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Exploring the Influence of the Visual Attributes of Kaplan’s Preference Matrix in the Assessment of Urban Parks: A Discrete Choice Analysis

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Abstract

A significant majority of the literature on natural environments and urban green spaces justifies the preferences that people have for natural environments using four predictors defined by Kaplan’s preference matrix theory, namely coherence, legibility, complexity, and mystery. However, there are no studies implicitly focusing on the visual attributes assigned to each of these four predictors. Thus, the aim of this study was to explore the influence of nine visual attributes derived from the four predictors of Kaplan’s matrix on people’s preferences in the context of urban parks. A discrete choice experiment was used to obtain responses from a sample of 396 students of Golestan University. Students randomly evaluated their preferences towards a set of potential scenarios with urban park images. The results of a random parameter logit analysis showed that all of the attributes of complexity (variety of elements, number of colors, and organization of elements) and one attribute each of coherence (uniformity), mystery (visual access), and legibility (distinctive elements) affect students’ choices for urban parks, while one attribute each of mystery (physical access) and legibility (wayfinding) did not affect the choices. Furthermore, the results indicated a preference for heterogeneity of the attributes. The findings of this study can provide instructions for designing parks.
Citation: Shayestefar, M.;
Pazhouhanfar, M.; van Oel, C.;
Grahn, P. Exploring the Influence of
the Visual Attributes of Kaplan’s
Preference Matrix in the Assessment
of Urban Parks: A Discrete Choice
Analysis. Sustainability 2022,14, 7357.
https://doi.org/10.3390/su14127357
Academic Editors: Nikos A.
Salingaros, Alexandros A. Lavdas,
Michael W. Mehaffy
and Ann Sussman
Received: 11 March 2022
Accepted: 27 May 2022
Published: 16 June 2022
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sustainability
Article
Exploring the Influence of the Visual Attributes of Kaplan’s
Preference Matrix in the Assessment of Urban Parks: A Discrete
Choice Analysis
Marjan Shayestefar 1, Mahdieh Pazhouhanfar 1, *, Clarine van Oel 2and Patrik Grahn 3
1Department of Architecture, Faculty of Engineering, Golestan University, Gorgan 4918888369, Iran;
marjan8970@gmail.com
2Faculty of Architecture and the Built Environment, Delft University of Technology, P.O. Box 5043,
2600 GA Delft, The Netherlands; c.j.vanoel@tudelft.nl
3Department of People and Society, Swedish University of Agricultural Sciences, P.O. Box 190,
234 56 Alnarp, Sweden; patrik.grahn@slu.se
*Correspondence: m.pazhouhanfar@gmail.com or m.pazhouhanfar@gu.ac.ir
Abstract:
A significant majority of the literature on natural environments and urban green spaces
justifies the preferences that people have for natural environments using four predictors defined by
Kaplan’s preference matrix theory, namely coherence, legibility, complexity, and mystery. However,
there are no studies implicitly focusing on the visual attributes assigned to each of these four
predictors. Thus, the aim of this study was to explore the influence of nine visual attributes derived
from the four predictors of Kaplan’s matrix on people’s preferences in the context of urban parks. A
discrete choice experiment was used to obtain responses from a sample of 396 students of Golestan
University. Students randomly evaluated their preferences towards a set of potential scenarios
with urban park images. The results of a random parameter logit analysis showed that all of the
attributes of complexity (variety of elements, number of colors, and organization of elements) and
one attribute each of coherence (uniformity), mystery (visual access), and legibility (distinctive
elements) affect students’ choices for urban parks, while one attribute each of mystery (physical
access) and legibility (wayfinding) did not affect the choices. Furthermore, the results indicated a
preference for heterogeneity of the attributes. The findings of this study can provide instructions for
designing parks.
Keywords:
information processing theory; landscape design; multinomial logit model; predictors
of preference
1. Introduction
Today, the growth of urbanization and city development have reduced green spaces
in cities, and the surrounding of cityscapes with buildings have separated individuals from
contact with nature and natural environments. Therefore, the existence of urban green
spaces, including parks, as structures affected by nature is considered one of the main needs
in citizens’ lives, and due to their significant position, can enhance people’s well-being and
provide a place full of tranquility and vitality for residents [
1
,
2
]. What makes urban parks
important today and makes people choose to use them for their leisure time, in addition
to their principled design and desired vegetation quality, is their visual quality [
3
5
]. The
visual expression of the landscape can be considered the main component for determining
the identity of the environment and is a means of communication between the environment
and its users. The relationship between the landscape and its perception and interpreta-
tion by users is very important, so this perception of the landscape is directly related to
recognizing the landscape’s aesthetic characteristics [
6
8
]. Therefore, the investigation and
study of individuals’ landscape preferences can be considered an effective and vital way to
Sustainability 2022,14, 7357. https://doi.org/10.3390/su14127357 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 7357 2 of 19
identify the landscapes with different visual value in order to organize, design, and manage
these landscapes. Kaplan worked during the end of the 20th century to develop knowledge
about the connections between preferences for parks and natural environments and their
aesthetic attributes, with the aim that the knowledge could be used in practice. The result
became one of the most referenced theories in environmental psychology, Kaplan’s prefer-
ence matrix, which is based on an information processing model [
9
]. This theory shows
that individuals have two types of basic needs in relation to the environment: a need to
understand and a desire to explore [
10
]. Together, understanding and exploration form
the framework of the informational preference matrix, which has four key information
variables: “coherence”, “complexity”, “mystery”, and “legibility” [
11
]. Several studies of
natural environments and urban green spaces have justified the preference choices that
people have for such environments using the four predictors [
10
,
12
,
13
]. The four predictors
provide information to understand why people prefer and choose such environments.
However, the visual attributes of these predictors that play an important role in people’s
preferences are not known yet.
Aim
According to Kaplan’s perceptual model, people like and prefer landscapes based on
four predictors: coherence, legibility, complexity, and mystery. As far as we know, there
is no study that implicitly focuses on the visual attributes assigned to each of these four
predictors. The purpose of this study is to first find specific visual attributes derived from
the four predictors in Kaplan’s preference matrix and to divide them into different levels of
hypothetical influence, then to explore the influence of these visual attributes on people’s
preferences for urban park environments using discrete selection methods. The aim is
to investigate to what extent the different visual attributes affect people’s preferences, as
measured by choice behavior.
2. Materials and Methods
2.1. Literature Review
This article focuses on the aesthetic predictors in physical environments that lead to
high preference in humans. This is a classic question in aesthetics, which has engaged
researchers in experimental Psychology since Gustav Fechner’s studies in the 19th cen-
tury [
14
]. Over the years, many studies have been devoted to finding out which specific
design attributes define these predictors and how the acquired knowledge can be used
in practice, for example in architecture and landscape architecture. Through an extensive
literature review, we will establish visual attributes related to Kaplan’s preference matrix
for further studies.
2.1.1. Mystery
One of the most important predictors of preference is mystery [
15
]. Kaplan defined it
as hiding part of the landscape to recognize it. The mystery results from a lack of complete
perception, due to the inability to see part of the landscape. This provides an opportunity
to learn something that is not quite apparent from the present vantage point [
16
]. Mystery
(inferred exploration) arouses one’s curiosity and encourages people to go deeper into the
scene to explore and learn more about the content. Mystery does not mean the presence of
new information, but the promise that new information exists. In fact, mystery means the
promise of more information, and in particular the opportunity to gather new information
in a landscape [
17
]. According to Kaplan’s theory, curvature paths and visual elements that
partially obscure visual accessibility are the most important mystery attributes.
