- Email: [email protected]
Application of wildland scenic assessment methods to the urban landscape
Landscape Planning, 10 (1983) 2 1 9 -237 Elsevier Science Publishers B .V., Amsterdam - Printed in The Netherlands
219
APPLICATION OF WILDLAND SCENIC ASSESSMENT METHODS TO THE URBAN LANDSCAPE L-M . ANDERSON USDA Forest Service, Southeastern Forest Experiment Station, Forestry Sciences Laboratory, Carlton Street, Athens, GA 30602 (U.S . A .) HERBERT W . SCHROEDER USDA Forest Service, North Central Forest Experiment Station, 5801 N . Pulaski St., Chicago, IL 60646 (U.S.A .) (Accepted for publication 26 May 1983)
ABSTRACT Anderson, L .M . and Schroeder, H .W ., 1983 . Application of wildland scenic assessment methods to the urban landscape . Landscape Plann ., 10 : 219-237 . This research explores the feasibility of studying the urban landscape using procedures from wildland scenic quality assessment, and ascertains the influence of some physical characteristics of urban landscapes on esthetic evaluations . Diverse groups of raters, including a men's civic club and a group of minority high school students, evaluated the scenic quality of 240 photographs of a small Georgia city . The slides were scored for physical features using both objective measurements of the area of each image covered by different elements, such as pavement, structures and vegetation, and subjective ratings of physical characteristics such as lawn and shrub maintenance . The results indicated a high degree of agreement among observers in their evaluations of the city scenes . Linear regressions of scenic quality on physical features accounted for about half of the variance, with development intensity detracting from scenic quality, and the amount of vegetation in the scene enhancing it . Landscape maintenance also emerged as an important factor. Suggestions for improvements in future application of the method are discussed .
INTRODUCTION
One of the most widely recognized benefits of urban vegetation is its contribution to the visual quality of the city landscape . The esthetic enhancement trees provide is translated into economic support for the landscaping and nursery industry, and higher property values within a community (Payne, 1973) . However, when budgets are tight, other municipal services often meet with more funding success . Because urban forestry and other tree-related programs must operate within increasingly stringent budget constraints, it is important to develop an understanding of all the benefits of urban vegetation, so that what monies are available for management can be used effectively to increase these benefits . 0304-3924/83/$03 .00
© 1983 Elsevier Science Publishers BY .
220
The research reported here was designed to increase understanding of the esthetic benefits of urban vegetation . Our objectives were, first, to help establish the feasibility of studying the overall urban landscape using procedures adopted from other areas of scenic quality research, and second, to explore the influence of physical characteristics of urban landscapes, including vegetation, on esthetic evaluations . Some specific questions concerning land use and vegetative maintenance quality were also addressed . This study addresses the total urban landscape, not being limited to particular land uses or design problems associated with particular structural or landform characteristics . Procedures for assessing the quality of environments are well developed in the context of wildland scenic quality . One of the most successful approaches has been to solicit direct evaluations of scenic quality from users, or potential users, of the scenic resource (e .g . Daniel and Boster, 1976 ; Buhyoff and Leuschner, 1978 ; Brush, 1979) . A few studies involving direct assessment (Rabinowitz and Coughlin, 1970 ; Kaplan et al., 1972 ; Brush and Palmer, 1979) have compared evaluations of urban and non-urban landscapes . Urban or built scenes commonly receive lower ratings than vegetated or non-built scenes . Nevertheless, observers readily discriminate among urban scenes, finding some more attractive than others . Daniel and Boster (1976) developed and tested a scenic quality measurement technique for wildland scenery . Their statistical procedure, called Scenic Beauty Estimation (SBE), is an adaptation of Thurstone's (1927) law of categorical judgment (Torgerson, 1958) . Using ordinal-level ratings of scenic quality, the SBE method can provide interval-level values corresponding to the degree of perceived scenic quality in landscapes . One successful application of this and similar methods has been the development of prediction models for scenic quality, using physical characteristics of the landscape . The physical features used to predict scenic quality sometimes follow readily from standard management practices . For instance, Arthur (1977) and Schroeder and Daniel (1981) found that scenic quality could be predicted with reasonable accuracy from variables recorded in standard resource inventories made on U .S . national forests, including estimates of tree density, vegetative ground cover, and downed wood. Linear regression models, using these variables, can predict over half of the variance in scenic quality evaluations of forest areas . Another means of characterizing scenes was used by Shafer et al . (1969) in one of the early studies of landscape evaluation . Their predictive variables included measures of the length of edge between, and the area covered by, different elements in the photograph, such as water and vegetation in the foreground, middle ground and distance . This method was recently re-evaluated by Brush (1981), who showed that large-scale landform features, such as mountains, account for additional variance . In this study we used a combination of variables, including both photo measurements and subjective ratings .
22 1
One consideration in applying observer-based methods concerns how well different observer groups' evaluations represent the values of the public. Because it is usually impractical to obtain statistically random samples of the public, most research uses non-random sampling techniques . An alternative approach to randomization has been discussed by Schachter (1982) . Under this approach, one studies existing public groups that differ in what are presumed to be important characteristics . Comparing results from the diverse groups then indicates the potential for generalization of the findings . If two different groups produce similar results, one's confidence in generalizing increases ; if the groups diverge, one has data with which to begin determining empirically the limits of generalization of the results . In the research described here, we have taken the approach suggested by Schachter, using groups whose age, sex, ethnic and economic characteristics differ greatly . Several existing groups have been studied in wildland scenic research, sometimes revealing differences, but usually not . Zube (1973, 1974) found little difference between scenic judgments of diverse groups composed of U.S. citizens . In later work, Zube and Pitt (1981) found some crosscultural differences in scenic evaluation . Daniel and Boster (1976) compared a number of different U .S. groups and found close agreement among them, with a few limited exceptions . Brush (1979) found close agreement between forestry students and small woodlot owners in Massachusetts . Buhyoff et al. (1978) found substantial agreement between students of landscape architecture and other students, but less agreement between experienced landscape architects and other students . Overall, the picture that has emerged from this research is one of considerable agreement between diverse groups evaluating wildland scenic quality . METHOD
In this research we obtained evaluations of photographs of scenes within the city limits of Athens, Georgia, a manufacturing and university town with a population of 70000, covering an area of about 12 square miles in the southern Piedmont region of the U .S . Residential, commercial, office/industrial, and vacant or undeveloped land uses were represented, along with a few scenes of the university campus . Tree cover varied, with none visible in some scenes, others with park-like stands of pines or mixed hardwoods, and still others showing densely wooded undeveloped land . Slide collection
Many studies of wildland scenic quality have used a randomizing scheme for selecting photographs to represent sites . Such methods are designed to overcome photographer bias in the selection of photographs by setting up an algorithm that determines which photographs will be taken before
2 22
one reaches the field . Following this practice, we used a pair of transects drawn at random across a road map of Athens, one roughly north-south and one east-west . These transects determined 60 sample-point locations where the transects intersected streets on the map . On the shorter northsouth transect, a sample point was located at each point where the transect crossed a street ; on the east-west transect, sample points were selected at every second street intersection . Four color slides were taken at each sample point, each at 90° from each other and all at a 45' angle to the street . The photographs were taken from the side of the road, so that at most sample points, two photographs showed none of the road-bed, while the other two showed a substantial amount of road in the immediate foreground . A total of 240 slides, four for each of 60 sample points, was collected . Figures 1-4 are examples of these photographs. The photographs were taken in late August and early September, so that foliage was in . late summer green . Eighteen of the photographs were somewhat under-exposed, a problem not discovered until autumn coloration had set in . Inclusion of these slides had only slight statistical effects on the outcome, adding only slightly to the unexplained variance . Slide evaluation
The observers for this study comprised five different groups . Two groups of undergraduate psychology students at the University of Georgia each evaluated 140 slides . Of the 140 scenes, 100 were unique to each group and 40 were viewed by the groups in common . Three other groups were also included . First, 22 members of a men's civic club in Winder, Georgia, a small town about 20 miles from Athens. This group consisted exclusively of white males of various ages . Another group was the summer staff of an Atlanta, Georgia, nature center, 70 miles from Athens . Fifteen of the 19 people in this group were black temporary employees aged 18-20, 12 of them female, assigned to the center by a federally funded work/ training program . The remaining four were supervisory staff teachers, including one black and three whites. The final group was a class of eight graduate students in landscape architecture at the University of Georgia . The graduate student group evaluated all 140 slides shown to the second group of psychology students . The nature center and men's club groups saw only the first 60 slides of the same set, because less time was available for those groups. If scenic evaluations are found to be similar across the boundaries of age, sex, ethnicity and economic background evident in these groups, then there would be substantial grounds for generalizing the results of this research to the broader public . Each group of observers received rating response sheets showing a scale from 0 (lowest scenic quality) to 9 (highest scenic quality), and a set of numbered response blanks appropriate to the number of slides they were
22 3
. Fig. 1 . One of the highest rated residential scenes (SBE = 105)
Fig . 2 . One of the highest rated commercial scenes (SBE - 9) .
2 24
Fig. 3 . One of the lowest rated residential scenes (SBE = -58).
Fig . 4 . One of the lowest rated commercial scenes (SBE = -100) .
