

as the scenrs with a greatrr divcnity ofstreetscape camponrnts. cxcrpt for the scenes

u ith trees prcsent. rhe scrnes with trees distinctly show a higher prefrrcnco. tlius. rc-

cnipliasizing tlie iiiiportancc or positive effcct of this strtxtsçape componrnt un \ isuül

prelkreiiçe.

1.1.2 Frequency Anulysis

fhc t'rcy iicnc! clinris shou n in Figures 6 throrigh I 1 help siippon the ti tili Ii! potticsis.

\vliicli prrdictcd n positiw correlation betwren the numbrr ofdifferent strcctscnpc

soniponcnts cind isiinl prefcrences. Figures 6 throuyh I I dcscribe the prclkrciicr riiiings

iii relation to ihc pcrccntngr of rcspondents.

Figiircs 6 und 7 rcprcscnt the scrnes of both streetscüpes ui th no coniponent prcscnl and

thc scencs \rith one t ) pc ot'coniponent prrsent. Figures 8 and 9 rrprrsrnt ihc sçenes d'

Strestsciipc One ;ind Tuo. n hich show the various combinations of trio types O!'

cornponents. Figiires I O and I 1 represent the strert scenes ol'both strcetscnpcs \\ i th tlircc

or tour t) pcs «ï coniponents prcsrnt.

Tlicse chans Iiclp i l lustrait. how visual preferences tended to increasr as the nuni hcr ol'

iupes of strrrtsclipr components increased. except for the trres. which individiiall!

rrceiwd a Iiiyh prcfrrcnçe rating (Figure 6). Again. the tree factor showed a siyni ticünt

positive etlkct on respondrnts' visual preferences.

Figure 6. Frcqucncy Charts for O or t Type of Element Present - Strectserpc Onc.

None

5 Second m l 0 Second

s

4 5 6 7

Preference Rating

Benches

0 5 Second 10 Second

Roceptacles

a 5 Second I O Second

1 3 J ? 5

Preference Raling

Lamps

5 Second m l 0 Second

40 00 ---

Preference Rating

T rees

0 5 Second 10 Second

Preference Rating

2 3 4 5 6 7

Preference Rallng

Figure 7. Frequency Chsrts for O or 1 Type of Element Present - Strcetscripe Twu.

None [3 5 Second 10 Second

Benches

0 5 Second . f O Second

: 3 4 6 7

Preference Rating

T rees

0 5 Second 10 Second

Receptacles

a5 Second rn 10 Second

Lamps

a 5 Second 1 10 Second

Preference Ratlng

3 4 5 6 7 -

Preference Rating

H 01 Respondents

% of Respondents W of Respondents 'A, of Respondentr

Figure 9. Frequency Churts for 2 Types of Elements Present - Strectscape T w .

Receptacles-Benches

[7 5 Second 8 10 Second

1 1 4 5 6 7

Preference Rating

Benches-Trees

0 5 Second l 10 Second

: 3U , 1

i d 5 6 7

Preference Rallng

Recep tac les -Lamps

0 5 Second m l 0 Second

50 O0

45 00 F I "'

7 1 3 4 C

Preterence Rating

Lamps-Trees

O 5 Second 1 O Second

Preference Rating

Receptacles-Tress Benches-Lamps

r] 5 Second ~ 1 0 Second 0 S Second t O Second

Preference Rating Preference Rating

Figure 10. Frcquency Charts for 3 o r 4 Types o f Elements Present - Streetsenpe Onc.

Receptacles-Benches-Lam ps

0 5 Second D l 0 Second

J Ù :U .

4 5 6 7

Preference Ratlng

Receptacles-Benches-T mes

n5 Second W 10 Second

- - - - . -- - - -

- -----.-

- -

rl

,,, i- ;a (

4 5 6 7

Preference Rating

All Four

0 5 Second 8 10 Second

- C 5 XI :a

25 311 O a :O 50

2 * s o c w 0 15JO

oe 4 ;a

Receptacles-Lampi-Trees

0 5 Second 10 Second

-

Preference Ratlng

- :cl .

Benches-Lamps-T rees

5 Second . l a Second

I -.

1 2 3 4 i a

Preterence Rating

Preterence Rating

Figure II. Frequcncy Charts for 3 o r 4 Types of Elements Present - Streetsrapc TIVU.

Receptacles-Benches-Lamps

5 Second 10 Second

- 3 4 5 ' 3 7

Preference Rating

Receptacles-Benches-T rees

gS Second a 10 Second

.a 5 6 1

Preference Rating

All Four

0 5 SecCind m l 0 Second

Receptacles-Lam ps-T rees

a 5 Second 1 O Second

-

1 : l 4 5 '2

Preference Rating

Benches-Lam ps-Trees

0 5 Second m l 0 Second

1 2 3 4 7 4

Preference Rating

t Z 3 4 5 6 7

Preference Rating

44.3 S pcurmun's Rank Correlation Cocfficien ts

Speürman's rün k correlation corfficients were genrratrd using S PSS data anal! sis

çoiiipiitrr s«St\urc. Inter-photograph Speamian's tank correlation coet'ticients h r h d i

Strcctscüpc One and Strertscapt, Two slidrs are outlined in Tables 6 and 7. Tlic tiiblcs

illustrüte tlitit ihc visuiil pre ferencc scores for images that have similür num bcrs 01.

difircni bu-cctsupc cunipuncnis are iiiglily sorrrlatèd. Associations arc cspcçinll~

evidcnt hei\\ccii tlic slidcs that sliow zero to t~vo types ot'components. ns uell ns hci~iccii

i tic. slidcs t l i ~ i t slimt iwo to Ibur t!.prs ot'components.

Likwisc. rlic prctkrcncs scorcs for images thüt contain relütivel>l unrqiiiil nunihcrs ol'

di fi'crc.nt strcciscupc somponrnts arc rithrr not correlateci or ncgativel! sorrclütcd. Ilicsc

corrcl;itim scic tiicitmts Rinhcr support the A R h hypothesis. N hich stnirs the pi~siii\ c

conclaiion h c ~ w n t hs niimber of di fkrent strtxtscapr: components md visilal

prclèrcnccs.

['hcsc. resiilts arc quite signiticünt and rrliabk considering that Speûrniün's rüiik

conclaiion soeftiçisnt is a very etTective analyticül mrthod to evaluüir çorrelüiions

hetu ren ru o dilTerent variables using ordinal data (Gibbons. 1993 ).

'Table 6. Inter-photograph Spearman9s Correlations for Streetseape One.

,

Sig. (2-tailed) N

G-L Correlation Coefficient Sig. (2-tailed)

N B-L-T Correlation Coefficient

srg. (2-tai~ed) N

T Cordation Coefficient Sig- (2-tailed) N

G-L-T Conelaîion Coefficient Sig. (2-talled) N

B C66lation%ffident Sig. (2-tailed) N

G-T Correlation Coefficient Sig. (2-tailed) N

L Carrelalion Coefficient

GL-T .388'

. 38 .W .O26

Sig. (2-tailed) .- N

Gû-T Correlation Coefficient Sig. (Ztailed) N

NONE Conelation Coefficient Sig. (2-tailed) N

G-6-L Corretatlon Coefficient Sig. (2-tailed)

B ,462

E L - -367

ËL-T .609*" peaman's rho B-T Correlation Coefficient

37 .6ûP -000

37 .608" .O00

37 .38F .O1 8

37 .462" .O05

36 .308 .O63

37 .343*

N Correlation Coefficient Sig. (2-tailed) N

ü-i corteiatiari Coefficient Sig. (2-Wled) N

AU CmlationCoeffident Sig. (2-tailed)

T .W8**

B-T 1 .O00

.O26 ,37

+ 1.000

.O41 36

.W

.O00 37

.236

.160 37

,292 ,080

N G CorrelatCon Coefficient

Sig. (2-tailed)

'. Correlation is signifipnt at the .05level(2-tailed).