Physical Access (Shape of Paths)
Physical access refers to the defined paths in a landscape through which the observer
can discover this landscape [
18
]. Based on Kaplan’s definition of mystery, curved paths
have more mystery than straight ones, so this study will investigate the paths in two forms,
Sustainability 2022,14, 7357 3 of 19
straight and curved, as most studies of environmental preferences suggest that curved
paths are the most important visual attribute of mystery.
According to Kaplan’s concept of mystery, Gimblett et al. [
18
] introduced five physical
attributes of mystery, including screening, distance of view, spatial definition, physical
access, and background radiance. The results of their study revealed that physical access
was the factor of discovery and involvement with the environment that had the strongest
effect on mystery. A study by Herzog and Miller [
19
] suggested that environments with
high levels of mystery must have curved paths with a high degree of visual access around
the boundaries. The results of their study showed a significant positive relationship
between mystery and the perceptual component of path curvature. A study by Eriksson
and Nordlund [
20
] found that predictors of mystery, such as curved paths, encourage
individuals into walking in and exploring the environment. Similarly, Kuper [
21
] evaluated
mystery via physical access and visual access with other factors at three different sites
(park, village, and art center). In a study of urban woodlands [
22
], the results showed that
curved paths increase aesthetic and recreational preferences, because curved paths provide
more opportunities for exploration and recreation and increase physical access in these
environments. Studies on urban streets [
23
] have also reported that people prefer curved
paths over straight ones, as defined by the mystery element; these paths firstly arouse
curiosity and secondly seem to reach destinations more quickly.
Visual Access
Visual access is a combination of the ability to see and influence vision [
24
]. Kaplan
defines visual access as the amount of the view of a landscape that is constrained by visual
barriers such as vegetation [11]. There are several ways to block or obstruct a view.
Mystery refers to hidden elements [
8
]. Gimblett et al. [
18
] showed that mystery is
affected by visual access, especially regarding vegetation. Similarly, the presence of factors
in the scene that create a sense of concealment and refuge, such as the presence of dense
vegetation, restricts visual access, which is an attribute of mystery [
25
]. The researchers also
claim that visual access affects the degree of mystery of the environment. Landscapes with
low visual access have high levels of mystery because these landscapes actually promise
more information [
16
,
24
]. In a study by Herzog and Miller [
19
], mystery was related to
visual access or openness. The results showed that if fewer hidden elements were observed,
the visual access increased, which led to the factor of mystery decreasing. In a study
on stone carvings and urban scenes, the results showed that occlusion views influence
mystery [
26
]. After several experiments, Stamps [
27
] stated that occlusion is an attribute of
mystery, believing that mystery is a visual function. One of the elements that can obstruct
one’s view and movement in the landscape is vegetation, a high density of which increases
the mystery of a landscape [
11
]. Many studies have shown that the presence of vegetation
partially obstructs one’s view and encourages one to explore the environment [
20
,
21
].
Studies on building facades have also shown that one’s visual perception and mystery is
influenced by vegetation density [28,29]. Because part of the facade is hidden and there is
the promise of more information, this increases one’s motivation to explore.
2.1.2. Legibility
Legibility is animportant indicator of urban perception that was examined by Lynch [
30
]
in the context of the visual quality of urban environments. This factor in Kaplan’s environ-
mental preference studies points to the possibility of an inferred understanding. According
to Kaplan’s definition of legibility [
17
], this is an area that is easy to view and form a mental
map of, which increases the possibility of wayfinding. In addition, the area should have an
appropriate spatial structure with distinct, identifiable elements and landmarks that increase
readability [10].
Sustainability 2022,14, 7357 4 of 19
Wayfinding
Kaplan states that finding a way through the landscape influences one’s perception
of legibility in an environment [
10
]. Additionally, in a study of neighborhood parks, the
entrance is an important factor influencing accessibility, which is one of the visual attributes
of legibility [
31
]. In recreational landscapes, finding a path through the presence of signage
is one attribute of legibility because it helps to create a positive mood towards service
providers [32].
Distinctive Elements
Scholars have stated that the presence of distinct elements such as landmarks makes
it easy to make a legible mental map [
30
,
33
]. Subsequently, many studies have been
conducted on the perceptions of cities, with the results showing that the presence of distinct
elements has a great impact on environmental legibility [
34
]. For example, tall buildings as
landmarks greatly impact the perceptions of legibility for commuters, conveying a sense of
direction to the destination [
35
]. The results of studies have shown that one of the visual
attributes of legibility in a landscape is the presence of salient elements. These elements are
effective in human perceptions of a landscape and are easily memorized [36].
2.1.3. Coherence
In 1998, Kaplan stated that a coherent environment means that there is an organized
order in specific regions. This allows people to identify specific areas and make them easier
to understand. Coherence is also enhanced by repetition and texture uniformity [
11
]. A
cohesive and orderly environment is easier for people to understand directly, as these
elements contribute to their ability to create meaning [10].
Uniformity (Texture)
Material or texture changes are attributes of the coherence in a scene. Texture changes
can define different areas, as each area can be identified by a similar material; therefore,
coherence is reduced if one of them has multiple materials [
11
]. In a study by Ode et al. [
7
],
the results showed that the unity of a scene, texture repetition, and color are correlated with
coherence. Subsequently, Huang [
37
] investigated the characteristics of rural landscapes
and their impacts on visitor preferences. Coherence in that study implied the organization
of elements and unity in the scene, which are features of rural landscapes.
Organization of Components—Order (Areas)
Kaplan et al. [
11
] compared the features of two nature scenes. The type and number of
elements in both scenes were the same. In high coherence areas, the same species of trees
came together to form distinct areas. In low coherence areas, trees from different species
were scattered in the area. Studies have also shown that organization and order have
a significant impact on coherence in natural environments [
7
,
38
]. Kuper [
39
] examined
people’s preferences of landscapes with three levels of coherence (high, medium, and low)
based on how plants were organized in the landscape (formal, clustered, or scattered) in
urban, residential, and field environments. The result showed that people had the highest
preference for environments where plants were organized in clustered units.
2.1.4. Complexity
The research on complexity includes a series of studies focusing on several areas in ad-
dition to natural and urban environments. Among other things, one of the most important
predictors of interior design [
40
], along with logos [
41
], websites [
42
], and photos [
43
] was
considered. Recent studies have shown that the complexity of the environment can have a
major impact on people’s mental health, attractiveness, and learning [44,45].
