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Characteristics of Urban Parks in Chengdu and Their Relation to Public Behaviour and Preferences

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Urban parks can offer a variety of ecosystem services such as beautifying the landscape, ecological regulation, leisure and recreation, and maintaining biodiversity. Understanding how urban parks affect people’s lives can help guide the construction and planning of parks in a direction that is more beneficial to the public. Therefore, it is worth studying the extent to which different urban parks with different characteristics affect public behaviour and preferences. This paper takes five typical urban parks in Chengdu and analyses the relationship between characteristics of the park ecosystem and public behaviour and preferences. The characteristics include the park scale, blue-green space ratio, plant diversity, and degree of re-wilding. Visit frequency, stay time, and park preference characterise public behaviour and preferences. The results show: (1) There are obvious differences in the ecosystem characteristics of the five parks: Qinglong Lake Wetland Park is the largest; the proportion of blue-green space in Jiangjiayiyuan Garden is the highest; the degree of re-wilding in Bailuwan Wetland Park is the highest; the proportion of green space and plant diversity in Guixi Ecological Park is the highest; and the proportion of blue space in Jincheng Lake Wetland Park is the highest. (2) There are differences in public behaviour and preferences for different parks. Tourists visit Qinglong Lake Wetland Park the most in spring and autumn and they choose Guixi Ecological Park instead in summer and winter. The public stays longer in Qinglong Lake Wetland Park and shorter in Jincheng Lake Wetland Park. (3) The scale of urban parks, the proportion of blue-green space, and the degree of re-wilding, especially the proportion of blue space, have a positive impact on the public’s evaluation and promote public visits. The results of the study could help improve public awareness of the relationship between park characteristics and ecological services and well-being.
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Citation: Li, Z.; Liu, Q.; Zhang, Y.;
Yan, K.; Yan, Y.; Xu, P. Characteristics
of Urban Parks in Chengdu and Their
Relation to Public Behaviour and
Preferences. Sustainability 2022,14,
6761. https://doi.org/10.3390/
su14116761
Academic Editor: Jakub Brom
Received: 14 April 2022
Accepted: 27 May 2022
Published: 31 May 2022
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4.0/).
sustainability
Article
Characteristics of Urban Parks in Chengdu and Their Relation
to Public Behaviour and Preferences
Zhiqiao Li 1,2 , Qin Liu 1, Yuxin Zhang 3, Kun Yan 1, Yangyang Yan 1and Pei Xu 1,*
1Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China;
lizhiqiao@imde.ac.cn (Z.L.); liuqin@imde.ac.cn (Q.L.); yankun@imde.ac.cn (K.Y.); yangzai@imde.ac.cn (Y.Y.)
2University of Chinese Academy of Sciences, Beijing 100049, China
3China Quality Certification Centre, Chengdu 610020, China; zhangyuxin11223@163.com
*Correspondence: xupei@imde.ac.cn; Tel.: +86-028-8522-8557
Abstract:
Urban parks can offer a variety of ecosystem services such as beautifying the landscape,
ecological regulation, leisure and recreation, and maintaining biodiversity. Understanding how urban
parks affect people’s lives can help guide the construction and planning of parks in a direction that is
more beneficial to the public. Therefore, it is worth studying the extent to which different urban parks
with different characteristics affect public behaviour and preferences. This paper takes five typical
urban parks in Chengdu and analyses the relationship between characteristics of the park ecosystem
and public behaviour and preferences. The characteristics include the park scale, blue-green space
ratio, plant diversity, and degree of re-wilding. Visit frequency, stay time, and park preference
characterise public behaviour and preferences. The results show: (1) There are obvious differences
in the ecosystem characteristics of the five parks: Qinglong Lake Wetland Park is the largest; the
proportion of blue-green space in Jiangjiayiyuan Garden is the highest; the degree of re-wilding in
Bailuwan Wetland Park is the highest; the proportion of green space and plant diversity in Guixi
Ecological Park is the highest; and the proportion of blue space in Jincheng Lake Wetland Park is the
highest. (2) There are differences in public behaviour and preferences for different parks. Tourists
visit Qinglong Lake Wetland Park the most in spring and autumn and they choose Guixi Ecological
Park instead in summer and winter. The public stays longer in Qinglong Lake Wetland Park and
shorter in Jincheng Lake Wetland Park. (3) The scale of urban parks, the proportion of blue-green
space, and the degree of re-wilding, especially the proportion of blue space, have a positive impact on
the public’s evaluation and promote public visits. The results of the study could help improve public
awareness of the relationship between park characteristics and ecological services and well-being.