". Correlation is signifiant at the .O1 level(24aüed).

37 ' -318

.055 8 37

389' ,017

37 .47 1" .O03

37 375' ,024

36 . I l 8 ,486

37 ,484"

37 3 5 " .O37

, . 37 ,195 254

1 - 36

, . 4 W .O04

. N L-T. . Correlation CoeMcientb

Slg. (2-tailed)

.O00 37

.318

.O55

.O03 36

.O45

.791 37 .MO .190

37 ,613" .O00

38- -1 07 .527

37 1 .O00

37 ,496" .O02

37 ,544" .O00

37 .O96 .577

36 .O30 A59

37 ,201

37 ,312 .O60

37

37 202

. .231

.O00 37

.38Q0

.O17

.239 36 503" .O02

37 -.232 .167

37 .O90 .597

36 326 .459

37 .496"" ,002

37 1 .O00

37 + .160

-343 37

.309

.O67 36

314 ,058

37 A1 5"

37 .IO5 ,538

37

37 306 ,066

.O18 37

.471"

.O03

.O1 2 36

.236

.lm 37

-255 .128

37 21 3 .205

36 . . . -.O32

, .850'

.O05 36

.375

.O24 37

.544"

.ooo * 37

,160 .3$3

37 1 .O00

37 -134 A37

36 ,184 -275

37 .167

37 -210 .213

37 902

37 393' .O16

36 .OS .m

3€ . 305

.06;

34 .13 .43'

3i 1.W

31 .41 .O1

3 -47

.330 36

3 3 2 .O45

37 -.O50 .768

37 .178 293

.495"

.O02 36

. t 98

.247

.O00 36

348' ,037

36 .O53 ,755 '

.OC 3

.OC

.8€ :

.6(

.O(

a

.31

.O: 37

221 -189

37

.237 36

-590" .O00

., 37 S49" .WO

.6

.O i

.O48

.?79 36

. 4 P

.O03 36

.O32 ,049

.1 1 .5

.C

.€

I

,* 37 .203 X29

4

,

.I

L-T ,202 -231

37 .306 .O66

32 393' .O16

37 .549 .O00

37 ,203 229

37 . 3 S .O32

36 .323 .O51

37 ,337' .O44

36 .O75 .660

37 .260 ,120

37 .135 .425

37 .271 .IO4

37 379' .O23

36 .353' .O35

a 36 .215 .202

a - 37 1 .O00

37

B-L -195 .254

36 .W' ,000

36 .202 .237

36 .495'* .O02

36 .O48 .n9

36 -100 .569

35 .O91 ,598

36 -527' .O01

35 .178 ,298

36 .374' .O24

36 .657' .O00

36 .409' .O1 3

36 1 .O00

. 36

246 .149

36 267 . i l 5

36 ,379' .O23

36

G-6-T ,580" .O00

37 .O45 .791

37 ,503" .O02

37 . .236

.160 37

,332' .O45

37 .O30 .863

36 .310 .O62

37 .153 ,374

36 1 .O00

37 .O43 -001

37 .261 .118

37 ,354' ,032 '

37 .178 2M

36 .612" .O00

36 .254 .130

37 .O75 ,660

. 37

NONE .236 .16Q

37 -220 .190 . 37

-.232 .167

37 -255 .128

37 -.O50 ,768

37 364" .O00

36 .320 .O54

37 .4 15' .O12

36 .O43 .a01

37 i .O00

37 .427" .O08

37 S61" .O00

37 374' .O24

36 -.IO7 .534

36 . 6 4 F .O00

. 37 . .26û

,120 37

-

B .462" ,005

36 .37F ,024

36 .O96 -577

36 .309 .O67

36 ,134 ,437

36 1 .O00

36 -417

- .O11 36

4 .O03

36 .O30 .a63

36 .664" .O00

36 . 3 W .O35

36 639" .O00

36 .IO0 ,568

35 - .O41

.014 35

.O00

7

- * - ALL 1 .4WW .O04

36 348' .O37

36 ,590" .O00

36 .198 .247

36 ,406" .O03

36 -041 .814

35 -240 .158

36 .140 .423

35 .612" -000

36 -.IO7 -534

36 350' .O36

36 .214 -21 1

36 -246

- .149 36

1 .O00

36 ' -.062.

,724 36

.353'

.O35 36

E T .308 ,063

37 -1 18 .486

1 37 .O30 ,059 .

37 314 .O58

37 .184 .275

37 A1 7' .O1 1

36 1 .O00

a 37 . . I l 9

,491 36

.310

.O62 37

,320 .O54

37 .133 .431

37 .309

' .O62 37

.O91

.598 36

,240 .158

36 -207 .218

EB-L 1 ,292 .O80

37 .613'* .O00

37 ,090 ,597

37 .213 .205

37 ,178 ,293

37 ,353' .O35

36 .133 ,431

37 51T' .O01

36 .261 . I l8

37 .427" .O08

37 1 .O00

37

.O01

G ' .IO7

.527 37

.126

.459 37

-.O32 A50

37 .O53 .755

37 .O32 .a49

37 .567** ,000

36 .207 .218

37 S25" ,001

36 ,254 .130

37 .MFm .O00

37 354' .O31

37 .6W* .O00

37 .267 . I l 5

36 -.O62 .720

36 1 .O00 '

37 .215

. 202 37

' L 343' .O41

36 ,484" .O03

36 .201 -239

36 .41S .O1 2

36 .167 .330

36 .47?" .O03

36 . I l 9 .491

36 1 .O00

36 -153 ,374

36 .41 5. .O1 2

36 *

,517" .O01

36 .431" .O09

36 .527" .O01

35 ,140 .423

35 325" ,001

E B 345' .O37

37 3 1 2 .O60

37 .IO5 -538

37 -21 0 .213

37 ,221 .189

37 .639" .O00

36 ,309 .O62

37 -431 * .O09

' 36 .354' ,032

37 ,561'" ,000

37 .506'* .O0 1

37 1.000

36 337 .044,

- 36

36 .358' .O32

36

, 37 . 6 P

- .O00 36

.350*

.O36 36

,354' .O31

37 ,135

. -425 37

- 37 ,323 .O51

' 37

, 37 . 4 y '.013 t

36 2 .Si1 36

-Wn .O00

37 8 . .27?

-104 ?-r .

" Table 7. Inter-pbotograph Spearman's Correlations for .+ Streetscape . Two.

IV.