2 25
going to view . The room was made as dark as possible, and the first 60 slides were shown for 8-s intervals . For groups viewing more than 60 slides, the remaining slides were shown at a slightly faster rate of 5 s, as the observers gained experience in their task . In a follow-up questionnaire, observers were asked to list those features of the scenes that led them to give high or low ratings of scenic quality . The ratings were transformed to interval-scale scores using the SBE computer program of Daniel and Roster (1976) . The scaled data, named scenic beauty estimates (SBE's), are standardized indices of scenic quality, which combine observers' ratings of the scenes to produce a single score for each scene .' Because our two college student groups had evaluated 40 of the scenes in common, we used these 40 scenes to establish a common metric scale of scenic quality for all 240 scenes . Thus, the SBE's for all 240 scenes rated by the two student groups were placed on a single scale of perceived scenic quality . Physical feature evaluations
The complex of vegetation, landform and man-made elements in the urban landscape suggests use of a large set of variables for characterizing the scenes. We used a total of 49 physical feature variables, including objective measurements of photograph features and subjective ratings of other features of the scenes . Some of the features were suggested by the responses to the followup questionnaire, in which participants reported features that led to high or low ratings of scenic quality . The objective measurements used a modification of the method used by Shafer et al . (1969) . Each slide was projected on to a 16 X 23 grid of 1-inch cells, with the 16th row not completely filled. The number of grid cells containing an identifiable quantity of each of several landscape elements was counted . A cell containing more than one element was counted once for each element it contained . The elements included such things as sky, vegetation, pavement, commercial and residential architecture, and others (see Table I for a complete list) . Some of these counts were made separately for the top and middle five rows and the bottom six rows of each scene . Cells containing other features, such as roads, fences or vehicles, were counted as a single total over the entire image . The objective measurements also included counts of the number of vehicles, buildings, people and signs (or parts thereof) in the scenes. These features are also described in Table I . ' SBE scores are derived by averaging the standard normal deviates (z-stores) associated with the cumulative proportion of ratings at each level of the rating scale . The mean of average deviates for an arbitrarily selected subset of the slides (in this case every fifth slide shown in random order) is subtracted from the average deviate of each slide and multiplied by 100 to produce the SBE . Within the context of a given experiment, the SBE scale has interval properties . While the range of SBE's is not predetermined, in most applications they range from approximately -150 or -100 to 100 or 150 .
2 26 TABLE I Descriptions of objective physical-feature variables used to develop the model of urban scenic quality for Athens, Georgia Description of variable
Average value
Grid count variables Sky' 48 .9 Vegetation' 295 .0 Commercial architecture' 7 .3 Residential architecture' 43 .2 Total architecture 54 .2' Signs' 1 .9 Poles and wires' 15 .1 Streets and roads 30 .6 Paved driveway 4 .0 Paved sidewalk 9 .4 Parking lot 2 .9 Curb 7 .8 Vehicles 10 .5 Fence and retaining walls 13 .2 Bare dirt 9 .4 Litter 0 .8 Other variables Number of structures Number of signs Number of vehicles Number of people
1 .4 0 .2 1 .0 0 .1
Standard deviation
Highest value
Correlation with SBE
43 .9 73 .6 27 .4 49 .8 55 .4 7 .2 21 .9 42 .2 12 .7 17 .2 12 .2 12.1 23 .5 47 .2 18 .2 3 .7
180 368 249 222 352 73 121 171 91 95 95 62 154 368 118 36
-0 .27 0 .41 -0 .27 n .s . -0 .26 -0 .19 -0 .27 -0 .15 -0 .14 n,s . n-s. n .s . -0 .30 -0 .25 ns. -0 .22
1 .1 0 .9 2 .1 1 .3
5 10 13 20
-0 .16 -0 .20 -0,25 n .s .
' Grand total reported here, but divided into the top, middle and bottom thirds of the scene for analyses . 'Includes commercial, residential and undifferentiated architecture .
A second group of features included subjective evaluations of maintenance quality, urban tree cover, and compositional characteristics of the scenes. These variables are described in Table II . Maintenance of lawns, shrubs and structures was rated by two judges using a 5-point scale . Little variation in maintenance quality of structures was apparent in the photographs, so this variable was dropped . Lawn maintenance was evaluated in terms of mowing, edging, and bare or weedy patches . Shrub maintenance concerned mainly pruning, but ratings were not lowered for shrub varieties that are not normally intensively pruned (e .g. crape myrtle, Lagerstroemia indica L .) .
Ratings of tree cover were taken, including the density and species mix (hardwood, pine or mixed) of trees in each scene . Two other tree variables, distribution and age, were attempted, but inter-judge agreement was too low (r <0 .70) for these ratings to be included . The judges' dis-
227
TABLE 11 Description of subjective physical characteristics used to develop a model of urban scenic quality Description of variable
Interpretation of scale
Average Standard Correlavalue deviation tion with SSE
Species mix of trees' Density of trees' Prominence of flowers Prominence of litter Prominence of dead vegetation Scale of view Best lawn maintenance' Worst lawn maintenance' Best shrub maintenance 2 Worst shrub maintenance' Development Intensity'
I-all pine to 3-mixed to 5-all hardwood I=low to 5-high density 1-low to 3-high prominence I=low to 3=high prominence 1=low to 3=high prominence 1-under t§ city lot to 6-over 5 city lots 1-bad condition to 5-exceuent condition 1-bad condition to 5=exceuent condition 1-bad condition to 5-excellent condition 1-bad condition to 5-excellent condition O=undeveloped to E=higldy developed
4.2 2.9 0.2 0.2 0.2 3.2 3.4 3 .1 3 .5 3 .0 3.1
1 .0 1 .0 0 .5 0 .4 0 .5 1 .2 1 .0 1 .0 1 .0 0 .9 2 .4
us . 0 .27 n .s . -0 .29 -0 .21 u .s . 0 .22 0 .26 0 .22 0 .21 -0 .47
' Based on the 221 scenes where sufficient land area was visible to evaluate species mix and tree density . 'Assigned only to scenes of developed property (164 scenes) . 'Based on zones assigned by county planning commission.
agreement in the tree evaluations usually occurred on scenes where little land area was visible . For instance, when only the shadow of a tree appeared in the photograph, one judge might infer the most likely size of the unseen tree, while the other judge simply reported that the size could not be established . Those scenes showing an area of less than one city lot were problems in other ways also, as we will discuss later . Ratings from 0 (none) to 3 (very prominent) were assigned to the amount of dead vegetation, litter and flowers in each scene . The scale of view, i.e. the amount of land area visible in the scene, was rated from 1 (less than lA a standard urban lot) to 6 (more than 5 such lots) . Each photograph was classified according to the type of property visible, i .e . whether the scene was of undeveloped urban land, such as a freeway easement or vacant lot ; whether the scene was clearly of commercial or residential property ; or whether the scene showed a close-up of vegetation or other objects so that the nature of the property could not be determined . Finally, we included a variable reflecting the intensity of development in the area where each photograph was taken . The local planning commission had assigned land-use zone designations to all areas in the study, and these served as the basis for the development-intensity variable . Each of four of the zone categories (undeveloped, residential, commercial and office/industrial) had a short hierarchy of zones already ranked by development density (e .g . zones R1-R5 referred to residences on decreasing lot sizes) . The categories of zones were unified into a single hierarchy on the basis of the average number of grids containing man-made elements across all scenes for each zone type . More details of the scoring scheme for these variables, along with some descriptive statistics, are shown in Table II .
228 RESULTS AND DISCUSSION
The results we discuss address our two objectives, one concerning the feasibility of the procedure, and the other concerning apparent influences of physical features of the urban landscape on its scenic quality . One test of the feasibility of the method concerns the extent of inter-group agreement or reliability in the evaluations of the landscapes . Our observer groups represent several presumably disparate populations, and consensus in ratings of scenes would add to our confidence in the results. Our second question concerns the relation between esthetic evaluations and characteristics of the urban landscape . The analysis for this question involved computing correlations between SBE's and slide features . An analysis of variance was calculated to determine the scenic quality differences between areas assigned different land-use zones by the planning commission . A factor analysis and regression analyses of the physical features and SBE's were also conducted to provide estimates of the degree of relationship among the slide features and their relation to scenic quality . Correlations among observer groups
Table III shows the results of the inter-group correlations. The different number of pairs in each sample is a function of the different number of slides seen in common by each of the pairs of groups . All of the correlations are high, and are in the range often found in wildiand scenic research. The two groups hypothesized to be the most divergent (the men's club of Winder and the Atlanta nature-center staff) produce the highest correlation, while the landscape architects showed the least extensive agreement with most other groups . The discrepancy involving the landscape architecture students may be due to small sample size rather than any substantive differences in esthetic criteria . There were only 8 landscape architects, so that idiosyncratic ratings by a single evaluator will have a more pronounced effect on the group's averaged index . TABLE III Inter-group correlations of Scenic Beauty Estimates (SBE's) for five groups of evaluators Group
1. 2. 3. 4. 5.
