Tourists’ landscape perceptions and preferences in a Scandinavian coastal region

Landscape and Urban Planning 91 (2009) 202–211

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Tourists’ landscape perceptions and preferences in a Scandinavian coastal region Aslak Fyhri a,∗ , Jens Kristian Steen Jacobsen a , Hans Tømmervik b a b

Institute of Transport Economics, Gaustadalleen 21, 0369 Oslo, Norway Norwegian Institute for Nature Research (NINA), 9296 Tromsø, Norway

a r t i c l e

i n f o

Article history: Received 12 February 2008 Received in revised form 6 January 2009 Accepted 12 January 2009 Available online 12 February 2009 Keywords: Conceptualisation Sorting task Multidimensional scaling Re-growth Reforestation Typicality

a b s t r a c t European cultural landscapes have been subject to change since the middle of the twentieth century, and among the most significant alterations are general re-growth, reforestation, and overgrowth. Such changes might lead to landscape loss for locals and deterioration of vistas for sightseeing holidaymakers. This article responds to a lack of academic research on landscape perceptions among tourists. The main objective is to explore international tourists’ landscape perceptions in a coastal area in northern Scandinavia, focusing on three different concepts thought to be important for tourists’ landscape preferences: typicality, vegetation lushness, and degree of human influence. A combination of free and directed sorting procedures was employed. Quantitative and categorical data derived from the multiple sorting methods were subjected to multidimensional scaling analysis. The results indicate that foreign tourists might have an understanding of re-growth in the case area. Preference ratings gave mixed results in relation to vegetation and human influence as important features for landscape preferences, as found in previous studies. The findings emphasise the need for taking into consideration typicality of setting in future landscape research. © 2009 Elsevier B.V. All rights reserved.

1. Background European landscapes have been subject to change during the last few decades, both physically and perceptually, related to alterations of local ways of life and dissemination of romantic attitudes towards nature (Wang, 2000). Among the most significant visual changes in many parts of Europe during this period are general re-growth and overgrowth (Losvik, 1999; Mottet et al., 2006). Both in Norway and in several other European countries, changes in agricultural practices since the 1950s have led to a number of alterations in the biological diversity of flora (Jensen et al., 2001; Sickel et al., 2004) and fauna (Tømmervik et al., 2004; Fry et al., 2004; Tombre et al., 2005). This is mainly caused by more intensive use of the most productive land, over-fertilisation, invasive plants, spruce plantations, and most importantly abandonment with subsequent natural reforestation of marginal agricultural land (Sickel et al., 2004). Besides changes associated with agriculture, climate variations have affected the vegetation composition of land cover. For instance, indications of increased greenness (expanding forest areas) in northern Norway have been detected through weather satellite data (Myneni et al., 1997; Zhou et al., 2001; Karlsen et al., 2006).

∗ Corresponding author. Tel.: +47 22573800; fax: +47 22570290. E-mail addresses: [email protected] (A. Fyhri), [email protected] (J.K.S. Jacobsen), [email protected] (H. Tømmervik). 0169-2046/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.landurbplan.2009.01.002

Such changes may have consequences for several landscape stakeholders, including tourists and tourism-related enterprises, as overgrowth might lead to among other things deterioration of vistas for sightseeing holidaymakers. For numerous urban travellers, certain landscapes represent a vital missing element of the modern present: a readily identifiable “shrine to the past” (Lowenthal, 1982), thus constituting a livelihood for tourism-related industries with finite accessible and safe locations at their disposal (Krippendorf, 1984). Much previous research on tourism has concentrated on the sense of sight (e.g. Urry, 1990), to see for oneself, as vision is a dominant mode of consciousness in the modern world. According to Simmel (1968, p. 486), modern social life implies an increasing significance of purely visual impressions. Sightseeing has even been regarded as a fundamental ritual in modern society (MacCannell, 1976) and “landscape” seems to be one of the most important kinds of locations in contemporary non-urban tourism (Jacobsen, 2001). While general studies of landscape perceptions and scenic beauty represent some well-established traditions, only very few academic studies of tourists’ perceptions of and preferences for agriculturally related landscapes have been conducted (Squire, 1994), and even fewer encompassing visualisations of landscapes and conducted through representational options such as photographs. Consequently, little is known about how more or less “ordinary” people perceive the landscapes they experience during their holiday tours (Norton, 1996). In addition, Koch and Jensen (1988) have shown that nature management staff and several other landscape-related decision-makers have relatively unclear