8-T N ~ N E ;.. G . GB-T 1 .O00 -. I l0 -.O83 .Sr

- G-L -

.O24 ,891

36 .288

. .O88 36

.268

. I l9 35

- -.O83 ,716

36 1 .O00

36

.wo, 35

,652 ,000

36 -.O48 ,781

36 .148 390

36 .O32 -852

36 .148 396

35 .500' .O02

36 ,138 -421

36 .230 -177

36 -.O01 397

36 .144 -404

36 -

$pearman's fia B-T Conelaüon Coefficient

NONE Correlation Ccmffident Sig. (2-tailed)

\ N G Conelation Coefficient

Sig. (2-tailed) N

EB-T Conelation Coeffident Sig. (2-tailed) N

G L Correlation Coefficient Sig. (2-tailed) N

L Correlation Coefficient Sig. (2-tailed) N

G-6-l Correlation Coeffldent Sig. (2-tailed) N

T Correlation Coefficient Slg. (Ztailed) N

G-T Correlation Coeffident Sig. (2-tailad) N

6 Conelation Coeflident Sig. (2-bila¶) N

B-L-T Correlation Coeffident Sig. (2-tailed) N

., B-L Correlation Coefficient Sig. (2-tailed) N I

AL1 Conelalion Coefficient Sig. 12-triieb) \ N

Sig. (2-tailed) N

L-T Carrelation Coefficient Sig. (2-talled)

N -T Conelation Coefficknt

Sig. (2-fôiled) N

'- Correlation is significant at the -05 level(2-tailed).

G-L .O24 391

36 .288 .O88

36 ,268 ,119

35 -.O63 .716

36 1 .O00

36 .W .O00

35 .65P*

' .O00 36

-.O48 ,781

36 .148 .390

36 .O32 .a52

36 ,148 ,396

35. .500" ,002

36 .138 -421

36 - 230

.in 36

-.O01 997

, 36 ,144 .404 36

G-T -387' .O20

36 .O70 686

' 36 ,109 -534

35 .217 .203

36 -148 ,390

36 .217 .210

35 .O52 .764

36 S96" .O00

36 1 .O00

36 -.O94 .584

36 .419' .O1 2

35 .. .3û4

.O72 36

.361'

.O30 36

-.If3 .Si 1

36 .669* .O00 - 36

'. .Ml" -007

36

T .378' .O23

36 -.O12 -943

36 -.O45 .795

35 - .268

,114 36

-.O48 .781

36 229 .187

35 -.O22 ,899

36 1 .O00

36 .596;" ,000

36 -.O79

, -646 38

.34V

.O42 35

.282

.O95 36

-112 317

36 -.O23 436

36 .490" .O02

36 .230

* .A76 36

L -.O02 .991

35 .38S .O23

35 .209 .235 '

34 -. 1 34 4 3

35 . 6 W .O00

35 1 .O00

35 .581gg

,000 ' 35 229 .187

35 .217 ,210

35 .160 -358

35 .245 .155

35 .463"

' .O05 35

.IO2

.559 35

-259 .132

35 -.O42 .a11

3 5 .O51 -773 - * 35

w

B .O04 .983

36 -174 .310

36 .42r .O1 1

35 .294 ,082

36 .O32 .852

36 .160 .358

35 ,349' .O37

36 -.O79 646

36 -.O94 ,584

36 1 .O00

36 -.264 .125

35 .431H .O08

36 -.246 .149

36 .48OW .O03

. 36 -.il3 S I0

36 -.156 363

36 a..

. G-B-L -.O27 .875

36 -.O62 .719

36 .27 1 . I l 6

35 .O89 .606

36 ,652" .O00

36 S81" .O00

35 1 .O00

36 -.O22 .899

36 .O52 .764

36 .349' .O37

36 .217 -21 1

35 S35" .O01 . 36 -338' .O44

36 .217 .203

36 -.O14 .936

36 - .209

221 ' 36

8-L-T -435- .O09

35 -.276 .IO8

35 -.O81 A47

34 .O81 .642

35 -148 .396

35 ,245 ,155

35 .217 .211

35 .34V .O42

35 .419* .O1 2

35 5264 .125

35 1.000

35 .249 .149 :35

.57F

.dao 35

-200 ' ,250

35 374- -000

35 .612*' .O00 -

35

51 ,312

' .O64 36

.O44 ,800 -36

-21 2

-21 35

.4W

.O14 36

.500g*

.O02 36

.463"

.O05 35

535'" .O0 t

36 282 .O95

36 .304 .O72 '36

,431" .O09

36 .249 .149

35 1 .O00

36 .272 .IO9

. 36 .W

.,.O02 36

.287

.O90 36

226 .184

36

.*.

AL1 *' .275 .IO4

36 -.431" .O09 36

-.244 .t58

35 ,361' .O31

36 .138 .421

36 .IO2 .559

35 .338' ,044

36 . i l2 .51 7 38

.361' ,030

36 -,246 ,149

36 .570" .O00

35 .272 ,109

36 . 1.000

36 -. 158 358

36 _ - .301 .O74

36 .492" ,002

36

t"rB .MO .81 5

36 .259 .128

36 -481" .O03

35 .294 .O82

3 6 .230 .177

36 .259 .132

35 ,217 .203

36 -.O23 ,896

36 - . I l3 .511

36 .W" ,003

36 -.200 250

35 .5Wa .O02

36 -.158 358

36 1 .OOO'

. 36 -3 39 .420

36 -.197 .250

36

L-T .43Wd. .O07

36 - . l e : 334

36 -.O16 ,926

35 .368' .O27

36 -.O01 ,997

36 -.O42 .81 1

35 -.O1 4 336

36 .49OW ,002

36 ,669" ,000 '36

-.Il3 .510

36 .574" .O00

35 .287 .O90

36 301 .O74

36 -.139 .920

' 36 1 ;O00 . . . 36

.570M

.O00 36

1 EL-T .44S .O06

36 -.174 310

36 .O16 328

- - 35 ,353' ,035

36 .144 .404

36 .O51 .7?3

35 .209 221

36 .230 .176

36 .#la .O07

36 -.156 ,363

36 .612 -000

35 ,226 .184

36 .49$ ,002

36' -.197 .250

3d .SV .O00

36 1 .O00

36

4.5 Contcnt .-\nalysis

Contcnt analyis \vas iisd to ewluate the respondents' bricf descriptions in rclütioii t i j

the scenes ilic) iiiost prelrrred and the scrnes the); least prel'rrred. This iechniyiir. ticlps

idcnti @ ilni y iic rlicnies or patterns concealctd behind the data. as wrll as pro\.idc Iiirilic.1-

esplünütions ns IO \th! certain scenes are more prefrrred tlian othcr scrnes.

l i t o disiincti\ c. ~ I I C I I I C S \\ ert. identi tird within respondents' replies conçerning ilicir

rcüsons Ibr sclsciiiig tlirir most and lrast preferrrd images. Thcse thcrncs nrs SCL'IIC

çornpositioii and cniotiond responses. Respondrnts tendrd to iissocintc Jescriptit c

uords 11 iili ihc conlp~siti~n OF the different components or with the eniotions ilic!

t.spcrirncc.d l'riim the scenes. Tables 8 and 9 illustrate the typical positive iind ncgnti\ c

rcspoiiscs \\ i t l i in cadi ut' thc IWO themes.

T ~ b l c 8. Contcnt Anrlysis ïor Thcmc 1: Scene Composition.

1 Like Descriptors / Dislike Descripton

Trees, benches, lamps Trees All elements Trees and lamps Trees and benches

EWty Lack of trees Lack of elements Without trees No trees Garbage receptacles No elements

Table 9. Content Andpis for Theme 2: Emotional Responses.

1 Like Descriptors 1 Dhlike Descriptors

Cold, uninviting Stark Empty, cold Barren, dark Uninviting Uninteresting Non-inviting Desolate Unfriendly, non-welcoming

P

i

a

!

Rrspondcnts renxdui ihüt tliey rnostly prelèrred the scrncs with irccs or thc sccncs \\ iili

al1 tour çomponents prcsent in the Street. However. respondcnts ülso comnicnic.d oii ilic

barrcn. eiiipt!. iiniiiviting kcling that the scenrs with no trees or no comp»iiciiis

ponrqsd.

