Tourists' Spatial-Temporal Behavior Patterns Analysis Based on Multi-Source Data for Smart Scenic Spots: Case Study of Zhongshan Botanical Garden ...
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Article
Tourists’ Spatial–Temporal Behavior Patterns Analysis Based on
Multi-Source Data for Smart Scenic Spots: Case Study of
Zhongshan Botanical Garden, China
Jie Zheng , Xuefeng Bai, Lisha Na and Hao Wang *
School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China; zj_1229@njfu.edu.cn (J.Z.);
njfu_xuefengbai@163.com (X.B.); nls_2017@163.com (L.N.)
* Correspondence: wh9816@126.com; Tel.: +86-138-05161-9757
Abstract: The data based on location/activity sensing technology is exploding and integrating
multi-source data provides us with a new perspective to observe tourist behavior. On the one
hand, tourist preferences can be extracted from the attractions generated by clustering. On the other
hand, potentially extracted tourist information can provide decision-making support for tourism
management departments in tourism planning and resource development. Therefore, developing
smart tourism services for tourists and promoting the realization of “smart scenic spots.” A field
survey was conducted in Zhongshan Botanical Garden, China, from 3 February to 3 April 2019.
This empirical study combines a handheld GPS tracking device and questionnaire survey using
SEE to optimize k-means clustering algorithm and explores the spatial–temporal behavior patterns
of tourists. The results showed that tourists in the botanical garden could be divided into three
behavioral patterns. They are recreation and leisure, birdwatching and photography, and learning
and education. The spatial–temporal behavior patterns of different tourists have obvious differences,
Citation: Zheng, J.; Bai, X.; Na, L.; which provides a basis for the planning and management of smart scenic spots.
Wang, H. Tourists’ Spatial–Temporal
Behavior Patterns Analysis Based on Keywords: smart scenic spot; spatial–temporal behavior pattern; multi-source data; global position-
Multi-Source Data for Smart Scenic ing system; Zhongshan botanical garden
Spots: Case Study of Zhongshan
Botanical Garden, China. Processes
2022, 10, 181. https://doi.org/
10.3390/pr10020181
1. Introduction
Academic Editors: Shengfeng Qin “Smart” has become a new buzzword to describe technology-driven economic and
and Marcin Perzyk social development, relying on sensors, big data, open data, and other methods [1]. While
Received: 31 December 2021
smart scenic areas stem from the smart city concept, on the one hand, they consider tourists
Accepted: 15 January 2022 to support mobility, resource availability and distribution sustainability, and quality of
Published: 18 January 2022 life/access [2]; on the other hand, smart garden tourism is a social phenomenon generated
by the integration of information technology and tourism experience. Technology is the
Publisher’s Note: MDPI stays neutral
intermediary to focus on the experience through personalization, situational awareness,
with regard to jurisdictional claims in
and real-time monitoring [3]. The smart park tourist experience efficiently, as the connota-
published maps and institutional affil-
tion is rich, can be understood as the innovation of tourism destination based on advanced
iations.
technology and infrastructure, optimizing resources, increasing the tourist’s visit enthusi-
asm, promoting tourist interaction and blending in with the surrounding environment, and
improving the quality of the park experience, thus ensuring the sustainable development
Copyright: © 2022 by the authors. of scenic spots. Tourists are active participants in the creation of smart scenic spots. They
Licensee MDPI, Basel, Switzerland. consume and create, annotate, or otherwise enhance the data that forms the basis of the
This article is an open access article experience. With the rapid development of China’s tourism industry and holiday economy,
distributed under the terms and public holidays increase the number of tourists in scenic spots and decrease the quality
conditions of the Creative Commons of tourist experience, which is an important problem for tourists and scenic spot manage-
Attribution (CC BY) license (https:// ment [4,5]. Therefore, the construction of smart scenic spots based on good planning and
creativecommons.org/licenses/by/ accurate service is particularly important.
4.0/).
Processes 2022, 10, 181. https://doi.org/10.3390/pr10020181 https://www.mdpi.com/journal/processes
Processes 2022, 10, 181 2 of 15
The growing popularity of location/motion-sensing technology has produced a wealth
of location-based big data (LocBigData) [6], such as tracking or sensing data (for example,
GPS tracking of people and other moving objects, as well as cellphone signaling data), social
media data, and other geographic information data. Multiple studies have demonstrated
that LocBigData can better understand human spatial–temporal behavior patterns [7],
regulate and optimize landscape management services [8], improve the quality of tourists,
and try to provide practical solutions to problems such as congestion and varying activity
levels. Currently, the most common tracking or sensing technologies are GPS devices
using the Global Navigation Satellite System and mobile phone devices. Compared with
traditional data (such as questionnaires and surveys) [9], tracking or sensing data provide
a more expansive and objective method [10] and can describe human behavior in space
and time more completely. Such data create unprecedented opportunities for research
on the construction of smart scenic spots. Mobile signaling data has the advantages of a
large sample size and real-time performance, but it is difficult to obtain the data because
of confidentiality and low accuracy [11]. As a new investigation method for studying
individual human behavior, GPS positioning tracking has attracted extensive attention
in the research field due to its portability, diversification, and availability. In terms of
human behavior, GPS data tends to have better data quality in both spatial and temporal
dimensions than cell phone signaling data [12]. Aczanowska studied a national park
and compared a GPS track and hand-drawn map of the questionnaire to highlight the
advantage of GPS in data accuracy [13]. However, the acquired GPS data cannot reveal
tourists’ personal and social attributes, affecting the integrity of the analysis. Therefore, the
research method needs to consider the combination of multi-source data.
