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Visual impact of hillside development: Comparison of measurements derived from aerial and ground-level photographs
Landscape and Urban Planning, 15 ( 1988 ) 119- 126 Elsevier Science Publishers B.V.. Amsterdam - Printed in The Netherlands
119
Visual Impact of Hillside Development: Comparison of Measurements Derived from Aerial and Ground-Level Photographs
HERBERT
W. SCHROEDER
North Central Forest Experiment Station, Chicago, IL (C!S.A.) (Accepted for publication
18 February 1987)
ABSTRACT
Schroeder, H. W., 1988. Visual impact of hillside development: comparison of measurements derived from aerial and ground-level photographs. Landscape Urban Plann., 15: 119-126.
Development on forested hillsides in and around cities has a serious impact on the visual quality of the urban landscape. The amount of visual impact, however, is not necessarily easy to predict, because development may be partially masked by vegetation and terrain. In this research I made measurements of development
INTRODUCTION Forested hillsides are an important feature of the landscape of many American cities. By providing attractive views of natural and manmade features of the landscape, hillsides can enhance the quality of the city as a place to live or visit. The scarcity of undeveloped land for new construction in some cities, however, has created pressure for development on previ-
0169-2046/88/$03.50
0 1988 Elsevier Science Publishers B.V.
intensity and visual impact from aerial and ground-level photographs offorested hillsides in Cincinnati, Ohio. Observers’ ratings.of the perceived visual quality of the hillsides showed a clear preference for hillsides with little or no visible development. The amount of visual impact in ground-level views could be predicted reasonably well from aerial photographic measurements of the percentage of land area on the hillside that is artiJicially surfaced. Additional research to expand and refine these measurements could provide useful information jar guiding development policies to protect the visual quality offorested hillsides in cities.
ously unused hillside sites. Given the importance of hillsides in many city landscapes and the potential high visibility of hillside structures, city planners need to be able to evaluate the visual impact of proposed hillside development plans, and to set guidelines and limits for the intensity and kinds of development that are allowed on hillsides. Development intensity can be measured and regulated in terms of the density (buildings per
acre) of various kinds of structures, and in terms of the percentage of land area in natural or artificial ground cover. It is not necessarily easy, however. to predict how much visual impact will result from a given level of development intensity, because some of the development on the hillside may be hidden from the observer by terrain and vegetation. Also. different kinds of structures may have different effects on the viewer’s subjective perceptions of the visual quality of the hillside. Therefore, research is needed to learn how measurements of development intensity are related to visual impact and to perceived quality of urban hillsides. In this research. I compared measurements of development intensity taken from aerial photographs of urban hillsides in Cincinnati, Ohio. with measurements of visual impact taken from ground-level photographs of the same hillsides. I then used measurements of both development intensity and visual impact to predict observers’ ratings of the perceived attractiveness of the hillsides. An understanding of the relationships between these different measures should be useful to urban planners trying to anticipate and regulate the visual impact of proposed new hillside developments. There is already considerable research showing that development and man-made features detract from the perceived quality of landscapes. Kaplan et al. ( 1972 ) found that photographs of environments in which nature predominated were almost always rated higher than scenes with mainly man-made features. Studies of residential areas have shown that vegetation has a consistently positive influence and that buildings and urban development detract from visual quality (Anderson and Schroeder, 1983: Schroeder and Cannon, 1983 ). Similar results have been observed for landscapes in the Northeast (Civco, 1979 ) and for urban parks (Schroeder. 1982; Schroeder and Anderson, 1984 ) . The impact of development has been found to depend not just on the amount, but also on
the contrast, obtrusiveness, and fittingness of man-made elements in a natural environment (Wohlwill and Harris, 1980: Vining et al., 1984). A study by Brush and Palmer ( 1979) is unusual in finding that buildings, had a positive influence on perceived landscape quality. They suggest that this may be due to the character of the buildings, many of which were substantial old homes. Trees and other vegetation, however. were still the strongest positive influence on landscape quality. The study presented here was carried out in Cincinnati, Ohio. a city in which forested hillsides are a key element of the landscape. Previous studies and reports have addressed the visual characteristics of the hillsides (May and Noe. 1974 ). the perception of the hillsides by Cincinnati residents (Cincinnati Institute. 1976), and guidelines for hillside development (Cincinnati Institute. 1975). The present study is a step toward quantifying the assessment of development intensity and visual impact as a further aid in land-use planning. DATA COLLECTION Measurements of hillside development intensity were made from aerial photographs of Cincinnati’s hillsides taken in April 1982. Measures of visual impact were made from ground-level photographs taken in the summer of 1982. Important vistas and viewpoints were identified from the report by May and Noe ( 1974 ) and through consultations with personnel from the Cincinnati Urban Forestry Board and the Cincinnati City Planning Commission. Viewpoints included designated overlooks in parks as well as views from cemeteries, schools, riverbanks, and streets on the hillsides, in the valleys, and in the central basin. At each viewpoint one or more photographs were taken to capture the predominant views at the point. Approximately 150 photographs were taken from 60 viewpoints scattered over the city. From this set of photographs I selected a
121
