Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model

: Aiming at the problems in current research into low-carbon and water scenery tourism, this paper brings forward a low-carbon tour route algorithm of urban scenic water spots based on an improved Divisive Analysis clustering model. Based on the ecological attributes of scenic water spots, the clustering model is set up to create scenic spot clusters. Via the clusters, the low-carbon tour route algorithm of urban scenic water spots based on the optimal energy conservation and emission reduction mode is proposed, and it provides the optimal scenic water spots and low-car-bon tour routes for tourists. The model can thus realize the optimization of vehicle exhaust emission in urban travel and reduce exhaust emission damage to urban water bodies and natural environments. In order to verify the advantages of the proposed algorithm, this paper performs an experiment to compare the proposed algorithm with the frequently used route planning methods by tourists. The experimental results show that the proposed algorithm has great advantages in energy conservation, emission reduction and low-carbon travel and can reduce the exhaust emission and the damage to the urban water bodies and the natural environment, realizing low-carbon tourism. The main findings and contributions of the proposed work are as follows. First, an improved clustering algorithm is set up, and the urban scenic water spots are clustered according to attribute data, which could optimize the scenic spot recommendation spatial model. Second, combining with the specific characteristics of scenic water spots, the scenic spot mining and matching algorithm is set up to satisfy tourists’ needs. Third, a method that could reduce emission exhaust by optimizing self-driving tour routes is proposed, which could control and reduce the damage to urban environments and protect water ecosystems. The proposed algorithm could be used as the embedded algorithm of tour recommendation systems or the reference algorithm for planning urban tourism transportation. Especially in peak tourism season, it could be used as an effective method for tourism and traffic management departments to direct traffic flow.