As a large green space in cities, the botanical garden has been transformed into a
scenic spot with diversified functions such as scientific research, protection, popular science,
entertainment, and culture. In recent years, the growth of the nature tourism market (such
as birdwatching tourism) [14] and popular science tourism for children has further boosted
the popularity of botanical gardens. In order to survive in the ever-changing environment,
it has become an effective way to construct smart scenic spot management to understand
the spatial and temporal behavior patterns of tourists and adjust the spatial layout of
facilities according to their preferences. Therefore, this paper takes Zhongshan Botanical
Garden in China as an example, combines GPS data and traditional data, uses K-means
clustering algorithm and Square Sum Error (SSE), and explores the tourist spatial–temporal
behavior patterns of micro-scale scenic spots from the perspective of scenic space units, to
provide decision support for the planning of smart parks. In short, the purpose of this study
was to (1) conduct a field survey of tourists to discover their behavioral characteristics
and demographics; (2) visually depict the spatial and temporal spatial–temporal behavior
patterns of tourists; (3) provide planning strategies for smart scenic spots based on tourists’
spatial–temporal behavior patterns
2. Data and Methods
2.1. Study Area
Founded in 1929, Nanjing Zhongshan Botanical Garden is located in Purple Mountain,
Xuanwu District, Nanjing, also known as the Institute of Botany of the Chinese Academy
of Sciences in Jiangsu Province (Figure 1). It is the first national botanical garden and one of
four major botanical gardens in China [15]. Zhongshan Botanical Garden is the “National
youth science and technology education base,” “national science education base,” is a
collection of plant resources protection, botanical garden construction, and natural science
education, one of the comprehensive public welfare institutions.
Zhongshan Botanical Garden is also one of the 48 scenic spots of Jinling. It is a scenic
spot with beautiful scenery and environment in China, receiving more than 300,000 tourists
from home and abroad every year. This garden not only hosts over 700,000 herbariums
and cultivates over 3000 species of plants, but also bird species are abundant. The garden
covers an area of 186 hectares, comprising two parts. The research area used for this study
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was the North Garden of the Zhongshan Botanical Garden located in Nanjing, China. The
North Garden is older than the South Garden. Located in the Zhongshan scenic region of
Nanjing’s Xuanwu District, the botanical garden faces a mountain and a lake adjacent to
of traffic between the starting and ending points [16]. “O” indicates the starting pla
the ancient city wall. In addition, this botanical garden is surrounded by universities and
the
residences. Hence line
theand “D” indicates
tourists the line’s
are expected to bedestination.
diverse. Therefore, the research results
should show clear patterns.
Figure 1. Location and1.zoning
Figure of the
Location andstudy area.
zoning of the study area.
2.2. Data Source
A field survey was conducted, and we designed a questionnaire that would accompany
the research team’s handheld GPS devices. Then, the research team surveyed travelers
four times a month (including weekdays and weekends, when the weather is cloudy and
sunny) from 3 February to 3 April 2019 in the Zhongshan Botanical Garden. Handheld GPS
and matching questionnaires were used to obtain tourists’ spatial and temporal behavior
data with personal attributes. To ensure the sample’s representativeness, researchers
should not choose the respondents subjectively; therefore, the researchers in the study
were instructed to go to the garden’s gate with a ready GPS device and questionnaire to
find willing tourists. Volunteers were asked to bring a GPS device to the entrance to the
north side of the botanical garden, return it at the end of the tour, and then complete their
questionnaire. A supported GPS device recorded all participants’ temporal and spatial
paths. The handheld GPS handsets (China, Victory Technology Co., Ltd.) used in this
survey had a horizontal positional accuracy of 2.5 m for 95% of the tracking points. They
were updated at a one-second interval, monitoring the tourist’s accurate time and location.
A total of 220 sets of GPS and questionnaire data were obtained. After deviations were
corrected and data format converted, the trajectories were loaded in ArcGIS Pro to generate
the spatial distribution of 220 tourists’ trajectories in Zhongshan Botanical Garden.
2.3. Methods Figure 2. Framework for the study.
Figure 2 shows the framework of this study. The first step was the field investigation
2.3.2.
