Assessment of landscape preferences is widely studied in environmental perception research. Landscape preference studies aim to investigate how and why people prefer some environments to others. People judge and interpret their environments and they respond to environments in terms of affective responses. Environmental preference is not luxury for people but essential and tied to basic concerns (R. Kaplan & S. Kaplan, 1989). Kaplan sees preference as an indicator of aesthetic judgment (1988b) and as a complex process which involves perception of things and space and reacting to them in terms of their potential usefulness and supportiveness (1988a). According to Charlesworth (1976), species has to be able to both recognize and prefer environments in which it functions well (S. Kaplan, 1988a). Preference for specific landscapes is about the organization of the space, rather than the individual elements (R. Kaplan & S. Kaplan, 1989), hence designers should focus on the integrity of different landscape elements.
The bio-evolutionary perspective on landscape preferences were explained in the previous section (Section 2.2): long history of human evolution is believed to be the reason for why we prefer some environments to others. One consistent finding of environmental preference research is that people prefer naturalness or natural environments to human-modified environments (e. g. R. Kaplan & S. Kaplan, 1989; van den Berg et al., 2003). Presence of water also increases the preference ratings (Hull & Stewart, 1995; Yang & Brown, 1992). Natural scenes are also assumed to contribute to well-being by reducing stress levels, and to have positive influence on functioning and behavior (Ulrich et al., 1991). It is assumed that preferences for savanna-like landscapes are linked to human evolutionary history, as an adaptation to East Africa savannas for survival (Falk & Balling, 2010). Ulrich (1979) found that homogenous ground texture, medium to high levels of depth, presence of a focal point, and moderate levels of mystery leads to high level of preferences in natural scenes (Porteous, 1996).
Complexity has been one of the central concepts in environmental preference research. Although R. Kaplan & S. Kaplan (1989) have found that coherence is more significant in explaining preferences, Ode & Miller (2011) suggest that landscape preferences have a relationship between measurements of complexity. Their study on rural landscapes showed that "a landscape with an unequal distribution of land cover, a moderate amount of land cover, and a low level of aggregation is more likely to be preferred over a landscape with many land-cover classes, equal distribution, and strong aggregation”. Complexity is also found to have a positive influence on urban landscape preference (Falk & Balling, 2010).
Environmental preference research generally focuses on natural or rural environments and there is little research on urban landscape preferences. This might be due to the fact that urban environments are highly complex structured; there are too many kinds of elements (both natural and cultural) that form urban structure. Moreover, social dynamics have important influence on shaping urban environments. Hence, it is rather difficult to measure and to assess landscape preference determinants in urban landscapes. One of the preference studies in urban environment was conducted by Nasar and his colleagues (1988a). They investigated the visual preferences for urban street scenes. Nasar used bipolar adjectives to describe the environments; closed-open, simple-diverse, chaotic-orderly, dilapidated-well – kept, vehicles prominent-vehicles not in sight, and nature (greenery) not in sight – nature (greenery) prominent. He found (just like he expected) that people preferred ordered, natural, well-kept, and open scenes with vehicles not prominent. However, Nasar was cautious about the interdependence of the variables; he expressed the need for further research for explanation of the relationship between these variables. Nevertheless Nasar suggested that moderate novelty, increase diversity, increased contrast among buildings, good maintenance, order, more vegetation and reduced vehicle prominence might produce highly preferred urban environments.
According to Bourassa (1990), aesthetic response occurs at both biological and cultural levels. Falk & Balling (2010) also state that ‘‘that human landscape preferences is best understood as a continuous progression of aesthetic ideals, tempered by social convention, passed on from one generation to the next through human culture”. But do culture and socio-demographic factors really affect preferences? There are several cross-cultural studies that investigate preferences for landscapes and landscape elements. Generally, the results show that despite cultural differences, people seem to have similar preferences for specific landscapes; however the concepts of novelty and familiarity can affect preferences for people from different cultures. Familiarity plays an important role in feeling secure and safe. People feel comfortable and relaxed in environments which they are familiar to (Kaplan et al., 1998). On the other hand too much familiarity may become boring and people seek for novelty. For instance, Yang & Brown (1992) found that traditional Japanese style landscapes and water presence were highly preferred by people from both Korean and Western cultures. However they also found that while Koreans preferred Western style landscapes, Western tourists preferred Korean style landscapes. A similar result was found by Nasar (1988a). His study results showed that although there were consensus on preferences for ordered, natural, open and well-kept scenes; Japanese subjects highly preferred the American scenes and vice versa. His findings supported Berlyne’s assumption that people prefer novelty to familiarity. He also pointed out that the results would have been different if subjects had been chosen from older population since Sonnenfeld (1966) claims that younger people prefer novelty and others familiarity (Nasar, 1988a). In their study Yang &Kaplan (1990) investigated landscape style preferences of Korean and Western individuals. They found a cross-cultural similarity in preferences in favor of landscapes with natural styles. Landscapes with rectangular or formal designs were less preferred by both groups.
