The new path of tourism planning development based on MSPA-connectivity-space syntax

Abstract

As China’s tourism industry is on the right track, the country has gradually paid more attention to the ecological protection of tourism areas. Under the concept of sustainable development, the research on environmental adaptability of tourist attractions has become a hotspot. This study took Huanglongxi Ancient Town in Shuangliu District, Chengdu City, Sichuan Province as the research object, and determined seven ecological protection spaces of Huanglongxi Ancient Town by MSPA method, and then used the landscape connectivity method to identify the priority of ecological sources. The high green space and water are the “source”, and finally the path network is constructed using space syntax, and the relationship between the flow of people and the path resistance disturbance is calculated. After analysis, it is concluded that Huanglongxi Ancient Town has 2 green spaces with higher priority and 1 water area with higher priority. The route layout can meet the current annual reception volume and will not cause obvious congestion during the daily peak. Huanglongxi Ancient Town has 6 enterprises above designated size and 20,000 square kilometers of arable land. The average dLLC of the green space in Huanglongxi Ancient Town is 19.10, the average dPC is 20.92, the maximum time resistance is 0.951 $+$ 1.703*10 ${}^{-7}$ *V ${}_{151.3}$ , and the maximum time resistance disturbance is 0.999. Huanglongxi Ancient Town can pass between paths 7–8. Add new paths to improve the path situation.

Keywords

MSPA landscape connectivity ecological source area space syntax path network environmental adaptability

1. Introduction

In the past 20 years, China has paid more and more attention to natural resources and environmental conditions, and the development of natural resources has also changed from uncontrolled to sustainable development [1]. The relationship between the environment and tourism is getting closer and closer, and the rise of nature reserves and rural tourism has gradually reduced environmental conflicts [2]. In addition to the ecological environment, whether the spatial structure of tourist attractions is reasonable is also an important factor in attracting tourists [3]. Reasonable spatial structure can relieve congestion and improve tourist experience, and the correct identification of the ecological source priority of scenic spots can enable the government to develop and plan scenic spots more rationally. This study proposes a development path of tourism planning based on Morphological Spatial Pattern Analysis (MSPA), combined with landscape connectivity and space syntax, to analyze the planning of Huanglongxi Ancient Town from the perspectives of ecological protection and path planning. Firstly, the path integration degree, main path time impedance and main path time impedance disturbance of Huanglongxi Ancient Town is calculated by using space syntax and landscape connection degree, and then MSPA is used to obtain the priority of green space and water area of Huanglongxi Ancient Town, and finally tourism planning is carried out. In order to provide reference for the development of green tourism planning under the idea of sustainable development.

2. Related work

With the acceleration of urbanization, prioritizing ecological sources has become an important method to alleviate urban ecological pressure and improve land use efficiency [4]. Many scholars at home and abroad have conducted research on how to identify and plan the ecological space of the city. Huang et al. [5] believed that forest fragmentation is a trend of global change, and understanding the patterns and dynamics of forest fragmentation is of great significance for maintaining ecosystem stability. Using the morphological spatial pattern analysis method and K-means clustering algorithm, the forest landscape spatial morphology and urban forest fragmentation pattern in Qujing City, Yunnan Province, China from 2006 to 2016 was studied. The results show that the landscape area changes in Qujing City are 1.17%, 0.02%, 0.30%, 1.65%, 0.20%, 0.19% and 0.05%. Tang et al. [6] believed that human activities and climate change have led to increasingly fragmented forest landscapes, and the conflict between biodiversity conservation and economic development has become more prominent. Taking Jindong Forest Farm as the research area, the forest ecological suitability index, morphological and spatial pattern analysis method, area method and landscape connectivity index were combined for analysis. Eleven forest patches with highly suitable habitats were identified as ecological source areas, and 54 potential corridors were extracted. Yang et al. [7] constructed and optimized the regional ecological security model by combining morphological spatial pattern analysis and loop theory. The study area was Shuozhou City, located in the east of the Loess Plateau of China. The results showed that from 2010 to 2017, the number of ecological resources in the study area decreased from 21 to 20, and the total area of ecological sources decreased from 1,923.35 square kilometers to 1,869.37 square kilometers. Reference for resource-based cities by improving regional ecosystem services and biodiversity. Li et al. [8] believed that with the acceleration of urbanization leading to fragmentation of urban landscapes, the construction of ecological networks is of great significance for alleviating the degradation of urban habitats and protecting the natural environment. The ecological source area was extracted by the morphological spatial pattern analysis method and the landscape index method, and the ecological network was constructed using the minimum cumulative resistance model and the gravity model. The experimental results show that the optimized network has 11 important corridors, 34 general corridors and 7 hidden corridors, and the suitable ecological corridor width is 60–200 meters. Pratic et al. [9] believed that rural landscape can reflect rural ecological functions and rural culture. The trend of farmland abandonment from 1995 to 2014 was studied by morphological spatial pattern analysis. The results showed that the farmland area decreased from 813.25 hectares to 118.79 hectares. The reduction in the area of the MSPA core region leads to an increase in the relative importance of unstable classes. Therefore, morphological spatial pattern analysis is usually used to identify the priority of ecological sources, which can effectively distinguish the importance of nature reserves or municipal green space. However, Huanglongxi ancient town has the dual attributes of nature reserve and urban green space, which is suitable for the priority identification of its ecological source by morphological spatial pattern analysis.