Keywords: urban parks; ecosystem characteristics; behaviour and preferences; ecosystem service
1. Introduction
Urban parks are an important part of urban ecosystems and urban landscapes [
1
]. They
serve ecological functions such as water conservation, climate regulation, environmental
purification, and biodiversity maintenance. They can provide urban residents with scenery,
leisure, exercise, entertainment, social networking, and other ecosystem services [
2
,
3
] that
ensure urban ecological security and promote the sustainable development of human and
natural systems [
4
]. Disturbed by different degrees of human activity, urban parks have
dual attributes of both natural and artificial ecosystems. There are also great differences
in the characteristics of different types of park ecosystems. Parks dominated by natural
ecosystems are larger in scale, with high vegetation coverage and rich biodiversity [
5
], while
parks dominated by artificial ecosystems are smaller in scale, with fragmented landscapes
and more infrastructure [6].
Urban parks can affect people’s emotions and perceptions [
7
] and change public
behaviour [
8
]. These effects may be associated with park features such as flowers [
9
], dense
Sustainability 2022,14, 6761. https://doi.org/10.3390/su14116761 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 6761 2 of 16
trees [
10
], and convenient leisure facilities [
11
]. Parks with the above characteristics are more
popular among tourists. Natural landscapes are generally more attractive to the elderly,
while young people prefer artificial facilities [
12
]. Most researchers study the influence of
urban park characteristics on public behaviour through the relationship between spatial
structure characteristics of parks and visitors’ behaviour. Greg et al. [
13
] used the public
participatory geographic information system (PPGIS) to study the relationship between
urban park types and public participation in sports activities. They found that linear parks
provide the most significant overall physical benefit. Hou et al. [
14
] obtained data such
as morning exercise types and space allocation using the behavioural annotation method.
Long et al. [
15
] used space syntax theory to quantify the characteristics of urban spatial
organisation, obtaining differential characteristics of walking and non-walking spaces and
walking space preference in the elderly. They found that people tend to choose parks
with high accessibility and large open spaces for morning exercises, especially those who
use group dance as a morning exercise program. Research into the relationship between
urban park characteristics and public preferences is based on public physiological and
psychological indicators. Song et al. [
16
] measured tourists’ heart rates to obtain heart
rate variability and tested the influence of urban parks on people’s minds and bodies
through the semantic differential (SD), Profile of Mood States (POMS), and State-Trait
Anxiety Inventory (STAI). This research shows that heart rate was significantly lower while
walking in the urban park than in the city street. Furthermore, the urban park walk led
to higher parasympathetic nervous activity and lower sympathetic nervous activity. Wu
et al. [
17
] explored the influence of colour characteristics on tourists’ perception through
11 evaluation factors of the three spatial dimensions of the South Part of Minjiang Park.
The factors included ambient color characteristics (coordination, color, richness, memory,
and attractiveness) and tourist perception (comfort, pleasure, security, dullness, annoyance,
and depression). Their results showed that factors correlated with tourist perception are all
related to coordination. At present, research on ecosystem services of urban parks mainly
adopts computer technology and network technology, including social media, geographic
information technology, and modeling, as research methods [12,18,19]. They demonstrate
that urban parks have an impact on the public and provide a theoretical basis for this study.
People are the main beneficiaries of urban parks. The comprehensive evaluation of
physiology, psychology, and geographic information systems are used to obtain public
behaviour trajectories. The differences in public behaviour and preferences among different
parks can be revealed in this way but the relationship between park characteristic factors
and the public is not intuitive. The current research aims to study the degree of influence of
urban park characteristics on public behaviour and preferences and reveal which character-
istic factor has which influence. It will make the construction of future urban parks better
meet public needs; in this way, the government will improve the quality and quantity of
urban parks. Urban parks are an important provider of ecosystem services [
2
4
], so they
will enhance park ecosystem services and promote sustainable urban development. There-
fore, this paper combines field research with participatory questionnaires (Appendix A) to
identify the ecosystem characteristics of typical parks in Chengdu, reveal the differences in
public behaviour and preferences for different types of parks, and explain the relationship
between important characteristic factors and public behaviour. It also provides a scientific
basis for park construction and management.
2. Materials and Methods
2.1. Study Area
Chengdu (102
54
0
E~104
53
0
E, 30
05
0
N~31
26
0
N) was the first place to propose the
concept of the “Park City” in China. It is also the national demonstration area of China’s
park cities, showing a rapid increase in the number and area of its parks [
20
]. This study
selected five urban parks (Figure 1), namely Jincheng Lake Wetland Park, Qinglong Lake
Wetland Park, Guixi Ecological Park, Bailuwan Wetland Park, and Jiangjiayiyuan Garden
(Figure 1). The above parks are all parts of Chengdu Huancheng Ecological Park. The
Sustainability 2022,14, 6761 3 of 16
construction of Chengdu Huancheng Ecological Park is a project in progress. These five
parks are relatively complete (Figure 2), while most others are still under construction. Their
construction time, distance from the main urban area, infrastructure, and management
levels are similar; however, the park scale, ecosystem composition and structure, and
biodiversity are different. As the park conditions are similar or identical except for the few
characteristics to be studied, they offer the representativeness and typicality needed for this
research. Moreover, these five parks are the most popular in Chengdu, which is beneficial
for sending out questionnaires.