Complexity was first studied in aesthetic studies, and in general this component was
recognized as the principal factor determining aesthetic responses. Berlyne [
46
] suggested
that environments with intermediate levels of complexity would be judged as being the
Sustainability 2022,14, 7357 5 of 19
most beautiful; that is, humans would prefer medium complexity more than both low
and high complexity. In 2016, a study by Alpak et al. [
47
] found that people’s preferences
indeed were the highest for urban landscapes of intermediate complexity. This study also
showed that important predictors of complexity were the experience of coherence and the
organization of the components of the urban landscape. Fechner [
14
], in his pioneering
aesthetic studies, argued that beauty is the result of something conveying an expression
containing two factors, complexity and order, where order, as stated above, is a significant
part of coherence. Later studies suggested that beauty is the balance between these two
factors [
30
]. Complexity and coherence thereby seem to be related. However, this negative
correlation does not simply mean that more of one will automatically lead to less of the
other. Something that is experienced as disordered is not necessarily a result of too much
complexity, but could instead be a result of low coherence (Kaplan and Kaplan, 1989 [
10
]). In
addition, a high amount of complexity will not necessarily lead to low preference, as long as
there is also high coherence. Information on the spatial organization of landscape patterns
can, therefore, be seen as an important component for describing perceived complexity [
48
].
Berlyne [
49
] explored why organisms have different levels of arousal when exposed to
stimuli with different characteristics of novelty, complexity, surprise, and so on. In 1971, he
introduced three sets of variables that had the potential for arousal through the amount
of information transmitted to an organism. One of them included complexity, and he
suggested that the perception of complexity was related to factors such as order, the amount
of elements in a scene, and asymmetry [
30
]. In studies on environmental preferences,
Kaplan [
9
] considered complexity as being an immediate exploration component. He
introduced the features of complexity, namely diversity, the number of different visual
elements in a landscape, and organization. Additionally, when complexity is high in a
scene, that scene offers more different things to increase the sense of exploration [10].
Variety of Elements
Complexity in a scene involves the number of elements present in the scene [
50
]. Addi-
tionally, the variety of places is an attribute of complexity [
51
]. In general, the presence
of different elements with different shapes that have different functions represents the
presence of complexity in a scene [
52
]. In studies of visual indicators predicting land-
scape preferences, the results showed that the number and variety of landscape elements
that represent diverse features are strong predictors of complexity [
53
55
]. Kuper [
38
]
considers vegetation diversity as a visual attribute of complexity. He states that a high
number of different plant species give more landscape information for exploration [
48
].
Visual complexity in urban landscapes depends on the amount of information one receives
from the environment. This information includes the number of visual elements and their
variety [
56
58
]. Studies of the number of elements in facades also show that the number
affects the experience of complexity [
59
]. The design characteristics for the perception of
complexity in interior design are the number and variety of elements. In environments
with low complexity, there is very little furniture or accessories [60].
Number of Colors
Weil [
61
] considered complexity as the diversity of five attributes, and in a study of
natural environments and artworks to measure complexity, color was one of the factors
found to influence diversity. Many studies have suggested that the color diversity of plants
affects individuals’ aesthetic preferences [
62
64
] and that plants covered with colorful
flowers are attractive and stimulating for most people and provide high levels of aesthetic
preference [
65
]. In a study of regenerated industrial landscapes, the results showed that
color diversity is an important factor in people’s preferences [
66
]. Subsequently, another
study was carried out on urban roof landscapes that showed that plant characteristics such
as color increase landscape diversity, meaning they are most preferred and restorative [
67
].
One of the visual attributes of complexity in urban streets is the number of colors that
significantly impact one’s perception of complexity in an urban setting [
68
]. Kuper [
69
]
Sustainability 2022,14, 7357 6 of 19
used the color changes of plants in 4 different visual modes at several different sites to
predict the level of complexity. The results showed that the scene’s complexity was low
when the numbers of flowers and colors were fewer. In his second study on the complexity
of plant diversity (flowering, leafy, or autumn-colored plants), the results showed that
flowering plants (yellow and purple) and autumn colors (yellow and red), through their
color, provide more environmental information than leafy greenery. This information can
also provide an incentive to explore the environment [
39
]. Color also plays an important
role in determining the level of visual complexity in abstract images.
Organization (Symmetry–Asymmetry)
Many studies have suggested that the organizational level is one of the attributes
of complexity (e.g., [
7
,
55
]). How complexity relates to symmetry has been shown to be
one of the most important factors influencing aesthetic judgment and preference [
49
,
70
].
The organization of a setting can be symmetrical or asymmetrical. In studies of tourist
destinations, the results showed that symmetry is an important factor in aesthetic judg-
ment [
71
,
72
]. Many studies have addressed the impacts of this component on complexity.
For example, in a study of nature images, the results showed that complexity is affected by
symmetry [
73
]. In another study on product packaging, symmetrical information items
had a significant impact on the complexity of the packaging [74].
Studies have also examined the three attributes of complexity simultaneously
[75,76]
.
For example, in a study of natural landscape preferences and restoration in these environ-
ments, the predictors of complexity were defined as the number of elements, the level of
organization, and the number of colors [75].
2.2. Discrete Choice Experiment
Choice modeling is a method for modeling stated preferences, which are generally
elicited as DCEs. DCEs are now commonly used in transportation research, health eco-
nomics, energy research, and environmental economics, and recently in landscape archi-
tecture. The DCE approach is based on Lancastrian consumer theory [
77
] and random
utility models and allows the analysis of the stated choices under utility maximization [
78
].
Lancaster [
77
] stated that individual utility levels for goods do not relate to the goods
themselves but to attributes of the goods. The basic idea of a DCE is to choose the most
preferred alternative from a number of alternatives, whereby each alternative contains the
same attributes differentiated by attribute level. The indirect utility function formally is
representing as:
Uni = Vni +εni j.
Here, U
nm
refers to the true utility of the alternative ito the respondent n. V
ni
is
the deterministic or observable portion of the utility and is calculated by multiplying the
parameters
β
m(1,
· · ·
, m) of the variables (observable and important visual attributes) that
are presented to respondents in the DCEs and xnji (levels of visual attributes).
Vni =βnm xnmi.
Here,
εni
is the error or the portion of the utility that is unknown and that represents
the random components of the utility, also called the error term. Error terms are unobserved
and unmeasured. A wide range of distributions can be used to represent the error terms
over individuals and alternatives. Considering a specific parametric distribution of the
observed component, a probabilistic analysis of individuals’ choices is possible. Random
utility
εni
has a probability distribution. In the framework of the DCE approach, it is
assumed that if alternative (i) is preferred to alternative (j), the utility of alternative (i) is
greater than the utility of alternative (j).
Pni =Prob (uni >unj j6=i),
=Prob (Vni +εni >Vnj +εnj) = Prob (Vni Vnj >εnj εni j6=i).
Sustainability 2022,14, 7357 7 of 19
The type of DCE determines the probability distribution. There are several different
probability distributions in the various models, namely standard conditional, logit, multi-
nomial probit, nested logit, and mixed logit models or random parameter logit models.
If the error term is an identically and independently distributed type I extreme value
(Gumbell distribution) with scale parameter (
µ
), the probability of individual nchoosing
an alternative (i) over another (j) takes the well-known form of the multinomial logit:
Pni =eµβxni
j
j=1eµβxnj
Constant (homogeneous) parameters were assumed for each attribute over all respon-
dents. According to the current literature on DCE, the conditional logit model, often called
the multinomial logit (MNL) model, has been widely used when research samples have
mean preferences. It is well-known that the standard MNL model has the limitation of
providing a point estimate for each coefficient, which is equivalent to assuming preference
homogeneity for the entire sample. This condition does not likely hold in all scenarios;
therefore, analysists are often concerned with estimating more flexible models that account
for taste heterogeneity. Among the various options is the random parameter logit (RPL)
model. The RPL model assumes that coefficients are individual-specific and follow a
random distribution, for which a location and a scale parameter are estimated. Thus, the
probability that a given individual chooses any alternative becomes:
Pni =eµβxni
j
j=1eµβxnj f(β)dβ
F(
β
) shows a distribution density that can include any continuous probability, and the
model can be estimated using the maximum simulated likelihood method.