Introduction
In tourism projects, water scenery tourism is a very popular type. It is a collective name for tourism activities that rely on water resources in scenic spots. Water scenery tourism mainly relies on water resources; thus, scenic spots constructed on water not only have the function of providing sightseeing locations but also aim to maintain urban ecological environments, regulate water circulation, regulate the urban temperature and beautify the urban environment. Thus, it is necessary to protect urban scenic water spots [1]. Urban water scenery tourism resources are relatively scarce. People have hydrophilic psychology; thus, urban scenic water spots must not only have features of landscape design and sightseeing but also other features. Their multiple attributes determine that the urban scenic water spots can provide recreation and rest places and also provide other tourism functions, such as culture, sports, health, etc. In addition to these attributes, since scenic water spots are distributed in the city geospatial environment, they have geospatial features, namely spatial location, distance relation, water area, etc., which assigns them to the research category of tourism geographic information systems. Some scenic water spots are classical and popular ones, which not only attract local tourists but also attract a large number of out-of-town tourists [2]. When the vast majority of scenic spots in a city are scenic water spots, the city can be identified as a water tourism resource city. Urban water tourism resources should be reasonably developed and effectively protected. As an important component of ecosystems, the water cycle plays an important part in regulating the urban natural environment, in which scenic water spots play a key role [3]. Every year, tens of thousands of tourists come to visit these scenic spots, and their activities will impact the urban ecosystems. Under the condition of huge quantities of tourists, it is important to reduce exhaust emission and the damage to urban ecosystems while meeting tourists' needs and finally to realize low-carbon travel [4][5][6].
Currently, the research on urban water scenery tourism and low-carbon tourism mainly focuses on the following aspects. The first is the development of the mode and mechanism of water scenery tourism or the products of water scenery tourism. The second is research on the spatial distribution of water tourism resources. The third is the present situation and mechanism of low-carbon tourism. Li [7] analyzed the problems associated with water tourism resources and proposed development prospects. This study mainly addressed existing problems in rural water tourism resources and proposed new ideas for optimizing spatial patterns, changing the mode of production and adjusting the industrial structure. Zhou [8] classified water tourism resources, used the AHP method to conduct an investigation on Zhangjiajie water tourism resources, and brought forward improvement suggestions. The study reached the following conclusions: Zhangjiajie's water resources are abundant, but the overall quality is low. The main improvements should include water quality monitoring, developing new modes of water tourism and improving the basic transportation system. Xu [9] studied the usage and development of Chizhou city's water tourism resources and found that that resource integration, brand building and increasing capital investment are necessary. Li [10] studied the development strategy of urban water ecotourism, identifying the issues with water tourism resources and the corresponding countermeasures. Cao [11] conducted statistical analysis of tourists' cognition and studied urban water scenery tourism. Cao proposed the principles and ideas behind urban water tourism development, development methods for water tourism activities and purification methods for water environments. Fan [12] conducted data mining on the cultural attributes of water tourism resources in the process of tour planning. By mining the cultural attributes of the scenic water spots, Fan reached the conclusion that cultural attributes are indispensable factors for tourism activities. Li [13] studied the spatial distribution of water tourism resources in megacities and proposed an optimization method for water tourism spatial distribution. Li also determined the key issues that should be addressed in water tourism planning in the future. Song [14] conducted research on the products of water culture and reported the following conclusions: cultural attributes should be considered as critical concerns, and the role of cultural attributes should be emphasized in water tourism development. Dai [15] studied the development mode of water sports tourism, focusing on Hubei province, and proposed the possibility of further developing and promoting Hubei province water sports tourism. Huang [16] used the MCN mode to study the innovation in low-carbon tourism marketing and proposed innovative ideas for Heilongjiang province, including personnel training, establishing supply chains for low-carbon tourism and regulating marketing policies and systems. Tao [17] studied tourists' low-carbon behaviors and came to the conclusion that the Harbin low-carbon tourism could be enhanced by increasing publicity, improving tourism facilities, improving public transportation networks, etc. Tian [18] conducted research on the feasibility and development of low-carbon tourism in Guilin and proposed strategies for promoting the construction of Guilin as a low-carbon tourism city. Wang [19] studied the intrinsic motivation of tourists' low-carbon traveling activities and found that subjective norms, behavior attitudes and moral norms as well as knowledge of low-carbon options influence and drive low-carbon activities, while perceptual behavior control doesn't have a significant impact. Ren [20] studied transformation strategies for low-carbon tourism in cities and proposed strategies for resource-exhausted cities to develop low-carbon tourism. Liu [21] conducted research on the construction and application of low-carbon tourism evaluation systems, determining the associated problems and proposing suggestions for improvement. The current studies on water scenery tourism and low-carbon tourism mainly focus on development modes, spatial distributions, low-carbon construction methods, etc., which have certain limitations. First, there has been no targeted research on the feature attributes of scenic water spots and their clustering modes. The clustering of scenic water spots is vital, since it is a precondition for low-carbon tour route research. Second, tourists' behaviors when choosing scenic water spots in terms of the aspects of low carbon, lowest cost and the most benefits have not been studied. These are also preconditions for low-carbon tour route research, since identifying tourists' needs is the key to realizing the lowest cost and the greatest benefits. Third, the optimization of the tour route via different transportation choices to ensure energy conservation and emissions reduction (ECER) has not been studied. The choice of transportation method is critical in low-carbon tourism research. When a majority of tourists choose optimal routes, it greatly reduces exhaust emissions. Aiming at the current problems and deficiencies of water scenery tourism, this paper proposes a low-carbon tour route algorithm for urban scenic water spots based on an improved DIANA clustering model. It mainly studies the optimization of scenic water tour routes in terms of scenic feature attributes, tourist choices of scenic spots, low-carbon tour route planning, etc. It aims to reduce exhaust emissions and help to protect urban ecosystems. Compared with the methods from the literature, the proposed algorithm focuses on quantitative study. Compared with [7], the research object of the proposed algorithm is urban water tourism resources, not rural water tourism resources. Urban water resources may be influenced by multiple factors and can be easily contaminated. Thus, they need specific strategies. Compared with [8], the proposed method involves an intensive quantitative study of tour routes and transportation modes and puts forward a specific algorithm. Compared with [9][10][11], the proposed method designs a specific algorithm for scenic water tour routes. It provides an optimized model of urban water tour routes based on geographic information and algorithm optimization. Compared with [12][13][14][15], the proposed method mines water tourism's cultural attributes and sets them as labels to set up the algorithm. It is a concretization of the ideas presented in the literature. The literature [16][17][18][19][20][21] has analyzed the existing problems in the water tourism industry in terms of carbon imprint and has put forward optimization countermeasures and development paths for low-carbon tourism spatial optimization. Ref. [22] set up a mode of two-stage supply chain management to reduce greenhouse gases emissions. Ref. [23] proposed a closed-loop supply chain, which connects herbal medicine with biofuels. It can effectively increase waste utilization and reduce environmental pollution. Ref. [24] studied the different production strategies of two kinds of innovative green products and then developed three models to promote the reproduction of the innovative green products to realize environmental protection. Ref. [25] aimed to make pure biofuel with a smaller amount of carbon emission and energy utilization through a smart multi-type biofuel manufacturing framework. By using this method, the percentage of impure biofuel can be decreased through the minimized energy consumption. Compared with the literature [22][23][24][25], the proposed algorithm has a similar research idea, in which an optimization model is set up and the aim is to reduce greenhouse gas emissions. Refs. [22][23][24][25] mainly focused on biofuels, waste recycling, developing innovative green products and smart multi-type biofuel manufacturing, while the proposed algorithm reduces vehicle exhaust emission by optimizing tour routes and tourist activities.
The research objectives, innovations and novelties are as follows. First, aiming at the specific attributes of scenic water spots, this research proposes an improved DIANA algorithm, which generates scenic spot clusters on the basis of scenic water spot attributes. Thus, the clustering results are more accurate, conforming to tourists' needs. Second, based on the clustering results, a tourist interest matching algorithm is set up to precisely mine the scenic water spots and enhance tourist satisfaction. Third, aiming at the issues surrounding low-carbon traveling, a low-carbon tour route planning algorithm based on search optimization is established, which can decrease exhaust emissions from self-driving vehicles to protect urban ecosystems and water resources.