Dawes et al. [10] used the rational floor plan method to test the spatial properties and topological relationships of Renaissance villas to confirm the importance and flexibility of the villas. Its complex scale system and construction rules are analyzed through history and mathematics, and its texture is analyzed in space syntax. Eldiasty et al. [11] presented a standard model for decision making to preserve the old Rosetta Market in Egypt. In this model, the ideal solution of similar preference order technology combined with spatial syntactic analysis method is used to obtain the best position of market selection. Through this model, the social needs of urban development can be met, and the balance between tourism and ecological protection can be realized. Gao et al. [12] believed that urbanization has led to changes in the cultural life atmosphere of urban historical and cultural blocks, the collective memory of blocks is gradually lost, and there is a crisis of local homogeneity. Selecting Beiyuanmen block as a case, using space syntax and in-depth interviews to conduct empirical research on the collective memory of residents. The study found that the spatial storage structure level of collective memory in Beiyuanmen neighborhood continued as a whole, but the subconscious habitual memory faced different degrees of damage, and the spatial elements of collective memory changed significantly. Othman et al. [13] believed that reasonable street layout is an inherent feature of sustainable cities, which can promote population mobility and social behaviors to strengthen economic structure. Darrel et al. [14] used spatial syntax to describe the overall landscape connectivity of an area can better manage human-modified landscapes. He combined fine-scale motion patterns to construct a connectivity modeling method, which can effectively describe how fine-scale vegetation supports connectivity. Wang et al. [15] built an InVEST model around the functional connectivity of green space network structure in Haidian District of Beijing. Research shows that the model can identify 6 important connecting nodes and 3 important links. Gis combined with spatial syntax can help to study the effectiveness of spatial analysis in urban space. It can be concluded that the current studies on morphological spatial pattern analysis at home and abroad are mostly applied to regional ecological protection, while the studies on spatial syntax are mostly focused on the planning and protection of urban green space and cultural sites. The two analysis methods are rarely applied to tourism development planning following the concept of sustainable development. This paper proposes a morphological spatial pattern analysis-connectivity-space syntax method to analyze the spatial structure, priority of scenic spots and environmental adaptability of Huanglongxi ancient town in Shuangliu District of Chengdu.

3. Environmental adaptability analysis method based on space syntax-landscape connectivity-MSPA

3.1 The relationship between the spatial path distribution of scenic spots and the distribution of tourist flow

Space syntax is a theory that describes the internal social logic of space, and its essence is to analyze spatial elements using configurational relations [16]. Topological graphs used to describe spatial configurations are called $J$ graphs, as shown in Fig. 1.

Figure 1.

Spatial configuration.

In Fig. 1, the two $J$ graphs look different, but are spatially identical. Figure 1a and b is the same plane from different perspectives, and the topological maps obtained by observing the same spatial configuration from different perspectives are different. The accessibility of the spatial configuration network is determined by the depth value and the degree of integration. Assuming $J$ that the step distance of two adjacent nodes in the graph is 1, the shortest step distance from one node to any node is the depth value of the two nodes. The depth value cannot be considered as an independent morphological variable, it is an intermediate variable. The average depth value of a spatial configuration is the average of the shortest steps from one node to other nodes, as shown in Eq. (1).

$\displaystyle\bar{D}_{i}=\frac{\sum\limits_{j=1}^{m}{d_{ij}}}{m-1}$ (1)

In Eq. (1), $\bar{D}_{i}$ is $i$ the average depth value of any node $m$ , is the total number of nodes in the spatial configuration, and is $d_{ij}$ the minimum number of paths from node $i$ to node. $j$ Integration reflects the convenience and discreteness of one unit space to another, as shown in Eq. (2).