2.3. Study Erea
The study was conducted within Gorgan city. This city is the capital of Golestan
Province, which is located in the north of Iran, and is a large city bordering (approximately
30 km) the Caspian Sea (Figure 1). Golestan University is a major institute of higher
education in Gorgan with about 4000 students.
Sustainability 2022, 14, x FOR PEER REVIEW 8 of 20
Figure 1. Location of study.
2.4. Participants
Participants for this study consisted of 396 students from Golestan University.
According to Table 1, the age group under 25 years old accounted for 82% of the sample,
while more than 70% of the sample were undergraduate students. Furthermore, 55% of
the respondents were female.
Table 1. Demographic profile of the participants (n = 396).
Age
<25 Years
82%
2530 Years
14.5%
>30 Years
3.5%
Gender
male
45%
female
55%
Occupation
Undergraduate
70.5
Graduate
19.5
2.5. Questionnaire
The questionnaire consisted of two parts. The first part was related to the
demographic information of the participants (gender, age, field of study, and level of
education), while in the second DCE part of the questionnaire a digital questionnaire was
shown to the participants. These images were modeled based on experimental design to
create different scenarios. Each participant was shown 6 sets of choices with 2 choice
options each (2 images representing different scenarios) and had to choose the urban park
that represented their preferences. Each participant evaluated 12 urban park images.
2.6. Experimental Design
Visual Attributes and Levels
Based on the literature review, a total of nine visual attributes derived from the four
predictors of the preference matrix and their levels were considered. Table 2 shows an
overview of the attributes and their levels.
Figure 1. Location of study.
Sustainability 2022,14, 7357 8 of 19
2.4. Participants
Participants for this study consisted of 396 students from Golestan University. Ac-
cording to Table 1, the age group under 25 years old accounted for 82% of the sample,
while more than 70% of the sample were undergraduate students. Furthermore, 55% of the
respondents were female.
Table 1. Demographic profile of the participants (n= 396).
Age <25 Years
82%
25–30 Years
14.5%
>30 Years
3.5%
Gender male
45% female
55%
Occupation Undergraduate
70.5 Graduate
19.5
2.5. Questionnaire
The questionnaire consisted of two parts. The first part was related to the demographic
information of the participants (gender, age, field of study, and level of education), while
in the second DCE part of the questionnaire a digital questionnaire was shown to the
participants. These images were modeled based on experimental design to create different
scenarios. Each participant was shown 6 sets of choices with 2 choice options each (2 images
representing different scenarios) and had to choose the urban park that represented their
preferences. Each participant evaluated 12 urban park images.
2.6. Experimental Design
Visual Attributes and Levels
Based on the literature review, a total of nine visual attributes derived from the four
predictors of the preference matrix and their levels were considered. Table 2shows an
overview of the attributes and their levels.
Table 2. Description of research variables.
Mystery
Physical access [2023]
Visual access [20,21,29]
Complexity
Variety of elements [38,47,48,51,53,54,56,75,76,79]
Number of colors [39,62,63,65,67,75,76]
Organization [71,72,7476,80]
Coherence
Uniformity [7,37]
Organization [38,47,81]
Legibility
Wayfinding [31,32]
Distinctive elements
Mystery can take on two attributes—visual access and physical access. The aspects of
physical access were straight and curved paths. Visual access in an urban park is highly
affected by dense vegetation at eye level; therefore, full visibility is provided by tall trees
and visual obstructions are created by short trees.
Complexity can take on three attributes—the variety of elements, number of colors,
and organization of elements. The attribute variety of elements included the number of
Sustainability 2022,14, 7357 9 of 19
elements in the scene. For example, the presence of 3 types of elements shows low diversity
and 9 types of elements show high diversity. The number of colors equaled 2 or 4 colors in
the flowers. The organization of elements was designed symmetrically and asymmetrically.
Coherence can take on two attributes—uniformity and organization of elements.
Uniformity consisted of two levels, a single material and 3 materials, and there were three
ways of organizing natural elements, namely scattered, clustered, and formal.
Legibility can take on two attributes—distinctive elements and wayfinding. Distinctive
elements such as landmarks include two levels, presence and absence, while wayfinding
through signs also has the same levels. It should be noted that the amount of trees and
vegetation in all scenes was equal to avoid the influence of greenery in the images. Table 3
shows the attribute levels used in the DCE on urban parks.
Table 3. Attribute levels used in the DCE on urban parks.
Predictors Visual Attributes Levels
Mystery Physical access Curved Straight
Visual access None Full
Complexity Variety of elements 3 9
Number of colors 2 4
Organization Asymmetry Symmetry
Coherence Uniformity 1 3
Organization Scattered Clustered Formal
Legibility Distinctive elements Included Excluded
Wayfinding Included Excluded
The full factorial design included all possible combinations (2
8×
3 = 768); thus, a
D-optimal orthogonal design was employed and evaluated using the program SAS Version
9.2 to reduce the number of combinations to 72. The created design consisted of a final
set of 72 choice profiles, presented in 12 choice sets with 2 choice sets in 6 blocks to
which participants were randomly assigned. ‘No choice’ was not a realistic option in
the context [
82
,
83
] and the participants selected one of the urban parks. For each choice
option, the images were constructed using Sketchup and Photoshop software programs.
All renderings were taken from the observer’s point of view. This allowed the participants
to evaluate the simulation from one point as if they were standing on the pathway. Figure 2
presents an example of a choice task in the choice experiment. The list of the 9 attributes
was also shown.
Sustainability 2022, 14, x FOR PEER REVIEW 10 of 20
[82,83] and the participants selected one of the urban parks. For each choice option, the
images were constructed using Sketchup and Photoshop software programs. All
renderings were taken from the observer’s point of view. This allowed the participants to
evaluate the simulation from one point as if they were standing on the pathway. Figure 2
presents an example of a choice task in the choice experiment. The list of the 9 attributes
was also shown.
Figure 2. Overview of the different levels for 2 stimuli images: (left) high visual access, straight path,
high variety of elements, high number of colors, symmetrical, high uniformity, organization
scattered, and without signage and distinctive elements; (right) low visual access, straight path, low
variety of elements, low number of colors, symmetrical, low uniformity, organization clustered,
with signage, and without distinctive elements.
In the discrete choice experiment, an alternative from the choice set represents the
alternative with the highest level of preference. In the stated preferences approach,
individuals choose an alternative from various alternatives, and choosing one alternative
over the other alternatives indicates that the chosen alternative has the highest utility [84].
In this study, utility refers to the scene inside the image, not the image itself, and
participants are asked to choose the environment inside the image. The utility derived
from the environment is the only difference between the two alternatives [38,82,85].
2.7. Data Collection
Data were collected from students at Golestan University. First, aim and a short
description of the survey procedure were presented. Then, participants were required to
give their informed consent, and were informed that they were free to leave at any time.