Psychology students I Psychology students II Landscape architects Atlanta nature center staff Winder, Georgia, men's civic club
n
24 23 8 19 22
Correlations (number of pairs of slides) 2
3
4
5
0 .89 (40) -
0.79 (40) 0 .88 (140) -
0 .90 (18) 0 .87 (60) 0 .74 (60) -
0.88 0 .86 0 .70 0.90 -
(18) (60) (60) (60)
229
Clearly, however, these results do not support a hypothesis that different groups of evaluators differ greatly in their evaluation of the urban environment . Instead, it appears that evaluations of the urban scene are quite consistent. The extent of inter-group agreement is sufficient to proceed with the other analyses using the largest data-set available, namely that of the combination of evaluations by the two groups of psychology students . The combined evaluations of these two groups, using the average SBE value for scenes that were rated by both groups, provide a set of SBE's for all 240 slides . Relationship of physical features with SBE's
Tables I and II show the simple correlations, significant at 0 .05 or less, between physical features and scenic quality evaluations . Over half of the variables show a significant relationship to scenic quality, although no feature accounts for more than 22% of the variance, and most account for less than 10% . The two strongest correlations with scenic quality are produced by vegetation in the middle third of the scene (r = 0 .44) and development intensity or zone (r = -0 .47) . These two variables also correlate significantly with each other (r = -0.32), so that the percentages of variance they each account for (22% for zone and 19% for vegetation) are not independent quantities. Vegetation in the middle thrid of the scene can roughly be considered the lawn, foundation plantings, and under-story for the typical urban scene as collected by our photographic procedure, as in Fig . 3. Where this space is not occupied by vegetation, the most common features are buildings, pavement and vehicles, all of which detract from the scenes . The role of vegetation was also found to be important in the urban scenic quality model developed by Brush and Palmer (1979) . The influence of zone was further explored in a one-way analysis of variance . The analysis was highly significant (F = 12 .75, df 7/224, P < 0 .001), showing the major distinction between the lowest-density residential zone and the two highest-density residential zones, the commercial zones and undeveloped property (mainly road easements and vacant lots) . The main characteristic distinguishing the more attractive office/industrial zones from commercial zones is the amount of open land, usually landscaped, surrounding the structures . The highest-rated commercially zoned scenes had a high degree of landscape maintenance on a small area of parking median or a few square feet of garden that happened to dominate the photograph . Scenes of commercial sites with lower ratings showed more parking lot and less vegetation, with poorer maintenance of the vegetation that did exist . The planning commission zones upon which we based our development-intensity variable were determined by lot size for residential areas, so that property values and development intensity are highly
2 30 correlated . It follows readily that income levels of the households in the less developed zones are higher than incomes in the more developed zones . We cannot sort out the different influences of lot size, property values and household income, as we do not have independent data on the latter two variables . The rather strong negative relationship between scenic quality and amount of sky probably arises from both the visibility of overhead poles and wires against sky and the reduced tree cover associated with clear views of the sky . The signs of the remaining significant simple correlations are negative for all but one man-made feature, paved driveway . Paved drives were most common in low-density residential zones, which may explain their positive association with scenic quality . The natural material features, including maintenance measures of lawn and shrubs, are positive, with the exception of dead vegetative material, which was high for scenes where yard prunings or leaves had been piled for trash collection . The characteristics of some of the lowest- and highest-rated scenes, shown in Figs . 1-4, can be deduced from the simple correlations . Figure 1 shows one of the highest-rated residential scenes, with extensive tree cover and well-maintained lawn and shrubs . Figure 2 shows one of the highest-rated commercial scenes, with small but well-maintained plantings . Figure 3 is one of the lowest-rated residential scenes, with tree cover but very poor maintenance of structures and lawn . Figure 4 shows one of the lowest-rated commercial scenes, with very little vegetation and that not in good condition . In general, highly preferred scenes containing structures also contained intensively managed plant materials . Factor analysis To determine the relationships among the physical characteristics and their joint effect on scenic quality, the physical features and scenic quality scores were factor analyzed . Unlike regression analysis, which uses a number of independent variables to make predictions about a single targetted dependent variable, factor analysis treats all variables equally, clustering them together according to similarities in their pattern of variation across cases . Variables cluster together by loading highly, in terms of absolute magnitude, on one or another factor emerging from the analysis . The results of our factor analysis' indicated that several features vary together, using our criterion of a factor loading in excess of 0 .50 . These clusters are summarized in Table IV, which shows the first and strongest nine factors to emerge from the analysis (each of which accounted for 3% or more of the variance) . Two factors concerned the amounts of architecture ; one residential and one commercial . Both of the architecture factors show scenic quality ' The factor analysis was calculated using the Statistical Package for the Social Sciences (SPSS) FACTOR subroutine, with quartimax rotation, to maximize the probability that each variable would load highly on at least one factor .
231 TABLE IV Results of factor analysis on SBE's and physical characteristics of scenes of Athens, Georgia Factors and variables loading over 0 .50 on each
Factor Percent explained loading variance
Factor 1 Commercial architecture, top Commercial architecture, middle Commercial architecture, bottom Commercial architecture, total Number of signs Number of people
0 .86 0 .73 0 .80 0 .90 0 .70 0 .77
Factor 2 Residential architecture, top Residential architecture, middle Residential architecture, bottom Residential architecture, total Number of structures
0 .76 0 .88 0 .58 0 .85 0 .54
Factor 3 Sky, top Sky, middle Sky, total Poles and wires, middle
0 .85 0 .88 0 .53 0.50
Factor 4 Vegetation, bottom Vegetation, total Scale of view Paved road Curb
19 .3
-0 .21
10 .6
-0 .14
10 .4
-0 .21
6 .4
-0 .04
5 .3
-0 .07
3 .9
-0 .16
3 .8
-0 .16
3 .6
0 .21
3 .3
0 .67
-0 .80 -0 .61 0 .50 0 .80 0 .62
Factor 5 Signs, top Signs, middle Signs, bottom Signs, total
0 .80 0 .89 0 .63 0 .63
Factor 6 Poles and wires, top Poles and wires, middle Poles and wires, bottom Poles and wires, total
0 .68 0 .63 0 .64 0 .79
Factor 7 Paved parking lot Vehicles (grid count) Number of vehicles
0 .74 0 .67 0 .66
Factor 8 Species mix of trees Density of trees
0 .69 0 .58
Factor 9 Paved driveway Development intensity SBE
Factor loading of SBE's
0 .57 -0 .61 0 .67
232
with a negative loading, indicating that the more buildings and other structures in a scene, the lower its scenic quality rating . A third factor concerned view distance, with high amounts of sky and poles and wires loading together . Presumably because of the poles and wires, scenic quality loaded negatively on this factor also. Scenic quality loaded highest on a factor it shared with development intensity and paved driveways, which were usually found in low-density residential zones . Separate factors emerged for signs, poles and fires, tree species and density, and vehicles and parking lots . A final factor reflects an artifact of the way in which the photographs were collected . In each set of four slides taken at a sample point, two show the scenes across the street and two show scenes on the same side of the street as the photographer . The cross-street scenes contain a substantial amount of pavement from the road-bed in the immediate foreground, while the other two scenes show no road-bed at all, in most cases . The factor analysis captured this artifact, showing an inverse relationship between pavement and vegetation in the bottom third of the scenes . However, this factor shows little relationship to scenic quality, which loaded only -0 .05 . Hence, this artifact of the photographic procedure poses very little threat to the accuracy of the scenic quality ratings . The factor analysis reveals a considerable degree of independence among the urban features we included in this study . It shows SBE's to be affected by development intensity and, to a lesser degree, by overhead wires, commercial and residential architecture and vehicles . SBE's were less influenced by the photo-sampling artifact of how much road-bed showed in the photograph and by signs, probably because there were few signs in the scenes we used . Regression analyses
Finally, to determine the extent to which scenic quality can be predicted from physical features, we performed two regression analyses . Stepwise regression was used, including only the predictors contributing 1% or more to the Rz . The first model in Table V is for those 190 scenes that show an area of developed property at least one average city lot in size . The second model is for all 240 scenes . The regression equation for all scenes accounts for about half of the variance, while in the more selected data-set the physical characteristics account for an additional 9% of the variance . The positive effects of the amount and degree of vegetation are evident in both equations . Conversely, man-made elements, such as fences, vehicles and (through the sky variable) poles and wires had a negative effect . Development intensity had the strongest negative impact in both models, although as mentioned before, this variable corresponded closely with property values for the predominant residential-zone categories .
233 TABLE V Results of forward stepwise linear regressions predicting scenic quality from objective and subjective physical characteristics Predictor variables
Standardized regression coefficient
R'
Change in R'
Simple r
Model for scenes of developed areas with view of at least one city lot (n=190) Development intensity 0 .28 -0 .32 0 .28 -0 .53** Maintenance of worst lawn in scene 0 .42 0 .30 0 .14 0 .48** Vegetation in middle third of scene 0 .23 0 .50 0 .08 0 .41** Sky in top third of scene -0.19 0 .54 0 .05 -0 .31** Fence and retaining walls -0 .16 0 .56 0 .02 -0 .31** Number of vehicles -0 .15 0 .57 0 .01 -0 .24** Scale of view 0 .15 0 .59 0 .02 -0 .21** Model for all scenes (n=240) Development intensity Vegetation, top third of scene Maintenance of worst lawn in scene Sky, top third of scene Vegetation, middle third of scene Fence and retaining walls Number of vehicles
