THE SPATIAL STRUCTURE
CHARACTERISTIC AND ROAD TRAFFIC
ACCESSIBILITY EVALUATION OF A-LEVEL
TOURIST ATTRACTIONS WITHIN WUHAN
URBAN AGGLOMERATION IN CHINA
Wanying Liao
Faculty of Arts and Social Sciences, University of Malaya.
50603 Kuala Lumpur, Malaysia.
wanyingliao1998@gmail.com - https://orcid.org/0009-0002-3259-9663
Hongtao Wang
College of Urban and Environmental Sciences, Central China Normal University.
430079 Wuhan, Hubei, China.
hongtaowang2001@gmail.com - https://orcid.org/0009-0008-6454-0598
Jiajun Xu*
Institute for Advanced Studies, University of Malaya.
50603 Kuala Lumpur, Malaysia.
jiajunxu2000@gmail.com - https://orcid.org/0009-0000-9130-1025
Reception: 21/05/2023 Acceptance: 22/07/2023 Publication: 13/08/2023
Suggested citation:
Liao, W., Wang, H., and Xu, J. (2023). The Spatial Structure Characteristic
and Road Traffic Accessibility Evaluation of A-Level Tourist Attractions
within Wuhan Urban Agglomeration in China. 3C Tecnología. Glosas de
innovación aplicada a la pyme, 12(3), 388-409. https://doi.org/
10.17993/3ctecno.2023.v12n3e45.388-409
https://doi.org/10.17993/3ctecno.2023.v12n3e45.388-409
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
388
THE SPATIAL STRUCTURE
CHARACTERISTIC AND ROAD TRAFFIC
ACCESSIBILITY EVALUATION OF A-LEVEL
TOURIST ATTRACTIONS WITHIN WUHAN
URBAN AGGLOMERATION IN CHINA
Wanying Liao
Faculty of Arts and Social Sciences, University of Malaya.
50603 Kuala Lumpur, Malaysia.
wanyingliao1998@gmail.com - https://orcid.org/0009-0002-3259-9663
Hongtao Wang
College of Urban and Environmental Sciences, Central China Normal University.
430079 Wuhan, Hubei, China.
hongtaowang2001@gmail.com - https://orcid.org/0009-0008-6454-0598
Jiajun Xu*
Institute for Advanced Studies, University of Malaya.
50603 Kuala Lumpur, Malaysia.
jiajunxu2000@gmail.com - https://orcid.org/0009-0000-9130-1025
Reception: 21/05/2023 Acceptance: 22/07/2023 Publication: 13/08/2023
Suggested citation:
Liao, W., Wang, H., and Xu, J. (2023). The Spatial Structure Characteristic
and Road Traffic Accessibility Evaluation of A-Level Tourist Attractions
within Wuhan Urban Agglomeration in China. 3C Tecnología. Glosas de
innovación aplicada a la pyme, 12(3), 388-409. https://doi.org/
10.17993/3ctecno.2023.v12n3e45.388-409
https://doi.org/10.17993/3ctecno.2023.v12n3e45.388-409
ABSTRACT
Against the backdrop of the post-pandemic COVID-19, regional short-distance tourism
has become more prevalent. This paper used Wuhan Urban Agglomeration (WUA) as
the research area and explored spatial structure characteristics and road traffic
accessibility issues of A-level tourist attractions within WUA. The geospatial analysis
methods of Average Nearest Neighbour (ANN) and Kernel Density Estimation (KDE)
were used to identify the spatial structure distribution of A-level tourist attractions.