Appealing Friendly. inviting Natural Pleasant. pleasing Greenery

The fillou,ing pic cliürts (Fiyiires 12 and 13) display the "like" and "dislikc." Jcscripiions

or comniciits mode b) thc respondcnts. Srparatr pie chans were created for positivs

( Figiirc I ? ) and negaiive çomments (Figure 13).

lnviting Welcoming . cornfortable Full of life

Figure 12. Content Anily sis of Respondents' "Like" Descriptors.

CI Trees (A)

T r e e . lamps. Benches (B)

O Trees, lamps (C)

Appealing . pleasing . friendly (D) Inviting, interesting . welcoming (E) Natural, greenery (F)

All elernents (G)

No response (H)

hl1 four cornponcnts prcscnt in the scene \vas the most cornmon nason g i w n h) tiic

rcspundcnts îbr lihiiig ;i scrne followed by the presrncc of trees. This üyaiii rc-

mphüsizcs thc hiylicr prekrencti rating for the images with twrs and tlie images shou ing

Ü I I tlic dillèrciii ~ p c s 01' strcetscape components.

Figure 13. Content Anulysis of Respondents' "Dislike" Descriptors.

ElNo trees ( 1 )

No elernents/amenities (J)

Cl Stark, barren, empty, (KI desolate (K)

Non-welcoming, uninviting (L)

I I Cold, dull. boring, plain (M)

No response (N)

Unfriendly (O)

A çonsidc.rcthlc iiiini ber of respondents (39%) commentrd nrgativel y im t hc barrsn. u r

dcsolutc üppeüriinw i) 1' thcir Ieast preferred images. Another 16% of the rcspondcnis

çonimtmtrd im the non-udcorning. uninviting nature of their Ieast prckrred sccncs.

Tliesc dcscripiors iend r« rcintbrcc the low visual preferences with thc laçk ol'dillCrcnt

strcctsçripc conlpuncnts.

5.0 Discussion

TIIL. L d l o ~ iiiy ssctions discuss the associations of the outlined results to theory and

prcçcdent rcscürch. limitations and recommrndations Ior future resrarch. and tlic

iinpiications tli;ir thcsc lindings niay have to other professionals.

5.1 Hcsults in Hclrtion to Thoory

One nrcds io r d k r hück to theory in ordrr to help explain the reponed results. 'l'lie t k t

thiit tlir trecs had the highest positive rtTect on preferences followed by the Innips.

hrnçbcs. mi garhugc rcçt.ptücles. respectivrly. is soniewhüt rsplained bjr the tlircc

tlic1)rit.s disc~issed carlicr. In relation to h.laslow's theory of needs. the trws tend ti)

provide shndc. slic.ltcr. and protection. whilr lamps tend to provide scciirit) . thiis.

sütist)ing I ~ L . m«rc important nreds. When these nt.& are met. «ne ih rn tends to scnrch

for other nccds likc corntort and aesthetics. which are funher reint'orced when tlic

bcnç hrs and yiirhüg~ rcccptacles are present.

1 n relation to 1.1 iicli's thcory of "Lqibility": hypothesis (5). whiçh proposed ü posi t iw

correlation brtuecn the number of difkrent components and visual preferençes. is

partial 1) ex plained. The yreater number of di tfrrent components present in the image

tends to rcinhrcr the linrürity and order within the streetscape. and pcrhaps. müking it

more lrgiblti and cohrrent in relation io a more pedestnan friendly strcetscape.

It has besn slioun that the more legible and coherent the scrnt: the hiyber the visiid

pret'rrence ( Lynch. 1 960. Kaplan and Kaplan. 1989). A slight contradiction is srcii in ilic

slides depicting ml' trres. wliich tttnded to have a highrr prekrence thün the sccncs u ith

two or sometiiiies tlirer diî'krent componcnts (besides trees) present. Ikhaps i t could he

iirgiied tliat the strects with only trees present might actudly br more coherent tliün ilic

strerts uiih ;idditional components. The ddit ion of other cornponcnts might lia\ L. c;iiiscd

thcse sccncs il) hecornt. sornewhat incoherttnt by causing di l'ticulty in undsrstanding tlic

rclütionsliip h c t ~ csii ihe components and the sumunding environment.

Thc inipon;intx c i t' trws çün ülso be linked to other factors. Somtt rescürc h m ( Knplün

;ind Kaplan. I W) ) I i i i w cstablishrd connections betwccn trws and the natiirül

cn\ i roniiit.iii. ;ind lion pcopli: tcnd to associate trees with nature and restornti~ c îixit LIW.

I t nia' also hc nrgiird rhot irees tend to bz thé largest component visible in tlic

strcctscape. ~ind pcrhnps having ü greater size and visual iniprict than the tuo or thrcs

Ji ttkrent çoniponcnts conibinrd. in turn. possibly providing sutbficic.nt niüss aiid i oliinic

to ponra' «r&r and Icgibility in the scrne.

This scction rchtcs the tindinys to Kaplan and Kaplan's prcferencr throry. Kiiplon and

Kaplan ( 1988: 1089) drscribe tour possible patterns or informational Ficiors thot

in tlurnce prefirence-Co hcrence. Complexity. Legibili ty. and M y s t q . Corn plexit). is

detheci --in tttrms of the number of dityerent visual etements in a scene: how intricaic the

sçrinr is: and ils riçhness" (Kaplan and Kaplan. 1989). Coherence hrlps in providing a

sense ot'ordrr and in dincting one's attention to tùrther exploration (Kaplan and Kaplan.

1 ) In relation io this stiidy. one could order according to physical cornplexit>. tioiii

morc complrs to Ieüst comples the phpsical factors: trers. larnps. bcnchcs. and yürhiiuc. C

rcceptacles. respect ive1 y. rlccording to Kaplan and Kaplan's prekrrncc thei~r! . t lic iiiorc

çoniples the sceiw is. (wliiçh could be related to having more diî'îiirent componcnts

preseiit withi II i t ) the larger the positive e tfect on visual preferences. 'Tliiis. rsplüiii iiig tlic

ordcr ot'prcti.rt.iicc cl'tixts outlined in this study: trees. lamps. bençhes. rind giirbiigc

rccrptüclcs. rcspcctiwly.

Knplün and Kiiplii i i '~ tlicor); of complexity can also btt usrd to esplain ilie Ii titi

hyotlicsis (çorrrliitian brtucen number of types of componrnts and preièrencc). u Iicrc

scctics slioi\ iris more component types. along the strect edge. represcntcd niorc

coniplesii> n i t l i i i i tlic scene. thus. contributiny to highrr visual prrferençes.

5.2 Results in Relation to Precedent Reselirch

Tlic stündordized conirasts or ttî'tkct sizes for trees. lamps. benches. and yürbügc

rccrptüclrs \i rrc 3 6 . 0.73.0.47. and 0.16. respective1 y. Al1 four conirasts art.

siynificani at the 0.00 1 level. Since numerical estimates ot'etkct sizts are di fticult to

interprct. it is iisctiil io compare the cffect sizes obtained in this study wiih ct'fcci siïss

reportrd in other studirs. In the standard reference of effect sizes. Cohen ( 1988) S L I ~ ~ L ' S ~ S

that standardizcd differenccs of 0.20.0.50. and 0.80. constitutr small. medium. and large

rffects. In thrsc terms. trecs have a very large positive rffect (C4=2.36) on prclercnces

of downtown strcetscapes. with lamps having a large efkct (C3=0.73 ). bt.nc1it.s liai-iny ü

mcdiitm rffcct (C'2=0.17). and garbage receptacles having a small r f k c t ( C I =O. 16) lin

\-isiiül prekrcnçrs of douniown streetscapes.