-0 .35 0 .10 0 .32 -0 .14 0 .23 -0 .16 -0 .10
0 .22 0 .32 0 .41 0 .44 0 .47 0 .49 0 .50
0 .22 0 .10 0 .09 0 .03 0 .03 0 .02 0 .01
-0 .47** 0 .40 0-26** -0 .27** 0 .44** -0 .25** -0 .25
**P < 0 .01 .
CONCLUSIONS
The conclusions of this research are presented in terms of our two initial questions - the feasibility of research on the urban scene and the physical characteristics of urban areas that contribute to scenic quality . Both of these questions will be addressed in separate sections devoted to management implications and implications for further research . Management implications
Our different groups of evaluators provided very similar evaluations of scenes of the city of Athens, Georgia . As has been found in wildland scenic research, there is strong agreement about preferred scenes among public groups . Thus, the public may often agree in its evaluations of land managers' impact on scenic quality . It should be noted, however, that the correlations between our observer groups are for the aggregated data . If one were to compare the evaluations of any two individuals in this study, there would probably be less agreement as to the scenic quality of particular scenes . The use of aggregate preferences is, nevertheless, appropriate in urban design . While home owners may be able to plan a landscape to
2 34
satisfy their own taste, managers of public-oriented facilities and public urban land must be sensitive to the fact that one individual's preference, even a designer's, may not be satisfactory to the majority of users . Managers and designers can readily claim that no design will please all individuals, but our research indicates that it is not impossible to please a majority of users . Better understanding of what people like is an attainable goal, and one that will enable managers to adopt designs that will maximize the esthetic benefit brought about by their investment . Direct evaluation methods, similar to those presented here, could be applied to test species and planting arrangements for different landscape situations, in order to improve the effectiveness of municipal tree-planting programs . The particular features we identified that contribute to, or detract from, urban visual quality offer little that is new or striking . Detractors having the greatest effect are overhead wires and poles, vehicles and parking lots . Underground placement of utilities may be too expensive for many urban areas, but our results show that trees can function as a mask for poles and wires ; when viewed against a background of trees, poles and wires are much less visually obtrusive . Parking lots are another visual blight, but again there are remedies in judicious use of vegetation for screening . Parking lots for new buildings could be placed to the rear, rather than on the street side . Thus, only users of the lot need to see it . The use of plantings in existing parking lots has a palliative effect . Several scenes of Athens business zones were rated more attractive if they had even small plantings in gardens or parking medians . Such small green spaces may be less costly to establish and maintain than larger plantings . They are less enhancing than larger areas devoted to plantings, but they do improve commercial property appearance for viewers from the road . Finally, our results show that viewers are sensitive to the maintenance of vegetation . Lawn maintenance concerned both mowing and bare or weedy patches, while shrub maintenance dealt mainly with pruning of species where appropriate . Maintenance quality ratings of lawns and shrubs were highly correlated with each other, but not with other features . Thus, maintenance levels and development intensity or zone are fairly independent . The implication is that even properties in generally unattra, ;t ve zones can realize esthetic benefits from well-maintained vegetation . The influence of maintenance quality applies only to developed property, since we did not assign a rating of maintenance quality to undeveloped sites or sites whose development status could not be determined . Several wooded lots were included in the photo-sample, mostly from the edges of the city . Many of these sites received high ratings of scenic quality, as would be expected from other studies that have consistently shown natural areas to be more attractive than urban scenes . Grass- and herbcovered but tree-less undeveloped property, such as land that had been graded but never built upon, was not rated as highly as tree-covered land .
2 35
However, even tree-covered scenes that had litter or vehicles in evidence were found to be less attractive . There is no such thing as a no-maintenance landscape, but if small lots of relict forest are surrounded by urban development, they may require only removal of litter to retain a high degree of attractiveness . Research implications
The different observer groups produced similar evaluations of urban scenes . The inter-group correlations suggest that, even though some of the groups would seem to have very little in common, in most cases at least 80% of the variance in each group's SBE's is accounted for by consistent preferences shared among groups . By comparison, the grid counts of physical characteristics in the scenes, plus a small number of subjective ratings, could account for only 50-60% of the total variance in the psychology groups' SBE's . What features might account for the other 30 or 40% that our observers tapped, but that our physical characteristics of scenes failed to capture? The 50% of variance accounted for by our model for all scenes is comparable to several studies of wildland scenic quality . For instance, some of Arthur's (1977) models of scenic quality for ponderosa pine forests account for about half of the variance . On the other hand, in a study using scenes selected to vary in the extent of southern pine beetle damage, Buhyoff and Leuschner (1978) were able to obtain R' of 0 .84 using only insect damage to predict scenic evaluations by students informed of the cause of damage . Buhyoff and Leuschner's study differed from ours in several ways . The landscapes were much more homogeneous, being highly similar views of completely forested hillsides, differing mainly in the amount of visible insect damage . The physical feature measurements were made more precisely using a finer grid than ours, and a non-linear (logarithmic) function was used to model the impact of insect damage on scenic quality . By focusing on a single feature (insect damage) and carefully controlling the set of landscapes, Buhyoff and Leuschner achieved a high degree of precision in their model . The complex character of the urban environment precluded such careful control of the landscape sample and rendered very precise measurement of each physical feature unfeasible . Hence, a part of the unexplained variance in our models may be due to the limitations of our sampling and measurement methods . Comparing the two models we report in Table V suggests that restricting the selection of landscapes can increase the variance accounted for by the model . When we omitted scenes of undeveloped urban property and those showing an area less than one city lot in size, the regression accounted for an additional 9% of the variance . An increase of about 4 of the 9% results from dropping the scenes of undeveloped property, where struc-
236
ture grid counts were all 0 and landscape maintenance was unevaluated . The other 5% comes from deleting several close-up scenes of structures and vegetation . Such scenes had the highest standard deviations in untransformed ratings (over 2 .0), as some observers liked such close-ups and others did not . Adjusting the photo-sampling procedure to take all four scenes at each point from the opposite side of the road would eliminate the problem of close-up photographs, while retaining the unbiased quality of the sample procedure . Elements with potential symbolic and emotional associations, such as churches, graveyards, slums and liquor stores, may also add unsystematic variability to the data . Small or ambiguous elements in the photographs could increase this variability, as observers might differ in their probability of detecting a minor detail, or in their interpretation of an ambiguous feature . Some cultural influences have been shown to affect wildland scenic evaluations (Anderson, 1981), and we were concerned that in urban areas, the effects of such variations in interpretation would be magnified . Our findings, however, suggest that while symbolic elements and affective associations may influence evaluations of urban scenes, these meaningful associations are probably consistent across groups - our observer groups were quite consistent in their evaluations of scenic quality . Our results suggest that the urban environment is as conducive to esthetic study as the natural environment, although the researchers' ingenuity in calibrating the visual characteristics of the scenes may be taxed to a greater extent . Visual quality judgments of urban scenes appear to be as consistent across diverse evaluators as are those of natural scenes . Finally, our results suggest that much remains to be learned about how the elements of the urban scene affect scenic evaluations of the city landscape . The particular challenge here is likely to be understanding the role of symbolism in the urban setting, although broad categories of urban elements - vegetation and structures, land-use planning zones - can have a considerable influence on preferences for urban scenery . ACKNOWLEDGEMENTS
The authors wish to thank Kathy Stewart and Dr . B .E. Mulligan of the Department of Psychology, University of Georgia, for their assistance in obtaining slide ratings from the undergraduate students, Sharon Lewis of the same program for her assistance with the data analysis, and Antoinette Watkins of the Young American Conservation Corps (YACC) program for her painstaking efforts in providing the grid counts used in this study . Preliminary results of this research were presented at the Southeastern Recreation Researchers Conference, Asheville, NC, 18-19 February 1982 .
2 37 REFERENCES Anderson, L .M ., 1981 . Land use designations affect perception of scenic beauty in forest landscapes . Forest Sci ., 27 : 392-400 . Arthur, L .M ., 1977 . Predicting scenic beauty : some empirical tests. Forest Sci ., 23 : 151-160 . Brush, R .O ., 1979 . The attractiveness of woodlands : perceptions of forest landowners in Massachusetts . Forest Sci ., 25 : 495-506 . Brush, R .O ., 1981 . Landform and scenic preference : a research note . Landscape Plann ., 8 : 301-306 . Brush, R .O . and Palmer, J.F ., 1979 . Measuring the impact of urbanization on scenic quality : land use change in the Northeast . In : G .H . Elsner and R .C. Smardon (Editon:), Proceedings of Our National Landscape : a Conference on Applied Techniques for Analysis and Management of the Visual Resource . USDA Forest Se" . Gen . Tech . Rep . PSW-35 . Buhyoff, G .J . and Leuschner, W .A ., 1978 . Estimating psychological disutility from damaged forest stands . Forest Sci ., 24 : 424-432 . Buhyoff, G .J ., Wellman, J .D ., Harvey, H . and Fraser, R .A ., 1978 . Landscape architects' interpretations of people's landscape preferences . J . Environ . Manage ., 6 : 255-262 . Daniel, T .C . and Boster, R .S ., 1976 . Measuring landscape esthetics : the scenic beauty estimation method . USDA Forest Sam . Res . Pap . RM-167 . Kaplan, S ., Kaplan, R . and Wendt, J.S ., 1972 . Rated preference and complexity for natural and urban visual material . Percept . Psychophys ., 12 : 354-366 . Payne, B .R ., 1973 . The twenty-nine tree home improvement plan . Nat . Hist., 82(9) : 74-75 . Rabinowitz, C .B . and Coughlin, R.E ., 1970 . Analysis of landscape characteristics relevant to p reference . Reg. Sci. Res. Inst . Discuss . Pap . Ser . 3 8 . Schachter, S ., 1982 . Recidivism and self-cure of smoking and obesity . Am . Psychol ., 37 :436-444 . Schroeder, H .W . and Daniel, T .C ., 1981 . Progress in predicting the perceived scenic beauty of forest landscapes . Forest Sci ., 27 : 7 1-80 . Shafer, E .L ., Jr ., Hamilton, J .F . and Schmidt, E .A ., 1969 . Natural landscape preferences : a predictive model . J . Leisure Res ., 1 : 1-19 . Thurstone, L .L ., 1927 . A law of comparative judgment . Psychol . Rev ., 34 : 273-286 . Torgerson, W .S ., 1958 . Theory and Methods of Scaling. Wiley, New York . Zube, E .H ., 1973 . Rating everyday rural landscapes of the northeastern U .S . Landscape Archit ., 63 : 370-375 . Zube ., E .H ., 1974 . Cross-disciplinary and inter-mode agreement on the description and evaluation of landscape resources . Environ . Behav ., 6 : 69-89. Zube, E .H . and Pitt, D .G ., 1981 . Cross-cultural perceptions of scenic and heritage landscapes . Landscape Plano., 8 : 69-87-
219
APPLICATION OF WILDLAND SCENIC ASSESSMENT METHODS TO THE URBAN LANDSCAPE L-M . ANDERSON USDA Forest Service, Southeastern Forest Experiment Station, Forestry Sciences Laboratory, Carlton Street, Athens, GA 30602 (U.S . A .) HERBERT W . SCHROEDER USDA Forest Service, North Central Forest Experiment Station, 5801 N . Pulaski St., Chicago, IL 60646 (U.S.A .) (Accepted for publication 26 May 1983)
ABSTRACT Anderson, L .M . and Schroeder, H .W ., 1983 . Application of wildland scenic assessment methods to the urban landscape . Landscape Plann ., 10 : 219-237 . This research explores the feasibility of studying the urban landscape using procedures from wildland scenic quality assessment, and ascertains the influence of some physical characteristics of urban landscapes on esthetic evaluations . Diverse groups of raters, including a men's civic club and a group of minority high school students, evaluated the scenic quality of 240 photographs of a small Georgia city . The slides were scored for physical features using both objective measurements of the area of each image covered by different elements, such as pavement, structures and vegetation, and subjective ratings of physical characteristics such as lawn and shrub maintenance . The results indicated a high degree of agreement among observers in their evaluations of the city scenes . Linear regressions of scenic quality on physical features accounted for about half of the variance, with development intensity detracting from scenic quality, and the amount of vegetation in the scene enhancing it . Landscape maintenance also emerged as an important factor. Suggestions for improvements in future application of the method are discussed .