Constructing Weighted Network Analysis to measure the traffic access time between
tourist attractions and traveler origin and further using Network Analysis to measure
the traffic access time between different tourist attractions. The traffic access time
results were spatially visualized using Inverse Distance Weight (IDW). The study
results were as follows. (1) The spatial structure of A-level tourist attractions in WUA
indicated a core-periphery distribution in general. All tourist attractions showed
clustering characteristics of the spatial distribution pattern. The spatial clustering
degree was highest for human tourist attractions and lowest for nature tourist
attractions. (2) Traffic access time results exhibited significant centrality with Wuhan
as the core and regional differences in WUA. The road traffic accessibility of human
tourist attractions was better than that of natural tourist attractions. (3) The spatial
distribution and road traffic accessibility of tourist attractions in WUA indicated a circle
structure centered on Wuhan, which aligned with the general rule of regional
development. The accessibility of the north-south direction was weaker than the east-
west direction in WUA. (4) Human tourist attractions were mainly concentrated in
urban areas with high connectivity and intensive road networks. But natural tourist
attractions were separated from traveler origin and other different tourist attractions.
Most were in mountainous and hilly areas with poor accessibility, which could attract
more tourists with better road networks and traffic infrastructure.
KEYWORDS
Tourist Attractions; National A-Level; Spatial Structure Characteristic; Road Traffic
Accessibility Evaluation; Wuhan Urban Agglomeration (WUA), China
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INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. METHODOLOGY
2.1. Description of the Study Area
2.2. Data Collection
2.3. Design of the Study
3. RESULTS AND DISCUSSIONS
3.1. Spatial Structure of A-Level Tourist Attractions
3.1.1. Average Nearest Neighbour Analysis of Tourist Attractions
3.1.2. Kernel Density Estimation Analysis of Tourist Attractions
3.2. Road Traffic Accessibility of A-Level Tourist Attractions
3.2.1. Road Traffic Accessibility Analysis between Tourist Attractions and
Traveler Origin
3.2.2. Road Traffic Accessibility Analysis between Different Tourist
Attractions
4. CONCLUSIONS
4.1. Conclusion
4.2. Limitation and Prospect
5. DATA AVAILABILITY
6. CONFLICT OF INTEREST
REFERENCES
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INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. METHODOLOGY
2.1. Description of the Study Area
2.2. Data Collection
2.3. Design of the Study
3. RESULTS AND DISCUSSIONS
3.1. Spatial Structure of A-Level Tourist Attractions
3.1.1. Average Nearest Neighbour Analysis of Tourist Attractions
3.1.2. Kernel Density Estimation Analysis of Tourist Attractions
3.2. Road Traffic Accessibility of A-Level Tourist Attractions
3.2.1. Road Traffic Accessibility Analysis between Tourist Attractions and
Traveler Origin
3.2.2. Road Traffic Accessibility Analysis between Different Tourist
Attractions
4. CONCLUSIONS
4.1. Conclusion
4.2. Limitation and Prospect
5. DATA AVAILABILITY
6. CONFLICT OF INTEREST
REFERENCES
https://doi.org/10.17993/3ctecno.2023.v12n3e45.388-409
1. INTRODUCTION
Accompanied by the rapid development of the Chinese economy, its economic
growth mode was gradually inclined to the tertiary industry, and the position of the
tourism industry was increasingly important in its social development [1]. In recent
years, tourism has become a pillar industry of national development, and the
theoretical study of urban tourism in China started late but developed very rapidly [2].
As an essential symbol for measuring the quality of attraction spots, Chinese national
A-level tourist attractions were an essential indicator of the unique rating standard and
resource standardization management, which played a positive role in promoting the
construction of tourist attractions and the development of the tourism economy since
its promotion in 1999 [3]. As one of the basic prerequisites for tourism operations,
regional transportation was a critical factor in relating tourists to tourism destinations
[4]. In the increasingly fierce competition faced by global tourist attractions, the
requirements of consumers for tourist attractions were becoming higher and higher,
with accessibility, attraction service quality and sustainable development serving as
important competitive advantages [5]. Some scholars conducted forward-looking
analyses to capture the supply and demand for regional tourism based on the
importance of transportation elements to tourism facilities and the potential impact on
regional tourist attractions [6]. Therefore, a correct understanding of the attraction's
situation was essential in achieving optimal allocation and development of tourism
resources.