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comprehension of various recreationists’ landscape preferences, indicating the importance of the research reported here. In the tourism-related industries in northern Norway, there has recently been a growing concern that visual landscape changes such as regrowth and reforestation of previously open farmland might make the region less attractive to tourists (Arnstad, 2006). Predominantly, it has been assumed that decline in coastal agriculture might influence foreign and domestic visitation negatively since re-growth and reforestation might reduce tourist views from the roads. Moreover, some tourism industry representatives have thought that fewer active farms might make the northerly littoral less interesting for sightseeing holidaymakers, particularly passengers on coastal liners (Norsk Rikskringkasting, 2004). The present study thus responds to a deficiency of empirical academic research on how agriculturally related landscapes are interpreted by visiting non-local holidaymakers. The main focus here is on international tourists’ perceptions, preferences and assessments of re-growth and overgrowth in two northerly archipelagos. In doing so, the study throws light on an important theme within previous landscape research: the assumed preference people show for more or less natural landscapes above more human-influenced landscapes. In the following paragraphs we will review some of the previous literature that may throw light on this topic. 1.1. Landscape perception among tourists Within landscape perception research, there are several traditions and paradigms (Zube et al., 1982; e.g. Bourassa, 1990). Two of them, the psychophysical and the cognitive paradigms, seem particularly relevant for the study of tourists’ landscape perceptions and preferences. Within the psychophysical and the cognitive paradigms, several studies have shown which qualities or factors of given landscapes that can best predict aesthetic preferences. One of the key distinctions in Western peoples’ environmental perceptions is that between natural scenes and built scenes (Kaplan and Kaplan, 1989). A number of studies have found that landscapes that are perceived as natural are considered more scenic than clearly human-influenced (cultural) landscapes (Zube et al., 1982; Hull and Revell, 1989; Ulrich et al., 1991; Purcell et al., 1994; Kent and Elliot, 1995; Real et al., 2000). Nevertheless, the distinction between natural scenes and human-influenced scenes might sometimes be problematic or blurred, as it is not always clear to respondents if and how landscapes are human influenced. Clearly related to the issue of perceived naturalness is the argument that degree of vegetation is an important feature for landscape perception. The presence of trees is often found to be valued (Yang, 1992), and scenes with much foliage are preferred (Abelló and Bernáldez, 1986). In general, lavish vegetation is considered as an important element in what has been termed a “prototype of beauty” (Múgica and DeLucio, 1996). Forests have also been found to be a favoured type of landscape among some holidaymakers, in the Greek littoral (Eleftheriadis et al., 1990). It has also been suggested that the strongest preference is for landscapes with an optimal, rather than a high, level of vegetation or forestation, and Hunziker (1995) has lent support to this line of reasoning through qualitative interviews with tourists and local residents in Switzerland. Even if common preferences for more or less untamed nature areas with little visible human influence have been established, it might be argued that agricultural activities should be considered as a special case of human impact. Early studies in Connecticut River Valley found increased preferences for rural agricultural landscapes (Zube et al., 1974). This is perhaps not surprising, as farm tourism has ideological roots in the romanticism of nature (Nilsson, 2002). For instance, a study of visual preferences for traditional and

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agrarian landscape scenes among Norwegian students found strong preferences for traditional human-influenced settings (Strumse, 1996). Often, landscapes characterised by small-scale and traditional farms are perceived as rural idyll (Bell, 2006) and this might well be the situation for many tourists. Moreover, Salzman (2000) has elucidated conflicts between locals’ and visitors’ perceptions of agriculturally related landscapes. In a study of preferences for traditional Swiss cultural landscapes, Gehring (2006) found that afforested landscape images were preferred to a greater extent than more traditional or typical cultural landscape images. However, the most afforested images in Gehring’s study retained a degree of cultural landscape (winding country roads, meadows, houses, a church, and low bushes and flowers at the roadside). Both tourists and locals were more positive to a local farm that maintained its status as an agricultural enterprise than they were to the same farm turned into a tourism facility. The third variety in this study, the same farm as abandoned, was the least preferred by both locals and tourists alike (Gehring, 2006). In relation to tourists’ landscape interests, the perception of typicality (Andsager and Drzewiecka, 2002) or prototypicality might be of particular interest. A number of studies have looked at typicality in relation to preference formation (Wellman and Buhyoff, 1980; Purcell, 1987; Herzog and Stark, 2004; Roth, 2006), and two opposing models have been proposed to explain the relationships: preference-for-prototypes and preference-for-differences. A review of such studies concluded that there is mixed evidence for both models (Peron et al., 1998). Moreover, a study of variability in differences in preference scores among a random sample of Swedes judging a typical Swedish landscape type (pastureland) (Hägerhall, 2001) found that mean preference raised with increased typicality of a setting. A later study (Herzog and Stark, 2004) replicated these findings. It should be noted that this positive correlation probably only existed for what was considered a positively valued setting category (such as parks). For a negatively valued category, the relationship was the opposite, that is, increased typicality led to decreased preference. Moreover, mode of experience also influences peoples’ perceptions, preferences, and assessments (Hartig and Staats, 2006). For instance, being a tourist has been associated with various modes of experience (Cohen, 1979), some of them influenced by romanticism, with a focus on aesthetic pleasure (Taylor, 1994; Jacobsen, 1994) and scenic experiences (Towner, 1985). It is here essential to keep in mind that sightseers often sense the external world by relying heavily on picture media such as film and television, literary texts, and other tourists’ accounts as well as tourism advertising (Krippendorf, 1984; Dann, 1999; GalaniMoutafi, 2000). At a fundamental level the current study aims at improving our understanding of how people construe their physical surroundings. The ability to categorise the environment is crucial for one’s ability to interact with the social and physical world (Kelly, 1955; Rapoport, 1982). Preference can be defined as ‘liking one thing more than another’, and is the result of an evaluative judgement aimed at a specific object. Thus, a number of studies have looked at the connection between conceptualisations and preference. Young (1978) showed that enjoyment of buildings was positively linked with the ability to understand their function. Wilson (1996) demonstrated that architectural students, who previously categorised buildings into four main stylistic categories, subsequently based their stylistic evaluations on these categories, a finding that was later replicated in a cross-cultural comparison of Norwegian and British architects (Fyhri, 1994). All of these studies have been concerned with architectural preference. Expanding this approach to include a consideration of landscape perceptions may therefore be useful to improve our knowledge of basic cognitive processes.