Fig. I. View of a Cincinnati hillside with little or no development
subset of 24 views for use in this study, including some that were almost completely undeveloped (Fig. 1) and some with substantial development (Fig. 2 ). The area of hillside visible in each slide was outlined on a topographic map so that information from aerial photographs of the area could be matched to the ground-level photographs. Table 1 shows the measurements that were made from the aerial and ground-level photographs. Small structures are defined as buildings less than three stories tall and occupying a small area (e.g. single-family dwellings). Large horizontal structures are buildings less than three stories and occupying a large area (e.g. factories and schools). Large vertical structures are buildings three or more stories tall oc-
cupying any amount of area (e.g. high-rise apartments). The percentages of the photographic image showing developed features, vegetation, and bare soil were measured by projecting the ground-level photographs onto a 64x48 grid of square cells, and counting the number of grid cells occupied by each of the features. Only the portion of the photograph showing the hillside was measured; foreground features and sky were excluded. Similar methods of measuring photograph content have been used in other studies of landscape preference (e.g. Buhyoff and Leuschner, 1978; Buhyoff et al., 1982). The perceived attractiveness of the hillsides was evaluated by showing the 24 ground-level photographs to a panel of eight students in a
Ftg. 2. View ofa
heavily
developed
Cinctnnatt
hillside
T.ABLE I Dcvelopmcnt
Intensity
and visual impact
Measure
Mean
Building densttl (per acre) SMALL: small structures HORIZ: horizontal structures VERTI: vertical structures Ground cover (‘%I) 4RTIF: arttficial cover HERB.& herbaceous cover C.&NOR: tree canop) GSOIL: bare sot1 \Tistble fcaturcs (percentage VDEVE: development VVEGE: vegetation VSOIL: bare sot1 httracttvencss 1TTR.A: mean rating
measurcmcnts
1.1 I 0.09 0.01 25.25 69.87 52.91 4.87
of photographic 10.39 88.23 I .46 6.49
Standard
0.865 0.135 0.023 13.38 14.19 18.76 6.21 Image) 9.51 9.56 2.62 I.54
deviation
recreation management class at the University of Illinois at Champaign. The students rated the attractiveness of each hillside on a scale from 0 (very unattractive) to 9 (very attractive). Past research has shown that students’ ratings of landscape esthetics are usually very similar to the general public’s (Daniel and Boster, 1976). The correspondence has not. however, been specifically tested for scenes such as the ones used here. Therefore we must be somewhat cautious about generalizing from these ratings to public preferences. In particular, the students’ ratings might differ from those of long-time Cincinnati residents who would be very familiar with some of the hillside scenes. The ratings obtained for this study are probably more representative of the perceptions of
123
visitors and newcomers to Cincinnati. I averaged the eight observers’ ratings together to reflect the group’s consensus about the attractiveness of the scenes. The reliability of the mean rating can be estimated from the average correlation between individual raters, using the Spearman-Brown formula (Nunally, 1967 ). The average between-rater correlation for this group was 0.45, giving an estimate of 0.87 for the reliability of the mean attractiveness rating. ANALYSIS
AND DISCUSSION
I examined the relations among the measures of development intensity and visual impact by doing a series of statistical regression analyses, which determine how well one measurement can be predicted from a combination of other measurements. The results of the analyses are presented in Tables 2-5. Measures of herbaceous ground cover, tree canopy, and visible vegetation were not included in these analyses because they were highly correlated (i.e. redundant) with other measures. Variables were retained as predictors in the regression models if their coefficients were statistically significant, or if they produced a simultaneous increase in R and decrease in PRESS (R2 expresses the proportion of variance in the dependent variable that is accounted for by the set of independent variables used to derive the model. PRESS is a statistic that indicates the amount of error that would be expected if the model were used to predict values for a new set of data. For a given dependent variable, smaller PRESS values indicate greater predictive power). The number of predictors in any model was limited to three or less to avoid overfitting the models. The first model (Table 2) shows that the percentage of artificially surfaced land area is strongly related to the density of small structures and large horizontal structures. The density of large vertical structures is not a good predictor of extent of artificial cover, perhaps
TABLE 2 Regression of artificially surfaced land area on building densities (dependent variable: ARTIF) CONST SMALL (P) 10.00
HORIZ (P) 30.26 (0.043)
II.10
(0.000)
R’
PRESS
ADJ. F R’ (PI
0.64 0.62
18.33 (0.000)
1956
because overall there are fewer of them on these hillsides than of the other building types. The coefficients of the model indicate that, on average, an increase in density of one small structure per acre on a hillside is associated with an 11% increase in artificially surfaced ground area, and on average a large horizontal building is associated with about three times as much artiIicially surfaced area as a small structure. These amounts reflect not only the presence of the buildings themselves, but also the roads, parking lots, and other developed features that accompany them. The models in Table 3 show that visible development is better predicted by the percentage of land artificially surfaced than by building densities. Adding the density of vertical structures to this model produces a modest increase in its predictive power. The model accounts for about 50% of the variance in visible development across the 24 hillsides. To do a better job of predicting visual impact would probably require a considerably more complex model that takes into account the topography of the hillside, the spatial distribution of structures, and TABLE
3
Regression (dependent CONST
-
1.82
of visible variable: ARTIF
SMALL
VERTI
(P)
(P)
(P)
on development
0.483 (0.001)
2.89 -2.42
development VDEVE)
4.69 (0.029) 0.436 (0.001
)
122.75 (0.117) 95.48 (0.156)
R’
ADJ. HL
0.46
0.46
0.34
0.31
0.51
0.49
intensity
F
IndIces
PRE!