INTRODUCTION
One of the most widely recognized benefits of urban vegetation is its contribution to the visual quality of the city landscape . The esthetic enhancement trees provide is translated into economic support for the landscaping and nursery industry, and higher property values within a community (Payne, 1973) . However, when budgets are tight, other municipal services often meet with more funding success . Because urban forestry and other tree-related programs must operate within increasingly stringent budget constraints, it is important to develop an understanding of all the benefits of urban vegetation, so that what monies are available for management can be used effectively to increase these benefits . 0304-3924/83/$03 .00
© 1983 Elsevier Science Publishers BY .
220
The research reported here was designed to increase understanding of the esthetic benefits of urban vegetation . Our objectives were, first, to help establish the feasibility of studying the overall urban landscape using procedures adopted from other areas of scenic quality research, and second, to explore the influence of physical characteristics of urban landscapes, including vegetation, on esthetic evaluations . Some specific questions concerning land use and vegetative maintenance quality were also addressed . This study addresses the total urban landscape, not being limited to particular land uses or design problems associated with particular structural or landform characteristics . Procedures for assessing the quality of environments are well developed in the context of wildland scenic quality . One of the most successful approaches has been to solicit direct evaluations of scenic quality from users, or potential users, of the scenic resource (e .g . Daniel and Boster, 1976 ; Buhyoff and Leuschner, 1978 ; Brush, 1979) . A few studies involving direct assessment (Rabinowitz and Coughlin, 1970 ; Kaplan et al., 1972 ; Brush and Palmer, 1979) have compared evaluations of urban and non-urban landscapes . Urban or built scenes commonly receive lower ratings than vegetated or non-built scenes . Nevertheless, observers readily discriminate among urban scenes, finding some more attractive than others . Daniel and Boster (1976) developed and tested a scenic quality measurement technique for wildland scenery . Their statistical procedure, called Scenic Beauty Estimation (SBE), is an adaptation of Thurstone's (1927) law of categorical judgment (Torgerson, 1958) . Using ordinal-level ratings of scenic quality, the SBE method can provide interval-level values corresponding to the degree of perceived scenic quality in landscapes . One successful application of this and similar methods has been the development of prediction models for scenic quality, using physical characteristics of the landscape . The physical features used to predict scenic quality sometimes follow readily from standard management practices . For instance, Arthur (1977) and Schroeder and Daniel (1981) found that scenic quality could be predicted with reasonable accuracy from variables recorded in standard resource inventories made on U .S . national forests, including estimates of tree density, vegetative ground cover, and downed wood. Linear regression models, using these variables, can predict over half of the variance in scenic quality evaluations of forest areas . Another means of characterizing scenes was used by Shafer et al . (1969) in one of the early studies of landscape evaluation . Their predictive variables included measures of the length of edge between, and the area covered by, different elements in the photograph, such as water and vegetation in the foreground, middle ground and distance . This method was recently re-evaluated by Brush (1981), who showed that large-scale landform features, such as mountains, account for additional variance . In this study we used a combination of variables, including both photo measurements and subjective ratings .
22 1
One consideration in applying observer-based methods concerns how well different observer groups' evaluations represent the values of the public. Because it is usually impractical to obtain statistically random samples of the public, most research uses non-random sampling techniques . An alternative approach to randomization has been discussed by Schachter (1982) . Under this approach, one studies existing public groups that differ in what are presumed to be important characteristics . Comparing results from the diverse groups then indicates the potential for generalization of the findings . If two different groups produce similar results, one's confidence in generalizing increases ; if the groups diverge, one has data with which to begin determining empirically the limits of generalization of the results . In the research described here, we have taken the approach suggested by Schachter, using groups whose age, sex, ethnic and economic characteristics differ greatly . Several existing groups have been studied in wildland scenic research, sometimes revealing differences, but usually not . Zube (1973, 1974) found little difference between scenic judgments of diverse groups composed of U.S. citizens . In later work, Zube and Pitt (1981) found some crosscultural differences in scenic evaluation . Daniel and Boster (1976) compared a number of different U .S. groups and found close agreement among them, with a few limited exceptions . Brush (1979) found close agreement between forestry students and small woodlot owners in Massachusetts . Buhyoff et al. (1978) found substantial agreement between students of landscape architecture and other students, but less agreement between experienced landscape architects and other students . Overall, the picture that has emerged from this research is one of considerable agreement between diverse groups evaluating wildland scenic quality . METHOD
In this research we obtained evaluations of photographs of scenes within the city limits of Athens, Georgia, a manufacturing and university town with a population of 70000, covering an area of about 12 square miles in the southern Piedmont region of the U .S . Residential, commercial, office/industrial, and vacant or undeveloped land uses were represented, along with a few scenes of the university campus . Tree cover varied, with none visible in some scenes, others with park-like stands of pines or mixed hardwoods, and still others showing densely wooded undeveloped land . Slide collection
Many studies of wildland scenic quality have used a randomizing scheme for selecting photographs to represent sites . Such methods are designed to overcome photographer bias in the selection of photographs by setting up an algorithm that determines which photographs will be taken before
2 22
one reaches the field . Following this practice, we used a pair of transects drawn at random across a road map of Athens, one roughly north-south and one east-west . These transects determined 60 sample-point locations where the transects intersected streets on the map . On the shorter northsouth transect, a sample point was located at each point where the transect crossed a street ; on the east-west transect, sample points were selected at every second street intersection . Four color slides were taken at each sample point, each at 90° from each other and all at a 45' angle to the street . The photographs were taken from the side of the road, so that at most sample points, two photographs showed none of the road-bed, while the other two showed a substantial amount of road in the immediate foreground . A total of 240 slides, four for each of 60 sample points, was collected . Figures 1-4 are examples of these photographs. The photographs were taken in late August and early September, so that foliage was in . late summer green . Eighteen of the photographs were somewhat under-exposed, a problem not discovered until autumn coloration had set in . Inclusion of these slides had only slight statistical effects on the outcome, adding only slightly to the unexplained variance . Slide evaluation
The observers for this study comprised five different groups . Two groups of undergraduate psychology students at the University of Georgia each evaluated 140 slides . Of the 140 scenes, 100 were unique to each group and 40 were viewed by the groups in common . Three other groups were also included . First, 22 members of a men's civic club in Winder, Georgia, a small town about 20 miles from Athens. This group consisted exclusively of white males of various ages . Another group was the summer staff of an Atlanta, Georgia, nature center, 70 miles from Athens . Fifteen of the 19 people in this group were black temporary employees aged 18-20, 12 of them female, assigned to the center by a federally funded work/ training program . The remaining four were supervisory staff teachers, including one black and three whites. The final group was a class of eight graduate students in landscape architecture at the University of Georgia . The graduate student group evaluated all 140 slides shown to the second group of psychology students . The nature center and men's club groups saw only the first 60 slides of the same set, because less time was available for those groups. If scenic evaluations are found to be similar across the boundaries of age, sex, ethnicity and economic background evident in these groups, then there would be substantial grounds for generalizing the results of this research to the broader public . Each group of observers received rating response sheets showing a scale from 0 (lowest scenic quality) to 9 (highest scenic quality), and a set of numbered response blanks appropriate to the number of slides they were
22 3
. Fig. 1 . One of the highest rated residential scenes (SBE = 105)
Fig . 2 . One of the highest rated commercial scenes (SBE - 9) .
2 24
Fig. 3 . One of the lowest rated residential scenes (SBE = -58).
Fig . 4 . One of the lowest rated commercial scenes (SBE = -100) .