and analysis of the Tourist Spatial–Temporal
study area, which helped Behavior Pattern Clustering
with the questionnaire design. According to
the garden’s layoutTheand local themes,
clustering we divided
algorithm theapplied
has been North Garden
in manyinto 28 and
fields unitshas
of scenic
penetrated the
spots under four scenicbehavior
of tourist zones; namely, thespots
in scenic scientific research
[17–20]. zone,widely
The most the natural reserves
used and relatively sim
clustering method is K‐means clustering. K‐means clustering was first proposed by L
in 1957 and was fully described and applied in research by MacQueen in 1967. Minim
the error function aims to divide the original dataset into several clusters and calc
Processes 2022, 10, 181 4 of 15
zone, the culture and education zone, and the recreation and leisure zone (Figure 1). Next,
we handed out GPS devices along with the questionnaires. We then extracted tourists’
movement trajectories and used the method of cluster analysis. Considering the existing
feature zones of the botanical garden (study area), we classified tourists’ spatial–temporal
behavior patterns in our final step. Specifically, (1) we extracted the effective movement
trajectories based on the GPS recording time and labeled the scenic areas with ID tags. (2)
We selected the optimal K value using the SSE method. We conducted K-means cluster
analysis combined with the unit map and origin and destination (OD) distribution map
of the divided scenic spots to classify tourists’ spatial–temporal behavior patterns and
extract typical behavior types for visual analysis. (3) Based on the above analysis results,
we provided
Figure reasonable
1. Location suggestions
and zoning forarea.
of the study the construction strategy of the smart scenic spot.
Figure 2. Framework for the study.
Figure 2. Framework for the study.
2.3.2.
2.3.1. Tourist
The ODSpatial–Temporal
Analysis of Tourist Behavior Pattern Clustering
Spatial–Temporal Behavior Chain
The clustering
In the process algorithm
of visiting,has been applied
tourists have stayin many fieldsinand
behaviors has penetrated
different the area
spaces. The stay
of tourist behavior
behaviors in scenic
have a certain spots [17–20].
frequency The most
and sequence widely used
relationship, and “behavior
called relatively chain.”
simple
clustering
Firstly, themethod is K‐means
serial number clustering.
information of K‐means clustering
all the spatial units was first proposed
of tourists’ by Lloyd
stay behavior is
in 1957 and
counted, was fully
forming thedescribed
behaviorand applied
chain. in research
Secondly, each by MacQueen
behavior in 1967.
chains’ Minimizing
departure space
uniterror
the and arrival space
function aimsunit
to are screened
divide out from
the original all samples’
dataset behavior
into several chains.
clusters Finally,
and the
calculate
frequency of each OD selection is counted, and the OD chart is visualized. OD (origin-
destination) chart is also called OD traffic volume survey; OD chart refers to the amount of
traffic between the starting and ending points [16]. “O” indicates the starting place of the
line and “D” indicates the line’s destination.
2.3.2. Tourist Spatial–Temporal Behavior Pattern Clustering
The clustering algorithm has been applied in many fields and has penetrated the
area of tourist behavior in scenic spots [17–20]. The most widely used and relatively
simple clustering method is K-means clustering. K-means clustering was first proposed
by Lloyd in 1957 and was fully described and applied in research by MacQueen in 1967.
Minimizing the error function aims to divide the original dataset into several clusters and
calculate similarity (i.e., distance) between variables [21]. We divided the original data
into a predetermined number (K) of clusters in the K-means clustering analysis. Variables
of high similarity would be in the same cluster, and those of low similarity would be in
different clusters. To avoid a randomly determined K leading to an optimal local solution,
Processes 2022, 10, 181 5 of 15
a K-means clustering algorithm with an SSE method (i.e., the elbow method) was used to
predetermine the optimal number of clusters.
To obtain the optimal K, it was first assumed that k belonged to a set [1, M]. Next, the
SSE of the data was calculated (a total of M numbers). Finally, a k-SSE curve was plotted.
The SSE method equation is shown in Equation (1).
SSE = ∑ p ∈ Ci | p − mi |2 (1)
where Ci represents cluster i; p is a sample point in cluster Ci ; and mi is the cluster’s centroid,
the mean of all the samples in Ci . SSE is the clustering error for all the samples, which
represents the accuracy of the clustering effect.
As the number of clusters, K, increases, the similarity of each cluster gradually in-
creases, and the mean square of error and SSE decrease progressively. When K is smaller
than the optimal number of clusters, K, K will increase the degree of similarity of each
cluster. Therefore, the decrease in the SSE will be large. When K reaches the optimal
number of clusters (i.e., k = K), the similarity of clusters obtained by increasing K will
decrease rapidly. Therefore, the decrease in the SSE will slow down. As the value of K
continues to grow, the curve is flat. In an elbow-shaped curve, the relationship between the
SSE and K can be described. The K value at the turning point is the optimal number, K,
of clusters.
After K was determined, we randomly selected data of K tourists from the 225 valid
tourist data sets as the initial cluster centers. Then, SPSS software was used to calculate
the clustering results. The calculation process is as follows: the distance between each
tourist’s data and each cluster center was calculated, and the data of each tourist were
allocated to the nearest cluster center. Subsequently, data of all tourists in each cluster were
averaged, and the mean value became the new cluster center and was used to calculate the
values of the standard measure function. The mean square error was generally adopted as
the standard measure function [22]. The steps of allocation of tourist data to clusters and
calculating new cluster centers were repeated. Finally, the clustering results were output if
the values of the cluster centers and aim functions remained unchanged.
3. Results
3.1. OD Analysis of Tourists’ Spatiotemporal Behavior
Figure 3 shows the OD map of the tourist movement trajectories. In Figure 3, the
shade of the lines between two scenic units represents the degree of association between
the scenic units. The lighter the color, the higher the association between the scenic units.