(P) 18.74 (0.001) 5.30 (0.014) I 1.02 (0.001)
1491 1879 1454
T.L\BLE 4 Regression
of attractivenesson
C‘ON ST
.ARTIF
SMALL
HORIZ
(1’)
(P)
(P)
-0.631 (0.046
x.34
R’
4D.l. R2 0.43
0.40
-6.07
)
- 0.061 (0.003 )
0.38
(0.008) -4.18 (0.044
0.53
0.51
)
5
Regrewon ofattractlveness able: ATTR -\ )
x. I’
indices
0.43
7.78
C‘ONST
intensity
-0.075 (O.OOl )
x.39
TqBLE
development
VDEVE (I’) -0.1’0 (0.000 )
on hlllslde features
VSOIL (1’) -0.261 (0.002 )
K2
-\DJ. R2
0.66
0.63
(dependent
F (PI 20.38 (0.000 )
vari-
PRESS
‘I.8
the masking effect of various kinds of vegetation. The coefficients of the models suggest that each 1% increase in developed surface area produces slightly less than a 0.50/o increase in visible development. This probably reflects the masking effect of vegetation and topography. Increasing the density of small structures by one building per acre increases visible development by about 5% and a large vertical building on average has roughly 20 times the impact of a small structure. Artificial cover is a better predictor of perceived attractiveness than are building densities (Table 4). The best model for predicting perceived attractiveness takes into account both the amount of artificial ground cover and the density of horizontal structures. The negative signs on the coefficients indicate that higher levels of development result in lower preference ratings. It also appears that on average a horizontal building has about ten times the impact on perceived attractiveness as does a small structure. The last model (Table 5) shows that the amounts of development and bare soil visible
(dependent
variable:
F
ATTRA)
PRESS
(1’) 16.60 (0.001 7.13 (0.005 Il.74 (0.001
37.2
) 44.3 ) 31.6
)
to a ground-level observer are strongly and negatively related to perceived attractiveness. This model is a better predictor of viewer preference than are the models based on aerial photograph measures of development intensity. This makes sense because we would expect preferences to be best predicted by what is immediately visible to the viewer. We also attempted to find models for predicting the amount of visible bare soil from aerial photograph measurements. but these were not successful. SUMMARY
AND CONCLUSIONS
This research looked at measurements of development intensity taken from aerial photohillsides. and graphs of 24 Cincinnati measurements of visual impact derived from ground-level photographs of the same hillsides. The amount of development visible at ground level could be reasonably well predicted from the aerial photographs, but development intensity is certainly not a perfect predictor of visual impact. For the observers who rated the slides in this study. development on the hillsides clearly detracted from the attractiveness of the hillsides. This result is consistent with the studies cited earlier, that show widespread preference for landscapes with vegetation and natural features rather than man-made features. If this result also holds for a majority of city residents, then an appropriate goal for hillside develop-
125
ment planning would be to minimize the visibility of development on hillsides. There may be cases in which development on a hillside would enhance the appearance of the hillside, but this did not appear to occur for the hillsides photographed in this study. Much of the variance in visual impact seems to be related to small structures such as single family homes. While large buildings such as high-rises may be important in certain views, the pervasiveness of smaller structures makes it important that their influence on the visual environment be accounted for in hillside planning. Vertical structures have a stronger impact on the extent of visible development than horizontal structures. This may be because horizontal structures are sometimes hidden from view by topography, vegetation, and other buildings. while vertical structures are more likely to be visible from ground level. Horizontal structures do, however, have a fairly strong negative impact on viewer preferences. Apparently, attractiveness is not just a function of how much development is visible, but also depends on the nature of the structures. Perhaps horizontal buildings have a stronger negative impact because when they are visible they may occupy a larger surface area than vertical buildings, giving the impression of greater impact on the hillside itself. In particular, more vegetation may be affected when constructing a horizontal structure than when constructing a vertical one. The percentage of artificially surfaced land area is the best single predictor of both visible development and attractiveness, and could be a useful criterion for evaluating the potential visual impact of a proposed development. Visible areas of bare soil also detract from the attractiveness of the hillside, but could not be predicted from the aerial photographic measures of ground cover. This may be because the most visible areas of soil in the ground photographs were the result of prominent cuts in steep slopes on the portion of hillsides closest