Strimps ( 1W7) aiid Orland el al ( 1992) reported trees. versus other compunrnts. 3s fin\ iny

thc greiltcst posili\ c ci'fcci on prcferencrs with contrasts ol'O.35 and 0.30 rcspccti\ cl!.

iliis S I L I ~ > rcportcd trws nith a much larger efiçt size (C-kZ.36) thün Strinips :iiid

Orlüiid ct al. Ilic rrüson lbr this could prrhaps be explaincd hy the tiict that Siniiip niid

Orliind et al borli icstcd tlic physical factors on neighbourhood streets ~ I i i l r tliis s t ~ i d ~

tcsted tlicni un Jo\r iitoun streets. When comparing a downtown sircet sccnc to n

ne igIiboiirliuod sircci sçenc. the amoiint of vegetaiion diî'îiirs si pni licantl>..

Nciyhbourliood stirrts tend to have more vegetation depicted in thc hiickground. in iiirii.

compcting u i t l i ttic natricil et'fect givrn by trres. In a downtown setiing. trws \riiiild

lia\ c ~i grciter coiitnist n i th its surrounding rlemcnts. in tum. haviny u 1lirgr.r slli.ct i i i

dou ntou 11 strcctscnpes ~crsus ncighbourhood streeis. This and the liiçt thüt trccs. iii

gencrül. lia\ c. ihc grutest posi t ivr eftfct on preferrnce. is fiirthcrr supportrd b! cstcnsi\ c

rescürcli (Appleton. i98-l: Kaplan and Kaplan. 1989: Kaplan. 1 988: Goid. 1977: Nüsar.

1988: 'I'hayr. I97X: lilriçh. 1983: Wohlwill. 1983) showiny natucil coniponents hein-

more preferred tlian built componrnts.

The resrilts from trsting hypothesis ( 5 ) . which showed a positive correlation hetwrn

cornpoiient t> pes (niore coniples scenes) and pnferencrs. çan also bc cornparcd to otlicr

rccrnt tindiiigs. I:iw csümple. Oriand et al (2000) investigated visual complcsit) ;inil

perwiwd scr.iiic bt.üut)-. and tbund that a positive correlation esisted betwen \ isiiül

coniplesit> and perceiwd scrnic quality . The more visuall y corn ples sccncs rccei\uJ

Iiighcr prckrcncc rntiiigs in relation to percrivcd scenic beliuty (Orland et ü1. 2000).

5.3 Limitations and Recommendations

Ont. possible. thrcüt to thc validity of this study is the estent to which the present tindings

nia). not gcnerol i ~ c to otlicr people in Guelph. local downtown residents. or coniniiiniiics

elscu hcrc. Stlinips ( l 992) provides relevant data on this issue. by using tuo indcpcndciii

sani plcs d ' S m I:riincisco rcsidcnts. onc group using a semant ic dill'rrcniiül scalc io

c\duütc prcli.rt.iicc.s ;ind ifie other yroup using a comparative choicr prutocol. l'tic

çorrclatiim ot'the prclérencr rütings between the two experiments \vas 0.90 (Stanips.

7 . In nnotlicr study (Stamps. 1906) two sets of prekrences (by redents l i i iiig i t ~ tlic

sanic arrü u ert. o btüinrd: thc tirs1 completed in 1990 and the second in I W 5 . .-1

pre frrcnçr cornlütion of 0.88 resulted between the two studies.

Nasar ( 1988) reponed tinding siyniticant correlations between on-site c~,aluütions b'

people living N itliin ri block of each scene and evaluations by people who livsd 100 niiles

away. Stamps and Nasar ( 1997) completed a study regarding genenlizability \i hcrc

residential sçrnrs tiom one Celifomia city were evaluated by residrnts from another

Calilornia city and h~ those from a city in the Midwest. The comlation olpreti.rcnccs

brtuern the t\i O sornples of respondents was 0.88 (Stamps and Nasar. 1997). Phosc u ho

\\ isli to in\.r.stiglitt. the genttralizability of this study are recommrnded to repeai thc stiid!

using di t'krent organizations and groups within the City of Guelph to providc othcr

rcsiilts tor c~mpürison.

0tht.r possihlc ilirtsiis to wlidity are the use of visual simulations and thc rangc oi'

stiniiili. C'orrclüti«ns hetwren prefirrtnctts obtained from colour slides. coloiir

photoprüplis. and on-site waluations appear to be brtwern 0.84 io 0.03 (Stamps. 1L)L17 ).

Ont' u h o u ishcs IO ilse cornputcrized visual simulations and çolour iniagcs is

rcconimendcd to iisc tiiyli yiiality images. dong with high qiiality oiitp~ii. u hctlicr hot hc

printcd iiiatrriol or cimpiitrr projectrd images.

Thc csperinicntol design tor this study had tisrd variables t'or the stiitiuliis factors ciiid ~i

ründom \ tirinhic l'or tlis respondcnts. Since the stimulus factors wrre Asrd. tlic presciii

rcsults tippl! anl? io the conditions studird. Reseürchers who want to estend tlic Iindinys

of this rescrirch. ulons \vit11 othrr siniilar findings (Stamps. 19~?7). will nczd to rcpliçutc

ihis study in otlier sitiés using other ranges of streetscape components.

In tlic. casc ot'this stiidy. the time allocated to the slide presentations to [loimiinin

Guelph conmittrrs t'or trvaluation was a limiting tàctor. Evaluating downtown

cornmittees pnor to thrir schedultd meetings was very efficient. Unfortunatcly one of

thc major conditiuns \\ith this method is the time limitation given to prttsrnt thc visiiül

images. In the prcscni study. only i 5 minutes was allocated during eüch committcc

session t'or tlic waluution of the stimulus photographs. This. in tiirn. limiis tlic nunihcr 01'

slides one is dlo\wl to present. as well as the number of stimulus factors one cün

in\rsiigate. :\ rccommendation for future work in this areii is ro perhaps schcdulc ;i

yencril mcctiiip. \rlierc ull the cornmittees of interest are invitcd to take pan in thç stiid'..

Tliis çould ilIll)\\ Iiw a longer timr period for the viewiny of the slidcs. as uell as ;in

opportiiiiit! io inwstigütr mort' stimulus factors.

5.4 I mplicii tians for Future Reseirch

I'herc arc lblir iii~tin iiiipliçations that are usehl for future rcseürch. I'hc tirst ini plicoi iun

is rc1:itc.d i i ~ tlie t! pc ot'approxh that \vas used in this studp io provide heneticinl tincliiigs

in rcliitiim io coinniiiniiy design. Inçorporating and involving local orgüiiizütions iintl

çomn~ittces in tlic rcsctircli procrss. more speci tically. using thsse groiips as tlic somplc

population çüii acconiplish this. By using this type of approach one is able to cili.çti\ cl!

idcntit') and c\ üliinie henrhkd design factok. which in turn can br incorporü~ed into

cornmunit> drsign and landscape planning.

.A second implication for future rewarch is the use of standardized contrasts or

stündürdizcd mt"m di tl'rrrnces to report Findings. By using a standardizrd iinal) sis it

ri1lou.s one tu cimipare thsir rcsults with other reported findings. Thus. allouing thcsc

rtisiilts to more applicable and genenlizable to other areas and other populations.