In addition, the heat map color of red to blue shows the cumulative stop frequency of the
tourists. A darker red color indicates that tourists more frequently stop there. The OD
map shows that tourists were concentrated around the Sculpture of Sun Yat-sen and its
surroundings, such as the System Garden and the Crepe Myrtle Garden.
It can be seen from the overall OD map that units 2, 3, 14, and 27 were a few of the
more remote locations. The common feature of these scenic spots is a relatively low tourist
rate. A statistical analysis of the GPS tracking data was conducted to evaluate the scenic
units (Table 1). Scenic units with an F < 2 and p > 0.05 were removed, and the repre-
sentative tourist movement trajectories were finally obtained. These scenic spots would
cause no obvious differences in clustering elements and interfere with the clustering of
spatial–temporal behavior patterns. Therefore, it was necessary to remove these interfering
elements. Combined with statistical analysis and OD map visualization results, it was
found that the results were consistent. Finally, four spatial units, namely 2, 3, 14, and 27,
were deleted, and 24 spatial units were retained.
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Table 1. The difference analysis of the 28 scenic units.
Dependent Variable Mean Table
Square F Value
1. The differencep‐Value
analysis ofDependent
the 28 scenicVariable
units. Mean Square F Value p‐Value
0 3.570 24.179 0.001 14 0.019 0.124 0.883
1 Variable
Dependent 3.768
Mean Square F Value
18.146 p-Value
0.000 Dependent 15 Variable 1.540
Mean Square F8.870 p-Value
Value 0.000
20 1.006
3.570 4.299
24.179 0.015
0.001 16
14 1.148
0.019 6.314
0.124 0.002
0.883
31 3.768
0.308 18.146
2.642 0.000
0.073 15
17 1.540
3.385 8.870
22.663 0.000
0.000
42 1.006
1.155 4.299
6.064 0.015
0.003 16
18 1.148
9.636 6.314
67.293 0.002
0.000
3 0.308 2.642 0.073 17 3.385 22.663 0.000
54 7.230
1.155
48.818
6.064
0.000
0.003
19
18
4.060
9.636
33.282
67.293
0.000
0.000
65 3.894
7.230 17.911
48.818 0.000
0.000 20
19 1.048
4.060 9.854
33.282 0.000
0.000
76 1.633
3.894 7.785
17.911 0.001
0.000 21
20 0.257
1.048 3.310
9.854 0.038
0.000
87 1.633
7.562 7.785
46.215 0.001
0.000 21
22 0.257
1.990 3.310
13.832 0.038
0.000
8 7.562 46.215 0.000 22 1.990 13.832 0.000
9 4.290 46.162 0.000 23 3.578 17.012 0.000
9 4.290 46.162 0.000 23 3.578 17.012 0.000
10
10 3.061
3.061 14.577
14.577 0.000
0.000 24
24 0.394
0.394 2.230
2.230 0.110
0.110
11
11 1.411
1.411 17.233
17.233 0.000
0.000 25
25 1.661
1.661 10.901
10.901 0.000
0.000
12
12 1.641
1.641 13.275
13.275 0.000
0.000 26
26 1.602
1.602 16.978
16.978 0.000
0.000
13
13 13.029
13.029 101.780
101.780 0.000
0.000 27
27 0.387
0.387 3.002
3.002 0.052
0.052
TouristsOD
Figure3.3.Tourists
Figure ODfigure.
figure.
3.2. Spatial–Temporal Behavior Patterns
3.2. Spatial–Temporal Behavior Patterns
Before conducting the cluster analysis, the number of clusters was determined by
Before
plotting conducting
K against theFigure
SSE. As cluster4 shows,
analysis,
thethe number
turning of isclusters
point was determined
K = 3. When K < 3, there by
is a
plotting K against SSE. As Figure 4 shows, the turning point is K =
rapid downward trend, and when K > 3, the curve tends to be flat. Therefore, 3. When K 3,
of clusters was determined to be 3. the curve tends to be flat. Therefore, the optimal
number of clusters was determined to be 3.
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Processes 2022, 10, 181 7 of 15
Figure 4. The K- SSE (sum of the squared errors) curve.
Figure 4. The K‐ SSE (sum of the squared errors) curve.
The spatial–temporal behavior patterns of tourists were grouped into a single type
The spatial–temporal
and a mixed behavior
type. Tourists patterns
of the single of choose
type touriststhewere
samegrouped
type ofinto a single
scenic spots,type
while
and a mixed type. Tourists of the single type choose the same type of scenic
tourists of the composite type choose two or more scenic spots during their visit. Because spots, while
tourists of the28composite
there were type
scenic units, choose
there weretwo or more scenic
theoretically spots duringFrom
15 combinations. their the
visit.
SSEBecause
method,
there were 28 scenic units, there were theoretically 15 combinations.
the optimal clustering number was determined to be 3. Therefore, the K-means algorithm From the SSE
method,
was used thetooptimal
clusterclustering number was determined
three spatial–temporal to be 3.Then,
behavior patterns. Therefore, the scenic
the three K‐means spot
algorithm
units with wastheused to cluster
highest three spatial–temporal
probabilities were selected from behavior
all thepatterns.
various Then, the three
spatial–temporal
scenic spot units
behavior withasthe
patterns highestsample
effective probabilities were
behavior selected
data types.from all the various
We combined thesespatial–
with the
temporal behavior patterns as effective sample behavior data types. We combined
information obtained from the questionnaire. Therefore, three tour patterns of the tourists these
with the information obtained from the questionnaire. Therefore, three tour
were obtained, namely recreation and leisure, birdwatching and photography, and learning patterns of
theand
tourists were obtained,
education. namely
The specific recreationof
characteristics and leisure,
these threebirdwatching
tour patternsand photography,
are given in Table 2.
and learning and education. The specific characteristics of these three tour patterns are
given
Tablein2.Table
Three2.tour patterns and the visitation probabilities.
