WORLD’S BEST LANDSCAPES PROJECT Dr Andrew Lothian Scenic Solutions 2016 Cover: Top – Grand Canyon, United States Bottom – Torres del Paine, Chile © 2016 Dr Andrew Lothian, Scenic Solutions ABN: 55 275 407 146 [email protected] www.scenicsolutions.com.au World’s Best Landscapes WORLD’S BEST LANDSCAPES ASSESSMENT PROJECT EXECUTIVE SUMMARY Introduction preferences. Strict criteria apply to the photography of the landscape so that the The World’s Best Landscapes rating is of the quality of the scene, not the Assessment project was carried out during quality of the photograph. early 2016. Having measured and mapped landscapes in South Australia and the Photo selection Lake District in the UK, all of which averaged in the range 5 to 7, the question The project commenced in early February arose, what rating does the World’s best and was completed by the end of March, it landscapes achieve? What are the being much quicker than my other projects characteristics of such landscapes? as it did not involve taking original Where are such landscapes located? photography but rather relied on existing photographs from the Internet. The list of Concept of World class landscapes what may be the World’s best landscapes was prepared based on previous work on In examining the concept of World class the World Heritage list which includes landscapes, the scale and definition of landscapes that meet Criterion 7 of landscapes at varying scales, from landscape aesthetics. In addition, two immediate to continental in scale is dozen lists of the World’ Wonders had reviewed. It then examines landscape been previously reviewed and from these quality profiles, i.e. the proportions of a plus the World Heritage list and set of 45 given landscape that are of specific ratings locations were elected. For each site, on the 1 – 10 scale, e.g. 50% rating 5, 10% based on strict criteria, three photographs rating 7. The possible profile of World class for each site were chosen from the Internet landscapes is suggested. The components and some from people who had visited of a landscape – its land forms, land cover, various sites. In addition, three scenes land uses, the presence of water and so on were added from South Australia and two determine the quality of the landscape. from the Lake District, all five being of Mathematical models can define the known rating from previous projects. In all, contribution of each in a given landscape the survey comprised 143 images. and these are summarised. Internet survey Nature of landscape quality The survey was loaded onto the Survey Landscape quality is an aesthetic quality Monkey site and launched on 27 February. which is assessed via our affective Invitation emails were forwarded to over capacity, our likes and dislikes, our 1,000 individuals and organisations in aesthetic preferences, not via our cognitive Australia and England, using addresses abilities which analyse and logically from previous surveys. After 16 days, by comprehend the environment. Early the 14 March, there were only 146 attempts often measured every aspect of responses which was a rather the landscape – height, aspect, geology, disappointing response. vegetation, land use, in the hope that its scenic quality would emerge but it never Data characteristics did as it can only be assessed aesthetically. The scientific basis is There were 76 respondents that rated all psychophysics, the measurement of the 143 scenes, 52% of the total responses. effect on the brain of stimuli from the Fifteen respondents rated no scenes, senses – sight, sound, taste, touch and stopping the survey after entering their smell. Research has established the use of demographic information. This may have photographs as a vehicle for measuring been due to a malfunction of the Survey Dr Andrew Lothian, Scenic Solutions Page i World’s Best Landscapes Monkey survey instrument and Survey 7.93. These ratings were lower than Monkey found that the survey was correct expected – where were the 9’s and 10’s? and that the problem lay with the browsers of respondents. The answer may lie in the unfamiliarity by respondents of the sites shown in the Omitting the zero responses and 3 who survey. In previous surveys, the rated only one scene left 128 responses respondents were familiar with the which provided a confidence interval of landscapes where they lived or visited and 8.66; in other words, at a 95% confidence knew implicitly the likely high points in the level, the responses are +/-8.66% of the survey and this would guide their ratings. true value. While was less than the 5% target it is sufficient to give an indication of It was suggested that respondents saved community preferences. No strategic bias their 9’s and 10 ratings because