Figure 1.
Location of study area against the background of Chengdu Huancheng Ecological Park,
Chengdu, China.
2.2. Investigation and Analysis of Park Ecosystem Characteristics
Park characteristics are mainly divided into three categories: ecological characteristics,
service characteristics, and aesthetic characteristics [
21
]. Usually, the diversity and spatial
distribution of green plants have a greater impact on public behaviour and preferences [
22
];
therefore, this study analysed the characteristics of urban parks from the scale of the parks,
their proportion of blue-green space [
23
], plant diversity [
24
], and degree of re-wilding [
25
].
This study used ArcGIS 10.8 software to obtain the total scale of the study area. The
proportion of blue-green space was calculated based on land use data [
26
]. Green space
was divided into “parks and green spaces”, “bushland”, “orchards”, “adjustable orchards”,
“woodland”, “bamboo forest”, “other grassland”, “other woodland”, and “other gardens”.
Blue space was divided into “dry canal”, “ditch”, “river surface”, “pond water surface”,
Sustainability 2022,14, 6761 4 of 16
“reservoir water surface”, and “aquaculture fish pond”. The area of land which belongs to
the blue-green space was first calculated and then the obtained value was divided by the
total area to obtain the proportion of blue-green space. Richness is used as an evaluation
index of plant diversity [
27
]. The average and standard deviation can represent the level of
diversity. In this study, a total of 37 quadrat surveys (10
×
10 m quadrat) were conducted
in the five parks. The number of plant species in each quadrat was calculated, and the
average and standard deviation were calculated among quadrats in each park. The area
occupied by native plants [
28
] and the number of native species [
29
] are the keys to re-
wilding; the degree of re-wilding was calculated using the following formula: The degree
of
re-wilding = the
proportion of native plant distribution area
×
0.5 + the proportion
of native
species ×0.5
. The native plant land includes “bushland”, “woodland”, and
“bamboo forest”. We calculated the area of native plant land and divided the obtained
value by the green space area to obtain the proportion of native plant distribution area.
Then, we counted the number of native species in each park and divided it by the number
of all species in the same park to obtain the proportion of native species.
Figure 2.
Five research target parks (credit: Qin Liu). (
a
) Qinglong Lake Wetland Park; (
b
)
Jiangjiayiyuan Garden; (
c
) Bailuwan Wetland Park; (
d
) Guixi Ecological Park (
e
) Jingcheng Lake
Wetland Park.
Sustainability 2022,14, 6761 5 of 16
2.3. Participatory Questionnaire Survey and Analysis
From March to April 2021, a participatory questionnaire survey was carried out in
the five city parks. A total of 400 questionnaires were sent out, and 377 valid completed
questionnaires were collected, resulting in a response rate of 94.25%. The 377 questionnaires
consisted of 160 from Qinglong Lake Wetland Park, 104 from Jiangjiayiyuan Garden,
40 from
Bailuwan Wetland Park, 22 from Guixi Ecological Park, and 51 from Jincheng
Lake Wetland Park. We arrived at the parks and stratified respondents by gender and
age. Then, we had conversations with the respondents face to face and provided them
with questionnaires which they returned as soon as they completed. In order to motivate
people to participate in the survey, respondents who completed the questionnaires were
each given a bottle of water.
This survey adopted stratified random sampling [
30
]. Stratified random sampling is
to divide the units of the population into various categories according to specific standards,
and then select samples from each category according to the random principle. The cate-
gories in this research included basic demographic information (gender, age, occupation,
education, and accommodation), public behaviour (visit frequency and length of stay), and
public preferences (behavioural activity preferences, landscape preferences, bio-aesthetic
preferences, and functional value preferences). The research used a five-point Likert scale
(from “1 = very dislike” to “5 = very much like”) [
31
] to assign scores to respondents’
preferences in a positive order. Besides this, a descriptive statistical analysis was carried
out after summarising the results. According to the results, we calculated the average
Xi
,
standard deviation SD, coefficient of variation CV [
32
], and median Min order to study the
differences in public preferences in different parks.
2.4. Data Analysis
This study used IBM SPSS Statistics (IBM SPSS Statistics for Windows. Version 26.0.0.0,
IBM corp, Chicago, IL, USA, released 2019) to test the significance and undertake correlation
analyses of the survey results. The public preferences (behavioural activity preferences,
landscape preferences, bio-aesthetic preferences, and functional value preferences) of this
survey were characterised and compared using a non-parametric test (e.g., Chi-square tests).
A canonical correlation analysis (CCA) [
33
,
34
] was used to study the relationship between
the independent variables (different park characteristics) and the dependent variables
(public preferences).