Subsequently, participants completed the first part of the questionnaire, which included
demographic details. Next, participants were randomly assigned to a block. In this study,
each participant was shown only one block. Each block contained 6 choice sets, and each
set contained 2 alternatives. The block was selected randomly to be displayed to
individuals to avoid the effect of any order in the experiment. These random numbers are
given at www.random.org, for free. In selecting blocks to be displayed to participants, it
was always important that the number of times each block was displayed to participants
was approximately the same to avoid the potential impact of each blocks chances of being
displayed. The data collection process took approximately 4 min per person. This
questionnaire was shown to the students using Java software on a laptop (Figure 3). This
software was designed for collecting data using a jar format. By selecting blocks from the
menu and then selecting ‘start survey’, binary images are shown. Each participant chooses
one of the images. In the end, data are placed in an excel file related to the same block.
Each respondent was randomly assigned 6 choice sets with two varying alternatives,
leading to a total of 2376 observed choices (396 × 6 = 2376). The advantage of this data
collection method is that the number of unusable and discarded answers is reduced.
Figure 2.
Overview of the different levels for 2 stimuli images: (
left
) high visual access, straight path,
high variety of elements, high number of colors, symmetrical, high uniformity, organization scattered,
and without signage and distinctive elements; (
right
) low visual access, straight path, low variety of
elements, low number of colors, symmetrical, low uniformity, organization clustered, with signage,
and without distinctive elements.
In the discrete choice experiment, an alternative from the choice set represents the alter-
native with the highest level of preference. In the stated preferences approach, individuals
choose an alternative from various alternatives, and choosing one alternative over the other
alternatives indicates that the chosen alternative has the highest utility [
84
]. In this study,
Sustainability 2022,14, 7357 10 of 19
utility refers to the scene inside the image, not the image itself, and participants are asked
to choose the environment inside the image. The utility derived from the environment is
the only difference between the two alternatives [38,82,85].
2.7. Data Collection
Data were collected from students at Golestan University. First, aim and a short
description of the survey procedure were presented. Then, participants were required to
give their informed consent, and were informed that they were free to leave at any time.
Subsequently, participants completed the first part of the questionnaire, which included
demographic details. Next, participants were randomly assigned to a block. In this study,
each participant was shown only one block. Each block contained 6 choice sets, and
each set contained 2 alternatives. The block was selected randomly to be displayed to
individuals to avoid the effect of any order in the experiment. These random numbers are
given at www.random.org, for free. In selecting blocks to be displayed to participants, it
was always important that the number of times each block was displayed to participants
was approximately the same to avoid the potential impact of each block’s chances of
being displayed. The data collection process took approximately 4 min per person. This
questionnaire was shown to the students using Java software on a laptop (Figure 3). This
software was designed for collecting data using a jar format. By selecting blocks from the
menu and then selecting ‘start survey’, binary images are shown. Each participant chooses
one of the images. In the end, data are placed in an excel file related to the same block. Each
respondent was randomly assigned 6 choice sets with two varying alternatives, leading
to a total of 2376 observed choices (396
×
6 = 2376). The advantage of this data collection
method is that the number of unusable and discarded answers is reduced.
Sustainability 2022, 14, x FOR PEER REVIEW 11 of 20
Figure 3. Screen shots of the software and a choice task.
3. Results
3.1. Random Parameter Logit Model
Here, the observations from 396 students were obtained for data analysis, which was
a sufficient sample size for this type of experiment. A RPL model was used to estimate the
main effects model with visual attributes and to investigate the extent of the response
heterogeneity using Panda Biogem software [86].
First, the randomness of the corresponding parameters was measured to consider the
main effect of visual attributes as random (p < 0.1). An attribute of mystery (physical
access) and an attribute of the level of coherence (clustered) with non-significant. RPL
model had an acceptable fit (McFadden pseudo-R2 = 0.202, n = 2358, panel effects, 235
uniform draws, 10 attributes, normal distribution) .
3.2. Parameter Value
The RPL model probes the mean sample estimates and the presence of preference
heterogeneity among respondents. The levels of visual attributes were dummy-coded in
the utility function based on whether they were present (value 2) or not present (value 1).
Dummy coding makes it relatively easy to interpret the modeling results for part-worth
effects. The parameter value for each dummy-coded attribute level is by definition the
part-worth value of that attribute. Utility is added compared to the base alternative if the
value = 2 (present). Thus, the parameter value indicates the part-worth utility for that
visual attribute (βa). Table 4 shows the model parameters.
Table 4. Parameter estimates of the RPL model.
Attributes
Reference
Levels
PV a
SE
P
St.dv
SE
p-Value
Mystery
Visual access
(high)
Medium
β1
0.226
0.091
<0.01
1.49
0.549
<0.001
Physical
access
(curve)
Straight
0.009
0.058
0.875
-
-
-
Complexity
Variety of
elements
(9-high)
3-low
β2
0.559
0.089
<0.0001
1.7
0.442
<0.0001
Figure 3. Screen shots of the software and a choice task.
3. Results
3.1. Random Parameter Logit Model
Here, the observations from 396 students were obtained for data analysis, which was
a sufficient sample size for this type of experiment. A RPL model was used to estimate
the main effects model with visual attributes and to investigate the extent of the response
heterogeneity using Panda Biogem software [86].
First, the randomness of the corresponding parameters was measured to consider the
main effect of visual attributes as random (p< 0.1). An attribute of mystery (physical access)
and an attribute of the level of coherence (clustered) with non-significant. RPL model had
an acceptable fit (McFadden pseudo-R
2
= 0.202, n= 2358, panel effects, 235 uniform draws,
10 attributes, normal distribution).
Sustainability 2022,14, 7357 11 of 19
3.2. Parameter Value
The RPL model probes the mean sample estimates and the presence of preference
heterogeneity among respondents. The levels of visual attributes were dummy-coded in
the utility function based on whether they were present (value 2) or not present (value 1).
Dummy coding makes it relatively easy to interpret the modeling results for part-worth
effects. The parameter value for each dummy-coded attribute level is by definition the
part-worth value of that attribute. Utility is added compared to the base alternative if the
value = 2 (present). Thus, the parameter value indicates the part-worth utility for that
visual attribute (βa). Table 4shows the model parameters.
Table 4. Parameter estimates of the RPL model.
Attributes Reference
Levels PV aSE pSt.dv SE p-Value
Mystery
Visual access
(high)Medium β
1
0.226
0.091
<0.01 1.49
0.549
<0.001
Physical access
(curve)Straight
0.009 0.058
0.875 -- -
Complexity
Variety of
elements
(9-high)3-low β
2
0.559
0.089
<0.0001 1.7
0.442
<0.0001
Number of
colors
(4-high)2-low β
3
0.29
0.086
<0.0001 0.635
0.175
<0.0001
Organization
(symmetry)Asymmetry β
4
0.208
0.080
<0.001 - - -
Coherence
Uniformity
(3-high)1-low β
5
0.474
0.103
<0.0001 0.975
0.185
<0.0001
Organization
(formal)Scattershot
0.003 0.087
0.969 1.17 0.573 <0.05
Organization
(clustered)Scattershot
0.060 0.085
0.484 -- -
Legibility
Wayfinding
(included)Excluded β
6
0.168
0.094
<0.1 1.41
0.669
<0.05
Distinctive
elements
(included)Excluded 0.094 0.1 0.348 0.878 0.189 <0.0001
Number of observations = 2358; log likelihood = 2606.5937; LR chi2(10) = 197.46; pseudo R
2
= 0.0365;
a
: parameter value.