2 25
going to view . The room was made as dark as possible, and the first 60 slides were shown for 8-s intervals . For groups viewing more than 60 slides, the remaining slides were shown at a slightly faster rate of 5 s, as the observers gained experience in their task . In a follow-up questionnaire, observers were asked to list those features of the scenes that led them to give high or low ratings of scenic quality . The ratings were transformed to interval-scale scores using the SBE computer program of Daniel and Roster (1976) . The scaled data, named scenic beauty estimates (SBE's), are standardized indices of scenic quality, which combine observers' ratings of the scenes to produce a single score for each scene .' Because our two college student groups had evaluated 40 of the scenes in common, we used these 40 scenes to establish a common metric scale of scenic quality for all 240 scenes . Thus, the SBE's for all 240 scenes rated by the two student groups were placed on a single scale of perceived scenic quality . Physical feature evaluations
The complex of vegetation, landform and man-made elements in the urban landscape suggests use of a large set of variables for characterizing the scenes. We used a total of 49 physical feature variables, including objective measurements of photograph features and subjective ratings of other features of the scenes . Some of the features were suggested by the responses to the followup questionnaire, in which participants reported features that led to high or low ratings of scenic quality . The objective measurements used a modification of the method used by Shafer et al . (1969) . Each slide was projected on to a 16 X 23 grid of 1-inch cells, with the 16th row not completely filled. The number of grid cells containing an identifiable quantity of each of several landscape elements was counted . A cell containing more than one element was counted once for each element it contained . The elements included such things as sky, vegetation, pavement, commercial and residential architecture, and others (see Table I for a complete list) . Some of these counts were made separately for the top and middle five rows and the bottom six rows of each scene . Cells containing other features, such as roads, fences or vehicles, were counted as a single total over the entire image . The objective measurements also included counts of the number of vehicles, buildings, people and signs (or parts thereof) in the scenes. These features are also described in Table I . ' SBE scores are derived by averaging the standard normal deviates (z-stores) associated with the cumulative proportion of ratings at each level of the rating scale . The mean of average deviates for an arbitrarily selected subset of the slides (in this case every fifth slide shown in random order) is subtracted from the average deviate of each slide and multiplied by 100 to produce the SBE . Within the context of a given experiment, the SBE scale has interval properties . While the range of SBE's is not predetermined, in most applications they range from approximately -150 or -100 to 100 or 150 .
2 26 TABLE I Descriptions of objective physical-feature variables used to develop the model of urban scenic quality for Athens, Georgia Description of variable
Average value
Grid count variables Sky' 48 .9 Vegetation' 295 .0 Commercial architecture' 7 .3 Residential architecture' 43 .2 Total architecture 54 .2' Signs' 1 .9 Poles and wires' 15 .1 Streets and roads 30 .6 Paved driveway 4 .0 Paved sidewalk 9 .4 Parking lot 2 .9 Curb 7 .8 Vehicles 10 .5 Fence and retaining walls 13 .2 Bare dirt 9 .4 Litter 0 .8 Other variables Number of structures Number of signs Number of vehicles Number of people
1 .4 0 .2 1 .0 0 .1
Standard deviation
Highest value
Correlation with SBE
43 .9 73 .6 27 .4 49 .8 55 .4 7 .2 21 .9 42 .2 12 .7 17 .2 12 .2 12.1 23 .5 47 .2 18 .2 3 .7
180 368 249 222 352 73 121 171 91 95 95 62 154 368 118 36
-0 .27 0 .41 -0 .27 n .s . -0 .26 -0 .19 -0 .27 -0 .15 -0 .14 n,s . n-s. n .s . -0 .30 -0 .25 ns. -0 .22
1 .1 0 .9 2 .1 1 .3
5 10 13 20
-0 .16 -0 .20 -0,25 n .s .
' Grand total reported here, but divided into the top, middle and bottom thirds of the scene for analyses . 'Includes commercial, residential and undifferentiated architecture .
A second group of features included subjective evaluations of maintenance quality, urban tree cover, and compositional characteristics of the scenes. These variables are described in Table II . Maintenance of lawns, shrubs and structures was rated by two judges using a 5-point scale . Little variation in maintenance quality of structures was apparent in the photographs, so this variable was dropped . Lawn maintenance was evaluated in terms of mowing, edging, and bare or weedy patches . Shrub maintenance concerned mainly pruning, but ratings were not lowered for shrub varieties that are not normally intensively pruned (e .g. crape myrtle, Lagerstroemia indica L .) .
Ratings of tree cover were taken, including the density and species mix (hardwood, pine or mixed) of trees in each scene . Two other tree variables, distribution and age, were attempted, but inter-judge agreement was too low (r <0 .70) for these ratings to be included . The judges' dis-
227
TABLE 11 Description of subjective physical characteristics used to develop a model of urban scenic quality Description of variable
Interpretation of scale
Average Standard Correlavalue deviation tion with SSE
Species mix of trees' Density of trees' Prominence of flowers Prominence of litter Prominence of dead vegetation Scale of view Best lawn maintenance' Worst lawn maintenance' Best shrub maintenance 2 Worst shrub maintenance' Development Intensity'
I-all pine to 3-mixed to 5-all hardwood I=low to 5-high density 1-low to 3-high prominence I=low to 3=high prominence 1=low to 3=high prominence 1-under t§ city lot to 6-over 5 city lots 1-bad condition to 5-exceuent condition 1-bad condition to 5=exceuent condition 1-bad condition to 5-excellent condition 1-bad condition to 5-excellent condition O=undeveloped to E=higldy developed
4.2 2.9 0.2 0.2 0.2 3.2 3.4 3 .1 3 .5 3 .0 3.1
1 .0 1 .0 0 .5 0 .4 0 .5 1 .2 1 .0 1 .0 1 .0 0 .9 2 .4
us . 0 .27 n .s . -0 .29 -0 .21 u .s . 0 .22 0 .26 0 .22 0 .21 -0 .47
' Based on the 221 scenes where sufficient land area was visible to evaluate species mix and tree density . 'Assigned only to scenes of developed property (164 scenes) . 'Based on zones assigned by county planning commission.
agreement in the tree evaluations usually occurred on scenes where little land area was visible . For instance, when only the shadow of a tree appeared in the photograph, one judge might infer the most likely size of the unseen tree, while the other judge simply reported that the size could not be established . Those scenes showing an area of less than one city lot were problems in other ways also, as we will discuss later . Ratings from 0 (none) to 3 (very prominent) were assigned to the amount of dead vegetation, litter and flowers in each scene . The scale of view, i.e. the amount of land area visible in the scene, was rated from 1 (less than lA a standard urban lot) to 6 (more than 5 such lots) . Each photograph was classified according to the type of property visible, i .e . whether the scene was of undeveloped urban land, such as a freeway easement or vacant lot ; whether the scene was clearly of commercial or residential property ; or whether the scene showed a close-up of vegetation or other objects so that the nature of the property could not be determined . Finally, we included a variable reflecting the intensity of development in the area where each photograph was taken . The local planning commission had assigned land-use zone designations to all areas in the study, and these served as the basis for the development-intensity variable . Each of four of the zone categories (undeveloped, residential, commercial and office/industrial) had a short hierarchy of zones already ranked by development density (e .g . zones R1-R5 referred to residences on decreasing lot sizes) . The categories of zones were unified into a single hierarchy on the basis of the average number of grids containing man-made elements across all scenes for each zone type . More details of the scoring scheme for these variables, along with some descriptive statistics, are shown in Table II .
228 RESULTS AND DISCUSSION
The results we discuss address our two objectives, one concerning the feasibility of the procedure, and the other concerning apparent influences of physical features of the urban landscape on its scenic quality . One test of the feasibility of the method concerns the extent of inter-group agreement or reliability in the evaluations of the landscapes . Our observer groups represent several presumably disparate populations, and consensus in ratings of scenes would add to our confidence in the results. Our second question concerns the relation between esthetic evaluations and characteristics of the urban landscape . The analysis for this question involved computing correlations between SBE's and slide features . An analysis of variance was calculated to determine the scenic quality differences between areas assigned different land-use zones by the planning commission . A factor analysis and regression analyses of the physical features and SBE's were also conducted to provide estimates of the degree of relationship among the slide features and their relation to scenic quality . Correlations among observer groups
Table III shows the results of the inter-group correlations. The different number of pairs in each sample is a function of the different number of slides seen in common by each of the pairs of groups . All of the correlations are high, and are in the range often found in wildiand scenic research. The two groups hypothesized to be the most divergent (the men's club of Winder and the Atlanta nature-center staff) produce the highest correlation, while the landscape architects showed the least extensive agreement with most other groups . The discrepancy involving the landscape architecture students may be due to small sample size rather than any substantive differences in esthetic criteria . There were only 8 landscape architects, so that idiosyncratic ratings by a single evaluator will have a more pronounced effect on the group's averaged index . TABLE III Inter-group correlations of Scenic Beauty Estimates (SBE's) for five groups of evaluators Group
1. 2. 3. 4. 5.