The spatial structure and traffic accessibility of tourist attractions as the research
hotspot were explored by scholars through mathematical statistics and spatial
metrology. In terms of studies related to the spatial structure of tourist attractions,
some scholars constructed a system conceptual model to improve the governance for
regional tourism elements based on the development of land policies, which were
used to optimize the design specifications and functions of the region's tourism [7].
Some scholars quantitatively studied the spatial pattern and accessibility of auto
campsites in Chinese Beijing from the perspective of self-driving tourism [8]. Some
scholars summarised the influencing factors affecting the distribution by analyzing the
spatial distribution characteristics of tourism towns in the Wuling Mountains region of
China [9]. In addition, some scholars explored the distribution of tourist attractions, the
misallocation of resources and the future development trend from the perspective of
spatial allocation dynamics [10]. In terms of studies related to the traffic accessibility of
tourist attractions, some scholars explored the impact on regional spatial accessibility
differences by using traffic data with different attributes, such as highway networks
[11]. Some scholars used a potential model with different effective service radii to
measure the spatial traffic accessibility of care facilities [12]. Some scholars
investigated and designed tourism products by considering the accessibility problem
of shore excursions with the limited docking time of cruise ships as the background
[13]. Some scholars evaluated the accessibility issues of tourist attractions and
perceptions of consumer satisfaction through empirical studies from rural tourism
facilities, popular tourist attractions and protected tourism islands to optimize
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management initiatives of regional tourism [14, 15, 16]. In addition, some scholars
analyzed the impact of high-speed rail on regional transport accessibility and tourism
economic linkages and further explored the synergistic effect of accessibility and
tourism economic development through a coupled coordination degree evaluation
model [17]. Based on the intrinsic correlation between tourism efficiency and location
accessibility, some scholars quantitatively modeled the impact of accessibility on the
total tourism output of attractions areas and its efficiency change [18].
Tourist attractions were a core tourism component and a prerequisite for its
development. The spatial structure and accessibility of tourist attractions determined
the behavior of tourists and profoundly influenced tourism development strategies
[19]. Studies indicated that the development of short-distance tourism and the state of
transportation development were important factors that affected the evolution of the
spatial structure of attraction spots [20], and the short-term tourism trend of using
short public holidays was becoming increasingly obvious [21]. In addition, the
COVID-19 outbreak dramatically changed the behaviour of tourists [22], short-
distance tourism was gradually becoming more popular in tourism after the post-
pandemic. Therefore, this paper started from the research gap of a few empirical and
applied studies on short-distance tourism and selected WUA (nine cities centered on
Wuhan) as an appropriate scope of the study area for short-distance tourism. Through
an empirical case study of the WUA region, the spatial structure of A-level tourist
attractions was identified, and the road traffic accessibility of short-distance travel
within the region was further measured. The study results were helpful in suggesting
the development and optimization of the spatial layout of tourist attractions in WUA
and providing scientific guidance for creating and managing A-level tourist attractions
in the future.
2. METHODOLOGY
2.1. DESCRIPTION OF THE STUDY AREA
Wuhan Urban Agglomeration (WUA) was known as Wuhan "1+8" City Circle in the
eastern part of Hubei Province within China. WUA was centered on Wuhan and
covered Huanggang, Ezhou, Huangshi, Xianning, Xiantao, Qianjiang, Tianmen and
Xiaogan (Figure 1). According to statistical information, its geographical location was
in the Yangtze River's middle reaches and the Jianghan Plain's east-central part, with
an area of about 57800 km².
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management initiatives of regional tourism [14, 15, 16]. In addition, some scholars
analyzed the impact of high-speed rail on regional transport accessibility and tourism
economic linkages and further explored the synergistic effect of accessibility and
tourism economic development through a coupled coordination degree evaluation
model [17]. Based on the intrinsic correlation between tourism efficiency and location
accessibility, some scholars quantitatively modeled the impact of accessibility on the
total tourism output of attractions areas and its efficiency change [18].