Figure 4shows a graph of the parameter value related to each visual attribute on
which it was significantly based on regarding the results in Table 4. The variety of elements
(complexity attribute,
β
2 = 0.559, <0.0001) had the most positive influence on preference,
followed by uniformity (coherence attribute,
β
5 = 0.474, <0.0001. Next was the number
of colors (complexity attribute,
β
3 = 0.290, <0.0001) followed by visual access (mystery
attribute,
β
1 = 0.226, <0.01). Organization (complexity attribute,
β
2 = 0.208, <0.001) and
wayfinding (legibility attribute,
β
2 = 0.168, <0.1) had the lowest values. Two attributes,
organization (coherence attribute) and distinctive elements (legibility attribute), did not
significantly influence preferences.
Sustainability 2022,14, 7357 12 of 19
Sustainability 2022, 14, x FOR PEER REVIEW 12 of 20
Number of
colors
(4-high)
2-low
β3
0.29
0.086
<0.0001
0.635
0.175
<0.0001
Organization
(symmetry)
Asymmetry
β4
0.208
0.080
<0.001
-
-
-
Coherence
Uniformity
(3-high)
1-low
β5
0.474
0.103
<0.0001
0.975
0.185
<0.0001
Organization
(formal)
Scattershot
0.003
0.087
0.969
1.17
0.573
<0.05
Organization
(clustered)
Scattershot
0.060
0.085
0.484
-
-
-
Legibility
Wayfinding
(included)
Excluded
β6
0.168
0.094
<0. 1
1.41
0.669
<0.05
Distinctive
elements
(included)
Excluded
0.094
0.1
0.348
0.878
0.189
<0.0001
Number of observations = 2358; log likelihood = 2606.5937; LR chi2(10) = 197.46; pseudo R2 = 0.0365;
a: parameter value.
Figure 4 shows a graph of the parameter value related to each visual attribute on
which it was significantly based on regarding the results in Table 4. The variety of
elements (complexity attribute, β2 = 0.559, <0.0001) had the most positive influence on
preference, followed by uniformity (coherence attribute, β5 = 0.474, <0.0001. Next was the
number of colors (complexity attribute, β3 = 0.290, <0.0001) followed by visual access
(mystery attribute, β1 = 0.226, <0.01). Organization (complexity attribute, β2 = 0.208, <0.001)
and wayfinding (legibility attribute, β2 = 0.168, <0.1) had the lowest values. Two attributes,
organization (coherence attribute) and distinctive elements (legibility attribute), did not
significantly influence preferences.
Figure 4. Part-worth utility for visual attributes.
3.3. Evaluation of Attribute Levels
A positive sign of the coefficients throughout the model means that a higher value of
the variable increases the probability to choose a visual attribute compared to the
Figure 4. Part-worth utility for visual attributes.
3.3. Evaluation of Attribute Levels
A positive sign of the coefficients throughout the model means that a higher value of
the variable increases the probability to choose a visual attribute compared to the reference
level. Based on the results in Table 4regarding complexity attributes, the respondents
preferred 9 types of elements (high diversity) over the presence of 3 types of elements (low
diversity). They also clearly preferred environments with a high number of colors over
those with a low number of colors. Another property of complexity was the organization,
where respondents preferred symmetry over asymmetry. In terms of coherence attributes,
participants preferred environments with 3 materials over those with single materials.
Regarding the organization of natural elements, formal coherence attributes were found to
be significant at p< 0.05. Furthermore, the results regarding mystery attributes showed that
visual access was a significant visual attribute, with respondents preferring full visual access
over medium visual access. The second attribute of mystery, physical access, was not found
to be significant at p< 0.05. Finally, for the results regarding legibility attributes, respondents
preferred environments where distinctive elements were included over environments where
they were excluded. The result for the wayfinding attribute was not found to be significant
at p< 0.05.
3.4. Random Parameter
The results in Table 4show that the standard deviations of the visual access (1.49,
<0.001), visual elements (1.7, <0.0001), number of colors (0.635, <0.0001), uniformity (0.975,
<0.0001), and distinctive elements (1.41, <0.05) were significant. Although the means
for organization as a coherence attribute and wayfinding as a legibility attribute were not
significant, the standard deviations were significant (
1.17, <0.05, 0.878, <0.0001). A
significant standard deviation indicates preference heterogeneities for these attributes
and different choices of visual attributes from one individual to another. Although the
mean value for organization as a complexity attribute was significant at p< 0.05, standard
deviations of organization were not found to be significant.
3.5. Interactions
Interactions between levels of visual attributes were investigated. Among all inter-
actions, only one significant relationship found, between organization (an attribute of
complexity) and visual access (an attribute of mystery). However, this interaction was not
significant in ML.
Sustainability 2022,14, 7357 13 of 19
Although the sample included students at Golestan University, the interactions be-
tween each of the demographic variables including age, gender, and education were
estimated in ML. Their effect were not found be significant at p< 0.05.
4. Discussion
In this study, the effects of attributes derived from the four predictors of Kaplan’s
preference matrix on individuals’ preferences for urban parks were examined. As stated in
our review, complexity emerged early on as an important factor in people’s preferences,
followed by coherence. Kaplan’s work led to the development of a preference matrix, where
complexity and coherence were supplemented with the legibility and mystery factors. Our
comprehensive review led to three specific visual attributes being linked to complexity,
as well as two visual attributes each being linked to coherence, legibility, and mystery.
Since these visual attributes had clear levels, a preference study via DCE would increase
knowledge of these attributes, and using this knowledge urban parks should be designed
to increase people’s preferences for these areas.
Many studies have shown the relationships between the four predictors (complexity,
coherence, mystery, and legibility) and preferences [
10
,
13
,
58
]. However, this study also
showed the roles of certain visual attributes associated with these predictors, and how these
attributes increased people’s preferences for urban parks. People had high preferences for
urban parks with a high variety of elements. Furthermore, the parks should be colorful,
have a symmetrical organization of elements (complexity attributes), have high uniformity
(coherence attributes), a high level of visual access (an attribute of mystery), and should
facilitate wayfinding (an attribute of legibility). However, physical access (an attribute of
mystery) and distinctive elements (an attribute of legibility) did not contribute to people’s
decisions when choosing urban parks.
The present study investigated the complexity based on three visual attributes: a
variety of elements, the number of colors, and organization. The most important attribute
of complexity that predicted preference was the variety of elements in the environment,
which is consistent with the results of most studies that have measured complexity based
on the number and variety of elements [
38
,
48
,
51
,
53
,
55
,
57
,
60
,
79
,
87
]. A greater variety of
elements means that there is more to explore and that the viewer has a lot to discover, which
leads to people staying longer in the environment [
38
]. The second attribute of complexity
is color diversity. Color diversity in vegetation and flowers is an important factor that
affects the complexity of parks. Previous studies have shown that color diversity has
significant relationships with preferences and aesthetic preferences [
21
,
39
,
63
,
65
], and the
greater the color diversity the greater the preference [
57
,
58
]. Furthermore, researchers have
stated that scenes with more color diversity improve psychological well-being and have
restorative potential [
39
,
64
]. The third attribute regarding complexity is the organization
of components. In this study, the symmetrical organization of components was highly
preferred. Studies have shown that an area can have high complexity and high preference
if there is order, whereby the symmetry of the area is an important visual attribute that
reduces the feeling of chaos, increases understanding, and increases one’s ability to interpret
the content [
47
,
55
]. The study by Chen et al. [
73
], among others, showed that people prefer
symmetrical scenes to asymmetrical ones, and that the results are interpreted on the
basis that symmetrical environments are less complex. Complex and difficult-to-interpret
environments are tiring, and the symmetrical images contain fewer unique elements and
are less complex than asymmetrical images, making it easier for individuals to process
the scene.