Lyons (1983) showed that there is a strong relationship between age, gender, residential experience and landscape preferences. She found that preference levels changed in different age groups, adolescent male and females had different preferences, urban and rural residents had different preferences, familiar vegetational biomes were preferred highest, and there was no evidence that landscape preferences were shaped by innate or evolutionary factors. Yu (1995) also reported that people from different living environments (rural vs. urban) had different preferences; rural residents had high preference for novelty and modernity. He also indicated that landscape preferences were strongly influenced by education levels. However, his findings did not show any significant relation between gender and preferences.
Landscape preference studies are generally based on public or user (non-expert) evaluations. Ranking, rating or sorting of visual stimuli and verbal instruments are popular tools in determination of landscape preferences. Participants are asked to rank, rate or sort visual stimuli according to their preferences. The outcomes can be evaluated in terms of most and least preferred scenes, preference predictors (e. g. coherence, diversity, naturalness etc.), correlations between preference and predictors, content analysis of preferred environments or comparison of different landscape characteristics.
Although photographs and slides have been widely used as visual stimuli in preference research, there has been a constant debate on the representational validity of them. While some researchers have found that photographs can be adequate and valid resources to use (Dunn, 1976; Shuttleworth, 1980; Stewart et al., 1984), some others do not agree with this idea (Kroh & Gimblett, 1992; Scott & Canter, 1997). R. Kaplan (1985) points out that use of photographs is less in cost and easy to administer, however sampling of the environments and selection of photographs require careful attention. In-situ assessments are time consuming, expensive and not practical. Besides, other variables of the landscape, (such as air condition and brightness) may vary during assessments and that might affect visual preference judgments of observers. On the other hand, a landscape is definitely more than just a scene and it is dynamic, however photographs and slides reflect landscapes as more static. Sevenant & Antrop (2011) state that depending on the character of the landscapes, some vistas are better presented by panoramic photographs, while some by normal photographs; thus, horizontal angle of view should be considered while selecting photographs. Palmer & Hoffman (2001) also support using panoramic images to increase validity. They also suggest that comparing the ratings of representations and actual field conditions from several individuals would help to establish validity of representations.
Current technology allows further visualization techniques such as computer graphics, 3Dmodelling, virtual reality, GIS-based photorealistic visualisation, etc. (Sevenant & Antrop, 2011). However, validity issues remain the same. In their study, Bishop & Rohrmann (2003) concluded that "computer simulations do not necessarily generate the same responses as the corresponding real environment". On the other hand, detailing seems to be an important aspect in computer visualizations; higher detail levels are believed to increase the validity (Bishop & Rohrmann, 2003; Daniel & Meitner, 2001). Nevertheless photographs still seem to be the most popular tools as surrogates for actual landscapes. However as concern about validity increases, researchers will need to prove reliability of their results and we’ll see much more debate on this issue.
Alternatively, sometimes verbal instruments such as verbal descriptions and bipolar adjective scales are used for assessment of landscape preferences. People can explain their preferences better by using words rather than rating or ranking visual stimuli. Although verbal assessments are quick and low-cost, analysis of the data may not be easy. Different people may use different adjectives or descriptions for the same preference judgment. Therefore content analysis of verbal descriptions should be done by experts or trained individuals in order to improve accuracy of results. On the other hand bipolar adjective lists, or semantic differentials, have been criticized for presenting adjectives selected by the researcher and therefore limiting people. However, Echelberger (1979) states that semantic differential may contribute to landscape preference assessment. On the contrary R. Kaplan (1985) claims that using adjectives does not tell much about preferences.