to the point where the photograph was taken. These cuts may have been small in relation to the total land area of the hillside, but were a prominent feature of the ground-level view. These results suggest that quantitative measures of development intensity can be useful for predicting visual impact, and could be used to help set general guidelines and policies for permissable amounts of hillside development. Clearly, more research is desirable. Detailed models could specify more completely the characteristics of hillsides and structures that influence the visibility of structures and the attractiveness of developed hillsides. The research should be replicated with additional scenes of hillsides in Cincinnati as well as in other cities, and attractiveness should be evaluated from the point of view of residents as well as non-residents of the cities in question. The present study suggests that continued research about development intensity and visual impact on urban hillsides is worth pursuing. ACKNOWLEDGEMENTS I would like to thank Steve Sandfort of the .Cincinnati Urban Forestry Board and Robert Duffy of the Cincinnati City Planning Commission for their advice and cooperation in conducting the ground-level photography for this study; and Ralph Sanders of the Northeastern Forest Experiment Station for his assistance in obtaining aerial photographic data. REFERENCES Anderson, L.M. and Schroeder, H.W., 1983. Application of wildland scenic assessment methods to the urban landscape. Landscape Plann.. IO: 219-237. Brush, R.O. and Palmer, J.F., 1979. Measuring the impact of urbanization on scenic quality: land use change in the Northeast. In: G. Elsner and R. Smardon (Editors ). Our National Landscape. USDA General Technical Report PSW-35, Pacific Southwest Forest and Range Experiment Station, Berkeley, CA, pp. 358-369. Buhyoff, G.J. and Leuschner, W.A.. 1978. Estimating psychological disutility from damaged forest stands, For. Sci., 24: 424432. Buhyoff, G.J., Wellman. J.D. and Daniel, T.C., 1982. Pre-
126
dieting scenic quality for mountain pine beetle and western spruce budworm damaged forest vistas. For. Sci.. 28: 837-838. Cincinnati Institute. 1975. Cincinnati Hillside Development guidelines. Report for the City Planning Commission, Cincinnati. OH. Cincinnati Institute, 1976. A topography of the mind: The meaning of Cincinnati hillsides. The Cincinnati Post, 9 October. special section. Civco. D.L., 1979. Numerical modeling of eastern Connecticut’s visual resources. In: G. Elsner and R. Smardon (Editors), Our National Landscape. USDA General Technical Report PSW-35. Pacific Southwest Forest and Range Experiment Station. Berkeley, CA, pp. 263-270. Daniel, T.C. and Boster. R.S.. 1976. Measuring landscape esthetics: The scenic beauty estimation method. USDA Forest Service Research Paper RM-167. Rocky Mountain Forest and Range Experiment Station, Fort Collins, co. Kaplan, S., Kaplan, R. and Wendt, J.S., 1972. Rated prefer-
ence and complexity for natural and urban visual material. Percept. Psychophys.. 12: 354-356. May, H.B. and Noe, S.V., Jr.. 1974. The visual importance of Cincinnati’s hillsides. Report for the Cincinnati Institute. Nunally, J.C.. 1967. Psychometric Theory. McGraw-Hill, New, York. Schroeder, H.W., 1982. Preferred features of urban parks and forests. J. Arboric., 8( 12): 317-322. Schroeder. H.W. and Anderson, L.M.. 1984. Perception of personal safety in urban recreation sites. J. Leisure Res.. 16: 178-194. Schroeder, H.W. and Cannon, W.N.. Jr.. 1983. The esthetic contribution of trees to residential streets in Ohio towns. J. Arboric.. 9(9): 237-243. Vining, J.. Daniel, T.C. and Schroeder, H.W.. 1984. Predicting scenic values in forested residential landscapes. J. Lcisure Res.. 16: I24- 135. Wohlwill. J.F. and Harris. G., 1980. Response to congruity or contrast for man-made features in natural-recreation settings. Leisure Sci.. 3: 349-365.