Scenic Unit Pattern 1 Pattern
Table 2 tour patterns
2. Three Pattern 3and the visitation
Scenic Unit Pattern 1
probabilities. Pattern 2 Pattern 3
Scenic 0Unit 0.02
Pattern 1 0.36
Pattern 2 Pattern0.43
3 15
Scenic Unit 0.11
Pattern 1 0.11
Pattern 2 0.39
Pattern 3
1 0.25 0.37 0.75 16 0.64 0.64 0.73
04 0.02
0.23 0.360.21 0.43 0.48 15 17 0.11
0.05 0.11
0.05 0.39
0.52
15 0.25
0.26 0.370.80 0.75 0.50 16 18 0.64
0.02 0.64
0.02 0.73
0.43
46 0.27
0.23 0.210.6 0.48 0.73 17 19 0.15
0.05 0.15
0.05 0.57
0.52
57 0.30
0.26 0.800.24 0.50 0.57 18 20 0.02
0.02 0.02
0.02 0.16
0.43
8 0.05 0.05 0.66 21 0.05 0.05 0.18
69 0.27
0.09
0.60.02 0.73 0.55 19 22 0.15
0.04
0.15
0.04
0.57
0.27
7 10 0.30
0.26 0.240.33 0.57 0.7 20 23 0.02
0.22 0.02
0.22 0.16
0.36
8 11 0.05
0.01 0.050.1 0.66 0.32 21 25 0.05
0.14 0.05
0.14 0.18
0.45
9 12 0.05
0.09 0.020.54 0.55 0.39 22 26 0.05
0.04 0.05
0.04 0.36
0.27
13 0.04 0.04 0.5
10 0.26 0.33 0.7 23 0.22 0.22 0.36
Number of tourists 92 84 44
11 0.01 0.1 0.32 25 0.14 0.14 0.45
12 0.05 0.54For the recreation
0.39 and leisure 26
tour pattern (type 0.05 0.05
1, Table 2), the probability0.36
of visiting
13 0.04 0.04 0.5
units 16, 18, and 10 was 64%, 43%, and 26%, respectively. The landscape functions of these
three scenic units were allNumber
recreationof and
tourists
leisure. Unit9216 is a major84attraction around
44 the
Sculpture of Sun Yat-sen, and unit 18 is a sunny lawn, which is the only area that tourists
canFor thethe
feed recreation
pigeons.and
Asleisure
the GPStour patternresults
tracking (type 1,show
Table(Figure
2), the probability
5a), a typicalof visiting
tourist of
units
this16, 18, and
pattern 10 wasfrom
started 64%,the43%, and 26%,
entrance of respectively. The landscape
the North Garden functions of
at approximately these
2:40 p.m.,
three scenic
passed theunits wereof
Sculpture allSun
recreation
Yat-sen,and
andleisure.
stoppedUnit 16 is a major attraction
at Hongfenggang for 10 min.around the
The tourist
Sculpture
then walkedof Sunthrough
Yat‐sen,the
and unit 18
Crepe is a sunny
Myrtle Garden,lawn, which at
arriving is the only area that
sunshine lawntourists
at 15:20
can
andfeed the pigeons.
staying As the
for 40 min. GPS
The tracking
tourist thenresults
walked show (Figure
through the5a), a typical
Flower tourist
Garden and offinally
this
pattern started from the entrance of the North Garden
returned to the north gate of the garden along the main road. at approximately 2:40 p.m., passed
the Sculpture of Sun Yat‐sen, and stopped at Hongfenggang for 10 min. The tourist then
walked through the Crepe Myrtle Garden, arriving at the sunshine lawn at 15:20 and stay‐
ing for 40 min. The tourist then walked through the Flower Garden and finally returned
to the north gate of the garden along the main road.
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Processes 2022, 10, 181 8 of 15
Figure
Figure 5. Three
5. Three typestypes of tourist
of tourist spatial–temporal
spatial–temporal behavior
behavior patterns
patterns (typical
(typical sample):
sample): (a) pattern
(a) pattern 1
(recreation and leisure); (b) pattern 2 (birdwatching and photography); (c) pattern 3 (learning(learning
1 (recreation and leisure); (b) pattern 2 (birdwatching and photography); (c) pattern 3 and
and education).
education).Processes 2022, 10, 181 9 of 15
Most tourists chose units 5, 6, and 9 for the birdwatching and photography tour
pattern (type 2, Table 2). The probability of selecting unit 5 (the System Garden) was as high
as 80%. According to the questionnaire and interview, there are as many as 300 different
types of birds in the botanical garden, especially the precious red-billed blue magpie in the
System Garden, so there are often birdwatching groups irregularly carrying out activities.