Tourist attractions were a core tourism component and a prerequisite for its
development. The spatial structure and accessibility of tourist attractions determined
the behavior of tourists and profoundly influenced tourism development strategies
[19]. Studies indicated that the development of short-distance tourism and the state of
transportation development were important factors that affected the evolution of the
spatial structure of attraction spots [20], and the short-term tourism trend of using
short public holidays was becoming increasingly obvious [21]. In addition, the
COVID-19 outbreak dramatically changed the behaviour of tourists [22], short-
distance tourism was gradually becoming more popular in tourism after the post-
pandemic. Therefore, this paper started from the research gap of a few empirical and
applied studies on short-distance tourism and selected WUA (nine cities centered on
Wuhan) as an appropriate scope of the study area for short-distance tourism. Through
an empirical case study of the WUA region, the spatial structure of A-level tourist
attractions was identified, and the road traffic accessibility of short-distance travel
within the region was further measured. The study results were helpful in suggesting
the development and optimization of the spatial layout of tourist attractions in WUA
and providing scientific guidance for creating and managing A-level tourist attractions
in the future.
2. METHODOLOGY
2.1. DESCRIPTION OF THE STUDY AREA
Wuhan Urban Agglomeration (WUA) was known as Wuhan "1+8" City Circle in the
eastern part of Hubei Province within China. WUA was centered on Wuhan and
covered Huanggang, Ezhou, Huangshi, Xianning, Xiantao, Qianjiang, Tianmen and
Xiaogan (Figure 1). According to statistical information, its geographical location was
in the Yangtze River's middle reaches and the Jianghan Plain's east-central part, with
an area of about 57800 km².
https://doi.org/10.17993/3ctecno.2023.v12n3e45.388-409
Figure 1. Location Map of Wuhan Urban Agglomeration (WUA) in China
2.2. DATA COLLECTION
This paper's data included the national A-level tourist attractions list, the total
resident population and GDP. The list of national A-level tourist attractions within WUA
was based on publicly available data from the Hubei Provincial Department of Culture
and Tourism in China (http://wlt.hubei.gov.cn/)
. The national A-level attractions were
evaluated and recognized by the China National Tourism Administration based on the
indicators of landscape quality, historical and cultural value, service facilities provision
and management level. A-level attractions were detailed into 1A, 2A, 3A, 4A and 5A,
with 5A as the highest level of assessment. The total regional resident population was
obtained from the 7th Chinese Population Census Bulletin of the National Bureau of
Statistics (http://www.stats.gov.cn/)
. GDP data from Hubei Provincial Statistical
Yearbook 2021 published by the Hubei Provincial Statistics Bureau in China (https://
tjj.hubei.gov.cn/)
. This paper used the map projection coordinate system
WGS_1984_UTM_Zone_49N for geographic data processing.
The county-level unit under the administrative jurisdiction of nine cities was used as
the scale for this paper (48 county-level administrative units in total). As of 2021, 214
A-level tourist attraction spots existed in WUA. According to the different categories of
tourist attractions, the tourist attractions were classified into two categories: natural
and human. The final statistics were collected with 94 sites in the natural attractions
and 120 sites in the human attractions (Figure 2).
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Figure 2. Location of Tourist Attractions in WUA (National A-level of China)
2.3. DESIGN OF THE STUDY
The study design was divided into two aspects: spatial structure characteristics and
road traffic accessibility of the tourist attractions. The tourist attraction's spatial
distribution and pattern were simulated using Average Nearest Neighbour (ANN) and
Kernel Density Estimation (KDE) analysis.
ANN analysis measured the geospatial distribution and the proximity of point
elements to each other in the regional space [23]. The Average Nearest Neighbour
Ratio (ANN-R) was calculated as the observed average distance divided by the
expected average distance, which was used to measure the distribution of point
elements in geographic areas [24]. ANN-R less than 1 indicated that the distribution
was a cluster, greater than 1 indicated that the distribution was discrete and equal to 1
indicated that the distribution was random [25]. The math expression of the existing
model was as follows.