3. Results
3.1. Differences in the Characteristics of Urban Park Ecosystems
The characteristic factors of different park ecosystems were different (Table 1). The
area of Qinglong Lake Wetland Park was the largest (9.42 km
2
), while the area of Guixi
Ecological Park was only 1.02 km
2
. In terms of the proportion of blue-green space in the
park, the proportion of all five parks was more than 75%. The proportion of blue space in
Jincheng Lake Wetland Park and Bailuwan Wetland Park was relatively high, while the
proportion of green space in Jiangjiayiyuan Garden and Guixi Ecological Park was relatively
high. There were also differences in plant diversity among the parks. The abundance of
Guixi Ecological Park was the highest (11.00
±
3.52), while the value of Qinglong Lake
Wetland Park was relatively low (6.71
±
2.50). The re-wilding degree of Bailuwan Wetland
Park was the highest, reaching 0.61; the re-wilding degree of Jincheng Lake Wetland Park
was the lowest, at only 0.33.
3.2. Analysis of Public Behaviour and Preferences
3.2.1. Public Demographic Information
The proportion of males and females among the respondents was 48.54% and 51.46%,
respectively. The 21–40 age group accounted for the highest proportion, at 50.93%. The
proportion of those with a bachelor’s degree or above was highest, at 37.67%, and the
Sustainability 2022,14, 6761 6 of 16
largest proportion of the population was employed, at 41.64%. A total of 61.01% of the
respondents lived in the central city of Chengdu (see Appendix BTable A1).
Table 1. The Characteristics of Urban Park Ecosystems.
Total
Scale/km2
Proportion of
Blue Space
Proportion of
Green Space
Proportion of
Blue-Green Space
Plant Diversity
Index
Re-Wilding
Degree
Qinglong Lake Wetland Park 9.42 17.09% 60.11% 77.20% 6.71 ±2.50 0.51
Jiangjiayiyuan Garden 1.23 5.96% 86.66% 92.59% 8.67 ±2.89 0.39
Bailuwan Wetland Park 2.11 24.80% 63.69% 88.49% 8.47 ±2.35 0.61
Guixi Ecological Park 1.02 1.98% 86.84% 88.82% 11.00 ±3.52 0.39
Jincheng Lake Wetland Park 1.05 35.28% 45.91% 81.19% 10.50 ±2.38 0.33
3.2.2. Analysis of Public Behaviour
There was little difference in the frequency of public visits and the length of stay
among the five study areas. The respondents tended to visit the park in spring and autumn
and the length of stay was generally 1–6 h. In spring and summer, the proportion of visitors
who travelled to the study area with more than a 15-day interval between visits accounted
for 19.63% and 35.28%, respectively, while the proportion in autumn and winter was 26.26%
and 40.59%, respectively (Figure 3). Qinglong Lake Wetland Park accounted for 23.13%
and 13.75% of the daily visits to the park in spring and autumn, respectively. In summer
and winter, Guixi Ecological Park accounted for 13.63% and 13.64%, respectively (Figure 3).
Visitors who stayed for 1–3 h accounted for most of the visits, at 56.76%, and visitors who
stayed for more than 6 h accounted for the least, at 2.65% (Figure 3). A total of 6.88% of
the respondents stayed in Qinglong Lake Wetland Park for more than 6 h, while 15.69% of
visitors stayed in Jincheng Lake Wetland Park for less than 1 h (Figure 3).
3.2.3. Analysis of Public Preferences
By calculating the medians and coefficients of variation of all visitors’ preferences,
which were divided into “behavioural activity preferences”, “landscape preferences”, “bio-
aesthetic preferences”, and “functional value preferences” (Figure 4), the visiting motivation
and preferences of the respondents could be studied. The medians of behavioural activity
preferences were lowest for “Socialisation” in Jincheng Lake Wetland Park, Qinglong Lake
Wetland Park, Bailuwan Wetland Park, and Jiangjiayiyuan Garden and the coefficients of
variation were highest; however, in Guixi Ecological Park it was “Photography”. In terms
of landscape preferences, the median of “Recreational Park” was lowest and the coefficient
of variation was highest in Jincheng Lake Wetland Park, Qinglong Lake Wetland Park,
Bailuwan Wetland Park, and Jiangjiayiyuan Garden. In Guixi Ecological Park, the lowest
median was for “Wetland”, and “Countryside” had the highest coefficient of variation. The
five parks had the highest median score and the lowest coefficient of variation for visitors’
bio-aesthetic preferences in “Viewing Flowers” or “Lawn”. The medians of “Animal
Diversity” in the five parks were the lowest and the coefficients of variation were the
highest. The medians of “Landscaping” or “Protecting Environment” were the highest of
the functional value preferences in Jincheng Lake Wetland Park, Qinglong Lake Wetland
Park, Bailuwan Wetland Park, and Jiangjiayiyuan Garden and their coefficients of variation
were the lowest. The median of “Enhancing City Image” was the lowest and the coefficient
of variation was highest in Jincheng Lake Wetland Park, Qinglong Lake Wetland Park,
Guixi Ecological Park, and Jiangjiayiyuan Garden. There are certain differences in public
preferences between Guixi Ecological Park and the other four parks.