119
Visual Impact of Hillside Development: Comparison of Measurements Derived from Aerial and Ground-Level Photographs
HERBERT
W. SCHROEDER
North Central Forest Experiment Station, Chicago, IL (C!S.A.) (Accepted for publication
18 February 1987)
ABSTRACT
Schroeder, H. W., 1988. Visual impact of hillside development: comparison of measurements derived from aerial and ground-level photographs. Landscape Urban Plann., 15: 119-126.
Development on forested hillsides in and around cities has a serious impact on the visual quality of the urban landscape. The amount of visual impact, however, is not necessarily easy to predict, because development may be partially masked by vegetation and terrain. In this research I made measurements of development
INTRODUCTION Forested hillsides are an important feature of the landscape of many American cities. By providing attractive views of natural and manmade features of the landscape, hillsides can enhance the quality of the city as a place to live or visit. The scarcity of undeveloped land for new construction in some cities, however, has created pressure for development on previ-
0169-2046/88/$03.50
0 1988 Elsevier Science Publishers B.V.
intensity and visual impact from aerial and ground-level photographs offorested hillsides in Cincinnati, Ohio. Observers’ ratings.of the perceived visual quality of the hillsides showed a clear preference for hillsides with little or no visible development. The amount of visual impact in ground-level views could be predicted reasonably well from aerial photographic measurements of the percentage of land area on the hillside that is artiJicially surfaced. Additional research to expand and refine these measurements could provide useful information jar guiding development policies to protect the visual quality offorested hillsides in cities.
ously unused hillside sites. Given the importance of hillsides in many city landscapes and the potential high visibility of hillside structures, city planners need to be able to evaluate the visual impact of proposed hillside development plans, and to set guidelines and limits for the intensity and kinds of development that are allowed on hillsides. Development intensity can be measured and regulated in terms of the density (buildings per
acre) of various kinds of structures, and in terms of the percentage of land area in natural or artificial ground cover. It is not necessarily easy, however. to predict how much visual impact will result from a given level of development intensity, because some of the development on the hillside may be hidden from the observer by terrain and vegetation. Also. different kinds of structures may have different effects on the viewer’s subjective perceptions of the visual quality of the hillside. Therefore, research is needed to learn how measurements of development intensity are related to visual impact and to perceived quality of urban hillsides. In this research. I compared measurements of development intensity taken from aerial photographs of urban hillsides in Cincinnati, Ohio. with measurements of visual impact taken from ground-level photographs of the same hillsides. I then used measurements of both development intensity and visual impact to predict observers’ ratings of the perceived attractiveness of the hillsides. An understanding of the relationships between these different measures should be useful to urban planners trying to anticipate and regulate the visual impact of proposed new hillside developments. There is already considerable research showing that development and man-made features detract from the perceived quality of landscapes. Kaplan et al. ( 1972 ) found that photographs of environments in which nature predominated were almost always rated higher than scenes with mainly man-made features. Studies of residential areas have shown that vegetation has a consistently positive influence and that buildings and urban development detract from visual quality (Anderson and Schroeder, 1983: Schroeder and Cannon, 1983 ). Similar results have been observed for landscapes in the Northeast (Civco, 1979 ) and for urban parks (Schroeder. 1982; Schroeder and Anderson, 1984 ) . The impact of development has been found to depend not just on the amount, but also on
the contrast, obtrusiveness, and fittingness of man-made elements in a natural environment (Wohlwill and Harris, 1980: Vining et al., 1984). A study by Brush and Palmer ( 1979) is unusual in finding that buildings, had a positive influence on perceived landscape quality. They suggest that this may be due to the character of the buildings, many of which were substantial old homes. Trees and other vegetation, however. were still the strongest positive influence on landscape quality. The study presented here was carried out in Cincinnati, Ohio. a city in which forested hillsides are a key element of the landscape. Previous studies and reports have addressed the visual characteristics of the hillsides (May and Noe. 1974 ). the perception of the hillsides by Cincinnati residents (Cincinnati Institute. 1976), and guidelines for hillside development (Cincinnati Institute. 1975). The present study is a step toward quantifying the assessment of development intensity and visual impact as a further aid in land-use planning. DATA COLLECTION Measurements of hillside development intensity were made from aerial photographs of Cincinnati’s hillsides taken in April 1982. Measures of visual impact were made from ground-level photographs taken in the summer of 1982. Important vistas and viewpoints were identified from the report by May and Noe ( 1974 ) and through consultations with personnel from the Cincinnati Urban Forestry Board and the Cincinnati City Planning Commission. Viewpoints included designated overlooks in parks as well as views from cemeteries, schools, riverbanks, and streets on the hillsides, in the valleys, and in the central basin. At each viewpoint one or more photographs were taken to capture the predominant views at the point. Approximately 150 photographs were taken from 60 viewpoints scattered over the city. From this set of photographs I selected a
121
Fig. I. View of a Cincinnati hillside with little or no development