Urban Scenic Water Spot Spatial Clustering Based on the Improved DIANA Algorithm
Urban scenic water spots have different feature attributes from other scenic spots. Their main bodies are water areas. They have the functions of sightseeing, recreation, culture transmission, and sports and health activities. When tourists choose the scenic water spots to visit, they usually have an intrinsic motivation related to their specific attributes, such as enjoying the scenery, boating, watching waterfowl, experiencing marine cultures and customs, swimming, diving, tasting seafood, etc. Tourists' intrinsic motivations will directly determine the choice of scenic water spots. Usually, tourists' intrinsic motivations match scenic water spots' attributes [26,27].
According to their attributes, scenic water spots can be divided into several groups: recreation and sightseeing, boating, water culture and custom, and water sports. The recreation and sightseeing group includes all the leisure activities involved when visiting and viewing the water scenery and related supporting facilities. Boating includes sightseeing while on board a boat. The water culture and custom group involves appreciating the historical culture and customs of particular water areas. Water sports includes any physical sport played on, in or around water. Scenic water spots have four attributes: popularity degree, minimum cost, traveling time and spatial distance. The popularity degree is an average value that reflects tourists' enthusiasm for a particular spot. The minimum cost is the lowest cost of visiting one scenic spot for one tourist (unit: ¥ yuan). The traveling time is the average suitable and comfortable leisure time that could be consumed in visiting one scenic spot (unit: hour). The spatial distance is the geospatial distance between one tour route's starting point and one scenic spot, which is calculated by the longitude and latitude, and directly determines the traveling cost and exhaust emission. In the modeling of clustering and tour route planning, the groups and attributes should be quantified as a first step.

The Modeling of the Attribute Quantification Matrix for Scenic Water Spots
According to the principle of the clustering method, dots in space with the close attribute relationships can be classified into one cluster. The tour process is usually constrained by multiple factors. Thus, scenic spot recommendations should consider both classifications and attributes. The key to modeling the scenic water spot spatial clustering algorithm is quantifying the classifications and attributes, as is done in definitions 1.1-1.5 as follows. (1) Row rank and column rank: , .
(2) When obtains the maximum value , the row meets .
(3) Arbitrary row always has one non-zero element; the other elements are 0.
(4) Arbitrary rows and or arbitrary columns and are nonlinearly correlated.
According to the definition and conditions, the matrix for one scenic water spot is modeled as Equation (1). The parameters are introduced into the matrix and the normalized matrix is formed as specified in Equation (2). The matrix can be used to set up the clustering objective function. (2) According to the matrix and , the scenic water spot spatial clustering model based on the improved DIANA algorithm is set up.