The second most important characteristic that affects people’s preferences for urban
parks is the uniformity of the material, which is related to coherence. Researchers have
investigated and demonstrated the positive effects of coherence on preferences and aes-
thetics [
37
,
88
,
89
]. In this study, one chosen visual attribute was material, with one or
three materials in the image. According to the definition of coherence, immediate under-
standing easily arises in scenes with less information (one material), so people may prefer
Sustainability 2022,14, 7357 14 of 19
these scenes. However, the results of this study suggest that scenes with one material are
perceived as boring, so the respondents prefer, perhaps due to the recreational intention,
several different materials (up to three types of material). Again, there is a link between
complexity and coherence that has been investigated in many studies [
38
,
54
,
90
]. There
seems to be a golden mean in both cases. The second visual attribute of coherence was
organization at three different levels, and this attribute showed no significance in terms
of preference expect in terms of formality. Formal organization was possibly perceived as
boring, while clustered organization possibly followed the golden mean.
Visual access is an important attribute of mystery. People seem to prefer moderate
visual access in such settings because the promise of hidden information is more prevalent
in scenes with a partial view [
24
]. The promise of further information also encourages one
to explore and stay in the environment [
20
], and this exploratory behavior helps individuals
to build a mental map in the park [
91
]. In this study, we found that a large number of
elements in the park could partially block the participant’s view, while a high level of visual
access to vegetation modulates their view, which is related to the “partial hiding of the
scene” effect and the definition of mystery. Scenes with a medium level of dense vegetation
provide a medium level visual access. In this regard, many studies have indicated that
medium-density scenes have high preference [
92
]. Furthermore, according to Appleton’s
landscape theory, people’s chances of survival increase in landscapes where they are able to
see without being seen [
93
]. As such, high-density scenes that block people’s vision reduce
the person’s sense of security. Therefore, the presence of dense trees that block the entire
view of the person in parks is less preferred.
This study shows that people’s choices of urban parks relate to their ability to under-
stand and find signs in the environment. Therefore, such signs should be included in urban
parks. There are various visual attributes that increase legibility, such as the presence of
various odd elements, which help the visitor in the absence of artificial signs [
11
]. The forms
and materials used in artificial signs are important and influential factors. For example,
wooden materials often create more coherence between artificial elements and the natural
environment [
82
]. The entrance to the urban park in this study was designed in such a way
that the viewer’s vision was partially blocked at first, reducing the visual access and feeling
of security. However, on the other hand, blocking part of the vision can cause exploratory
behavior in individuals.
In addition to the above results, this study also shows preferences for heterogeneous
attributes. This showed that individual preferences are different. However, the factors and
personal characteristics can influence preference need to be studied more.
Method Discussion
The study was based on a literature review that included both classic studies by
Fechner [
14
] and Berlyne [
49
] and the research by Kaplan [
10
], as well as more current
studies. Unfortunately, literature reviews are often limited to recent years, which means
that important results from classical studies can be distorted or completely forgotten over
time. The review showed that there was good support in the research for the selection of
the nine visual attributes that could be linked to Kaplan’s preference matrix. It was also
possible to specify strength levels for these nine visual attributes, which made it possible to
use them in discrete choice experiments. These nine attributes could of course have been
expressed in different ways by manipulating the images, and could have been combined in
other ways. In addition, we may have missed some important visual attributes related to
the preference matrix.
In this study, we used images as the visual aspects of environments to examine
in this study. We used screen shots for several reasons. First, people must, within a
reasonable period of time, make a choice based on selected possibilities in the discrete
choice experiment. Hence, it was not possible to use more advanced equipment such as
immersive virtual environments. Second, image-based studies of preferences (e.g., on
screens) have been shown to be valid and reliable [
94
]. In a potential follow-up study, we
Sustainability 2022,14, 7357 15 of 19
could conduct the research in fields where multisensory aspects are included, based on
embodied, situated cognition [9597].
An innovation in this study is the choice to study manipulated images through a
discrete choice experiment. The results show that the DCE resulted in clear and significant
results, which showed that visual attributes related to complexity above all are of great
importance in the choice of park to visit.
The participants selected in this study are not representative of the population. How-
ever, a relatively homogeneous group is often preferred in experiments so that any differ-
ences found can be attributed to the chosen experimental situation rather than to differences
in the respondents. Hence, we used homogenous convenience sampling [
98
]. This kind of
relatively small experimental study with a homogeneous sample of respondents should
be followed by a more comprehensive study with a randomized sample of respondents.
Since university students were the sample in this study, it would be worthwhile conduct-
ing follow-up studies with other samples more representative of the general population,
reflecting the influence of the respondents’ sociodemographic or situational characteristics.
In addition, comparative studies are needed regarding the effects of visual attributes on
environmental preferences in different countries and cultures.
Some of the results found here stood out. The visual attributes that most influenced the
respondents’ choices were related to complexity and coherence. Kaplan argues that these
predictors are perceived two-dimensionally, while legibility and mystery are perceived
three-dimensionally; that is, when moving in the environment. This could be an explanation
for the result found here.
Another result that stood out was that our respondents had strong preference for
environments with many and strong colors. Completely different results have been reported
from therapeutic environments, where depressed and exhausted people cannot cope with
such environments at all but completely avoid them [
99
]. Probably the result from the
present study has to do with the fact that the respondents in this case were young, healthy
students, who had no problems with environments containing many strong stimuli.
5. Conclusions
With reference to the importance of urban parks in urban life and the effects on
people’s health and well-being, this study focused on recognizing the visual characteristics
that influence people’s choices and preferences for urban parks.
The main result was that people’s preferences are high for environments that are
perceived to have high complexity. However, there must be organization and order in
this complexity, for example through symmetry. Several results support our findings that
there is a fine balance between complexity, coherence, mystery, and legibility, not least
in terms of the high or low levels of attributes associated with these predictors. The area
should be perceived as interesting and easy to find without being perceived as boring,
chaotic, or even unsafe. The results of this study can be interpreted as meaning that there
is a golden mean. More research is needed to specify the design attributes that can reveal
this golden mean. Our study shows that image studies combined with DCE can be used
to develop and validate design attributes to be used in machine learning and AI, where
many developments now occur [
100
], or to validate design attributes in models used in the
practical design of urban parks [97,101].