Psychology students I Psychology students II Landscape architects Atlanta nature center staff Winder, Georgia, men's civic club
n
24 23 8 19 22
Correlations (number of pairs of slides) 2
3
4
5
0 .89 (40) -
0.79 (40) 0 .88 (140) -
0 .90 (18) 0 .87 (60) 0 .74 (60) -
0.88 0 .86 0 .70 0.90 -
(18) (60) (60) (60)
229
Clearly, however, these results do not support a hypothesis that different groups of evaluators differ greatly in their evaluation of the urban environment . Instead, it appears that evaluations of the urban scene are quite consistent. The extent of inter-group agreement is sufficient to proceed with the other analyses using the largest data-set available, namely that of the combination of evaluations by the two groups of psychology students . The combined evaluations of these two groups, using the average SBE value for scenes that were rated by both groups, provide a set of SBE's for all 240 slides . Relationship of physical features with SBE's
Tables I and II show the simple correlations, significant at 0 .05 or less, between physical features and scenic quality evaluations . Over half of the variables show a significant relationship to scenic quality, although no feature accounts for more than 22% of the variance, and most account for less than 10% . The two strongest correlations with scenic quality are produced by vegetation in the middle third of the scene (r = 0 .44) and development intensity or zone (r = -0 .47) . These two variables also correlate significantly with each other (r = -0.32), so that the percentages of variance they each account for (22% for zone and 19% for vegetation) are not independent quantities. Vegetation in the middle thrid of the scene can roughly be considered the lawn, foundation plantings, and under-story for the typical urban scene as collected by our photographic procedure, as in Fig . 3. Where this space is not occupied by vegetation, the most common features are buildings, pavement and vehicles, all of which detract from the scenes . The role of vegetation was also found to be important in the urban scenic quality model developed by Brush and Palmer (1979) . The influence of zone was further explored in a one-way analysis of variance . The analysis was highly significant (F = 12 .75, df 7/224, P < 0 .001), showing the major distinction between the lowest-density residential zone and the two highest-density residential zones, the commercial zones and undeveloped property (mainly road easements and vacant lots) . The main characteristic distinguishing the more attractive office/industrial zones from commercial zones is the amount of open land, usually landscaped, surrounding the structures . The highest-rated commercially zoned scenes had a high degree of landscape maintenance on a small area of parking median or a few square feet of garden that happened to dominate the photograph . Scenes of commercial sites with lower ratings showed more parking lot and less vegetation, with poorer maintenance of the vegetation that did exist . The planning commission zones upon which we based our development-intensity variable were determined by lot size for residential areas, so that property values and development intensity are highly
2 30 correlated . It follows readily that income levels of the households in the less developed zones are higher than incomes in the more developed zones . We cannot sort out the different influences of lot size, property values and household income, as we do not have independent data on the latter two variables . The rather strong negative relationship between scenic quality and amount of sky probably arises from both the visibility of overhead poles and wires against sky and the reduced tree cover associated with clear views of the sky . The signs of the remaining significant simple correlations are negative for all but one man-made feature, paved driveway . Paved drives were most common in low-density residential zones, which may explain their positive association with scenic quality . The natural material features, including maintenance measures of lawn and shrubs, are positive, with the exception of dead vegetative material, which was high for scenes where yard prunings or leaves had been piled for trash collection . The characteristics of some of the lowest- and highest-rated scenes, shown in Figs . 1-4, can be deduced from the simple correlations . Figure 1 shows one of the highest-rated residential scenes, with extensive tree cover and well-maintained lawn and shrubs . Figure 2 shows one of the highest-rated commercial scenes, with small but well-maintained plantings . Figure 3 is one of the lowest-rated residential scenes, with tree cover but very poor maintenance of structures and lawn . Figure 4 shows one of the lowest-rated commercial scenes, with very little vegetation and that not in good condition . In general, highly preferred scenes containing structures also contained intensively managed plant materials . Factor analysis To determine the relationships among the physical characteristics and their joint effect on scenic quality, the physical features and scenic quality scores were factor analyzed . Unlike regression analysis, which uses a number of independent variables to make predictions about a single targetted dependent variable, factor analysis treats all variables equally, clustering them together according to similarities in their pattern of variation across cases . Variables cluster together by loading highly, in terms of absolute magnitude, on one or another factor emerging from the analysis . The results of our factor analysis' indicated that several features vary together, using our criterion of a factor loading in excess of 0 .50 . These clusters are summarized in Table IV, which shows the first and strongest nine factors to emerge from the analysis (each of which accounted for 3% or more of the variance) . Two factors concerned the amounts of architecture ; one residential and one commercial . Both of the architecture factors show scenic quality ' The factor analysis was calculated using the Statistical Package for the Social Sciences (SPSS) FACTOR subroutine, with quartimax rotation, to maximize the probability that each variable would load highly on at least one factor .
231 TABLE IV Results of factor analysis on SBE's and physical characteristics of scenes of Athens, Georgia Factors and variables loading over 0 .50 on each
Factor Percent explained loading variance
Factor 1 Commercial architecture, top Commercial architecture, middle Commercial architecture, bottom Commercial architecture, total Number of signs Number of people
0 .86 0 .73 0 .80 0 .90 0 .70 0 .77
Factor 2 Residential architecture, top Residential architecture, middle Residential architecture, bottom Residential architecture, total Number of structures
0 .76 0 .88 0 .58 0 .85 0 .54
Factor 3 Sky, top Sky, middle Sky, total Poles and wires, middle
0 .85 0 .88 0 .53 0.50
Factor 4 Vegetation, bottom Vegetation, total Scale of view Paved road Curb
19 .3
-0 .21
10 .6
-0 .14
10 .4
-0 .21
6 .4
-0 .04
5 .3
-0 .07
3 .9
-0 .16
3 .8
-0 .16
3 .6
0 .21
3 .3
0 .67
-0 .80 -0 .61 0 .50 0 .80 0 .62
Factor 5 Signs, top Signs, middle Signs, bottom Signs, total
0 .80 0 .89 0 .63 0 .63
Factor 6 Poles and wires, top Poles and wires, middle Poles and wires, bottom Poles and wires, total
0 .68 0 .63 0 .64 0 .79
Factor 7 Paved parking lot Vehicles (grid count) Number of vehicles
0 .74 0 .67 0 .66
Factor 8 Species mix of trees Density of trees
0 .69 0 .58
Factor 9 Paved driveway Development intensity SBE
Factor loading of SBE's
0 .57 -0 .61 0 .67
232
with a negative loading, indicating that the more buildings and other structures in a scene, the lower its scenic quality rating . A third factor concerned view distance, with high amounts of sky and poles and wires loading together . Presumably because of the poles and wires, scenic quality loaded negatively on this factor also. Scenic quality loaded highest on a factor it shared with development intensity and paved driveways, which were usually found in low-density residential zones . Separate factors emerged for signs, poles and fires, tree species and density, and vehicles and parking lots . A final factor reflects an artifact of the way in which the photographs were collected . In each set of four slides taken at a sample point, two show the scenes across the street and two show scenes on the same side of the street as the photographer . The cross-street scenes contain a substantial amount of pavement from the road-bed in the immediate foreground, while the other two scenes show no road-bed at all, in most cases . The factor analysis captured this artifact, showing an inverse relationship between pavement and vegetation in the bottom third of the scenes . However, this factor shows little relationship to scenic quality, which loaded only -0 .05 . Hence, this artifact of the photographic procedure poses very little threat to the accuracy of the scenic quality ratings . The factor analysis reveals a considerable degree of independence among the urban features we included in this study . It shows SBE's to be affected by development intensity and, to a lesser degree, by overhead wires, commercial and residential architecture and vehicles . SBE's were less influenced by the photo-sampling artifact of how much road-bed showed in the photograph and by signs, probably because there were few signs in the scenes we used . Regression analyses
Finally, to determine the extent to which scenic quality can be predicted from physical features, we performed two regression analyses . Stepwise regression was used, including only the predictors contributing 1% or more to the Rz . The first model in Table V is for those 190 scenes that show an area of developed property at least one average city lot in size . The second model is for all 240 scenes . The regression equation for all scenes accounts for about half of the variance, while in the more selected data-set the physical characteristics account for an additional 9% of the variance . The positive effects of the amount and degree of vegetation are evident in both equations . Conversely, man-made elements, such as fences, vehicles and (through the sky variable) poles and wires had a negative effect . Development intensity had the strongest negative impact in both models, although as mentioned before, this variable corresponded closely with property values for the predominant residential-zone categories .
233 TABLE V Results of forward stepwise linear regressions predicting scenic quality from objective and subjective physical characteristics Predictor variables
Standardized regression coefficient
R'
Change in R'
Simple r
Model for scenes of developed areas with view of at least one city lot (n=190) Development intensity 0 .28 -0 .32 0 .28 -0 .53** Maintenance of worst lawn in scene 0 .42 0 .30 0 .14 0 .48** Vegetation in middle third of scene 0 .23 0 .50 0 .08 0 .41** Sky in top third of scene -0.19 0 .54 0 .05 -0 .31** Fence and retaining walls -0 .16 0 .56 0 .02 -0 .31** Number of vehicles -0 .15 0 .57 0 .01 -0 .24** Scale of view 0 .15 0 .59 0 .02 -0 .21** Model for all scenes (n=240) Development intensity Vegetation, top third of scene Maintenance of worst lawn in scene Sky, top third of scene Vegetation, middle third of scene Fence and retaining walls Number of vehicles