Sustainability 2022,14, 6761 7 of 16
Figure 3.
Visit Frequency and Length of Stay. Frequency of visits to the five parks in (
a
) spring; (
b
)
summer; (c) autumn; (d) winter; (e) Length of stay in the five parks.
In this study, Chi-square tests were performed on visitors’ scores for “Behavioural Ac-
tivity Preferences”, “Landscape Preferences”, “Bio-aesthetic Preferences”, and “Functional
Value Preferences” in the five study areas. “Socialisation”, “Recreational Parks”, “Animal
Diversity”, and “Enhancing City Image” were the categories most affected by different
parks, while “Viewing”, “Lawn Park”, “Lawn”, and “Relaxing” were the least affected
(Table 2). This result illustrated that differences in public preferences were affected by park
characteristics. The greater the coefficient of variation, the higher the level of influence.
Sustainability 2022,14, 6761 8 of 16
Figure 4.
The medians and coefficients of variation of visitors’ preferences in the five study areas for
(
a
) “behavioural activity preferences”; (
b
) “landscape preferences”; (
c
) “bio-aesthetic preferences”;
and (d) “functional value preferences”.
Sustainability 2022,14, 6761 9 of 16
Table 2.
Differences in Public Preferences on Behavioural Activity, Landscape, Bio-aesthetic, and
Functional Value Preferences.
Classification Differences
Behavioural Running/Cycling X2(5) = 486.443, p< 0.01
Activity Fitness X2(5) = 460.915, p< 0.01
Preferences Viewing X2(5) = 178.103, p< 0.01
Entertainment X2(5) = 193.859, p< 0.01
Socialisation X2(5) = 778.135, p< 0.01
Photography X2(5) = 459.865, p< 0.01
Landscape Wetland X2(5) = 175.716, p< 0.01
Preferences Forest Park X2(5) = 179.504, p< 0.01
Lawn Park X2(5) = 168.045, p< 0.01
Countryside X2(5) = 248.958, p< 0.01
Recreational Park X2(5) = 326.878, p< 0.01
Bio-Aesthetic Viewing Flowers X2(5) = 251.154, p< 0.01
Preferences Viewing Leaves X2(5) = 221.997, p< 0.01
Lawn X2(5) = 119.950, p< 0.01
Trees X2(5) = 267.292, p< 0.01
Shade X2(5) = 266.528, p< 0.01
Animal Diversity X2(5) = 729.021, p< 0.01
Functional Landscaping X2(5) = 80.385, p< 0.01
Value Preferences Protecting Environment X2(5) = 120.427, p< 0.01
Exercising X2(5) = 140.289, p< 0.01
Relaxing X2(5) = 50.496, p< 0.01
Leisure Activities X2(5) = 85.987, p< 0.01
Enhancing City Image X2(5) = 267.292, p< 0.01
3.3. Correlation Analysis
The parks’ characteristic factors are closely related to some public preferences and less
related to others (Table 3). The public’s evaluation of “Shade” was negatively correlated
with the proportion of blue space in the park (r =
0.881, p= 0.048 < 0.05) and positively
correlated with the proportion of green space (r = 0.958, p= 0.01 < 0.05). The public believed
that the larger the total scale of urban parks, the stronger the function of regulating relax-
ation (r = 0.938, p= 0.018 < 0.05). Besides this, they believed that the richer the biodiversity,
the less conducive to relaxation (r =
0.904, p= 0.035 < 0.05). Rich biodiversity led to a de-
crease in visitors’ interest in taking pictures (r =
0.94, p= 0.017 < 0.05), while the greater the
difference in the biodiversity, the more visitors preferred fitness (r = 0.953,
p= 0.012 < 0.05
)
and visiting recreational parks (r = 0.904,
p= 0.035 < 0.05
). The wetland area’s correlation
was smaller (r =
0.931, p= 0.022 < 0.05) under these circumstances. A park with a higher
proportion of blue-green space had a worse public evaluation of lawn parks (r =
0.887,
p= 0.045 < 0.05) (Figure 5). The correlations between other public preferences and park
characteristic factors were insignificant, indicating a loose relationship.
Sustainability 2022,14, 6761 10 of 16
Table 3. Canonical correlation analysis of park characteristics and public preferences.