The probability of choosing units 12, 1, and 0 (in the scientific research zone) was 54%, 37%,
and 36%, respectively. Because these two units are close to the Ming-Xiaoling Wall, many
tourists take pictures in winter. Compared with the other two tour patterns, the tourists
in this tour pattern had a higher preference for natural reserves areas and areas with tall
plants. As the GPS tracking results show (Figure 5b), a typical tourist of this pattern entered
the North Garden at 9:10 in the morning, passed through the System Garden to reach the
Sculpture of Sun Yat-sen, and stayed for two hours. Then, the tourist walked through the
Bonsai Garden and the Biyun pavilion to the well on the west outside the palace gate. The
typical tourist stayed near the wall for 3.5 h and left at 5:05 p.m.
The tourists chose a wide range of scenic spots for the learning and education tour
pattern (type 3, Table 2). The probability of selecting unit 16 was 73%, and other scenic units’
possibilities were nearly even. Compared with the two different tour patterns, tourists
of this pattern had relatively broad experience in the garden. They also spent the longest
hours in the botanical garden. As the GPS tracking results show (Figure 5c), a typical
tourist of this pattern entered the North Garden at 8:40 in the morning. He reached the
Medicinal Plants Garden along the main road and stayed there for 15 min. The tourist then
went through the Conifer and Flower Garden, and he reached the Crepe Myrtle Garden at
10:20 and stayed for 10 min. After a long walk, the typical tourist reached the Sculpture of
Sun Yat-sen at 11:10. They visited the System Garden, Bonsai Garden in turn, and Crepe
Myrtle Garden again. Finally, along with the Flower Garden and Sculpture of Sun Yat-sen,
he walked back to the main gate at 14:30 and left the garden.
3.3. Differences in Tourist Spatial–Temporal Behavior Patterns
According to the questionnaire statistics, 119 male and 101 female tourists took part in
the survey. As shown in Table 3, the overall ratio of males to females was 55:45. The male-
to-female ratio of tour pattern 1 was nearly the same as the overall ratio and the smallest
among the three. In patterns 1 and 3, there were more men than women. However, in tour
pattern 2, there were more women than men, showing that women were more interested in
science and education, while men preferred leisure and birdwatching-related attractions.
In terms of age, the ages of tourists in pattern 2 were primarily between 19 and
35 years old (accounted for 45.2%). In comparison, the ages of tourists in patterns 1 and
3 were primarily between 36 and 65 years old, accounting for 40.2%and 40.9%, respectively.
Therefore, this indicates that young tourists preferred tour pattern 2, while middle-aged
and older people preferred a walk or similar leisure time in the garden. Because most
tourists under the age of 18 were accompanied by their parents, this data was biased and
thus removed.
Regarding the frequency, most tourists were not regular tourists. The first-time tourists
primarily chose pattern 1 (81.5%). Regular daily tourists may have been residents who
lived nearby. They chose pattern 1, likely because they came to breathe fresh air and do
some exercise (the botanical garden allows free admission before 8:30 a.m.). The proportion
of first-time tourists in pattern 1 (18.5%) was higher than that in other patterns, which
shows that first-time tourists only visited special gardens along the main road and had no
interest in visiting the whole garden. According to the interview records, most first-time
tourists were from out of the city or were students, and they found the garden lacking
clear signs and attractive attractions. Pattern 1 was slightly more frequent than the other
two patterns for tourists who come every day or once a month. The reason is attributable
to middle-aged and older adults who exercise in the morning. Among the tourists who
visit once a week, a relatively high proportion (7.1%) exhibited pattern 2, which can beProcesses 2022, 10, 181 10 of 15
understood as birdwatching photographers mostly focusing on personal or community
activities on weekends.
Table 3. Social characteristics of the tourists in different patterns.
Pattern 1 Pattern 2 Pattern 3
Characteristics
n = 92 n = 84 n = 44
Male 54.3% 44.0% 59.1%
Gender Female 45.7% 56.0% 40.9%
Under 18 0 3.6% 2.3%
19–35 35.9% 45.2% 31.8%
Age 36–65 40.2% 33.3% 40.9%
>65 23.9% 17.9% 25.0%
Irregular 56.5% 63.1% 70.5%
First time 18.5% 14.3% 9.1%
Daily 4.3% 1.2% 2.3%
Frequency
Once a month 12.0% 10.7% 4.5%
2–3 times a week 5.4% 2.4% 6.8%
Once a week 3.3% 7.1% 6.8%
Employees of for-profit
26.1% 33.3% 22.7%
organizations
Retiree 30.4% 19% 34.1%
Occupation
Employees of nonprofit
14.1% 25% 18.2
organizations
Student 17.4% 11.9% 18.2
Other 5.4% 1.2% 6.8%
Visit time 1.03 h 2.8 h 2.2 h
Tourists to the botanical garden who choose patterns 1 and 3 were mostly retirees.
In pattern 2, the proportion of employees from for-profit and nonprofit organizations is
relatively high, showing that they are more inclined to favor specific activities and scenic
spots. Pattern 2 had the highest average visit time (2.8 h) and pattern 1 had the lowest
average visit time (1.03 h).