Scenic Water Spot Spatial Clustering Model Based on the Improved DIANA Algorithm
DIANA is a clustering algorithm with the mode from top to bottom. Using this process, all the dots are initially gathered in a cluster, and then they are divided into several clusters in the control of clustering objective function. The core step of DIANA is to establish the division criteria. The traditional DIANA algorithm relies on dots' spatial distance. Since scenic water spots have other special attributes in addition to spatial distance, the attribute factors are introduced into DIANA. The definitions 2.1-2.5 are introduced as follows.
Definition 2.1. Scenic water spot initial cluster and element . Take one city's downtown area as the research range. Absorb its urban scenic water spots into one cluster; this cluster is defined as the scenic water spot initial cluster . The arbitrary one scenic spot in is element .  According to the definition, the clusters meet the following conditions: (1) Elements in the same cluster have a relatively strong correlation, while elements in different clusters have a weak correlation.
(2) Each cluster is a non-empty set, namely .
(3) Arbitrary element only belongs to one cluster , namely , and only relates to one single combination .
(4) The union of all the clusters is , namely . Step 1: Confirm the vector and for the number of elements in the cluster .
Step 2: Set up the topology vector for each attribute factor .
Step 3: Form the matrix and the normalized matrix for element .
Step 4: Aiming at and , extract the non-zero elements from and . Store them in the dimension vector from the smaller footnotes to the bigger ones.
Step 5: Set up the norm relation of the Euclidean distance between matrix and as in Equation (3). It is the criterion and objective function for the improved DIANA algorithm. (3) The improved DIANA algorithm is as follows. Store all scenic spots in the initial cluster , calculate the distances between each scenic spot, and confirm number of dots, with the smallest average dissimilarity as the center points. According to the approach principle, calculate the correlations between each non-center point and center point and absorb points into related clusters. The termination condition for the algorithm is all the scenic spots having been absorbed into clusters. Establish the vectors "splinter group" and "old party", which are used to store the transition data, noted as and . Establish the transition matrix ; this is used to store clusters and meets the following conditions: (1) The dimension is .
(2) It contains number of elements, in which number of elements are used to store scenic spots , others are used to store 0, .
(3) The row rank is , and column rank is . The matrix should contains at least 2 clusters.
Row sequence is the footnote of cluster , and the column sequence is the footnote of . The footnote is determined by the clustering algorithm.
According to the above conditions, the matrix is formed, as in Equation (4) . In the process of clustering, each scenic spot is absorbed into a related cluster in a certain spatial sequence. Connecting the adjacent two scenic spots in the sequence will form an edge, which is defined as a cluster topology edge . In one cluster, all the scenic spots as well as all the edges form a cluster structure tree . Based on the structure, the tree expands to the city's geospatial range and forms each cluster's special shaped visualized distribution. It is defined as the cluster spatial range .
According to the definition and the modeling principle, the improved DIANA algorithm is set up as follows.
Step 1: Set up the objective function storage matrix . The dimension matrix is set up to store the values. The row numbers are , ,..., ; the column numbers are also , ,..., . The elements are values; when the row number equals the column number, the element is 0.
Step 2: Calculate the values between arbitrary scenic spots and in , traversing , . Store all values in the matrix .
Step 3: Set up dimension vector and dimension vector .
is used to store the number of center points , while is used to store the number of non-center points .
Step 1-Step 3 forms the first level of the improved DIANA algorithm.
Step 4: Based on , calculate each scenic spot's average dissimilarity . Average dissimilarity is used to evaluate the average dispersion extent between one object to others. If in a group, there are number of objects , one object's average dissimilarity is calculated by Equation (5). The represents the spatial distance between objects and . Then, each scenic spot's average dissimilarity in is calculated as in Equation (6).
Step 5: Search and confirm the steady-state vector . Vector stores center points , and each center point relates to one cluster . The choosing of the center point relies on the smallest average dissimilarity. This is the modeling process of the steadystate vector . Step 5 forms the second level of the improved DIANA algorithm. (1) If there is an scenic spot , its related meets , and then delete and from and , and store into , store into ; (2) If there is no scenic spot that meets , keep the and elements unchanged.
Sub-step 5: Traverse the vector . Compare each element of with all elements in the current . Delete and change the related elements. The algorithm termination conditions are and are fully ranked. Average dissimilarity for each in is smaller than that of every element in .
Step 6: Continue dividing levels. Absorb the non-center point into the cluster of and form matrix . Step 6 forms the third level of the improved DI-ANA algorithm.
Sub-step 1: Store number of elements in the steady-state vector into the first row's elements of .
Sub-step 2: Confirm the cluster for : (1) Take the first element in the steady , calculate the number of objective function values between and number of elements , .
(2) Search the minimum value and its related element and center point . Note the center point as relating to cluster .
in the first row element of the first column in . Store the element in the second element of the same row. Absorb into .
(4) Connect center point with non-center point , and form the first edge for .
Sub-step 3: Confirm the cluster for : (1) Calculate the number of objective function values between and number of elements , .
(2) Take the minimum value and its related center point . Make a judgement: one structure tree and spatial range . The algorithm termination conditions are as follows: (1) All elements in have been stored into the first row elements in .
(2) All elements in have been stored into the related rows. (3) is fully ranked both in row and column. (4) There is at least one edge that connects each scenic spot , or all scenic spots have been absorbed into the tree .