$\displaystyle I_{i}=\frac{m\left[{\log_{2}\left({\left({\frac{m+2}{3}}\right)-% 1}\right)+1}\right]}{({m-1})({\bar{D}_{i}-1})}$ (2)

In Eq. (2), is $I_{i}$ the integration degree $I_{i}$ of a node, and $i$ its value is proportional to the accessibility of the spatial configuration, that is, the higher the convenience of the spatial configuration in the entire system. When tourists have incomplete information on the topology network, routing behavior occurs. Usually, the Logit choice model and SUE choice model is used to characterize the choice behavior of tourists on the route. Suppose each tourist chooses the travel route with the least cost, as shown in Eq. (3).

$\displaystyle U_{k}=-C_{k}=W_{k}+\varepsilon_{k}$ (3)

In Eq. (3), $U_{k}$ is $k$ the utility $C_{k}$ of the path, is $k$ the perceptual cost of the path, $W_{k}$ is the fixed utility of the utility function of path selection, and $\varepsilon_{k}$ is a random variable. When $\varepsilon_{k}$ subject Gumbel to an independent distribution, the Logit selection model and SUE selection model is shown in Eq. (4).

$\displaystyle p_{k}=\frac{\exp({\theta U_{k}})}{\sum\limits_{k=1}^{l}{\exp({% \theta U_{k}})}}$ (4)

In Eq. (4), is $p_{k}$ the probability that $\theta$ the tourist chooses the route, $k$ is a fixed parameter, and $l$ is the total number of routes that the visitor can currently choose. The traffic flow of the $k$ path and the tourist flow of the first road is shown in Eqs (5) and (6).

$\displaystyle\sum\limits_{k=1}^{l}p_{k}=1,({0\leqslant p_{k}\leqslant 1})$ (5) $\displaystyle f_{k}=qp_{k}$ (6)

In Eq. (6), $q$ is the path traffic flow, $f_{k}$ is the tourist flow, and the unit is $\textit{person}/\min$ . Introducing the accessibility index into the Logit model and SUE the model can better describe the preference of tourists when choosing a route, and the utility expression is shown in Eq. (7).

$\displaystyle U_{k}=-C_{k}=-\left({\frac{t_{k}^{0}}{t_{\max}^{0}}-\frac{w_{k}}% {w_{\max}}-\frac{I_{k}}{I_{\max}}}\right)$ (7)

In Eq. (7), $t_{k}^{0}$ is $k$ the free travel time $t_{\max}^{0}$ of the path, is $k$ the maximum free travel time $w_{k}$ of the path, is $k$ the effective width $w_{\max}$ of the path, is $k$ the maximum effective width $I_{k}$ of the path, is $k$ the integration degree of $I_{\max}$ the path, and is $k$ the maximum integration degree of the path. When the tourist flow increases to the flow threshold of the route, there will be congestion and stagnation of the tourist group, and the walking speed will drop sharply [17].

$\displaystyle t_{k}(V)=t_{k}^{0}+B\left({\frac{V_{k}}{c_{k}}}\right)^{n}$ (8)

In Eq. (8), is $t_{k}(V)$ the transit time $c_{k}$ of the first path $k$ when the $k$ path flow is, is $V$ the pedestrian flow capacity per unit width of the first path, is $V_{k}$ the tourist flow on the path, and $n$ is $k$ a fixed parameter. When the tourist flow gradually increases and the ratio of the tourist flow to the path width is close 1, the growth of the time impedance is a power order, and the time impedance growth rate also increases. When the tourist flow reaches the maximum traffic flow, the time resistance group reaches the maximum value. In order to better identify the lines that will be congested in the line, it is necessary to calculate the time impedance disturbance value of each line. The value is determined based on the difference before and after the change in tourist flow. The disturbance will be inversely proportional to the tourist flow. If the degree of change is large, it means that the path has poor anti-interference ability; if the degree of change is small, it means that the path has strong anti-interference ability. The actual carrying capacity of the route to the tourist traffic changes dynamically, as shown in Eq. (9).

$\displaystyle\varphi(k)=\frac{t_{k}({V_{\max}})-t_{k}(V)}{t_{k}({V_{\max}})-t_% {k}^{0}}$ (9)

In Eq. (9), $\varphi(k)$ is $k$ the relative rate of change of the passage time of the route, and is $t_{k}({V_{\max}})$ the passage time under the maximum traffic flow of the $\varphi(k)$ route. $k$ is inversely proportional to the tourist flow. When it $\varphi(k)$ increases, it $|{\Delta\varphi(k)}|$ will also become larger, indicating that its negative evolution is stronger, and the anti-interference of the path is weaker. On the contrary, the anti-interference is stronger. Landscape connectivity is one of the important methods to identify the priority of scenic spots, which includes the structural connectivity of the landscape and the functional connectivity of the landscape. This study uses landscape functional connectivity to evaluate, including the overall connectivity index of the landscape, the possible connectivity index of the landscape, and the relative importance index of the landscape to evaluate the priority of the landscape. According to relevant studies, the probability of landscape connectivity is set as 0.3, and the three indicators are respectively $\alpha,\beta,\gamma$ , as shown in Eq. (10) [18].