subset of 24 views for use in this study, including some that were almost completely undeveloped (Fig. 1) and some with substantial development (Fig. 2 ). The area of hillside visible in each slide was outlined on a topographic map so that information from aerial photographs of the area could be matched to the ground-level photographs. Table 1 shows the measurements that were made from the aerial and ground-level photographs. Small structures are defined as buildings less than three stories tall and occupying a small area (e.g. single-family dwellings). Large horizontal structures are buildings less than three stories and occupying a large area (e.g. factories and schools). Large vertical structures are buildings three or more stories tall oc-
cupying any amount of area (e.g. high-rise apartments). The percentages of the photographic image showing developed features, vegetation, and bare soil were measured by projecting the ground-level photographs onto a 64x48 grid of square cells, and counting the number of grid cells occupied by each of the features. Only the portion of the photograph showing the hillside was measured; foreground features and sky were excluded. Similar methods of measuring photograph content have been used in other studies of landscape preference (e.g. Buhyoff and Leuschner, 1978; Buhyoff et al., 1982). The perceived attractiveness of the hillsides was evaluated by showing the 24 ground-level photographs to a panel of eight students in a
Ftg. 2. View ofa
heavily
developed
Cinctnnatt
hillside
T.ABLE I Dcvelopmcnt
Intensity
and visual impact
Measure
Mean
Building densttl (per acre) SMALL: small structures HORIZ: horizontal structures VERTI: vertical structures Ground cover (‘%I) 4RTIF: arttficial cover HERB.& herbaceous cover C.&NOR: tree canop) GSOIL: bare sot1 \Tistble fcaturcs (percentage VDEVE: development VVEGE: vegetation VSOIL: bare sot1 httracttvencss 1TTR.A: mean rating
measurcmcnts
1.1 I 0.09 0.01 25.25 69.87 52.91 4.87
of photographic 10.39 88.23 I .46 6.49
Standard
0.865 0.135 0.023 13.38 14.19 18.76 6.21 Image) 9.51 9.56 2.62 I.54
deviation
recreation management class at the University of Illinois at Champaign. The students rated the attractiveness of each hillside on a scale from 0 (very unattractive) to 9 (very attractive). Past research has shown that students’ ratings of landscape esthetics are usually very similar to the general public’s (Daniel and Boster, 1976). The correspondence has not. however, been specifically tested for scenes such as the ones used here. Therefore we must be somewhat cautious about generalizing from these ratings to public preferences. In particular, the students’ ratings might differ from those of long-time Cincinnati residents who would be very familiar with some of the hillside scenes. The ratings obtained for this study are probably more representative of the perceptions of
123
visitors and newcomers to Cincinnati. I averaged the eight observers’ ratings together to reflect the group’s consensus about the attractiveness of the scenes. The reliability of the mean rating can be estimated from the average correlation between individual raters, using the Spearman-Brown formula (Nunally, 1967 ). The average between-rater correlation for this group was 0.45, giving an estimate of 0.87 for the reliability of the mean attractiveness rating. ANALYSIS
AND DISCUSSION
I examined the relations among the measures of development intensity and visual impact by doing a series of statistical regression analyses, which determine how well one measurement can be predicted from a combination of other measurements. The results of the analyses are presented in Tables 2-5. Measures of herbaceous ground cover, tree canopy, and visible vegetation were not included in these analyses because they were highly correlated (i.e. redundant) with other measures. Variables were retained as predictors in the regression models if their coefficients were statistically significant, or if they produced a simultaneous increase in R and decrease in PRESS (R2 expresses the proportion of variance in the dependent variable that is accounted for by the set of independent variables used to derive the model. PRESS is a statistic that indicates the amount of error that would be expected if the model were used to predict values for a new set of data. For a given dependent variable, smaller PRESS values indicate greater predictive power). The number of predictors in any model was limited to three or less to avoid overfitting the models. The first model (Table 2) shows that the percentage of artificially surfaced land area is strongly related to the density of small structures and large horizontal structures. The density of large vertical structures is not a good predictor of extent of artificial cover, perhaps
TABLE 2 Regression of artificially surfaced land area on building densities (dependent variable: ARTIF) CONST SMALL (P) 10.00
HORIZ (P) 30.26 (0.043)
II.10
(0.000)
R’
PRESS
ADJ. F R’ (PI
0.64 0.62
18.33 (0.000)
1956
because overall there are fewer of them on these hillsides than of the other building types. The coefficients of the model indicate that, on average, an increase in density of one small structure per acre on a hillside is associated with an 11% increase in artificially surfaced ground area, and on average a large horizontal building is associated with about three times as much artiIicially surfaced area as a small structure. These amounts reflect not only the presence of the buildings themselves, but also the roads, parking lots, and other developed features that accompany them. The models in Table 3 show that visible development is better predicted by the percentage of land artificially surfaced than by building densities. Adding the density of vertical structures to this model produces a modest increase in its predictive power. The model accounts for about 50% of the variance in visible development across the 24 hillsides. To do a better job of predicting visual impact would probably require a considerably more complex model that takes into account the topography of the hillside, the spatial distribution of structures, and TABLE
3
Regression (dependent CONST
-
1.82
of visible variable: ARTIF
SMALL
VERTI
(P)
(P)
(P)
on development
0.483 (0.001)
2.89 -2.42
development VDEVE)
4.69 (0.029) 0.436 (0.001
)
122.75 (0.117) 95.48 (0.156)
R’
ADJ. HL
0.46
0.46
0.34
0.31
0.51
0.49
intensity
F
IndIces
PRE!