4. Discussion
4.1. Accurate Classification of Tourist Spatial–Temporal Behavior Patterns
Researchers seem to agree that studies of spatiotemporal behavior at the micro-scale
are important for location data and the construction of “smart scenic spots.” Our study
demonstrates the feasibility of combining multiple data sources to explore behavioral
patterns. A single data source cannot record the spatial behaviors of tourists, and technology
cannot replace the traditional questionnaire that reveals the social attributes of tourists.
Millonig and Gartner compared the tracking and questionnaire methods and concluded that
a combined approach was more likely to produce complete behavioral characteristics [23].
In one of the few studies on tourist spatial–temporal behavior patterns using GPS, Huang
could not accurately distinguish the difference in tourists’ choices of scenic spots. However,
he undertook a cluster analysis on the time dimension of tourist behaviors in the Ocean
Park in Hong Kong [10]. In a previous study on the spatial–temporal behavior pattern
of tourists, the value of K in the clustering algorithm relied on continuous attempts at
solving the value of K [24]. Compared with the method, the combination of the SSE and
K-means algorithm used in the present study can more accurately select the K value and
classify tourist spatial–temporal behavior patterns. From the perspective of the tourist
route (Figure 6), tourists in pattern 1 mostly travel along the main road. The tour routes of
tourists in pattern 3 cover the whole garden, and occasionally there are high-density areas.
This can be attributed to the fact that most tourists in pattern 3 visit for science education.
The three tourist spatial–temporal behavior patterns are as follows:bonsai garden, and rest area. A bonsai garden is a form of the garden within a garden,
which began to form in China in the 1960s. The art of bonsai can be traced back to the
Eastern Han dynasty 1900 years ago [30]. It demonstrates traditional Chinese culture and
aesthetics, and the garden combines landscape walls, pavilions, and corridors to construct
a space with artistic appeals typically found in Chinese landscape paintings. The tourists
Processes 2022, 10, 181 11 of 15
in pattern 3 are mostly non‐local tourists that choose the bonsai garden.
Figure
Figure6.
6.Three
Threebehavioral
behavioralpatterns
patternsof
ofspatial
spatialvisualization.
visualization.
4.2. Strategies
Pattern 1:forItBuilding Smart
can be seen fromScenic Spots
Figure 6 that the areas where tourists frequently stopped
in pattern 1 were the statue of Sun Yat-sen,
To help tourists travel through the scenic Sunshine lawn,
area and and RoseaGarden.
encourage more evenSundistribu‐
Yat-sen’s
statue is an iconic destination in the scenic area so that most tourists will take photos
tion of tourists, the garden can better serve tourists by establishing a guiding system (e.g., there,
and the Sunshine lawn is the only place in the garden to feed pigeons. Yao
smart electronic maps and icons) that leads tourists where they might like to go. The GPSpointed out
that human–human, human–animal, and human–plant interaction would make a flat site
attractive and cohesive [25].
Pattern 2: The purpose of tourists in pattern 2 is to take photos of birds and the city
walls. The mainstay spaces are the System Garden, Ming City Wall, and Bonsai Garden. In
winter, Zhongshan Botanical Garden is free from the cold of the North and has numerous
seeds, plants, and insects. It is a paradise for many types of birds.
Birdwatching, a long-standing pastime in Western countries [26], has become increas-
ingly important in China in recent years as a new variant of nature tourism. Birding is also
the epitome of ecotourism, as it is relatively unlikely to lead to negative environmental
changes. Zhao classified birdwatching tourism and proposed birdwatching ecotourism
strategies to cultivate the ecotourism market [27]. Liu even suggested that bird resources
be utilized to build a birdwatching tourism platform to form a distinctive ecological bird-
watching industry [28]. Furthermore, Zhongshan Botanical Garden is surrounded by the
wall of the Ming dynasty, which is the longest, largest, and best-preserved ancient city wall
in the world [29].
Pattern 3: The high stay area for tourists in pattern 3 is the statue of Sun Yat-sen,
bonsai garden, and rest area. A bonsai garden is a form of the garden within a garden,
which began to form in China in the 1960s. The art of bonsai can be traced back to the
Eastern Han dynasty 1900 years ago [30]. It demonstrates traditional Chinese culture and
aesthetics, and the garden combines landscape walls, pavilions, and corridors to construct
a space with artistic appeals typically found in Chinese landscape paintings. The tourists
in pattern 3 are mostly non-local tourists that choose the bonsai garden.
4.2. Strategies for Building Smart Scenic Spots
To help tourists travel through the scenic area and encourage a more even distribution
of tourists, the garden can better serve tourists by establishing a guiding system (e.g.,
smart electronic maps and icons) that leads tourists where they might like to go. The GPS
movement trajectories of tourists suggested that some were lost and trying to find their wayProcesses 2022, 10, 181 12 of 15
back. Therefore, the scenic spots need to be made more identifiable. A similar study also
indicated that tourists are more likely to use environments that they can easily navigate [31].
Additionally, the results showed that the garden’s vigor was not evenly distributed. Most
tourists passed by them without stopping. The eastern and western parts of the garden
had significantly fewer tourists. Therefore, the eastern and western parts of the garden
need better attractions and better connections between the central area scenic spots with
the periphery attractions. The botanical garden can provide tourists with a wide range of
learning, entertainment, and fitness services.