Tlic tliird implication br future resrarch is the use of computer trchnology in rcsrürch

nirtliodologirs. Modern computer technology allows resrarchers to. rfficientl! and

t. ffccti wly. Jcvrlop vrry soiind and dçfrnsi b k met hodologirs.

:\ tot-tli iniplication is relüted to thc content usrd within restrarch projccts. In tliis stiid! .

thc scinic hur coiiipimcnts (content) were repeated in the sircrt scsiies t« txüliiatc \ isiial

prcfère~~css. h t i ~ r c rcseürch is nredrd to further investiyatc content wriables. !:tir

cscin~plt.. proiccts coiild wiluatr strert scenrs that repcat identical coniponsnts \ m u 5

sçei~rs thüi dcpict ;in qua1 numbrr of non-identical componenis ( the sanie trcc rcptxtcd

wrsus :in cy uol niimhcr of diflrrent trces). Thrsr types of rrsults coiild pro\ idc tiiitlisr

clari~ic;itioiis to [hc cimsnt tactors that Iiavr a greüter impact on visuül prcî'crcncrs.

5.5 Implicatiuns fur Londscapc Architecture

~ \ s stüted b! Siünips ( IO97). a major implication of this type ofstudg rcliitrs io ihc rlcsign

ofcspcrinicnis on cnvironnicntal preferences: "In ordrr to obtain calculüted cstiiiiatcs cd'

tlic prs!i.rcncc ctli'cts of design components. i t is nrcrssary to use formol espcrinicntnl

drsigns S U C ~ US the completely bainnced factorial design employed in this stud!"

(Stümps. IL)')7).

. - I he use of thesr hctorial designs requires the stimulus factors to bc: constructed to 1 tir!,

imly in the design cornponents undrr study. This may have becn dillicult to ii~~oiiiplish

in the posi. hut due to modern compiitrr applications and digital technolog!.. it is no\\

qiiits kasihlc tu çrtzitc visual simulations that conform to the requirenicnts ot' brnial

t.spcrinieiitül dcsi yns.

.*\ scçond iiiipliçütion is tliüt as long as researchen report their results uith etli.ct s i ~ c s

iising standürdized ineün diHérences or correlations. ii is b s possible to cornparc aiid

trüiislütc Iii idiiip l'rom di llkrent studies. B y being able to compare tindings to otlicr

similür studics onc is iiblc to irnprow the vnlidity and the gcneralizahilit! ot'ilicir

tiiidinys.

:\ [hird iniplicatiim that is brought forth by Stamps (1997) is thet it is no\v possible IO

condiiçt prc-constr~içtion prekrrnce evaluations for streetscapes. For cstiniplc. iisiny

coiiipiitcriïccl soti\wrc: a cmmunity group interestrd in introducing ncw dssiyi

componrnts to il strectscüpc cm realistically lenerate simulated scrnrs depiciiris thc

proposrd çliangcs hchre doing an. physical moditications to the street. Thcsc gcnmicd

images çcin tlien wry citiciently be used in a study to rvaluate the propos4 cliüngrs N iih

\.isual prckreiiçcs.

5.6 Conclusions

Considering tlic initial hypotheses made at the start of this study and the analysis

disciissrd aboi-c. a conclusion can br made that trees had the greatcst positive ctfcct on

thc \ isucil prc ti.~.cnccs ut' Downtown Guelph Committres on Downtown Giicl ph

strectsctipes. Iollo\i.crl by Iürnps. benchrs. and garbage reccptacles. rcspsctiwlj. .\

!ùnlit.r ~tuiciiiciit wi hz iiiiidc regrtrding the number ot'di î'krent sirwtscapr conipiincnis

;mtl \isiinl prci'kreticcs. in prnrral. the four Downtown Guelph comniittcts prctkrrcd

du\\ nion n strcct scciies thüt shou-ed al1 four diffcrent design components vcrsiis tlic

scciies sliowing !Zn er types of componrnts.

Idlowing tlic çcimpletion of this study. it isrvidrnt thüt the lise of cornputer sinililnicd

imiiprs is u wry c t'fcctive rcsearch technique. considering i t allows stimulus I:.icii~rs IO hc.

controllcd ti~r rspliçit estimations of visual preferences.

Iiniil concl iisiun penüins to this study 's rrsearch rnctthodology . The mtlihodolog! .

u hich incorp«mtcd ii range of techniques. proved to br ver' usefiil and rlfi.cii\ c. I'hc

ttxliniqiit. ol'strindürdizcd contrasts in particular wûs very rlléctive. niakiny ii possiblc io

compare tlir rrported resiilts with tindings from othrr siudies. as long üs the! wrc

reportrd iisioy contrüsts or corrclations. It was also found that ~ising local oryanizüiioiis

and commi ttees as tlie samplr population. data was collected very efficiently and cost

rfkctively f r m the individuals that tend to have a great intluencr on the design decisions

in relation to downtow districts.

Relating back to ihc purposr of tliis study. the groups and iiidividuals invol\.ed in making

design drcisions c m now rnaks use of thesc results to hrlp jiis~ify the implemrnintion or

phnsing-in o l' the more iinponant streetscape components. and impruving people's

prrcrpiions o f Dowtown Guelph's visual quality. These results will ülso be i i d d to

go\ ernnient üiid ni~inicipiil agencirs that wish to propose nrw Jowntown design

wiideline.: \ k i t h i i l ihcir jiirisdiction. The results from siich a stiidy san also bcnelit iliosc 2

üyrnci t s or groiips thüt are king hcrd with more and more restrictiw fiinding ~ I J

incrcüsing prcssurc il) miikr valid. rational dccisions rrgarding the üllocütion 01' liinds.

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Appendix A: Photographs of Stimulus Factors.

Streetscape One Images

PHOTOA B-T

PHOTO 3 B-L-T PHOTO4 Trees

PHOTOS G-L-T PHOTO 6 Benches

PHOTO7 G-T PHOTO8 Lamps

PHOTO 9 G-6-T PHOTO I O None

PHOTO 11 G-BOL PHOTO 12 G-6

PHOTO 13 6-L PHOTO 14 G-6-L-T

PHOTO 15 Garbage Receptacles

PHOTO j6 L-T

Streetscape Two lmages

PHOTO 1 6-T

PHOTO 2 None

PHOTO 3 Garages Recept.

PHOTO 4 G-€3-T

PHOTO 5 G-C

PHOTO 6 Lamps

PHOTO 7 G-6-L

PHOTO 8 Trees

PHOTO 9 G-T

PHOTO I O Benchcs

PHOTO 11 6-1-1

PHOTO 6-L

PHOTO G-6-L-T

PHOTO 14 G-6

PHOTO 1-T

PHOTO 16 G-L-T

Appendix B: Visual Preference Survey --

PART 1 - Photoera~h Evaluation

The two sets of photos (each containing 16 photos) that you are about to evaluate depict two different Downtown Guelph streetscapes. The first set of photos will be viewed in 5-second intervals, where are you asked to evaluate them using the preference rating scale below. The same photos will then be repeated in 10-second intervals, in which time you will re-evaluate them and try to associate one word with each preference. This process will be duplicated for the second set of photos

Please rate each photograph based on how much you prefer it on a scale of 1 to 7 (1 = least preferred. 7 = most preferred).