Total Scale/km2Proportion of Blue Space Proportion of Green Space Proportion of Blue-Green
Space
Plant Diversity Index
(Average)
Plant Diversity Index
(Standard Deviation)
Re-Wilding
Degree
Correlation
Coefficient Significance Correlation
Coefficient Significance Correlation
Coefficient Significance Correlation
Coefficient Significance Correlation
Coefficient Significance Correlation
Coefficient Significance Correlation
Coefficient Significance
Running/Cycling 0.158 0.8 0.341 0.574 0.384 0.523 0.351 0.562 0.193 0.755 0.41 0.493 0.524 0.365
Fitness 0.382 0.526 0.692 0.196 0.65 0.235 0.343 0.571 0.669 0.217 0.953 * 0.012 0.64 0.245
Viewing 0.617 0.267 0.082 0.896 0.071 0.909 0.381 0.527 0.645 0.24 0.237 0.701 0.172 0.782
Entertainment 0.31 0.612 0.749 0.145 0.72 0.171 0.415 0.487 0.576 0.31 0.305 0.618 0.382 0.525
Socialisation 0.121 0.846 0.574 0.311 0.386 0.521 0.151 0.808 0.363 0.548 0.787 0.114 0.499 0.392
Photography 0.697 0.19 0.334 0.583 0.396 0.509 0.4 0.505 0.94 * 0.017 0.759 0.137 0.587 0.298
Wetland 0.341 0.574 0.7 0.189 0.679 0.208 0.409 0.495 0.592 0.293 0.931 * 0.022 0.236 0.702
Forest Park 0.329 0.589 0.43 0.47 0.346 0.569 0.048 0.939 0.216 0.728 0.333 0.583 0.654 0.231
Lawn Park 0.463 0.432 0.399 0.505 0.618 0.266 0.887 * 0.045 0.076 0.903 0.137 0.826 0.236 0.703
Countryside 0.142 0.82 0.365 0.546 0.354 0.559 0.215 0.729 0.346 0.568 0.545 0.342 0.307 0.615
Recreational
Park 0.488 0.404 0.655 0.23 0.651 0.235 0.424 0.477 0.696 0.191 0.904 * 0.035 0.756 0.139
Viewing Flowers 0.618 0.266 0.559 0.327 0.624 0.261 0.557 0.33 0.784 0.117 0.866 0.058 0.324 0.594
Viewing Leaves 0.135 0.829 0.206 0.739 0.312 0.609 0.439 0.459 0.055 0.931 0.121 0.846 0.761 0.135
Lawn 0.434 0.465 0.014 0.982 0.094 0.881 0.237 0.701 0.588 0.297 0.335 0.581 0.198 0.75
Trees 0.514 0.375 0.419 0.482 0.59 0.295 0.766 0.131 0.295 0.63 0.433 0.466 0.405 0.498
Shade 0.398 0.507 0.881 * 0.048 0.958 * 0.01 0.807 0.098 0.281 0.647 0.835 0.079 0.015 0.981
Animal Diversity 0.532 0.356 0.426 0.475 0.496 0.395 0.483 0.41 0.471 0.423 0.516 0.374 0.829 0.083
Landscaping 0.068 0.914 0.766 0.131 0.711 0.178 0.357 0.556 0.257 0.676 0.831 0.081 0.116 0.853
Protecting
Environment 0.243 0.694 0.417 0.485 0.323 0.596 0.012 0.985 0.705 0.184 0.797 0.106 0.562 0.324
Exercising 0.189 0.76 0.402 0.502 0.319 0.6 0.034 0.956 0.622 0.262 0.751 0.143 0.264 0.668
Relaxing 0.938 * 0.018 0.13 0.835 0.084 0.893 0.521 0.368 0.904 * 0.035 0.269 0.662 0.354 0.559
Leisure activities 0.292 0.633 0.543 0.345 0.526 0.363 0.314 0.607 0.448 0.449 0.241 0.696 0.77 0.128
Enhancing City
Image 0.028 0.964 0.553 0.334 0.415 0.487 0.023 0.971 0.249 0.687 0.636 0.248 0.795 0.108
*p< 0.05.
Sustainability 2022,14, 6761 11 of 16
Figure 5.
Correlations between park characteristics and public preferences which are significant at
the 0.05 level (p< 0.05).
4. Discussion
The results show that the frequency of visitors visiting five different types of urban
parks varied by season. Extreme weather is more likely in both summer and winter and
will affect public visits to urban parks; therefore, in order to control for the impact of climate
variables when studying the effect of park characteristics on the frequency of public visits,
the results in spring were compared with those in autumn, and the results in summer were
compared with those in winter. The results show that visitors visit more frequently in
spring and summer than in autumn and winter, indicating that plant growth promotes
public visits to urban parks [
35
]. Some studies have shown that excessive vegetation can
make tourists feel insecure because it may provide hiding spaces. Whether visitors have
negative feelings about dense vegetation may depend on the level of safety in the area
where the park is located [
36
,
37
]. The motivations of people visiting the parks are relatively
similar. Walking, running, cycling, and other physical activities, as well as viewing the
scenery, are the main reasons the public visit these five urban parks. Lawns and flowers
are the most popular urban park landscapes, while wetlands and lawn parks are the most
popular forms of urban park space utilisation among visitors. This survey has raised public
awareness of the high-level value of urban parks (such as beautifying and protecting the
ecological environment or relaxing) which will help urban park managers to further build
and plan spaces that are beneficial to urban populations.