(P) 18.74 (0.001) 5.30 (0.014) I 1.02 (0.001)
1491 1879 1454
T.L\BLE 4 Regression
of attractivenesson
C‘ON ST
.ARTIF
SMALL
HORIZ
(1’)
(P)
(P)
-0.631 (0.046
x.34
R’
4D.l. R2 0.43
0.40
-6.07
)
- 0.061 (0.003 )
0.38
(0.008) -4.18 (0.044
0.53
0.51
)
5
Regrewon ofattractlveness able: ATTR -\ )
x. I’
indices
0.43
7.78
C‘ONST
intensity
-0.075 (O.OOl )
x.39
TqBLE
development
VDEVE (I’) -0.1’0 (0.000 )
on hlllslde features
VSOIL (1’) -0.261 (0.002 )
K2
-\DJ. R2
0.66
0.63
(dependent
F (PI 20.38 (0.000 )
vari-
PRESS
‘I.8
the masking effect of various kinds of vegetation. The coefficients of the models suggest that each 1% increase in developed surface area produces slightly less than a 0.50/o increase in visible development. This probably reflects the masking effect of vegetation and topography. Increasing the density of small structures by one building per acre increases visible development by about 5% and a large vertical building on average has roughly 20 times the impact of a small structure. Artificial cover is a better predictor of perceived attractiveness than are building densities (Table 4). The best model for predicting perceived attractiveness takes into account both the amount of artificial ground cover and the density of horizontal structures. The negative signs on the coefficients indicate that higher levels of development result in lower preference ratings. It also appears that on average a horizontal building has about ten times the impact on perceived attractiveness as does a small structure. The last model (Table 5) shows that the amounts of development and bare soil visible
(dependent
variable:
F
ATTRA)
PRESS
(1’) 16.60 (0.001 7.13 (0.005 Il.74 (0.001
37.2
) 44.3 ) 31.6
)
to a ground-level observer are strongly and negatively related to perceived attractiveness. This model is a better predictor of viewer preference than are the models based on aerial photograph measures of development intensity. This makes sense because we would expect preferences to be best predicted by what is immediately visible to the viewer. We also attempted to find models for predicting the amount of visible bare soil from aerial photograph measurements. but these were not successful. SUMMARY
AND CONCLUSIONS
This research looked at measurements of development intensity taken from aerial photohillsides. and graphs of 24 Cincinnati measurements of visual impact derived from ground-level photographs of the same hillsides. The amount of development visible at ground level could be reasonably well predicted from the aerial photographs, but development intensity is certainly not a perfect predictor of visual impact. For the observers who rated the slides in this study. development on the hillsides clearly detracted from the attractiveness of the hillsides. This result is consistent with the studies cited earlier, that show widespread preference for landscapes with vegetation and natural features rather than man-made features. If this result also holds for a majority of city residents, then an appropriate goal for hillside develop-
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ment planning would be to minimize the visibility of development on hillsides. There may be cases in which development on a hillside would enhance the appearance of the hillside, but this did not appear to occur for the hillsides photographed in this study. Much of the variance in visual impact seems to be related to small structures such as single family homes. While large buildings such as high-rises may be important in certain views, the pervasiveness of smaller structures makes it important that their influence on the visual environment be accounted for in hillside planning. Vertical structures have a stronger impact on the extent of visible development than horizontal structures. This may be because horizontal structures are sometimes hidden from view by topography, vegetation, and other buildings. while vertical structures are more likely to be visible from ground level. Horizontal structures do, however, have a fairly strong negative impact on viewer preferences. Apparently, attractiveness is not just a function of how much development is visible, but also depends on the nature of the structures. Perhaps horizontal buildings have a stronger negative impact because when they are visible they may occupy a larger surface area than vertical buildings, giving the impression of greater impact on the hillside itself. In particular, more vegetation may be affected when constructing a horizontal structure than when constructing a vertical one. The percentage of artificially surfaced land area is the best single predictor of both visible development and attractiveness, and could be a useful criterion for evaluating the potential visual impact of a proposed development. Visible areas of bare soil also detract from the attractiveness of the hillside, but could not be predicted from the aerial photographic measures of ground cover. This may be because the most visible areas of soil in the ground photographs were the result of prominent cuts in steep slopes on the portion of hillsides closest