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1.2. Objectives

3. Method

Based on these previous findings, the objective of this study is to explore foreign tourists’ perceptions, preferences, and assessments of agriculturally related coastal landscapes in two archipelagos in northern Scandinavia. The study aims at attaining a classification of landscapes that international visitors most likely will encounter on a summer season trip in this region. Moreover, the classifications obtained through this procedure are used as a basis for understanding the preference assessments people give to the same landscapes. In doing so, the study aims at further exploring the proposed relationship between initial conceptualisations and preference. The study focuses on three different concepts thought to be vital for landscape preferences and especially relevant to tourists: typicality, vegetation (degree of re-growth, reforestation), and degree of human influence. The study further aims at gaining practical insight into international tourists’ perceptions, preferences and assessments of re-growth and overgrowth as a phenomenon, and to elaborate on previous research results showing a general preference for more or less natural landscapes above more human-influenced landscapes.

In this study, photo-based sorting procedures were employed, and the sorting interviews were conducted among international tourists while they were visiting the case region. In most research, landscape quality is indicated by the human observers’ expressions of preference (choice, like/dislike) or judgements/ratings of visual aesthetic quality (including scenic quality, visual quality, and scenic beauty) (Daniel and Meitner, 2001). However, in order to gain a deeper understanding of people’s evaluations and preferences, the techniques that have frequently been employed, such as semantic differentials and bipolar adjective scales, are not necessarily the most suitable (Wilson, 1996; Scott and Canter, 1997). By using researcher-imposed concepts, information about how people conceptualise and what they consider to be important might be lost. In order to permit flexible exploration of landscape impressions one might use approaches that allow the participants to make their own classifications (Canter et al., 1985; Dann, 1995; Jacobsen and Dann, 2003) rather than relying on constructs provided by the researchers. One often-used approach is to pre-select adjectives and items to be used from previous qualitative interviews (e.g. focus groups), and then to apply these to quantitative interviews. Another method that potentially handles the qualitative and quantitative aspects of the topic of study at the same time is the photo-based sorting procedure. Sorting procedures, employing pre-selected photographs as stimuli, have proved to be effective in eliciting environmental conceptualisations and judgments (Zube et al., 1974; Zube and Isgur, 1975; Canter and Monteiro, 1993; Hubbard, 1996; Wilson, 1996; Scott and Canter, 1997; Wilson and Mackenzie, 2000; Real et al., 2000; Green, 2005). In the case of tourism-related landscapes, photographic sorting techniques have mainly been employed to study how people categorise, describe, and evaluate the settings in question (Philipp, 1994; Fairweather and Swaffield, 2001; Green, 2005). Such sorting tasks might be forced or unforced, or directed or free (Scott and Canter, 1997). The free sorting task is based on the assumption that people’s initial categorisations are taken as true representations of their actual perceptions of a landscape, in other words, making it possible to use it as basis for testing what kind of features that are most important to landscape perceptions. The method has a theoretical basis in Kelly’s (1955) personal construct theory, that people’s preferences and evaluations derive from constructs that they hold about the world, or more specifically, about the landscapes they encounter. The validity of the use of photographic representations of environments has been well established in comparative methodological research (Shuttleworth, 1980; Kellomäki and Savolainen, 1984). Several challenges occur when an approach normally used in laboratories, homes, classrooms and offices is transferred to field research among itinerant travellers. While many photo-based landscape research interviews in laboratories and other off-site settings last for 1 h or more, it is generally considered difficult to conduct personal interviews of any length with holidaymakers (Harrison, 2003). Then again, one of the strengths of such qualitative en route or on-site research is its “naturalism”, contrasting more “artificial” experiments in laboratories. In the present research, both interview time constraints and other practical limitations (e.g. typical table sizes on board the coastal liners) implied that the number of photographs had to be restricted.