$\displaystyle\gamma=0.3\alpha+0.3\beta$ (10)

In Eq. (10), the overall connectivity index, the possible connectivity index of the landscape and the relative importance index of the landscape was all normalized by the index, and the index calculation software was Conerfor Sensinode 2.2.

3.2 The relationship between environmental adaptability of scenic spots and ecological flow resistance

MSPA is a method of classifying landscapes. Its principle is to analyze different land use patterns. In the process of specific analysis, land with excellent ecological service capacity is selected as “prospects” [19]. To get a correct interpretation of ecological processes, it is necessary to reasonably determine the pixel size and edge width of MSPA. The resistance surface is constructed according to the ecological environment quality of various types of land use in the study area, which can better reflect the gap between different land use and describe the difference of the same land use in different environments. The setting of the ecological resistance surface not only needs to consider the type of land cover vegetation, but also pay attention to the difference in resistance to ecological flow caused by the topography of the region and the degree of artificial disturbance [20]. Therefore, nighttime light data and slope data are used for correction. As shown in Eq. (11).

$\displaystyle R=R_{0}\times S_{\textit{per}}\times\textit{OLS}$ (11)

In Eq. (11), $R$ is the ecological flow resistance after correction of each grid in the study area, is the ecological flow resistance $R_{0}$ before correction, $S_{\textit{per}}$ is the slope percentage, and OLS is the normalized night light index. The environmental adaptability planning and analysis of scenic spots is to protect the environment and natural resources. Compared with the traditional development methods, its core goal is to ensure the environmental quality of the development area. It includes tourism planning model, regional strategic planning process and comprehensive tourism development planning. The key point of environmental adaptability of ecotourism planning is to define ecology, scenic spots and carrying capacity. The planning framework for environmentally compatible tourism is shown in Fig. 2.

Figure 2.

Environmental compatible tourism planning.

In Fig. 2, the main focus of the framework is not carrying capacity, limit analysis, and pattern analysis for land-use adaptability. Instead, it identifies and determines the environment-adaptive tourism of a region through three aspects: prominent features, key regions, and harmonious activities. The most basic element of a region’s environmental adaptability framework is the basis for sustainable development, as well as the basis for environment, community, human satisfaction and economic integration. Beyond that, it needs to be strategic, recapitulated, and educational [21]. Generally speaking, the construction of the framework is divided into four steps, which is to analyze the current situation of the research area, to confirm the outstanding environmental characteristics and tourism resources, to divide the important characteristics and key areas, and to evaluate the overall. In the first step, a regional class of land and water is classified, including special reserves, nature reserves, recreational areas, etc. to provide a basis for future environmental planning, tourism development, and regional development. In the second step, special areas, such as the heart of special protected areas, are marked through environmental inspections and visits to local residents. The ultimate goal of tourism is to attract tourists to the tourist area and ensure their stay and enjoyment of services in the tourist area. In the third step, the prominent features and key areas are combined to ensure that tourism activities are harmonious and bearable. In the fourth step, evaluate the overall location strategy, and inject the concentration or dispersion of tourism activities, expenses and labor requirements, management methods, educational activities, etc.

4. Case analysis – taking Chengdu Huanglongxi ancient town as an example

4.1 The spatial texture and main axis path of Huanglongxi

For this study, Huanglongxi Ancient Town, Shuangliu District, Chengdu City, Sichuan Province was selected as an example, with an area of about 49.7 square kilometers and a population of about 30,000. An aerial view of Huanglongxi is shown in Fig. 3.

Figure 3.

Aerial view of Huanglongxi ancient town.

As can be seen from Fig. 3, Huanglongxi Ancient Town takes water system and square system as the core to build a public space system. Buildings and green spaces are interlaced and distributed along the river. The main body is divided into two parts by a tributary, connected by a bridge corridor. The layout of the main scenic spots and the actual shooting scene are shown in Fig. 4.

Figure 4.

Layout and real photos of main scenic spots.