This study can also provide knowledge for designing parks, which would help land-
scape architects, architects, urban planners, and other professional designers to increase
use and user satisfaction among citizens when visiting urban parks. The overall result
of improving the structure of urban green spaces is to attract people to spend more time
in urban parks and to reap the benefits of such spaces. This study points to the signifi-
cant impacts of diversity, especially the diversity of elements in the park, and suggests
that designers should use different elements to create spaces that are more attractive and
multifunctional. This can be done by adding various types of equipment and facilities,
including sports equipment, as well as a variety of plant species, flowers, and vegetation. In
Sustainability 2022,14, 7357 16 of 19
addition, spatial diversity should be incorporated (spaces for exercise, games for children,
etc.) and different types of materials should be used. This study also shows the importance
of seeking a balance between order and ease of finding in a green area in order to increase
the feelings of trust and security, but at the same time to create interest in exploring the area.
This involves finding the balance between visual accessibility and the feeling of mystery in
the environment.
Author Contributions:
Conceptualization, M.P. and M.S.; methodology, M.P., M.S., C.v.O. and
P.G.; software, M.P. and C.v.O.; validation, M.P. and C.v.O.; formal analysis, M.P., C.v.O. and P.G.;
investigation, M.P. and C.v.O.; resources, M.P., M.S. and C.v.O.; data curation, M.S.; writing—original
draft preparation, M.P. and M.S.; writing—review and editing, M.P., M.S. and P.G.; supervision, M.P.
and P.G.; project administration, M.P. All authors have read and agreed to the published version of
the manuscript.
Funding:
Part of this study was funded by NordForsk, an organization under the Nordic Council
of Ministers that provides funding for research and research infrastructure: “NORDGREEN: Smart
Planning for Healthy and Green Nordic Cities Project” (NordForsk: ID-nr 95322).
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Not applicable.
Acknowledgments:
This article was inspired by the first author’s master’s thesis in architecture,
entitled “Design of Urban Park Approach to Perceived Spaciousness Role in Citizens’ Visual Pref-
erence of the Urban Natural landscape”, completed at the Department of Engineering, Faculty of
Architecture, Golestan University.
Conflicts of Interest: The authors declare no conflict of interest.
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... Legibility means that the information can be easily identified (i.e., via distinctive elements or way finding). Mystery refers to the accessibility of the information [49][50][51][52]. Recent scholars have not only adopted the preference matrix in research on the natural environment but have also verified that the factors of the preference matrix can be used to predict the environmental preference of people in built environments [51,53,54]. ...
... Mystery refers to the accessibility of the information [49][50][51][52]. Recent scholars have not only adopted the preference matrix in research on the natural environment but have also verified that the factors of the preference matrix can be used to predict the environmental preference of people in built environments [51,53,54]. As the four factors of the preference matrix are visual properties [51], they are adopted in this study to predict tourists' visual preferences with regard to historic districts. ...
... Recent scholars have not only adopted the preference matrix in research on the natural environment but have also verified that the factors of the preference matrix can be used to predict the environmental preference of people in built environments [51,53,54]. As the four factors of the preference matrix are visual properties [51], they are adopted in this study to predict tourists' visual preferences with regard to historic districts. Additionally, coherence, complexity, legibility, and mystery can be adopted as independent variables to predict people's environmental preferences [53]. ...
Article
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Historic districts should be sustainably developed by preserving historic architectural landscapes and developing tourism. Researchers have found that attachment to a place positively influences pro-tourism and pro-environment behaviors among tourists, indicating that exploring the landscape planning of historic districts from the perspective of place attachment is a noteworthy topic of sustainability. However, there are few studies on how historic district landscapes ignite tourists’ place attachment. Using a historical district named Taiping Old Street in Taiwan as an example, we investigated the association between tourists’ landscape evaluation and place attachment in historic districts. This study mainly adopted questionnaire surveys and used partial least squares structural (PLS) equation modeling for survey data analysis. (1) The study identified three dimensions of tourists’ landscape evaluation of historic districts: visual preference, cultural heritage value, and authenticity. (2) The stimulus–organism–response (SOR) model was combined with the studies by previous scholars and a conceptual model put forward for the relationship between tourists’ landscape evaluation, destination image, and place attachment. (3) The model was verified, and we found that (i) tourists’ landscape evaluation in terms of cultural heritage values and authenticity had significant positive effects on destination image; (ii) tourists’ visual preference, evaluation of authenticity, and destination image had significant positive effects on place attachment; and (iii) tourists’ destination image influenced the impact of authenticity and cultural heritage values on place attachment. This study provides both theoretical references for the formation process of place attachment from a landscape perspective and suggestions for landscape planning in the sustainable development of historic districts of a similar type.
Article
Full-text available
The importance of urban green areas to support people's health and wellbeing has been confirmed by many studies [1]. In addition to regulating functions regarding water, air, and climate, urban green spaces can also contribute through psychologically driven pathways to, e.g., aid restoration from stress and attention fatigue and to promote physical activity (ibid.). Close-by access and visit times have been identified as crucial factors to support such pathways [2]. Other important factors are size [3] and the internal qualities of the green area, aspects that are often closely related. At the same time, many studies claim that there is a lack of evidence-based tools that can guide work regarding perceived qualities of urban green areas [4-7]. Our paper [8] summarizes findings made over several studies during the past 35 years regarding the most important perceived qualities of urban green spaces, qualities that people wish to experience when visiting. It aims to present these findings as a coherent theoretical model and suggests eight fundamental principles, eight perceived sensory dimensions, to consider in the planning and design of urban green areas to support people's common needs. Among previous studies on connections between perceived green spaces qualities and people's health and wellbeing some studies have focused on experiences of diversity and species richness [9-11]. Other studies have focused on various social activities and include such things as restaurants, kiosks, toilets, seating, lighted roads, etc. (e.g., [12,13]). There are also many studies on the importance of a perceived naturalness in the green area (e.g., [14,15]). To offer opportunities for restoration researchers have suggested the importance of providing a sense of shelter [16] as well as of extent, of entering a coherent "different world" [17,18]. In a similar regard, experiences of tranquility in urban green spaces have also been studied (e.g., [19]). Furthermore, the need for large open areas that make possible various physical activities has been mentioned, as well as the importance of cultural influence in green areas, e.g., through decorations with flowers, fountains, etc. We believe all these aspects to be represented through the eight distinct perceived sensory dimensions proposed in our model, interrelated as illustrated in Figure 1. The model places these eight qualities along four axes. Each axis has two opposing qualities that need to be balanced against each other as they are often associated with opposite attributes. Adjacent qualities in the model on the other hand are seen as synergistic and often support each other in an environment. We propose that support for between two and four such closely related qualities can contribute to places with high aesthetic function and low conflict between different qualities. Not all qualities need to be supported within one and the same green area, but we suggest that all eight ideally should be represented in the outdoor environment within approximately 300 meters of the dwelling. A Natural quality describes places where the greenery appears to be natural and spontaneously grown-up, offering the experience of a relative absence of human influence. It is often associated with larger green areas and mature vegetation (Figure 2). Impressions of self-sown plants and undisturbed development over time characterize the place. A Serene quality is found in places that are perceived as peaceful, tranquil and with few people (Figure 3). Both naturalness and serenity are often linked to large areas and the two qualities seem to support each other. They thus appear as neighbors in the model. Citation: Stoltz J, Grahn P. Perceived sensory dimensions: Key aesthetic qualities for health-promoting urban green spaces. J Biomed Res 2021;2(1):22-29.