(1)
In the above expression (1), the observed average distance was the average
distance between the tourist attractions point and the centroid of its nearest neighbor
point, defined as . The expected average distance was the average distance in the
R
=
¯
D
o
¯
De
¯
Do
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Figure 2. Location of Tourist Attractions in WUA (National A-level of China)
2.3. DESIGN OF THE STUDY
The study design was divided into two aspects: spatial structure characteristics and
road traffic accessibility of the tourist attractions. The tourist attraction's spatial
distribution and pattern were simulated using Average Nearest Neighbour (ANN) and
Kernel Density Estimation (KDE) analysis.
ANN analysis measured the geospatial distribution and the proximity of point
elements to each other in the regional space [23]. The Average Nearest Neighbour
Ratio (ANN-R) was calculated as the observed average distance divided by the
expected average distance, which was used to measure the distribution of point
elements in geographic areas [24]. ANN-R less than 1 indicated that the distribution
was a cluster, greater than 1 indicated that the distribution was discrete and equal to 1
indicated that the distribution was random [25]. The math expression of the existing
model was as follows.
(1)
In the above expression (1), the observed average distance was the average
distance between the tourist attractions point and the centroid of its nearest neighbor
point, defined as . The expected average distance was the average distance in the
R=¯
Do
¯
De
¯
Do
https://doi.org/10.17993/3ctecno.2023.v12n3e45.388-409
random distribution of each tourist attraction point, defined as . The Average
Nearest Neighbour Ratio (ANN-R) was defined as .
Kernel Density Estimation (KDE) analysis assumed that within a certain spatial
range, a certain object could occur at any geographical location, but the probability of
occurrence differed at each spatial site [26]. Estimating probability density values
based on the distance between the element to be assessed and the sample element
[27], KDE converted discrete points in a region into a continuous density map based
on a cell [28]. It could provide a clear visual representation of the geospatial posture of
the A-level tourist attractions in WUA. Using the spatial property of the data sample to
explore its spatial evolution trend helped reveal the spatial concentration of the tourist
attractions [29]. The math expression of the existing model was as follows.
(2)
In the above expression (2), was the estimated density function at spatial
location . was the number of tourist attractions points in WUA.
was the
bandwidth that controlled the degree of smoothing and the effect range for the kernel
function [30]. was the kernel function for the spatial weights. was the
distance between data sites and .
The traffic access time was related to the average speed of the different road
classes, and the time cost of the highway access was quantified based on a
proportional relationship (Table 1). The traffic access time was constructed using
Weighted Network Analysis to measure the accessibility between tourist attractions
and traveler origin and further using Network Analysis to measure the accessibility
between different tourist attractions. The results were visualized using Inverse
Distance Weight (IDW).
Table 1. Time Cost of Traffic Access to Major Road Networks in China
Note: Road driving speed regarding the "Technical Standard Highway Engineering of the
People's Republic of China (JTGB01-2003)" and related studies.
¯
De
R
F
(x) =
1
Nh
n
∑
i=1
Kn
(x−x
i
h
)
F(x)
x
N
h
K
x−xi
x
xi
Highway Classification Road Speed (km/h) Time Cost (min)
Expressway 100 0.60
National Highway 80 0.75
Provincial Highway 60 1.00
County Highway 40 1.50
Township Highway 30 2.00
Other Highways 20 3.00
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Traffic accessibility was an indicator of the extent to which one element was
connected to other elements. To better measure the accessibility between the tourist
attractions and traveler origin, the travel intentions of tourists were not only considered
in terms of spatial location and transport networks but also in terms of population,
attractions level and accessibility time [31]. Using the weighted average access time
of existing scholars' model to measure the road traffic accessibility between the tourist
attractions and traveler origin by weighting total resident population, GDP and national
attractions assessment indicators [8, 32]. Due to the reversible road network
movements, using the tourist attraction site as the origin point and defined it as , and
the traveler origin site as the destination point and defined it as . The math
expression of the existing model was as follows.