2. The study area The archipelagos of Lofoten and Vesterålen in northern Norway, with both alpine and agricultural shores, fjords, sounds, and small fishing hamlets, include highly valued landscapes of importance to international tourists. Additionally, the shipping lane used by the express coastal liner (Hurtigruten) leads through this region. A recent study revealed four primary elements in the foreign tourism image of northern Norway, among them being one of these neighbouring archipelagos (Lofoten), and what has been described as wild and alpine coastal landscapes (Jacobsen et al., 1998), demonstrating the tourism value of the case region. Moreover, visitors to these islands represent substantial revenue (Dybedal, 2003). Several of the agriculturally related landscapes in the archipelagos are found in areas that numerous visitors travel through or by, such as the strandflats along the sounds between the islands. Typical challenges related to contemporary changes of the Scandinavian littoral are found in the area, such as re-growth of areas that were previously used as meadows and grassland. Based on preceding analyses (Rekdal et al., 2001; Tombre et al., 2005), several landscape components with high ecological and cultural significance and importance for the region have been identified: sea shore meadows, agricultural land including meadows, pasturelands, cultural heritage areas/sites, transitional forests, deciduous forests, and coastal and mountain heaths. Most of the farmland in the islands of Lofoten and Vesterålen is artificial and semi-natural grassland and pastureland used for sheep, cattle grazing, and hay production. Decrease and change in agricultural land and semi-natural pastureland since the mid-1980s is significant (Tombre et al., 2005). Re-growth and reforestation areas included in the present study consist of less productive and abandoned tufted-hair-grassland on poor and medium rich soils as well as abandoned meadows completely dominated by herb species such as Cow-Parsley, Northern Dock or Rosebay Willowherb on rich and nitrogen-rich soils. Such meadows have not been re-cultivated or harvested for 7–10 years (Jensen et al., 2001). Some 10 years after the last harvest the same type of fields are often reverting back to forests, beginning with scattered scrubs and small trees. Some 25–30 years after the last harvest, the outcome is often a closed forest (Jensen et al., 2001). Forests and woodlands in the study region (and often surrounding cultivated fields) consist mainly of tall herb woodland and bilberry woodland (Rekdal et al., 2001).

3.1. Images of tourism landscapes As a basis for the sorting procedures in this study, 12 colour photographs (13 cm × 18 cm) were employed, all presenting landscapes that tourists are likely to encounter if they travel along

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or in the vicinities of the route of the express coastal liner (Hurtigruten) through the archipelagos of Lofoten and Vesterålen (see photographs in Appendix A). One of the challenges in this study was related to the selection of photographs, because the case region encompasses landscapes with many potentially valued aspects. Previous research has for instance found preference for water as scenery (Zube et al., 1982; Smardon, 1987; Kent and Elliot, 1995). Within the budget and the time frames of the present project, it was not possible to obtain a set of relevant quality photographs that systematically varied on degree of water present, alongside with all other possible varieties of important influential features on tourists’ landscape perceptions and preferences, and would also be beyond the scope of the study. The majority of the photographs were thus manipulated to include the same type of background and/or foreground, predominantly water and some moderately spectacular mountains but also more or less similar light conditions. Besides agriculturally related landscapes representing various stages of re-growth and reforestation, representations of active agriculture (sheep grazing and hay production) were also included. Moreover, in order to anchor the agriculturally related landscapes that were the focal point in the present study, the sorting material also included three representations of landscapes (photo numbers 4, 6, and 9) that were found to epitomise how the study area regularly is portrayed in travel media such as guidebooks (Jacobsen et al., 1998; Dann, 2003). A local professional provided most of the photographs, partly on order, and an expert graphic designer manipulated the majority of them electronically. A wider range of photographs were tested in a pilot study involving a number of sorting interviews with foreign tourists in other parts of Norway. Photographs that participants in the pilot study judged to be too similar or of too low photographic quality were excluded in the process of narrowing the sample to 12 pictures. In contrast, previous studies based on similar procedures have used images in numbers ranging from 16 to 26 (Hubbard, 1996; Green, 1999; Wilson and Mackenzie, 2000). Even if the number of images in the current study is on the lower side it still seems acceptable for making substantial interpretations of the case, that is, agriculturally related and partly re-grown and reforested littoral landscapes of northern Europe. Increasing the number of images by using smaller photographs was also tested in a pilot study, but this solution was rejected as the pictures failed to elicit enough variance in the responses given by the test persons. Most of the test persons also stated verbally that they did not feel the smaller images were valid representations of the landscape in question. 3.2. Procedure The interviews with foreign tourists were carried out within the archipelagos in the early part of the peak period of the summer season, from 27 June to 6 July 2005. The participants were 43 international tourists visiting the Vesterålen and Lofoten archipelagos as part of their holiday tour in northern Norway. Two main strata of international visitors were asked to participate: express coastal liner passengers (21 holidaymakers interviewed on board certain liners) and motorists and motorcyclists, interviewed along the roads, on board car ferries, and at shoreline camping sites (22 road tourists). The sampling of interviewees was dependent on the visitors’ accessibility in a given situation, for instance motorists who had pulled off at rest areas and coastal liner passengers at moments when the ships were far from the main shoreline sights. The respondents were approached and interviewed individually. Nine persons declined to participate in the study even if they had the time. Most interviewees enjoyed participating, specifically in the photo sorting tasks.