The botanical garden can provide further differentiated services and management
to enrich the experience of different visiting patterns. The differences and imbalances in
the three existing spatial–temporal behavior patterns can offer references to formulating
management strategies. By providing different tourism route planning to tourists of these
three different patterns, space supply can be strengthened, and clear pattern selection also
provides convenience to individuals. For example, for bird and photography pattern 2,
Attract birders by provide ideal birding locations, birding devices, and electronic birdwatch-
ing. Sustainable development of the tourism industry for birds needs proper infrastructure
and tourism activities for travelers to experience bird habitat for wild birds [14]. Studies
have shown that novice birders and professional birders focus differently. Beginner birders
concentrate on various activities at the site, while experienced birders frequently visit the
site to track birds, and most stay overnight. As a result, well-designed birding trails can help
experienced birders throughout the area. However, since birders’ skill levels are negatively
correlated with their satisfaction with the plan [32], more professional birders are harder
to please and require a well-designed plan. Previous studies have shown that birders
are slightly older and more likely to be professional [33–35], highly educated, and have a
higher household income than the general public. This is largely consistent with the results
of this paper, except for the age of tourists regarding birding and photography. Therefore,
as the visiting experience deepens their knowledge for different groups, the best strategy
for experts to provide an enriching expertise may be an adaptive management approach.
In addition, holding cultural events can attract tourists to this study area [36]. As the
results suggest, the learning and education tour pattern has a specific purpose, encouraging
tourists to stay at a single spot for a long time. Tourists in the other two tour patterns
did not last for long at any spots. Therefore, the potential of this attraction needs to be
further explored. For example, cultural events can be held to attract additional tourists
and meet different needs of tourists of different ages. Table 3 shows that young people and
children usually are interested in cultural and educational events and hands-on activities.
The elderly primarily experience the natural ecological environment, which leads to inte-
grating the natural environment with the site space to produce more public facilities for
recreation. Through the study of the correlation between the garden walking space and
the walking behavior of the elderly, scholars concluded that a walking area with comfort,
convenience, and a beautiful environment could promote the leisure walking activities of
the elderly [37,38]. Exploring building a “smart scenic spot” centering on the interactive
experience of tourists becomes possible [39].
4.3. Limitations and Future Research
This study has certain limitations that must be discussed. First, although GPS can
accurately obtain tourists’ spatial and temporal tracks, some track segments are missing
since there are many plants in botanical gardens, especially large trees, which interfere with
the acquisition of GPS signals. Secondly, the collection of GPS data requires human and
material resources. It must go through data cleaning and optimization, e.g., artificial track
correction, so only a small amount of data can be processed. The format conversion and
processing of the data can only be done manually. The job is so hard and time-consuming
that it is hardly possible to deal with thousands of trajectories. This paper studies the
relationship between the functional landscape space of the botanical garden and the spatial–
temporal behavior pattern of tourists, paying attention to the common characteristics ofProcesses 2022, 10, 181 13 of 15
tourists’ spatiotemporal behavior. In future research, in-depth research should be conducted
on the individual spatiotemporal behavior and spatial interaction experience of different
types of tourists, to provide technical support for the planning and management of smart
scenic spots, especially tourist routes, and the smart guidance systems.
5. Conclusions
In this paper, a research method with tourist spatial–temporal behavior patterns as
the core was proposed to promote the planning and management of smart scenic spots
by creating an enhanced destination experience. Taking the Zhongshan Botanical Garden
as the research object, this paper integrated multi-source data, including a questionnaire
survey of tourists’ movement trajectories and social characteristics obtained by handheld
GPS devices. Using the optimized clustering analysis method, cluster analysis was carried
out on the tour itinerary of tourists in the scenic area. The spatial–temporal behavior
pattern of tourists was obtained. The main conclusions are as follows. Based on GPS
tracking and questionnaire data, the combination of the K-means clustering algorithm
and SSE can effectively identify tourist spatial–temporal behavior patterns. The spatial–
temporal behavior patterns of tourists in botanical gardens can be divided into three types:
recreation, birdwatching and photography, and learning. There are obvious differences in
their preferences and social attributes.
Based on the more accurate analysis of the spatiotemporal behavior information of
individual tourists, it can provide direction for the future planning and upgrading of smart
scenic spots and real-time support for the management and operation of smart scenic spots.
For example, tourists can get targeted services based on their preferences, and scenic area
managers can also effectively segment the market based on tourist information (such as
primary and professional birdwatching groups) to control and guide the flow of tourists to
reduce traffic congestion. Smart scenic spots worldwide are looking for feasible plans, but
the complexity makes technology and service innovation extremely difficult. This study
may provide a sufficient basis for the practice of smart scenic spots.
Author Contributions: Conceptualization, methodology, and writing—original draft preparation,
J.Z.; data curation and software X.B.; visualization, L.N.; supervision, H.W. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by the National Key R&D Program of China (No. 2019YFD1100404).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
author. The data are not publicly available due to privacy. Images employed for the study will be
available online for readers.
Acknowledgments: We would like to thank the management department of the Zhongshan Botanical
Garden for giving us their permission to do this research. We would also like to thank X. B., L.S. for
assistance during data collection and to thank H. W. for writing suggestions.
Conflicts of Interest: The authors declare no conflict of interest.
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