to the point where the photograph was taken. These cuts may have been small in relation to the total land area of the hillside, but were a prominent feature of the ground-level view. These results suggest that quantitative measures of development intensity can be useful for predicting visual impact, and could be used to help set general guidelines and policies for permissable amounts of hillside development. Clearly, more research is desirable. Detailed models could specify more completely the characteristics of hillsides and structures that influence the visibility of structures and the attractiveness of developed hillsides. The research should be replicated with additional scenes of hillsides in Cincinnati as well as in other cities, and attractiveness should be evaluated from the point of view of residents as well as non-residents of the cities in question. The present study suggests that continued research about development intensity and visual impact on urban hillsides is worth pursuing. ACKNOWLEDGEMENTS I would like to thank Steve Sandfort of the .Cincinnati Urban Forestry Board and Robert Duffy of the Cincinnati City Planning Commission for their advice and cooperation in conducting the ground-level photography for this study; and Ralph Sanders of the Northeastern Forest Experiment Station for his assistance in obtaining aerial photographic data. REFERENCES Anderson, L.M. and Schroeder, H.W., 1983. Application of wildland scenic assessment methods to the urban landscape. Landscape Plann.. IO: 219-237. Brush, R.O. and Palmer, J.F., 1979. Measuring the impact of urbanization on scenic quality: land use change in the Northeast. In: G. Elsner and R. Smardon (Editors ). Our National Landscape. USDA General Technical Report PSW-35, Pacific Southwest Forest and Range Experiment Station, Berkeley, CA, pp. 358-369. Buhyoff, G.J. and Leuschner, W.A.. 1978. Estimating psychological disutility from damaged forest stands, For. Sci., 24: 424432. Buhyoff, G.J., Wellman. J.D. and Daniel, T.C., 1982. Pre-
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dieting scenic quality for mountain pine beetle and western spruce budworm damaged forest vistas. For. Sci.. 28: 837-838. Cincinnati Institute. 1975. Cincinnati Hillside Development guidelines. Report for the City Planning Commission, Cincinnati. OH. Cincinnati Institute, 1976. A topography of the mind: The meaning of Cincinnati hillsides. The Cincinnati Post, 9 October. special section. Civco. D.L., 1979. Numerical modeling of eastern Connecticut’s visual resources. In: G. Elsner and R. Smardon (Editors), Our National Landscape. USDA General Technical Report PSW-35. Pacific Southwest Forest and Range Experiment Station. Berkeley, CA, pp. 263-270. Daniel, T.C. and Boster. R.S.. 1976. Measuring landscape esthetics: The scenic beauty estimation method. USDA Forest Service Research Paper RM-167. Rocky Mountain Forest and Range Experiment Station, Fort Collins, co. Kaplan, S., Kaplan, R. and Wendt, J.S., 1972. Rated prefer-
ence and complexity for natural and urban visual material. Percept. Psychophys.. 12: 354-356. May, H.B. and Noe, S.V., Jr.. 1974. The visual importance of Cincinnati’s hillsides. Report for the Cincinnati Institute. Nunally, J.C.. 1967. Psychometric Theory. McGraw-Hill, New, York. Schroeder, H.W., 1982. Preferred features of urban parks and forests. J. Arboric., 8( 12): 317-322. Schroeder. H.W. and Anderson, L.M.. 1984. Perception of personal safety in urban recreation sites. J. Leisure Res.. 16: 178-194. Schroeder, H.W. and Cannon, W.N.. Jr.. 1983. The esthetic contribution of trees to residential streets in Ohio towns. J. Arboric.. 9(9): 237-243. Vining, J.. Daniel, T.C. and Schroeder, H.W.. 1984. Predicting scenic values in forested residential landscapes. J. Lcisure Res.. 16: I24- 135. Wohlwill. J.F. and Harris. G., 1980. Response to congruity or contrast for man-made features in natural-recreation settings. Leisure Sci.. 3: 349-365.