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The qualitative personal interviews (sorting procedures) were conducted solely in, or in a combination of, the following languages: English, German, Swedish, Danish, and Norwegian. Additionally, some respondents supplemented a few of their statements with expressions in French. Some interviews were carried out in two languages, English and German (for German, Swiss, and Dutch visitors), or Swedish and English (for Finnish respondents). For those interviewees who did not have English, German, Swedish or Danish as their mother tongue, there were obvious, though not insurmountable, limitations in how they could express themselves. Even so, the sorting schedule and the questionnaire were designed to cater for cultural differences among the respondents, since it was acknowledged that multicultural research requires special considerations (Manaster and Havighurst, 1972; Becker and Murrman, 2000). The interviews mostly took from 20 to 35 min, even if a few of them lasted less than 15 min. The interviewees came from different countries: 2 from the United States, 7 from Nordic countries, 17 from Germany, and the remaining 17 from other northern European countries (UK, Poland, Switzerland and The Netherlands). Some 43% of the respondents were females, and 57% were men. The mean age was calculated to be 53 years. One third of the respondents had grown up in a rural area, one third in a suburban area and one third in an urban area. Distribution for present living environments was almost the same as childhood environments, with, however, a somewhat larger proportion living in suburban areas (40%). Statements were taken down verbatim, even when they contained grammatical errors or stylistic infelicities. Often, the responses were abbreviated and did not form complete sentences. Such brevity was due to various reasons such as pressure of time, vague knowledge of the area and the research topics, and a limited vocabulary in the interview language(s). All answers given in languages other than English were translated prior to the analyses. The participants were first asked to familiarise themselves with the 12 photographs, presented in randomised order (a shuffled pile), before the actual tasks started. Secondly, the tourists were asked to categorise the sample of landscapes (photographs) according to their own concepts, a free or unforced sort. The instruction was as follows: “I would like you to sort the photographs into groups according to your own impressions. The sorting should be done so that all the pictures in any one group are similar to each other in some important way and different from those in the other groups. You may create as many groups as you want, and each group may contain as many pictures as you like. Remember, it is your views that count; there are no right and wrongs here”. Subsequently, the pictures were reshuffled for each of three structured or directed sorting tasks. In the first of the structured sorts, the respondents were asked to consider the visual appeal of the landscapes, and then sort the photographs into five piles according to their degree of preference. The categories to be used were as follows: “like most” (1); “like” (2); “like moderately” (3); “like less” (4); and “like least” (5). These alternatives were also presented by cards. The second structured sort was related to the typicality of the photographs with regard to the landscapes in question. Respondents were then asked to sort the photographs according to their typicality into graduated piles, indicated by cards, according to what they would find “most typical” (1); “somewhat typical” (2); and “least typical” (3). A third structured sorting task was then conducted following the same procedure, but this time according to preference for vegetation types, with three degrees of preference as response values, “like most” (1); “like somewhat” (2); and “like least” (3). In the end, the participants were asked to fill in a small, selfadministrated questionnaire about the appeal of various types of landscapes and landscape elements, and also a few background variables such as country of residence, age, and education. This questionnaire was available in three languages: English, Ger-

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man, and Norwegian, the latter adapted to suit Danish- and Swedish-speaking respondents. Apart from the background variables reported previously in this section, the results from this questionnaire are not reported in the current article. Data were analysed by use of content analysis, cluster analysis, multidimensional scaling (MDS), and simple bivariate correlations. 4. Results 4.1. Structure of the free sort As preparation for the analysis the raw data matrix was transformed into a single symmetrical matrix (12 × 12) containing the number of co-occurrences between all photographs. Thus, each cell of the matrix represented the number of times any of the 43 respondents had placed two images in the same pile, giving a similarity measure ranging from 0 to 42. A hierarchical cluster analysis, using the median method, was conducted in order to provide a categorising structure of the 12 landscapes, based on the 12 × 12 similarity matrix. The analysis suggested a four-cluster solution (Fig. 1). The cluster analysis is represented as a horizontal tree (a dendogram), with each image placed at the left side and a single cluster containing every image at the other. The interpretation of the analysis involves cutting the tree at a given height, and inspecting the partitioning of the different elements (images). At each level or “fork” of the tree trunk, starting from the right side, an increasing amount of precision is obtained, and a decreasing amount of generalisability. Inspection of Fig. 1 from right to left reveals that the first distinction the participants made was between (a) “typical coastal landscapes” and “other landscapes”. Secondly, (b) “active agriculture” splits from the “other” category, which again finally splits into (c) “forests” and (d) “meadows”. The photo co-occurrence data from the multiple photo sorting were also used to explore underlying dimensions along which the respondents discriminated between landscapes. To achieve this, MDS was employed (Groat, 1995; Real et al., 2000; Green, 2005), using the alternating least squares scaling algorithm (ALSCAL) (Young et al., 1980). An MDS analysis (interval level) was conducted for the whole sample of participants. The analysis indicated that a two-dimensional solution was the most appropriate. This solution was not a perfect fit, as the stress value (Kruskal’s formula-1) was 0.16 (RsQ, 0.73). However, nothing was gained by increasing the dimensionality of the solution, indicating that the model represents fairly well the underlying dimensions of the data, and that the stress value indicates only random error (Borg and Groenen, 2005, p. 55). The partitioning of the plots clearly confirmed the distinctions shown in the cluster analysis, with four quite dissimilar regions (a polar partitioning) (Fig. 2). From upper left and clockwise one finds “meadows/flowers”, “forests”, “coastal landscapes”,