Water Tourism Route Algorithm Based on the Optimal ECER Model
Water tourism should meet tourists' needs and protect the ecological environments. Based on the clusters and tourists' needs, the mining algorithm is set up to search for the best matched scenic water spots. Under the constraints of limited traveling time, tourists desire to use as much effective time as possible on visiting scenic spots, not on the ferrying process between scenic spots. Thus, it is necessary for tourists to choose the most convenient and effective route under the condition of a certain transportation mode. Motor vehicle is the most frequently used transportation mode by tourists; this consumes large amounts of energy and produces much exhaust gas, damaging the air, water and other environmental elements. When the exhaust emissions exceed the standard, it causes greenhouse effects, acid rain, etc., influencing air and water quality as well as citizens' health [28,29]. Thus, reducing energy consumption and exhaust emissions is an effective measure to protect urban water resources and ecosystems. Reducing the ferrying time and the exhaust emissions of motor vehicles is critical to realize low-carbon tourism. From the perspective of spatial analysis, the problem becomes searching for the optimal tour route. The first step is to set up the water tourism space model based on scenic spot mining, making all scenic spots match tourists' needs. The second step is to set up the optimal ECER tour route model based on the water tourism space model [30][31][32].

Water Tourism Space Model Based on Scenic Spot Mining
The precondition to set up the water tourism space and the optimal model on ECER to mine the best matched scenic spots. Thus, it is critical to obtain data on tourists' interests relating to scenic water spots. The smart machine method provides interest labels for tourists to choose, which rely on scenic water spots' classifications and attributes. When tourists choose certain labels, the smart machine will recommend a certain number of scenic spots that best match the interest labels. Tourists could confirm a desired traveling sequence according to their schedule [33,34]. Definitions 3.1-3.4 are introduced as follows. (1) Its dimension is , in which the represents the number of classifications and represents number of attributes, .
(2) The former number of elements store the non-zero elements of the first row in matrix . The latter number of elements store the non-zero elements from the second row in the No. row in matrix .
(3) It must be fully ranked, namely . Each represents the unique label set for each scenic spot. The element of is noted as , , , .
(4) Matrix has rows and columns, and they are both fully ranked, namely , .
(5) Two arbitrary rows are nonlinearly correlated, and two arbitrary columns are also nonlinearly correlated.   is set up as Equation (7), in which is the selected classification factor by tourists, is the scenic water spot classification factor, is the selected attribute factor by tourists, is the scenic water spot attribute factor, and is the normalization parameter. (1) The element relates to the value .
(2) The initial state is a zero matrix, and the final state is fully ranked .
(3) The transition state is a dynamic state of value sequencing.
According to the above definitions, the water tourism space model based on scenic spot mining is set up to confirm the best matched scenic water spots for tourists and finally forms the tourism space of the downtown area.
Step 1: Encode each row's elements of matrix . The first row is the interest vector , and the other rows are vectors . The same column relates to the same attribute. The basic encoding method is as follows: Sub-step 1: Calculate the objective function value between the first row and row , and store the value into the element . Sub-step 2: Calculate the objective function value between the first row and row , and store the value in the element . Sub-step 3: Compare the value and .
(2) If , delete the values in and . Store in and store in .
Sub-step 4: Calculate the objective function value between the first row and row , and store the value in the element and then make a comparison. According to the clusters and scenic spots confirmed by the tourists' needs and the proposed algorithm, make visualization maps for the clusters, the selected scenic spots and the tourism space . Figure 1 shows the process to form the tourism space .