In Fig. 4, Huanglongxi Ancient Town is centered on the six old streets of Zhengjie, Xinjie, Hengjie, Shanghe Street, Xiahe Street and Fuxing Street. Processing, industry, and finance is the auxiliary industrial systems. Its main attraction is along the ancient neighborhoods, ancient dwellings and ancient temples since the Ming and Qing Dynasties. The average width of the ancient streets is more than three meters. Therefore, the analysis of the spatial structure of Huanglongxi Ancient Town is mainly analyzed from three perspectives: the spatial distribution of the path axis, the degree of path integration and understanding, and the evolution and disturbance of the path temporal impedance. The spatial texture of Huanglongxi Ancient Town block is shown in Fig. 5.

Figure 5.

Spatial texture.

In Fig. 5, limited by terrain conditions and water system division, Huanglongxi Ancient Town is divided into two groups, namely, the ancient buildings of Ming and Qing Dynasties in the south and the modern commercial and tourist blocks in the north. The axis and path of Huanglongxi Ancient Town block is shown in Fig. 6.

Figure 6.

Axis and path.

In Fig. 6, due to the wide operating range of shops in Huanglongxi Ancient Town, the actual width of the paths varies greatly, and the actual measurement is difficult, so the average width of all paths is 3.44 meters. The length of path 1 to path 2 is 82.37 m, the length of path 1 to path 3 is 78.46 m, the length of path 2 to path 3 is 70.25 m, the length of path 2 to path 5 is 71.37 m, the length of path 3 to path 4 is 73.55 m, the path 4 to path 5 is 80.29 meters long, path 5 to path 6 is 37.17 meters long, path 4 to path 7 is 45.82 meters long, path 6 to path 9 is 17.33 meters long, path 7 to path 8 is 10.01 meters long, path The length of path 8 to path 9 is 22.54 meters, the length of path 8 to path 11 is 36.15 meters, the length of path 9 to path 10 is 37.62 meters, and the length of path 10 to path 11 is 28.24 meters. Let the speed of tourists in free walking be 0.5 m/s, and the free walking time of each path is shown in Table 1.

Table 1

Free walking time of tourists

Route	Free walking time/s	Route	Free walking time/s
1–2	164.74	5–6	74.34
1–3	156.92	6–9	34.66
2–3	140.50	7–8	20.02
2–5	142.74	8–9	45.08
3–4	147.10	8–11	72.30
4–5	160.58	9–10	75.24
4–7	91.64	10–11	56.48

Since the choice of the route by tourists is based in fact that they cannot fully grasp all the route conditions, it is often difficult to make the optimal choice. The Logit model and SUE the model is used as the traffic distribution model of the route. The average depth value and integration degree of each route is as follows: shown in Table 2.

Table 2

Average depth and integration

Route	Average depth value	Integration degree	Route	Average depth value	Integration degree
1–2	2.6	1.37	5–6	2.9	1.53
1–3	2.1	1.52	6–9	1.8	1.54
2–3	2.3	1.41	7–8	1.2	1.80
2–5	2.1	1.19	8–9	2.2	1.74
3–4	2.4	1.61	8–11	2.4	1.73
4–5	1.7	1.28	9–10	1.6	1.34
4–7	1.9	1.26	10–11	2.5	1.68

On the basis of integration, in order to simplify the evaluation method of path time impedance evolution, the flow distribution is based on unidirectional flow, without considering bidirectional flow and crowd control measures. Due to the particularity of the ancient town of Huanglongxi, the flow of people varies greatly in different seasons and times. The annual number of tourists is between 7 million and 8 million, and the average number of tourists is between 600 and 1,200 on non-holidays, and tourists are received on peak seasons. Between 80,000 and 100,000 people. Assuming that the total number of tourists received each year is 7.5 million, the annual reception days are 365 days, the daily reception time is 10 hours, the average flow of people entering the route per minute is 34, and the maximum flow of people per minute is about 150 people. The people flow per minute is divided into five grades, and each grade increases by 30 people. The time impedance and time impedance disturbance values under different people flow are shown in Table 3.