1 2 3 4 5 6 7 least indiffeient most

preferred preferred

You may give more than one photo the same rating, however, please attempt to use the full range of the scale and differentiate between photos as much as possible. The differences between photographs are sometimes subtle, therefore, please look at each picture carefully Please note these photographs have been manipulated using computerized imaging software and are being seen through a cornputer projector, so please do not allow the image quality to influence your ratings.

Photo Set # 1: Preference

Please circle the appropriate number based on your preference (1 = least preferred, 7 = most preferred) .

Photo Set # 1 : Preference & Word Association

Please circle the appropriate number based on your preference (1 = least preferred. 7 = most preferred) .

Photo 1 -t

Photo 1-2

Photo 1-3

Photo 1-4

Photo 1-5

Photo 1-6

Photo 1 -7

Photo 1-8

Photo 1-9

Photo 1-40

Photo 1-1 1

Photo t -1 2

Photo 1-13

Photo 1-14

Photo 1 -1 5

Photo 1-16

In a few words, please describe what you liked about the photo(s) you most prefened in this set.

In a few words. please describe what you disliked about the photo@) you least preferred in this set.

Photo Set # 2: Preference

Please circle the appropriate number based on your preference (1 = least preferred, 7 = rnost preferred).

Pt~oto Set # 2: Preference 8 Word Association

Please circle the appropriate number based on your preference (1 = least preferred, 7 = most preferred) .

Photo 2-2

Photo 2-6

Photo 2-8

In a few words, please describe what you liked about the photo(s) you most preferred in this set.

In a few words. please describe what you disliked about the photo(s) you least preferred in this set.

PART 2 - Background Information

In order to anaiyze sub-group responses, please answer the following questions. Any information you provide will remai n strictly confidential.

1. Please indicate your age (check one category).

< 20 yrs. 21 - 35 yrs. 36 - 50 yrs. 51 - 65 yrs. > 65 yrs.

2. Are you (check one category)

Male Female

3. Please indicate which best describes where you live (check one category).

Downtown Guelph City of Guelph, not Downtown Other, specify

If you've lived or still live in the Downtown, please indicate tirne period (check one category)

O - 5 yrs. 6 - 10 yrs. 11 - 15 yrs. 16 - 20 yrs. 21 - 25 yrs. >25 yrs.

4. Which of the following best describes your prirnary role in the Downtown (check those that ~ P P ~ Y ) .

Consultant/Professional Elected Official

Design Professional City Councilor

Downtown Board Member City Management

City Employee Chamber of Commerce

Financial Services (banks, accountants, etc.) Downtown MerchantiRetailer

Business OwnerlTenant Building Owner

Business Employee Downtown Usericitizen

Other, please specify

THANK YOU VERY MUCH FOR YOUR PARTICIPATION IN THIS STUDY.

Appendix C : Data Sets

Streetscripr 1 - Fiw-secund iiittwal photo set.

l 1 ' 6 G-6-f G-i, NONE 8-L-T T B-T G-L-T j I 1 Photo 1 Photo 2 Photo 3 Photo 4 Photo 5 Photo 6 Photo 7 Photo 81

nespondent 1 1 1 Respondent 12 Respondent 13 Respondent 14 Respondent 15 Respondent 16 1 Respondent 17 Respondent 18 Respondent 19 Respondent 20 ' Respondent 2 1 Respondent 22 Respondent 23 : Respondent 24 Respondent 25 ,

Respondent 26 Respondent 27 Respondent 28 1 Respondent 29 : Respondent 30 1 Respondent 31 ;

Respondent 32 / Respondent 33 1 Respondent 34 1 Respondent 35 j Respondent 36 ;

Respondent 37 j Respondent 38 1 - 1

Streetscapt: 1 - Fiw-secoiid interval photo set (cont'd).

7 1 I j G B- L L-T ALL G-B L G-T G-6-L ! j Photo 9 Photo 10 Photo 11 Photo 12 Photo 13 Photo 14 Photo 15 Photo 16 l~es~ondent 1 ! ?espondent 2 '

qespondent 3 iespondent 4 ?espondent 5 ?espondent 6 ?espondent 7 qespondent 8 ?espondent 9 ?espondent 10 ' ?espondent 11 7espondent 12 : ?espondent 13 ' ?espondent 14 ?espondent 15 ?espondent 16 ?espondent 17 ?espondent 18 ?espondent 19

Respondent 2 1 Respondent 22 Respondent 23 Respondent 24 Respondent 25 Respondent 26 Respondent 27 Respondent 28 Respondent 23 Respondent 30 Respondent 3 1 Respondent 32 1 Respondent 33 : Respondent 34 i Respondent 35 1 Respondent 36 i

Respondent 37

Strertscape I - Ttmsecond interval photo set.

I -----

B-T G-L 6-L-T T G-L-T B G-T L 1

Photo 1 Photo 2 Photo 3 Photo 4 Photo 5 Photo 6 Photo 7 Photo 8 ; Respondent l Respondent 2 J?espondent 3 1 esp pondent 4 j~espondent 5 1 Respondent 6

'Respondent 13 ,

Res pondent 14 ' Respondent 15 Respondent 16 iRespcndent 17 'Respondent 18 i Respondent 19 j Respondent 20 {Respondent 21 '

Respondent 22 : i Respondent 23 Sespondent 24 Respondent 25 , ~Respondent 26 i Respondent 27 IRespondent 28 ,

<Respondent 29 1

~Respondent 30 1 1 ~es~ondent 3 1 ' 'Respondent 32

Respondent 34 Respondent 33

IRespondent 35 1 Respondent 36 1 IRespondent 37 1

Stretltscapi: 1 - l'en-scç»nJ intemal photo set (cont'd).

I ; G-B-T NONE G-6-L G-B 6-L ALL G 1-T 1 1 ' Photo 9 Photo 1 O Photo 1 1 Photo 12 Photo 13 Photo 14 Photo 15 Photo 161 l

n --..

I~espondent 2 ' 1 Respondent 3 1 Respondent 4 'Respondent 5 1 ~ e s ~ o n d e n t 6 1 Respondent 7 Respondent 8 Respondent 9 Respondent 10 Respondent 1 1 Respondent 12 Respondent 13 Respondent 14 Respondent 15 Respondent 16 Respondent 17 Respondent 18 Respondent 19 Respondent 20 Respondent 21 I

, Respondent 22 'Respondent 23 , Respondent 24 : Respondent 25 : Respondent 26 )Respondent 27 / ~ e s ~ o n d e n t 28 ' 1 Respondent 29 'Respondent 30 , Respondent 31 '

! Respondent 32 I Respondent 33 ' iRespondent 34 , i Respondent 35 I~espondent 36 : JRespondent 37 1

Streetsciipe 2 - Five-second intcrval photo set.

1 : G-B-L B-T G-6-T B NONE G-L-T B-L-T G-B l : Photo 1 Photo 2 Photo 3 Photo 4 Photo 5 Photo 6 Photo 7 Photo 8 ,

e es pondent 2 ' Respondent 3

; Respondent 4 ! Respondent 5 I Respondent 6 'Respondent 7 Respondent 8 Respondent 9

i Respondent 10 1 Respondent 1 1 IRespondent l 2 lRespondent 13 ' Respondent 14 Respondent 15

I Respondent 16 i Respondent 17 Respondent 18 1 i Respondent t 9 Respondent 20 RIS pondent 21 Respondent 22 Respondent 23 (Respondent 24 ' 1 Respondent 25 j Respondent 26 i Respondent 27 i~espondent 28 1

/Respondent 29 , ' Respondent 30 i 1 Respondent 3 1 Respondent 32 Respondent 33 1 e es pondent 34 / Respondent 35 i Respondent 36

( Respondent 37

Strretsclipc 2 - Fiw-sccond intemal photo set (cont'd).