Optimal ECER Tour Route Algorithm Based on the Water Tourism Space
When tourists travel in the water tourism space, they should travel along the routes with the lowest costs. Meanwhile, considering the protection of the urban ecosystems and low-carbon requirements, when tourists choose motor vehicles, it is necessary for them to reduce the consumption of energy and exhaust emissions, thus decreasing the damage to the city water resources. Usually, tourists tend to take motor vehicles to save travel time, as they are efficient and fast. However, this also brings exhaust emissions. Thus, in the constraint of the total time, decreasing the whole route distance and reducing the ferrying time could help increase the scenic spot visiting time and reduce exhaust emissions [35][36][37]. The optimal ECER tour route algorithm based on the water tourism space is set up according to definitions 4.1-4.6.
are selected to set up the algorithm; these are those which the tourists will be most likely to pass. (1) Effective intersections are abstracted as the space lattice with longitude and latitude, according to their spatial distribution.
(2) Its dimension is , the row and column's full ranks are both .
(3) Starting from the first element in the first row and ending at the last element of the No. row, the effective intersections are stored in spatial sequence.
(4) Empty elements are set as value 0. Figure 2 is the process of abstracting the tourism space into the model and . The model is the precondition to set up the optimal ECER tour route algorithm based on the water tourism space. Figure 2a shows the distribution of the scenic water spots, connecting roads and road intersections in the water tourism space . Figure 2b shows the scenic water spots' distribution. Figure 2c Figure 2d shows the road intersection set in the space and the effective intersection set between the scenic spots and , noted by the red points in the dashed frame. Figure 2e shows the built model based on Figure 2d.

Definition 4.4. Basic ECER unit
. When a motor vehicle takes tourists from to , the average exhaust emission volume of the motor vehicle per kilometer is defined as the basic ECER unit, noted as . In the algorithm, it is measured by the gas weight, unit: kg.
When the car model is confirmed, it could be considered that the is a constant value. When the constant value is confirmed, the total exhaust emission of the motor vehicle is directly proportional to the traveling distance in a tour route. Thus, setting up an optimal route model to reduce the total ferrying distance between scenic spots could effectively decrease the exhaust emission volume.  According to the definitions, the optimal ECER tour route algorithm based on the water tourism space is set up as follows: Step 1: Set up the ECER model for . The model is the substructure of the tourism space . The optimal tour route algorithm in is set up first. Sub-step 1: Build the tree and calculate the ECER volume . Figure 3 shows the process to build the tree . (1) The initial state is shown in Figure 3a. The space lattice is , containing and . The starting point is while the ending point is . Absorb into .
Sub-step 3: Repeat the above sub-steps. Build the tree and calculate the ECER volume ; store in the element of , and compare to . The element should meet the following conditions: (1) Vector is fully ranked, namely .
, , if , there is always .
Sub-step 4: Take the total ECER volume of the first element in . Its related tree is the optimal path between and .
Step 2: Set up the ECER model for . The number of scenic spots selected by the smart machine cover different clusters and have specific locations. When tourists travel in the space , they first choose the favorite sequence on the scenic spots, that is, in the model , the optimal tour route is searched by the model, which finally accumulates the optimal tour route in the whole with the lowest energy consumption and exhaust emission. When a tourist confirms a type of sequence, the ECER model for is set up as follows: Sub-step 1: Build the scenic water spot tour sequence vector based on . Build a dimension vector to store the number of scenic spots and the starting point. The constraints for the vector are as follows: (1) The element is , and it is fully ranked, namely .
(2) Element is used to store the starting point . Elements ~ are used to store the number of scenic spots in the tour sequence.
(3) Element and are the two adjacent scenic spots, and the spatial interval is .
(4) ~ as well as their spatial intervals form a complete tour sequence in the space . Sub-step 2: Calculate the total ECER volume between and , and store the value. Sub-step 3: Calculate the total ECER volume between and , , , and store the value. Sub-step 4: Calculate the total ECER volume of the complete tour sequence, as shown in Formula (9). (9)