Table 3

Time resistance and disturbance values

Route	Time resistance/min	30 person/min	60 person/min	90 person/min	120 person/min	150 person/min
1–2	0.721 $+$ 1.70310 ${}^{-7}$ V ${}_{21.3}$	17 (0.992)	35 (0.994)	72 (0.877)	99 (0.856)	131 (0.901)
1–3	0.668 $+$ 1.70310 ${}^{-7}$ V ${}_{31.3}$	29 (0.993)	47 (0.942)	83 (0.881)	105 (0.923)	143 (0.342)
2–3	1.021 $+$ 5.31110 ${}^{-8}$ V ${}_{41.3}$	12 (0.999)	23 (0.986)	58 (0.925)	77 (0.617)	84 (0.663)
2–5	0.937 $+$ 1.70310 ${}^{-7}$ V ${}_{51.3}$	8 (0.992)	14 (0.923)	42 (0.982)	56 (0.354)	71 (0.902)
3–4	0.325 $+$ 1.70310 ${}^{-7}$ V ${}_{61.3}$	1 (0.991)	2 (0.960)	3 (0.590)	3 (0.285)	4 (0.825)
4–5	1.027 $+$ 1.70310 ${}^{-7}$ V ${}_{71.3}$	14 (0.994)	29 (0.999)	70 (0.361)	85 (0.741)	122 (0.754)
4–7	1.132 $+$ 1.70310 ${}^{-7}$ V ${}_{81.3}$	4 (0.996)	9 (0.963)	11 (0.998)	17 (0.753)	19 (0.247)
5–6	0.517 $+$ 1.70310 ${}^{-7}$ V ${}_{91.3}$	4 (0.998)	4 (0.954)	6 (0.741)	9 (0.964)	13 (0.493)
6–9	0.397 $+$ 1.70310 ${}^{-7}$ V ${}_{101.3}$	20 (0.994)	29 (0.972)	34 (0.793)	41 (0.992)	45 (0.841)
7–8	0.611 $+$ 1.70310 ${}^{-7}$ V ${}_{111.3}$	21 (0.996)	51 (0.981)	82 (0.639)	114 (0.825)	147 (0.501)
8–9	0.585 $+$ 5.31110 ${}^{-8}$ V ${}_{121.3}$	15 (0.992)	18 (0.962)	18 (0.915)	26 (0.336)	29 (0.369)
8–11	1.134 $+$ 1.70310 ${}^{-7}$ V ${}_{131.3}$	16 (0.997)	37 (0.987)	74 (0.840)	101 (0.729)	124 (0.521)
9–10	0.826 $+$ 1.70310 ${}^{-7}$ V ${}_{141.3}$	9 (0.994)	18 (0.999)	41 (0.742)	79 (0.457)	92 (0.993)
10–11	0.951 $+$ 1.70310 ${}^{-7}$ V ${}_{151.3}$	11 (0.999)	25 (0.992)	67 (0.914)	103 (0.896)	139 (0.903)

In Table 3, with the increase of the pedestrian flow on the path, the negative evolution of the path time impedance becomes larger, and when the path flow reaches the maximum value, the impedance disturbance value is close to 0. Due to the high integration degree of some paths and their location at the entrance of scenic spots, it is easy for crowds to reach them, leading to a serious degree of negative evolution.

4.2 Confirmation and prioritization of Huanglongxi’s ecological “source”

The core is as of MSPA’s landscape classification are mostly large areas of woodland, water bodies and other areas with good natural ecology, which are an important source of ecological services in ancient town tourism. The proportion of MSPA landscape types in Huanglongxi Ancient Town is shown in Fig. 7 [22].

Figure 7.

Proportion of MSPA landscape types.

In Fig. 7, the core isas in the MSPA landscape classification are mostly areas with good natural ecology such as large areas of woodland or water bodies, which are important sources of ecological services for tourist attractions [23, 24, 25]. In the ancient town of Huanglongxi, the vegetation coverage rate is about 147,100 square kilometers, and the core area accounts for 29.60% of the total area of the scenic spot. Huanglongxi Ancient Town is surrounded by Fu River, with a water area of 85,700 square kilometers, and the edge area accounts for 17.25% of the total area of the scenic spot. Huanglongxi Ancient Town has a commercial building area of 73,200 square kilometers and a residential building area of 56,300 square kilometers. The isolated island area and branch area account for 14.72% and 11.33% of the total area of the scenic spot, respectively. The cultivated land area of Huanglongxi Ancient Town is about 25,000 square kilometers, and the bridge area accounts for 5.31% of the total area of the scenic spot. The perforated area and ring area accounted for 0.79% and 1.47%, respectively. The important green spaces and waters of Huanglongxi Ancient Town are numbered, as shown in Fig. 8.

In Fig. 8, the green space of Huanglongxi Ancient Town is mainly distributed on both sides of Zhenlong Street (No. 8), the scenic spot Huanglong swinging tail (No. 7), the scenic spot Pillow River Building (No. 5), the scenic spot Seven Stars with the Moon (No. 6) and on both sides of Longtan Lake (No. 1, No. 2, No. 3) and around Longxin Island (No. 4). The water area of Huanglongxi Ancient Town is mainly distributed in Fu River (No. 9, No. 10) and Longtan Lake (No. 1, No. 2, No. 3). In general, the water network of Huanglongxi Ancient Town is relatively dense, and the vegetation coverage rate is relatively high. The identification results of green space and water priority are shown in Table 4.