they did was found in the respondents. not know what was ahead in the rest of the survey. The respondents had no Both the respondent and scene knowledge of the survey’s coverage and distributions appeared normal. The what the survey contained. They therefore average time taken to complete the survey took a conservative careful approach to as 25.3 minutes and most respondents ratings which, together with the lack of fore- spent six to twenty seconds per scene. knowledge of the scenes yet in the survey, led to lower than expected ratings. While a comparison of respondent means and standard deviations (SDs) showed Surprisingly, the top rated scene was of the only a weak relationship, this was much Plitvice Lakes in Croatia, a relatively small stronger in the scenes. Here highly rated set of waterfalls. All three scenes rated scenes had low SDs, indicating narrow either 7 or 8 and the overall mean was 7.93. range of opinion but as the ratings decreased, the SDs increased, indicating a wider range of opinion. Thus people are fairly certain of what they like but have a greater range of opinion for lesser rating scenes. The respondents were generally middle age or older, with slightly more females than males participating, and most were fairly well educated. While 73% were located in Australia, there were also 10 from the UK, 9 from Ireland, and 6 from Israel plus 9 from other countries. Out of the 128 respondents, 28 wrote Plitvice Lakes – top rated scene 8.55 comments (22%). Eleven commented on their ratings and five made suggestions for The ratings of mountain scenes which was improvements to the survey. Respondents generally not as high as expected. The were equally critical or positive about the scene of the summit of Mount Everest, for photos (5 each). Three said the survey was example, the world’s highest mountain and too long and two asked about the purpose instantly recognizable, rated only 5.91. of the survey. Scene ratings The top rating of an individual scene was 8.55 and for a group of three scenes was Dr Andrew Lothian, Scenic Solutions Page ii World’s Best Landscapes waterfalls and lakes. The following figure summarises the mean ratings of scenes with water. 7.5 7.0 6.5 Rating scale Mt Everest 5.91 6.0 The Grand Canyon, which ranked first in the lists of World Wonders, rated only 6.68. Certainly it is difficult to capture the scale and vastness of the Grand Canyon in photographs and for this reason the scenes included a panorama but this Influence of water on ratings actually rated lower than the other two scenes. 3rd rated scene: Victoria Falls, Zambia/ Zimbabwe 8.20 Grand Canyon 6.88 Many of the scenes contained cliffs and high mountains, and scenes of mountains viewed across lakes or the sea. The following figure compares the ratings of these. It also shows the ratings of mountains which are conical in shape, including volcanoes, and also those which are a high narrow tower. The five top rated groups of scenes were: Plitvice Lakes (Croatia), Torres del Paine 2nd rated scene: Torres del Paine, Chile 8.25 (Chile), Norwegian Fjords, Victoria Falls (Zambia/Zimbabwe), Rockies (Canada). The presence of water was found to have a significant influence on ratings, increasing ratings by 5%. Of the top 20 scenes, 17 included water, particularly in the form of Dr Andrew Lothian, Scenic Solutions Page iii World’s Best Landscapes 7.0 forms, land cover, visual diversity and naturalness. 6.5 Rating scale 6.0 High, narrow towers: Guilin Mountains 7.92 Ratings of mountain scenes Comparing these scores with the ratings found that the correlation coefficient (R²) was generally low. All the scenes were low in land cover, fairly low in diversity but high in naturalness. The trend lines indicated that diversity had the strongest influence followed by water and then the steepness and height of land forms. Model development Based on the component scores along with the scene ratings, multiple regression Lake and mountains – Kasmir Ladakh 7.32 analysis was used to derive predictive models. The following model used all seven components and has an R2 of 0.53. Components Land cover, land form, diversity, naturalness, water, steepness & height, snow & ice R2 0.53 Equation y = 4.04 + 0.52 diversity + 0.14 water + 0.133 snow & ice + 0.132 steepness/height + 0.06 natural- ness + 0.044 land cover + 0.040 land form Significance F=21.4, df = 7, 135, p < 0.000 A model which used only one component, A conical mountain: Huascaran diversity, had an R2 of 0.40. National Park, Peru 6.64 Components Diversity Landscape components R2 0.40 Equation y = 4.87 + 0.70 Diversity The author scored the components of Significance F = 93.50, df = 1, 141, p < 0.000 scenes to ascertain their contribution to ratings.