-7

7 L-T L G-L G-T G T 6-L ALL ; / : Photo 9 Photo 10 Photo 11 Photo 12 Photo 13 Photo 14 Photo 15 Photo 16j

IRespondent 2 ,

i Respondent 3 l

1 ~es~ondent 4 1 Respondent 5 ~Respondent 6 ; Respondent 7 / Respondent 8 Respondent 9 Respondent 10 1 Respondent I l 1 Respondent 12 1 Respondent 1 3 i Respondent 14 I

Respondent 15 1 Respondent 16 I

, Respondent 1 7 Respondent 1 di !~es~ondent 191 1 es pondent 201 I~es~ondent 21 ' Res pondent 22: Respondent 231

, Respondent 241 1 Respondent 251 / ~eç~ondent 261 1 es pondent 27; 1 Respondent 28 i 1 Respondent 291 Respondent 30; Respondent 31 I

/ es pondent 321 1 ~es~ondent 331 Respondent 34 / Respondent 35 / Respondent 361

1 Respondent 37 (

Strretsciipe 2 - kn-second intçrval piioto set.

1 I B-T NONE G G-8-T G-L L G-6-L T l 1 , Photo 1 Photo 2 Photo 3 Photo 4 Photo 5 Photo 6 Photo 7 Photo 8 , i

Respondent 1 l

Respondent 2 1 IRespondent 3 ; IRespondent 4 ' Respondent 5 I Respondent 6 Respondent 7 Respondent 8 Respondent 9 Respondent 10 Respondent 11 Respondent 12 ! Respondent 13 1 Respondent 14 '

Respondent 15 1

Respondent 16 Respondent 17 Respondent 18 Respondent 19 Respondent 20 Respondent 21 , Respondent 22 Respondent 23 I Respondent 24 '

~Respondent 25 i i~espondent 26 i jRespondent 27 iRespondent 28 / ~Respondent 29 / Respondent 30 ; Respondent 31 i~espondent 32 1 Respondent 33 Respondent 34 l 1 Respondent 35 1

Respondent 36 1 esp pondent 37 1

Strertscapc. 2 - 'kn-second interval photo set (cont'd).

G-T B B-L-T B-L ALL G-B L-T G-L-T-. Photo 9 Photo Photo Photo Photo Photo Photo Photo '

L 10 11 12 13 14 15 16

Respondent 1 [Respondent 2 / es pondent 3 IRespondent 4 Respondent 5 !

I I Respondent 6 '

Respondent 7 Respondent 8 Respondent 9 Respondent 1 O 1

Respondent 11 Respondent 12 ' Respondent 13 Respondent 14 Respondent 15 Respondent 16 Respondent 17 Respondent 18 Respondent 19 Respondent 20 Respondent 21 Respondent 22 : Respondent 23 iRespondent 24 1 i Respondent 25 1 Respondent 26 Respondent 27

1 Respondent 28 Respondent 29 Respondenl 30 ! Respondent 31 1 ~Respondent 32 I Respondent 33 I Respondent 34 1 Respondent 35 1

Respondent 36 IRespondent 37 1

Appendix D: SAS Code and Output --

Code (input):

dritii block: Ji, block= 1 to 1 5 2 : doj=I to 16:

cnd : end: run:

ruii: * proc prinl:

run: proc iiniuriate dütü=neu normal plot: var resid: rim: proc plot dlita=new: ploi rcsid*pred: 'IirIT

-43661 36 1-85 140 16 18-95443 1702=: 16334 Content-'Type: TEXTIPLAIN: CHARSET=ISO-8859-1 Content-Transfrr-Encoding: QUOTED-PRINTABLE Content-ID: <Pintt.CIPP.3.95.1 O003301 OS439.l63~4F~cçshstOl~ Contrini-Description: Test from file 'peterout.sast

qJ t

3 'Ji + 'Ji 30 fIi

G

oi Y

0 - c3 1 3

3 L'

0 & O\ ui O\ t3 O\ 4 O\ 00 O\ cl 4 O 4 v

4 1 3 -J W

2 4 wl 4 O\

-4 4 4 OC 4 4

O? u

X - X 13 X '4 J

- - - c, u - - S 'J-

Tlic SAS S> stciii 1 O:23 l'hursday. March 30. 2000 12 t 'ni\ ariatc Priiccdur~'

N 3 5 5 Sum LVyts 2355 100% Max 5.09006 99% 2.3 57572 blwn O Sui11 O 75% Q3 0.565084 9jQ& 1 .40823c) Std De\. O.94 1385 V;iri;rnce 0,886205 50% Mcd -0.0 1 78 90U O

1.1 16925 S k w ness 0.0024 l Kiirtosis 1.673325 25%Q1 -0.61112 lO50- I.IOS31 LISS 3086.126 CSS 2086.126 0% Min -4.77 182 j O / O - 1 A308 C' V . Std Mean 0.0 19399 l ?/o -2.39243 'r: blt.m=3 DO O Pr>l'TI 1 .O000 Rang 9.870878 Num "=3D O 2355 Num > O 1145' Q3-Ql 1.176199 kl(Sign) -32.5 Pr>=3DJMI 0.1872 Mode 0.47 1 223 Sgn Rank - 14700 Pr>=3DISI 0.656 1 D:Norrnd 0.05 1664 Pr>D <.O 1

Appendix E: Definitions

Downtown Business Districts:

For the purposcs of tliis siiidy the term encompasses or also refus to the ternis Müiii Strt'ttts. Cgrntr;il E3usint.s~ Districts. and Town Centres.

Krkrs to tlic physicai improvrments thüt occur mainly in the public reuliii portioii d i l i c .

strectsçüpr or [lie x c o froni curb to the fronts of the buildings dong dounto\vn strcetsc;~pcs.

Rclrrs io thc clcmt.nts or streetscaptt tiirniture componrnts ihüt are usiiallp ploccd tir sccii almg tlic cdge d'il d « \ w t o ~ n street. In the case of this study. the coniponciits niriid! rckr ti) trccs. lani ppost. benc lies. and parbagr recrptaclcs.

'WC Jegre~ 10 \ \ h i d i ü vicwr or rrspondent likes or prrfrrs a downt«\rn strcsiscüpc swnc.

Stvndardized Nlciin Diffcrcnce or Standardked Contrasts:

Arc the calç~ilüicd nieün difkrcncrs of one e f k t subtract the another sllkct Ji\ idrd h> [lie squürc r«ot 01' tfic mcün square error or residual error (Rosenthiil and Rosnoir . I ).

Stimulus Factors:

Relèr to the delinitioii of streetscape components.

Formrl Espcrimentul Designs or Bvlaneed Crossed Facto rial:

For the purposes ot'rhis study. a balanced crossed factorial design r e k n to a cotitrollcd e~perimrnt thai hüs an rqud rimount of images and measures the s i x of an effeçt hy comparing images with a particular stimulus factor versus a same number ol'imügcs withoiit that paniculûr stimulus îàctor.

Computerkud Visuirl Simulation:

Compi~rrrized visual simulation is a computer technique by which full-colour pliotoyraphs are digitized and edited by a computer application to represent changes to ü

Street scene. Tiiis c.lt.çtronic mnnipulation i s often undetectable in the tinül image. ( Adapted from Hands. I 999. and Orland, 1994).