Table 4

Priority identification results of green space and water area

Green space number	Priority identification	Relative importance		Water area number	Priority identification	Relative importance
		dllC	dPC			dllC	dPC
1-2-3	3	13.29	18.03	1	3	8.41	7.39
5	4	4.82	6.33	2	2	8.76	6.41
6	5	1.53	1.97	3	1	92.36	93.10
7	2	28.65	31.41	9	4	1.12	0.94
8	1	47.23	46.37	10	5	1.02	0.87

Figure 8.

Water area and green space number.

In Table 4, the green space No. 8 is located at the entrance of the scenic spot and distributed along Zhenlong Street. The number of tourists is large, so it has the highest priority. The green space No. 7 is located at the connection between the commercial area and Zhenlong Street, where tourists are diverted, and the priority is second only to No. 1. The green space numbered 1-2-3 is located on both sides of Longtan Lake and is distributed in the main scenic spots, so it is the third priority. In terms of water priority, No. 3 waters are located in the main scenic spot, Longtiao, and are of the highest importance. Other waters surround the ancient town and are of similar importance. Among them, No. 1 and No. 2 are higher than No. 9 and No. 10. Combining the comprehensive connectivity and space syntax recognition results, the first-level priority of Huanglongxi Ancient Town is all composed of “source”, the second-level priority is distributed around the scenic spots in Huanglongxi Ancient Town, and the third-level priority is mainly composed of the waters surrounding Huanglongxi Ancient Town. constitute. Compared with the impact of landscape connectivity on priority, space syntax can reflect the unequal weight impact of environmental adaptability.

5. Conclusion

The core idea of space syntax is to quantitatively study the relationship between human activities and the allocation of environmental factors through the axis model, which is widely used in the analysis of urban and rural spatial allocation, rail transit, and human landscape design and optimization. Some scholars usually use it to quantitatively study the relationship between landscape element allocation and species migration behavior. The research results show that spatial syntax helps deepen the understanding and cognition of landscape connectivity. The space syntax makes up for the deficiency of MSPA in the planning direction of tourist attractions. By taking the element of “tourists” into account in the ecosystem, the overall planning is more reasonable compared with the simple MSPA method. The study combined MSPA and space syntax to analyze the spatial texture of Huanglongxi ancient Town, and then constructed the block axis and path map, numbered each path, and calculated the average depth and integration degree of each path. Finally, MSPA was used to analyze the importance of the ecological source of Huanglongxi ancient town. The results show that the average depth of all paths in Huanglongxi ancient Town is 2.12, and the average integration degree is 1.5. In the scenic area, the impedance disturbance value from path 1 to path 3 and path 7 to path 8 is high, and the negative evolution degree is serious. According to the MSPA method, the landscape types of Huanglongxi ancient town were analyzed. The core area accounted for 29.60%, the marginal area accounted for 17.25%, and the ecological environment accounted for 43.85% in total. In general, the route planning of Huanglongxi ancient Town is reasonable, but it is necessary to add new paths near the entrance of the scenic spot and the connection between the south and the north to divert tourists and improve tourists’ visiting experience. In addition, the proportion of ecological environment is relatively low, the degree of commercialization is high, which leads to the low efficiency of ecological protection. It is suggested improving the quality of existing commercial services, appropriately reduce the scale of commercial services, and expand the area of ecological environment, which has important practical significance for biodiversity and regional ecological security.

References

Pang

Dong

Liu

. Research on the conservation and sustainable development strategies of modern historical heritage in the Dabie Mountains based on GIS. Open Geosci. 2021; 13(1): 717-735.

Racz

Jurdana

Grdic

. Managers’ perspectives about the relationship between tourism and climate change: Case of the Republic of Croatia. Academica Turistica-Tourism Innovation J. 2021; 14(1): 53-70.

Shen

Zhang

. Determinants of workplace choice: How important is the city’s ecological environment in attracting jobseekers in China. Sustainability. 2022; 14(5): 1-15.

Dai

Cheng

. General trends in freshwater ecological restoration practice in China over the past two decades: The driving factors and the evaluation of restoration outcome. Environ Sci Eur. 2020; 32(1): 1-12.