(3)
(4)
The above expression (3) was explored the weight of tourist attractions and traveler
origin. was the total resident population of the traveler origin site ( ). was the
total GDP of the traveler origin site ( ). was the national assessment level of the
tourist attraction site ( ). The above expression (4) was used to calculate the average
accessibility time between the tourist attraction site ( ) and the traveler origin site ( )
after weighting. was the average weighted access time between the tourist
attraction site ( ) and the traveler origin site ( ). was the total number of the traveler
origin site ( ) (the geographical location of the traveler origin site was represented by
the administrative location of the local government). was the minimum access
time cost between the tourist attraction site ( ) and the traveler origin site ( ).
was the weighted result in the expression (3).
How to effectively organize a reasonable intra-city tourism spatial structure and
routes according to the characteristics of tourist flow combined with the distribution of
tourism resources was the key challenge to be solved in urban tourism development
and planning [33]. Tourists sometimes would not return directly to their homes or
hotels after arriving at one tourist attraction but continue traveling to another. Hence,
this paper continued to measure the accessibility between different tourist attractions
based on the movement trajectories of tourists. The road traffic accessibility of
different tourist attractions was determined by measuring the average access time
from one tourist attraction to another in the region [34]. The results of less average
access time indicated the tourist attraction in the area with advantageous locations
and more convenient accessibility for tourists [35]. Due to the reversible road network
movements, defining two different tourist attraction sites as and . The math
expression of the existing model was as follows.
(5)
x
y
Mx y =3Py ×Gy ×L x
A x y =
n
∑
y=1
(Tx y ×Mx y)/
n
∑
y=1
Mx
y
Py
y
Gy
y
L x
x
x
y
A x y
x
y
n
y
Tx y
x
y
Mx y
i
j
Aij =
n
∑
j=1
Tij/
n
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Traffic accessibility was an indicator of the extent to which one element was
connected to other elements. To better measure the accessibility between the tourist
attractions and traveler origin, the travel intentions of tourists were not only considered
in terms of spatial location and transport networks but also in terms of population,
attractions level and accessibility time [31]. Using the weighted average access time
of existing scholars' model to measure the road traffic accessibility between the tourist
attractions and traveler origin by weighting total resident population, GDP and national
attractions assessment indicators [8, 32]. Due to the reversible road network
movements, using the tourist attraction site as the origin point and defined it as , and
the traveler origin site as the destination point and defined it as . The math
expression of the existing model was as follows.
(3)
(4)
The above expression (3) was explored the weight of tourist attractions and traveler
origin. was the total resident population of the traveler origin site ( ). was the
total GDP of the traveler origin site ( ). was the national assessment level of the
tourist attraction site ( ). The above expression (4) was used to calculate the average
accessibility time between the tourist attraction site ( ) and the traveler origin site ( )
after weighting. was the average weighted access time between the tourist
attraction site ( ) and the traveler origin site ( ). was the total number of the traveler
origin site ( ) (the geographical location of the traveler origin site was represented by
the administrative location of the local government). was the minimum access
time cost between the tourist attraction site ( ) and the traveler origin site ( ).
was the weighted result in the expression (3).
How to effectively organize a reasonable intra-city tourism spatial structure and
routes according to the characteristics of tourist flow combined with the distribution of
tourism resources was the key challenge to be solved in urban tourism development
and planning [33]. Tourists sometimes would not return directly to their homes or
hotels after arriving at one tourist attraction but continue traveling to another. Hence,
this paper continued to measure the accessibility between different tourist attractions
based on the movement trajectories of tourists. The road traffic accessibility of
different tourist attractions was determined by measuring the average access time
from one tourist attraction to another in the region [34]. The results of less average
access time indicated the tourist attraction in the area with advantageous locations
and more convenient accessibility for tourists [35]. Due to the reversible road network
movements, defining two different tourist attraction sites as and . The math
expression of the existing model was as follows.