The findings also highlight the differences among urban parks and the different effects
of urban park characteristics on public perception and behaviour. Although Qinglong Lake
Wetland Park occupies the smallest proportion of blue-green space, its area of blue-green
space is the largest because of its largest total scale. According to the results of canonical
correlation analysis, visitors can relax more in larger parks, indicating that the overall
scale of urban parks has a positive effect on public perception and behaviour. Research in
Sustainability 2022,14, 6761 12 of 16
Chongqing, China, shows that several small parks promote urban park equity, while large
urban parks are conducive to improving quality [
38
]. In general, the area of blue-green
space and the number of biological species increases with the park area, which can provide
better ecosystem services; thus, visitors prefer large parks. Guixi Ecological Park occupies
the smallest proportion of blue space, with high plant diversity but a low degree of re-
wilding. It lacks wetlands which are popular with visitors, so few people associate Guixi
Ecological Park with the function of beautifying and protecting the ecological environment.
The blue space in Jincheng Lake Wetland Park accounts for the largest proportion and
the rest of its features are similar to Guixi Ecological Park. Many visitors believe that its
role in beautifying the environment is obvious, indicating that the blue space has a high
ornamental value from the perspective of public perception. In addition, people prefer blue
spaces, valuing them more than green spaces in Beijing, China. Thus, citizens are more
willing to pay for cultural ecosystem services associated with blue space [
39
]. People are
more likely to use the functions of blue space, such as having picnics along rivers or lakes,
fishing, and having a boat trip. Therefore, the blue areas are more highly valued. However,
the shading function of parks has a close positive relationship with the proportion of green
space [
40
]. The increase in the proportion of blue space leads to a decrease in the proportion
of green space and the function of shading decreases.
Bailuwan Wetland Park has the highest degree of re-wilding and Jiangjiayiyuan
Garden has the highest proportion of blue-green space. Most visitors in the two places think
that their role in protecting the ecological environment is significant (Table 3), indicating
that the degree of re-wilding and the total proportion of blue-green space are important
indicators of the public’s evaluation of their ecological protection capacity. According
to other research results, the higher the degree of re-wilding, the stronger the ecological
stability and the less susceptible it is to external disturbances [
41
]; the higher the proportion
of blue-green space, the stronger the ecological restoration [
42
]. Therefore, the two are the
influencing factors of the park’s ability to protect the ecological environment. Qinglong
Lake Wetland Park and Bailuwan Wetland Park have the highest degree of re-wilding and
the lowest plant diversity, indicating that plant introduction and cultivation [
43
] increase
the plant diversity of parks after the artificial disturbance [
44
]. Plant introduction and
cultivation are based on human behaviour, which can bring in species that cannot enter
Chengdu under natural conditions. However, it is necessary to introduce exotic species in
a reasonable way in order to reduce the risk of biological invasion [
45
]. Plant diversity is
inversely proportional to visitors’ willingness to take pictures and their degree of relaxation,
indicating that visitors are less fond of green spaces that are over-planned and transplanted
with lots of non-native plants [46].
5. Conclusions
Based on a field investigation and survey of five different types of parks in Chengdu,
this paper calculated and obtained the characteristic factors of these ecosystems. Combined
with questionnaires on public behaviour and preferences, our analysis draws the following
conclusions: (1) The scale of urban parks has a positive impact on the public’s evaluation
and promotes public visits. (2) From the public perspective, the area and proportion of
blue space is proportional to its aesthetic value. (3) The degree of re-wilding and the
total proportion of blue-green space are important indicators for the public to evaluate
the ability of parks to protect the environment. (4) The characteristics of urban parks
are closely related to some public behaviour and preferences (“Fitness”, “Photography”,
“Wetland”, “Lawn Park”, “Recreational Park”, “Shade”, and “Relaxing”) and linked less
to the others. Park ecosystem is a popular form of space utilisation in cities. Revealing
the relationship between park characteristics and public behaviour and preferences is
conducive to designing and managing urban parks accordingly and thus better meeting
public needs.
Sustainability 2022,14, 6761 13 of 16
Author Contributions:
Conceptualization, Z.L. and Q.L.; methodology, Z.L. and Q.L.; validation,
Q.L. and K.Y.; formal analysis, Z.L.; investigation, Q.L., K.Y., Y.Y. and Z.L.; resources, Q.L., Y.Z., K.Y.
and Y.Y.; data curation, Z.L.; writing—original draft preparation, Z.L.; writing—review and editing,
Q.L.; visualization, Z.L.; supervision, P.X.; project administration, Q.L.; funding acquisition, Q.L. and
Y.Z. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Youth Innovation Promotion Association of the Chinese
Academy of Sciences, grant number 2021375; Evaluation of Ecosystem Services and its Value Realiza-
tion in Ecological Belt Surrounding the Chengdu City, grant number JCJQ-21170; and Technology
Program of China Quality Certification Centre, grant number 2021CQC21-stzx.