Fig. 1. Dendrogram solution from cluster analysis.

Fig. 2. Representation of dimensions 1 and 2 of the MDS solution for initial (free) categorisations.

and “active agricultural landscapes”. The clarity of the partitioning, with relatively larger inter-category than intra-category distances, indicates that this categorisation structure was common to most of the visitors. 4.1.1. Content analysis of labels The actual words that the participants employed to describe their individual categories (sorting piles) were content analysed. This procedure involved a simple word count of all descriptions used on categories of images and, subsequently generating overall (exhaustive and mutually exclusive) construct categories from the individual descriptions, and finally placing the verbal descriptions into categories. The procedure involved some trial and error, before the final solution was achieved. All in all, 164 labels were used to describe the categorisations. The labels were more or less “impressionistic” or associative, some containing more than one item, for instance “small settlements related to the sea; rocky and rough”. Only 20 of the 164 descriptions were of an evaluative character (“I like this landscape”, or “this is unimpressive”) and 145 were purely descriptive. Of the evaluative labels, 5 were related to degree of preference or scenic beauty and 14 were related to degree of typicality (of the Lofoten and Vesterålen area) or familiarity. Agriculture was used as descriptor of a category 34 times (a further four subjects used the word animals to describe a category), all in all 38 out of 43 of the tourists used this category. Animals/livestock as such were used 13 times. Vegetation was employed as a descriptor 27 times. Reforestation was mentioned specifically by one subject. Some 14 subjects made some kind of characterisations of vegetation types, for instance forest, meadow, and grassland, while 19 subjects made a distinction between typical agricultural landscapes and more untamed nature. A number of the 164 original labels were clearly synonymous, making it possible to reduce the actual number of labels to 80. The 80 different labels could again be reduced to 10 main categories, and these were as follows: (a) agriculture/animals, also “landscape”; (b) fishing; (c) degree of habitation, human presence; (d) water, also coastal landscape; (e) mountains; (f) degree of vegetation, type of vegetation; (g), forests; (h) preference; (i) degree of typicality; and (j) other. A second rater, a researcher with no knowledge of the project, was given a brief introduction about the task given to the participants, and then asked to generate 10 categories from the 80 different descriptions, including one “miscellaneous” category.

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Fig. 3. Dendrogram solution from cluster analysis of categorical data.

Comparison of the results of category generation gave an exact match on four out of nine categories, and a partial match on two. The second rater was then asked to place all 80 labels into the 10 (9 + 1) categories provided by the first judge. This gave an inter-rater reliability score of 0.89 for the single-category labels, and 0.74 for the labels described by more than one category. The number of times every photograph was included in each category was used as input for a hierarchical cluster analysis of categorical data, in order to verify the MDS analysis and cluster analysis of the initial matrix of quantitative sorting data. The solution bestowed by the clustering method was clearly comparable to those provided by the MDS and the cluster analysis of quantitative data (Fig. 3), although the order of the splits of the various clusters differed slightly. The initial distinction was made between agricultural landscapes and others, followed by a split between coastal and others, thereafter between forests and meadows. 4.2. Preference for landscapes The participants were also asked to rate each image according to their preference, from (1) ‘like most’ to (5) ‘like least’. The results of this preference sorting are presented in Table 1. The most preferred image was that of a nearly barren coastal rock landscape, followed by two images of fishing hamlets by the shoreline (Table 1). The least preferred images were those of a meadow covered with tufted-hairgrass and a coastline hillside with a spruce plantation.

In order to explore the relationships between landscape preference and initial categorisations, a separate MDS analysis (ALSCAL, ordinal level) of the preference data was conducted (Fig. 4). The structure of the general preference MDS is comparable to the structure of the initial (free) sorting (Fig. 2), with a partitioning into four distinct landscape types. The three images in the “coastal” part of Table 1 Mean ratings of preference scores for images of landscapes.