(5)
x
y
Mx y =3Py ×Gy ×L x
A x y =
n
∑
y=1
(Tx y ×Mx y)/
n
∑
y=1
Mx y
Py
y
Gy
y
L x
x
x
y
A x y
x
y
n
y
Tx y
x
y
Mx y
i
j
Aij =
n
∑
j=1
Tij/n
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In the above expression (5), it explored the average accessibility time between
tourist attractions.
was the minimum access time cost between one tourist
attraction ( ) and another tourist attraction ( ) through the road networks.
was the
total number of tourist attractions in the region.
3. RESULTS AND DISCUSSIONS
3.1. SPATIAL STRUCTURE OF A-LEVEL TOURIST
ATTRACTIONS
3.1.1. AVERAGE NEAREST NEIGHBOUR ANALYSIS OF
TOURIST ATTRACTIONS
Based on the mathematical expression (1), the Average Nearest Neighbour (ANN)
analysis measured the observed average distance and expected average distance for
the A-level tourist attractions of WUA. The Average Nearest Neighbour Ratio (ANN-R)
was obtained as 0.892 for natural attractions, 0.764 for human attractions and 0.766
for all attractions (Table 2). The results showed that the spatial distribution of different
tourist attractions showed clustering under the condition of passing the significance
test. The human attractions had the highest degree of clustering, while the natural
attractions had the lowest.
Table 2. Average Nearest Neighbor (ANN) Results of Tourist Attractions in WUA
3.1.2. KERNEL DENSITY ESTIMATION ANALYSIS OF
TOURIST ATTRACTIONS
Based on the mathematical expression (2), the Kernel Density Estimation (KDE)
analysis was used to generate a kernel density distribution map and identify spatial
distribution hotspots of the tourist attractions (Figure 3). The overall trend of the A-
Tij
i
j
n
Classification
Natural Attractions
(A-level)
Human Attractions
(A-level)
All Attractions
(A-level)
Number of
Attractions/individual 94 120 214
Observed Average
Distance/m 12250.5340 9367.0158 7419.9275
Expected Average
Distance/m 13726.5459 12252.6485 9682.9361
Average Nearest
Neighbour Ratio 0.892470 0.764489 0.766289
Z-Score -1.994451 -4.935516 -6.540590
Distribution Trend Cluster Distribution Cluster Distribution Cluster Distribution
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level tourist attractions showed multiple core-periphery structures. The core high-
value areas of kernel density were mainly distributed in the central urban area of
Wuhan, while the sub-high-value areas were distributed in the surrounding regions of
Xiaogan, Huanggang, Ezhou and Xianning.
Figure 3. Kernel Density Estimation (KDE) Results for All Attractions in WUA
The natural and human attractions of WUA showed a core-periphery structure
(Figure 4). The degree of core-density clustering was more significant in human tourist
attractions than in natural tourist attractions. However, the core-density hierarchy of
natural tourist attractions was more complex than that of human tourist attractions.
The high core density areas of the natural tourist attractions were present in Wuhan
Huangpi within WUA's northern part. The sub-high core density areas were presented
in Xianning and Huangshi in WUA's southern region. The distribution results were
closely related to the topography because the topography of the northern, southern
and eastern parts of WUA was mainly mountainous and hilly. The unique natural
landscape was more conducive to developing distinctive natural tourist attractions. To
a certain extent, the shortage of natural tourist attractions in WUA's western part was
due to the flat local terrain and the human farming culture of Jianghan Plain. The high
core density of human tourist attractions was present in the central urban area of
Wuhan and showed a very significant clustering compared to other surrounding areas.
The distribution of human tourist attractions was related to Wuhan's deep historical
heritage and developed economic level as a famous Chinese historical and cultural
city.
https://doi.org/10.17993/3ctecno.2023.v12n3e45.388-409
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
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