Institutional Review Board Statement:
For this study, ethical approval was not required. The
study was conducted with visitors of urban parks. All participants were informed in advance that
participation is voluntarily and that data collection takes place anonymously.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors would like to thank Chengdu Municipal Bureau of Planning and
Natural Resources and Chengdu Tianfu Greenway Construction Investment Group Co., Ltd. for their
support and assistance with this study.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. Questionnaire
The original version of the questionnaire was in Chinese. We have translated it for the
convenience of readers of this journal.
Public Behaviour and Preferences in (Name of a park)
Name of Investigator:
Number:
You are: Male/Female
Your age is:
Your educational background is:
Your career is:
Where do you live:
1. Multiple choice questions:
a. How often do you visit this park in spring?
(1)
Every day
(2)
2–3 days apart
(3)
4–7 days apart
(4)
7–15 days apart
(5)
>15 days apart
b. How often do you visit this park in summer?
(1)
Every day
(2)
2–3 days apart
(3)
4–7 days apart
(4)
7–15 days apart
(5)
>15 days apart
c. How often do you visit this park in autumn?
(1)
Every day
(2)
2–3 days apart
(3)
4–7 days apart
(4)
7–15 days apart
(5)
>15 days apart
Sustainability 2022,14, 6761 14 of 16
d. How often do you visit this park in winter?
(1)
Every day
(2)
2–3 days apart
(3)
4–7 days apart
(4)
7–15 days apart
(5)
>15 days apart
e. How long do you stay during every visit?
(1)
<1 hour
(2)
1–3 hours
(3)
3–6 hours
(4)
>6 hours
2. Sorting questions:
Please sort the options below by preference.
a. Behavioural Activity Preferences
(1)
Running/Cycling
(2)
Fitness
(3)
Viewing
(4)
Entertainment
(5)
Socialisation
(6)
Photography
b. Landscape Preferences
(1)
Wetland
(2)
Forest Park
(3)
Lawn Park
(4)
Countryside
(5)
Recreational Park
c. Bio-aesthetic Preferences
(1)
Viewing Flowers
(2)
Viewing Leaves
(3)
Lawn
(4)
Trees
(5)
Shade
(6)
Animal Diversity
d. Functional value preferences
(1)
Landscaping
(2)
Protecting Environment
(3)
Exercising
(4)
Relaxing
(5)
Leisure Activities
(6)
Enhancing City Image
Appendix B. Basic Information on the Survey Respondents
Table A1. Demographic Information about Park Visitors.
Item Classification Number Percentage
Gender Male 183 48.54%
Female 194 51.46%
Age Group <20 49 13.00%
21–40 192 50.93%
Sustainability 2022,14, 6761 15 of 16
Table A1. Cont.
Item Classification Number Percentage
41–60 85 22.55%
>61 51 13.52%
Education
Primary School or less
56 14.85%
Junior and Senior
School 133 35.28%
Technical School 46 12.20%
Bachelor’s or more 142 37.67%
Occupation Student 54 14.32%
On-the-job 157 41.64%
Free-lancer 98 26.00%
Retired 68 18.04%
Accommodation Central City 230 61.01%
Suburbs 145 38.46%
Other Cities 2 0.53%
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Urban green spaces can improve residents’ health and well-being. However, international research shows that urban greening can produce gentrification effects. A dilemma for planners is determining whether the scale of greening or the characteristics of green spaces is driving gentrification. In this article, Canonical correlation analysis (CCA) and field investigations are used to assess the potential gentrification effects of a new public green space in the urban central area of Hangzhou, China. Hangzhou is one of China’s ‘garden cities’, but rapid urbanization and climate change are increasing urban heat-island impacts, requiring large-scale urban greening. The two-stage CCA not only confirms the green gentrification phenomenon within the study area but suggests that large green spaces appear to foster gentrification due to their functional benefits, favorable policy support, elaborate embellishments, and strict management and maintenance regimes. Appropriate policy responses may include using a ‘just green enough’ approach: whereby distributed smaller green spaces, with less stringent maintenance could resolve the green gentrification paradox.
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Urban green spaces provide a range of services to urban residents; however, how these distinct spaces provide different services, if any, to human wellbeing has been seldom addressed. Using large volumes of social media data considering broad user groups and publicly available content, we developed a method to transform unstructured online comments into a structured assessment of nine categories of ecosystem services. An ‘ecosystem services lexicon’ was created based on 6853 words under twenty-eight subcategories of parks’ services to human wellbeing using the word2vec model. The application of the ecosystem service lexicon to urban parks in Beijing revealed that all ecosystem services were perceivable to urban park users; however, the perception frequency of the ecosystem services varied across different parks. Additionally, the perceived ecosystem services were bundled together; four types of bundles were identified with varied dominant services. Technically, this study offered a novel technical procedure that can transfer unstructured free comments into a structured assessment of urban parks’ perceived services to human wellbeing. Theoretically, the study revealed small-scale ecosystem service bundles from users’ opinions and called for a further cross-scale understanding of ecosystem service bundles. Practically, the study findings can help inform evidence-based park policies, planning, and management.