-0 .35 0 .10 0 .32 -0 .14 0 .23 -0 .16 -0 .10
0 .22 0 .32 0 .41 0 .44 0 .47 0 .49 0 .50
0 .22 0 .10 0 .09 0 .03 0 .03 0 .02 0 .01
-0 .47** 0 .40 0-26** -0 .27** 0 .44** -0 .25** -0 .25
**P < 0 .01 .
CONCLUSIONS
The conclusions of this research are presented in terms of our two initial questions - the feasibility of research on the urban scene and the physical characteristics of urban areas that contribute to scenic quality . Both of these questions will be addressed in separate sections devoted to management implications and implications for further research . Management implications
Our different groups of evaluators provided very similar evaluations of scenes of the city of Athens, Georgia . As has been found in wildland scenic research, there is strong agreement about preferred scenes among public groups . Thus, the public may often agree in its evaluations of land managers' impact on scenic quality . It should be noted, however, that the correlations between our observer groups are for the aggregated data . If one were to compare the evaluations of any two individuals in this study, there would probably be less agreement as to the scenic quality of particular scenes . The use of aggregate preferences is, nevertheless, appropriate in urban design . While home owners may be able to plan a landscape to
2 34
satisfy their own taste, managers of public-oriented facilities and public urban land must be sensitive to the fact that one individual's preference, even a designer's, may not be satisfactory to the majority of users . Managers and designers can readily claim that no design will please all individuals, but our research indicates that it is not impossible to please a majority of users . Better understanding of what people like is an attainable goal, and one that will enable managers to adopt designs that will maximize the esthetic benefit brought about by their investment . Direct evaluation methods, similar to those presented here, could be applied to test species and planting arrangements for different landscape situations, in order to improve the effectiveness of municipal tree-planting programs . The particular features we identified that contribute to, or detract from, urban visual quality offer little that is new or striking . Detractors having the greatest effect are overhead wires and poles, vehicles and parking lots . Underground placement of utilities may be too expensive for many urban areas, but our results show that trees can function as a mask for poles and wires ; when viewed against a background of trees, poles and wires are much less visually obtrusive . Parking lots are another visual blight, but again there are remedies in judicious use of vegetation for screening . Parking lots for new buildings could be placed to the rear, rather than on the street side . Thus, only users of the lot need to see it . The use of plantings in existing parking lots has a palliative effect . Several scenes of Athens business zones were rated more attractive if they had even small plantings in gardens or parking medians . Such small green spaces may be less costly to establish and maintain than larger plantings . They are less enhancing than larger areas devoted to plantings, but they do improve commercial property appearance for viewers from the road . Finally, our results show that viewers are sensitive to the maintenance of vegetation . Lawn maintenance concerned both mowing and bare or weedy patches, while shrub maintenance dealt mainly with pruning of species where appropriate . Maintenance quality ratings of lawns and shrubs were highly correlated with each other, but not with other features . Thus, maintenance levels and development intensity or zone are fairly independent . The implication is that even properties in generally unattra, ;t ve zones can realize esthetic benefits from well-maintained vegetation . The influence of maintenance quality applies only to developed property, since we did not assign a rating of maintenance quality to undeveloped sites or sites whose development status could not be determined . Several wooded lots were included in the photo-sample, mostly from the edges of the city . Many of these sites received high ratings of scenic quality, as would be expected from other studies that have consistently shown natural areas to be more attractive than urban scenes . Grass- and herbcovered but tree-less undeveloped property, such as land that had been graded but never built upon, was not rated as highly as tree-covered land .
2 35
However, even tree-covered scenes that had litter or vehicles in evidence were found to be less attractive . There is no such thing as a no-maintenance landscape, but if small lots of relict forest are surrounded by urban development, they may require only removal of litter to retain a high degree of attractiveness . Research implications
The different observer groups produced similar evaluations of urban scenes . The inter-group correlations suggest that, even though some of the groups would seem to have very little in common, in most cases at least 80% of the variance in each group's SBE's is accounted for by consistent preferences shared among groups . By comparison, the grid counts of physical characteristics in the scenes, plus a small number of subjective ratings, could account for only 50-60% of the total variance in the psychology groups' SBE's . What features might account for the other 30 or 40% that our observers tapped, but that our physical characteristics of scenes failed to capture? The 50% of variance accounted for by our model for all scenes is comparable to several studies of wildland scenic quality . For instance, some of Arthur's (1977) models of scenic quality for ponderosa pine forests account for about half of the variance . On the other hand, in a study using scenes selected to vary in the extent of southern pine beetle damage, Buhyoff and Leuschner (1978) were able to obtain R' of 0 .84 using only insect damage to predict scenic evaluations by students informed of the cause of damage . Buhyoff and Leuschner's study differed from ours in several ways . The landscapes were much more homogeneous, being highly similar views of completely forested hillsides, differing mainly in the amount of visible insect damage . The physical feature measurements were made more precisely using a finer grid than ours, and a non-linear (logarithmic) function was used to model the impact of insect damage on scenic quality . By focusing on a single feature (insect damage) and carefully controlling the set of landscapes, Buhyoff and Leuschner achieved a high degree of precision in their model . The complex character of the urban environment precluded such careful control of the landscape sample and rendered very precise measurement of each physical feature unfeasible . Hence, a part of the unexplained variance in our models may be due to the limitations of our sampling and measurement methods . Comparing the two models we report in Table V suggests that restricting the selection of landscapes can increase the variance accounted for by the model . When we omitted scenes of undeveloped urban property and those showing an area less than one city lot in size, the regression accounted for an additional 9% of the variance . An increase of about 4 of the 9% results from dropping the scenes of undeveloped property, where struc-
236
ture grid counts were all 0 and landscape maintenance was unevaluated . The other 5% comes from deleting several close-up scenes of structures and vegetation . Such scenes had the highest standard deviations in untransformed ratings (over 2 .0), as some observers liked such close-ups and others did not . Adjusting the photo-sampling procedure to take all four scenes at each point from the opposite side of the road would eliminate the problem of close-up photographs, while retaining the unbiased quality of the sample procedure . Elements with potential symbolic and emotional associations, such as churches, graveyards, slums and liquor stores, may also add unsystematic variability to the data . Small or ambiguous elements in the photographs could increase this variability, as observers might differ in their probability of detecting a minor detail, or in their interpretation of an ambiguous feature . Some cultural influences have been shown to affect wildland scenic evaluations (Anderson, 1981), and we were concerned that in urban areas, the effects of such variations in interpretation would be magnified . Our findings, however, suggest that while symbolic elements and affective associations may influence evaluations of urban scenes, these meaningful associations are probably consistent across groups - our observer groups were quite consistent in their evaluations of scenic quality . Our results suggest that the urban environment is as conducive to esthetic study as the natural environment, although the researchers' ingenuity in calibrating the visual characteristics of the scenes may be taxed to a greater extent . Visual quality judgments of urban scenes appear to be as consistent across diverse evaluators as are those of natural scenes . Finally, our results suggest that much remains to be learned about how the elements of the urban scene affect scenic evaluations of the city landscape . The particular challenge here is likely to be understanding the role of symbolism in the urban setting, although broad categories of urban elements - vegetation and structures, land-use planning zones - can have a considerable influence on preferences for urban scenery . ACKNOWLEDGEMENTS
The authors wish to thank Kathy Stewart and Dr . B .E. Mulligan of the Department of Psychology, University of Georgia, for their assistance in obtaining slide ratings from the undergraduate students, Sharon Lewis of the same program for her assistance with the data analysis, and Antoinette Watkins of the Young American Conservation Corps (YACC) program for her painstaking efforts in providing the grid counts used in this study . Preliminary results of this research were presented at the Southeastern Recreation Researchers Conference, Asheville, NC, 18-19 February 1982 .
2 37 REFERENCES Anderson, L .M ., 1981 . Land use designations affect perception of scenic beauty in forest landscapes . Forest Sci ., 27 : 392-400 . Arthur, L .M ., 1977 . Predicting scenic beauty : some empirical tests. Forest Sci ., 23 : 151-160 . Brush, R .O ., 1979 . The attractiveness of woodlands : perceptions of forest landowners in Massachusetts . Forest Sci ., 25 : 495-506 . Brush, R .O ., 1981 . Landform and scenic preference : a research note . Landscape Plann ., 8 : 301-306 . Brush, R .O . and Palmer, J.F ., 1979 . Measuring the impact of urbanization on scenic quality : land use change in the Northeast . In : G .H . Elsner and R .C. Smardon (Editon:), Proceedings of Our National Landscape : a Conference on Applied Techniques for Analysis and Management of the Visual Resource . USDA Forest Se" . Gen . Tech . Rep . PSW-35 . Buhyoff, G .J . and Leuschner, W .A ., 1978 . Estimating psychological disutility from damaged forest stands . Forest Sci ., 24 : 424-432 . Buhyoff, G .J ., Wellman, J .D ., Harvey, H . and Fraser, R .A ., 1978 . Landscape architects' interpretations of people's landscape preferences . J . Environ . Manage ., 6 : 255-262 . Daniel, T .C . and Boster, R .S ., 1976 . Measuring landscape esthetics : the scenic beauty estimation method . USDA Forest Sam . Res . Pap . RM-167 . Kaplan, S ., Kaplan, R . and Wendt, J.S ., 1972 . Rated preference and complexity for natural and urban visual material . Percept . Psychophys ., 12 : 354-366 . Payne, B .R ., 1973 . The twenty-nine tree home improvement plan . Nat . Hist., 82(9) : 74-75 . Rabinowitz, C .B . and Coughlin, R.E ., 1970 . Analysis of landscape characteristics relevant to p reference . Reg. Sci. Res. Inst . Discuss . Pap . Ser . 3 8 . Schachter, S ., 1982 . Recidivism and self-cure of smoking and obesity . Am . Psychol ., 37 :436-444 . Schroeder, H .W . and Daniel, T .C ., 1981 . Progress in predicting the perceived scenic beauty of forest landscapes . Forest Sci ., 27 : 7 1-80 . Shafer, E .L ., Jr ., Hamilton, J .F . and Schmidt, E .A ., 1969 . Natural landscape preferences : a predictive model . J . Leisure Res ., 1 : 1-19 . Thurstone, L .L ., 1927 . A law of comparative judgment . Psychol . Rev ., 34 : 273-286 . Torgerson, W .S ., 1958 . Theory and Methods of Scaling. Wiley, New York . Zube, E .H ., 1973 . Rating everyday rural landscapes of the northeastern U .S . Landscape Archit ., 63 : 370-375 . Zube ., E .H ., 1974 . Cross-disciplinary and inter-mode agreement on the description and evaluation of landscape resources . Environ . Behav ., 6 : 69-89. Zube, E .H . and Pitt, D .G ., 1981 . Cross-cultural perceptions of scenic and heritage landscapes . Landscape Plano., 8 : 69-87-