6 9 4 3 5 7 10 1 12 11 2 8

Image

Coastal rock Fishing hamlet Raftsund hamlet Meadow buttercup meadow Cow-Parsley meadow Rosebay Willowherb meadow Dense forest Sheep mountain Round bale meadow Sheep field Spruce plantation Tufted-hair-grass

the plot were seen as quite distinct from the rest, as they were placed the furthest away from the others. Also the two forested images were seen as quite distinct from the other landscapes. In the free sorting MDS, the distinction between “active agricultural landscapes” and “meadows/flowers” was quite clear. In the preference MDS, the same partitioning is present but the image of tufted-hairgrass is placed together with the “active agricultural landscapes” rather than together with “meadows/flowers”. In order to test the association between the two variables “typicality” and “preference for vegetation” and “general preference”, mean scores of ratings for each image were calculated. The bivariate correlations (Pearson’s r) for these mean scores were 0.88 (p < 0.01) between general preference rating and typicality, and 0.07 (not significant) between general preference and preference for vegetation. 5. Discussion and conclusion

4.3. MDS analyses of preference rating scale

Photo number

Fig. 4. Representation of dimensions 1 and 2 of the MDS solution for preference scores. Stress value, 0.09; RsQ, 0.95.

Preference (1–5) Mean

Std. Dev.

1.28 1.67 2.12 2.40 2.58 2.60 2.63 3.07 3.07 3.16 3.23 3.37

0.55 0.87 1.18 1.26 1.12 1.14 1.33 1.22 1.33 1.19 1.29 1.25

5.1. Initial categorisations The multidimensional scaling analysis and the cluster analysis of the free sorts show that the tourists share a set of categorisation criteria for the landscape elements in the archipelagos. The criteria do not belong only to one dimension but are expressions of multidimensional conceptualisations. The categorisation criteria are reflected in four distinct landscape types: “typical coastal landscapes”, “meadows/flowers”, “forests”, and “active agricultural landscapes”. Thus, the categories can be seen as manifestation of the features “degree of human influence”, “type of vegetation”, and “prototypicality”. All of these have previously been identified as influential features for landscape preference (Kaplan and Kaplan, 1989; Múgica and DeLucio, 1996; Peron et al., 1998; see e.g. Real et al., 2000; Herzog and Stark, 2004). These categories can also roughly be said to represent different degrees of re-growth or overgrowth, with the typical coastal landscapes as the least re-grown, via active agriculture and meadow to forests as the most overgrown landscapes. The verbal descriptions that the participants gave for each category were to a certain degree reflected in the structure of the free sorting task. However, only a few of the participants actually verbally expressed the category “typicality” in their description of

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the pictures in the free sorting, even if this feature was strongly influential in the overall sorting structure. This finding emphasises the value of supplementing simple verbal information from participants with statistically advanced methods and analyses. Moreover, a supposed relationship between visitors’ initial constructs and their preferences was supported. The participants’ preference evaluations had a strong relationship with the structure of the initial free sort of categories. This finding supports previous similar findings related to the built environment (Young, 1978; Fyhri, 1994; Wilson, 1996) and indicates that the same relationships may exist for natural environments as well. 5.2. Preferences One might argue that the findings cannot be directly generalised to other tourist areas, nor even to all foreign tourists in northern Norway, due to methodological limitations. Most important of these are narrow selection of images and seasonal differences in holiday visitation to the case area. However, the results do point to important limitations of previously assumed relationships between (a) degree of vegetation and preference and (b) perceived naturalness and preference. The preference sorting disclosed that the most preferred images were those with less prominent vegetation in them, indicating a preference among international tourists in this region for the raw and the barren, as opposed to lush vegetation. This finding is in opposition to earlier research showing tourist preferences for images with lavish vegetation and forests (Eleftheriadis et al., 1990; Yang, 1992; Múgica and DeLucio, 1996). Even previous findings of a preference for an optimal level of vegetation (Hunziker, 1995) are somewhat refuted as the most preferred landscape is virtually barren and mountainous. This contradiction might partly be related to the types of international tourists who chose to travel to northern Norway. As correlations were fairly high between the participants’ ratings of preference and typicality, whereas it was virtually zero between appraisal of vegetation and general preference, a more promising explanation has to do with the participants’ expectations and the perceived typicality of the images. It should be noted that the preference for vegetation and degree of typicality ratings only consisted of three-point scales. Ideally, all scales should have been five- or seven-point rating scales, as is customary for this kind of research. This would have provided more refined results and improved reliability of the findings. However, it is not likely that the main conclusions regarding the relative importance of typicality versus vegetation would have been altered. As a general remark, the development of standardised and replicable methodological procedures would have been of great value for constructing a proper theoretical framework within the field of landscape preferences studies. In the same manner as for the degree of vegetation, the present study gives mixed results in relation to previous theories about human landscape influence and peoples’ aesthetic preferences. Both conceptual research and a number of empirical studies (Kaplan and Kaplan, 1989; Real et al., 2000) have suggested that the most natural (unaffected) landscape images are generally the most preferred. The findings in the present enquiry are partially in accordance with the findings of these studies, as the most preferred image among the visitors to the archipelagos was of a wild and seemingly uninhabited coastal rock landscape. However, the fact that the two next most preferred images (coastal hamlets) had the highest degree of human influence is in opposition to findings in several previous landscape studies. The preference sorting indicates that agricultural areas and images including domestic animals are somewhat less preferred than other images. This lack of interest in agricultural activities corresponds with the results of the work of Fleischer and Tchetchik