Huang

Zhang

Gao

Bogonovich

, et al. A township-level assessment of forest fragmentation using morphological spatial pattern analysis in Qujing, Yunnan Province, China. J Mountain Sci. 2021; 18(12): 3125-3137.

Tang

Liao

Wang

. Construction of a forest ecological network based on the forest ecological suitability index and the morphological spatial pattern method: A case study of Jindong Forest Farm in Hunan Province. Sustainability. 2022; 14(5): 1-14.

Yang

Bai

Shi

. Linking morphological spatial pattern analysis and circuit theory to identify ecological security pattern in the Loess Plateau: Taking Shuozhou City as an Example. Land. 2021; 10(9): 1-18.

Zhang

Jiang

Guo

Zhao

Yang

, et al. Integrating morphological spatial pattern analysis and the minimal cumulative resistance model to optimize urban ecological networks: A case study in Shenzhen City, China. Temporary Cover. 2021; 10(1): 862-876.

Pratic

Solano

Fazio

Modica

. A multitemporal fragmentation-based approach for a dynamics analysis of agricultural terraced systems: The case study of Costa Viola Landscape (Southern Italy). Land. 2022; 11(4): 1-19.

10.

Dawes

Ostwald

Lee

. Examining control, centrality and flexibility in Palladio’s villa plans using space syntax measurements. Frontiers Archit Res. 2021; 10(3): 467-482.

11.

Eldiasty

Hegazi

El-Khouly

. Using space syntax and TOPSIS to evaluate the conservation of urban heritage sites for possible UNESCO listing the case study of the historic centre of Rosetta, Egypt. Ain Shams Eng J. 2021; 12(4): 4233-4245.

12.

Gao

Han

Zhao

. Detecting the evolution of collective memory space using a space syntax-based analysis method in Beiyuanmen historical and cultural block. Current Urban Stud. 2021; 9(4): 744-758.

13.

Othman

Yusoff

Salleh

. Assessing the visualization of space and traffic volume using GIS-based processing and visibility parameters of space syntax. Geo-Spatial Inf Sci. 2020; 23(3): 209-221.

14.

Tiang

DCF

Morris

Bell

Ngibbins

Azhar

Mlechner

. Ecological connectivity in fragmented agricultural landscapes and the importance of scattered trees and small patches. Ecol Processes. 2021; 10(1): 274-289.

15.

Wang

Liu

Tian

. Functional connectivity analyses and construction strategies of the structure of green space network in Haidian district of Beijing. Frontier Landscape Archit. 2019; 7(1): 34-51.

16.

Wang

Mao

Hong

Wang

Zhao

, et al. The spatial pattern and governance of Zhongyuan urban-rural system in its development trajectory. J Geogr Sci. 2022; 32(7): 1261-1280.

17.

Ivanov

Golomidova

Terenina

. Influence of the COVID-19 pandemic on the change in volume and spatial structure of the tourist flow in finland and estonia in 2020. Reg Res Russia. 2020; 4(70): 121-128.

18.

Wag

Hou

Yang

Song

. Evolution characteristics of hydrological connectivity pattern of marsh wetland in Naoli River Basin. Global Geol. 2021; 24(2): 95-102.

19.

Bai

Zhou

Cui

Bao

Zhang

Zhai

. HOG-SVM-based image feature classification method for sound recognition of power equipments. Energ. 2022; 15(12): 4449.

20.

Rehman

Qin

Pervez

Khan

Ullah

Ahmad

, et al. Land-use/land cover changes contribute to land surface temperature: A case study of the upper Indus basin of Pakistan. Sustainability. 2022; 14(2): 934.

21.

Kruesi

Burstein

Tanner

. A knowledge management system framework for an open biomedical repository: Communities, collaboration and corroboration. J Knowl Manage. 2020; 24(10): 2553-2572.

22.

Huang

You

Xiao

Zhong

. Construction of rural landscape ecological corridor in water network plain area based on MCR Model. Trans Chin Soc Agric Eng. 2019; 35: 243-251.

23.

Wei

Qian

Tao

. Evaluating spatial priority of urban green infrastructure for urban sustainability in areas of rapid urbanization: A case study of Pukou in China. Sustainability. 2018; 10(2): 327-332.

24.

Rosot

MAD

Maran

Luz

NBD

Garrastazú

De Oliveira

YMM

Franciscon

, et al. Riparian forest corridors: A prioritization analysis to the landscape sample units of the Brazilian national forest inventory. Ecol Indic. 2018; 93: 501-511.

25.

. Optimization of spatial structure of tourist attractions in Binzhou City under the background of regional tourism. Meteorol Environ Res. 2019; (5): 87-92.