(2005), who found that farm activities on a working farm were of no value to visiting holidaymakers. In this respect the results also seem to be in accordance with those of Strumse (1996) who found high non-expert preferences for traditional human-influenced rural landscapes above more modern agricultural landscapes in a southwestern Norwegian setting. However, in the current study signs of human influence were mainly represented by agricultural activities and small, traditional coastal hamlets, while other examples of explicit human influence were not covered. More aggressive and recent intrusions such as factory buildings would possibly have been considered more alien to the landscape and assumingly have lead to more negative tourist judgements. This assumption is supported by the fact that none of the 12 images in the current study were negatively rated. Rather, the assessments covered a range from medium preference to strong preference. The current study did not test for aesthetic value of presence of water. Rather, water was included in all images, in order to use this element as a constant. However, variation in degree of water presence and also the amount of reflection from the water was not to be avoided. As this might influence overall preference (Nasar and Li, 2004), one should ideally have included an assessment of perceived amount of water presence. The images with most reflective water were both among the most preferred and the least preferred, making it problematical to predict what the outcome of such a test would be. The study lends support to the importance of taking into consideration the typicality of setting category in studies of landscape preferences, as proposed by Herzog and Stark (2004). Thus, predictions about general preferences for lush and densely vegetated landscapes may hold true, if the setting in question is one where such landscape types are expected or sought for. In partly barren and mountainous island regions such as Lofoten and Vesterålen, tourists might not seek out or prefer to see lush forests. The influence of typicality on tourists’ landscape categorisations and preference evaluations gives credence to previous research focussing on the importance of an icon status of certain tourism destinations, that numerous tourists search for idiosyncratic landscapes and extraordinary place experiences (Urbain, 1989). Thus, tourists in this area do express preferences according to a mode of experience typically influenced by romanticism (Jacobsen, 1994), with a focus on aesthetic pleasure (Taylor, 1994, pp. 9–10) and scenic experiences (Towner, 1985). 5.3. Conclusions The findings of the present work suggest that the joint assessment of quantitative and qualitative data is particularly relevant for identifying and assessing visual environmental impacts as well as for scrutinising the mental constructs used in peoples’ impact assessments. Such research is vital in areas where tourism is a key economic factor but it is also relevant for the understanding of local residents’ awareness and assessment of re-growth and other environmental challenges that have visual consequences. Even though the concepts “re-growth” and “overgrowth” were not used actively by the participants, these issues could be detected as underlying themes for international tourists’ understanding of landscapes in the case region. This distinction is also reflected in the preference sorting procedures, where the active agricultural landscapes turned out to be the least preferred, and the moderately re-grown landscapes the most preferred of the agriculturally related areas represented here. The preferences for coastal hamlets and barren coastal landscapes lend support to the importance of taking into consideration the typicality of setting category in studies of tourist landscape assessments. The findings here have landscape managerial implications regarding measures for countryside maintenance and restoration.

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Even if the first stages of re-growth of agricultural areas were seen as quite favourable by the foreign visitors, further re-growth and reforestation seem to reduce tourist preferences thus leading to a longer term reduction in the attractiveness of the specific environment. Reduced attractiveness might in turn have negative consequences not only for the regional tourism-related industry. It might also have an impact on the regions’ image in a national context. Moreover, overgrowth will reduce access for other recreationists to favoured areas for activities such as hikes and berry-picking, and also make some areas lesser suited for cross-country skiing. Future research with larger numbers of photographs and indepth personal interviews might be necessary to possibly verify the departure of the results of the present study from previous research findings regarding certain preferences for culturally modified landscapes. Tourism-related landscape studies might, for instance, include photo-realistic representations in self-administrated questionnaires, making statistically representative visitor samples

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achievable. Future research should also include more tests, either in the form of subjective measurements or in the form of standardised expert assessments (which is the closest we can get to objective measures in such a field of research) in order to improve control for central predicting landscape variables, such as amount of water (e.g. fjords and sounds), degree of vegetation, and human influence. Acknowledgements This study was granted financial support from the Norwegian Research Council (TopCoast project 165786) and the Centre Program for The Polar Environmental Centre. The authors thank Alf Oxem and Josef Leupi for providing and manipulating photographs for the sorting procedure. The authors also thank Dr. Pål Ulleberg for helpful assistance with the cluster analysis. Appendix A. Images used in the interviews

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