Experiment and Result Analysis
In order to verify the feasibility and advantages of the proposed algorithm, an experiment was designed. We chose the tourism city Chengdu and its 24 urban scenic water spots as the research range and objects. They cover multiple tour functions, classifications and attributes, which can satisfy different tourists. The basic principle of the experiment is as follows: Confirm the scenic water spots and quantify their classifications and attributes. Use the proposed improved DIANA algorithm to form scenic spot clusters. Confirm the best matched scenic spots according to tourists' needs, and then search the optimal ECER tour routes. Finally, the tourists' frequently used electronic maps are set as the control group to allow comparison and verify that the proposed algorithm is feasible and has advantages. The raw data for the experiment are obtained from the basic geographic information data and traffic information data of Chengdu city. The names and attributes of the scenic water spots are obtained from the official website of Baidu Encyclopedia and the Chengdu City Platform for Common Geospatial Information Services. The traffic information data is used to calculate the results are obtained from the Chengdu City Platform for Common Geospatial Information Services and Chengdu City Public Data Open Platform, as well as Chengdu City's electronic map.

The Acquisition of the Scenic Water Spots and Calculation Results
The selected typical scenic water spots in the Chengdu downtown area are: : Jincheng Lake Park; : : Shibashan Cross-Country Park. Table 1 shows each scenic water spot's normalized classifications and attributes, in which relates to the classification and relates to attributes: : recreation and sightseeing; : boating experience; : culture and custom; : water sports; : the popularity degree; : the minimum cost; : the traveling time; : the spatial distance. The starting point of the tourist is Chengdu railway station, which is used to calculate the spatial distance. The spatial distance is calculated from the longitude and latitude. Figure 4 shows the scenic water spots map of Chengdu city; Figure 4a shows the scenic water spot geographic spatial distribution, and Figure 4b shows the initial cluster formed by the extracted scenic water spots.

The Results of Scenic Water Spot Clustering and Cluster Visualization
Calculate each scenic spot's average dissimilarity using the data in Table 1; the results are shown in Table 2. When the cluster quantities are set as , , , , the clusters and related scenic spots are formed, as shown in Table 3. Figure 5 shows the visualization results of the clusters under different cluster quantity conditions; the small blue ranges are the miniature scenic spots. After connecting the two ranges in the sequence of clustering, the topology edges, structure trees and cluster topology ranges are obtained.

The Results of the Optimal Scenic Spots and Tour Routes
In a tour day, the tourist confirms the interest labels according to the output clusters and scenic spot attributes. The smart machine calculates and generates the interest vector and interest matching matrix and calculates the objective function values . Table 4 shows the output results of the objective function values . Based on the Table 4 data, the smart machine recommends each cluster's optimal scenic spots for the tourist. The tourist confirms five scenic spots to be visited according to the calculation results and then confirms the tour sequence according to their interests. In the experiment, the interest labels confirmed by the tourist are (1) Enjoy the scenery and take photos; (2) do not enjoy boating or splashing; (3) there are no requirements for appreciating water scenery cultures or customs; (4) desire to wander around and do leisure sports. In addition, for one single scenic spot: (5) the popularity degree is set at 0.60; (6) the lowest cost is set at 0; (7) visiting time in a scenic spot is set at 1.50; (8) the maximum distance to the starting point is 20 km, and the nearer the better. Based on the interest labels, the interest vector is output = {1.00, 0.00, 0.00, 1.00, 0.60, 0.00, 0.15, 0.00}.

Comparison of Results
Based on the Table 5 data and the tour sequences, the models and are set up. In the downtown area, the road network is set up to connect the scenic spots, and then the set and effective intersections are formed. Collect the basic data of the city roads, and use the optimal tour route ECER algorithm to output the low-carbon tour routes. The experiment takes the frequently used motor vehicle as an example and sets the ECER unit as , which is the exhaust emission volume of a 1.6 L swept volume motor vehicle that travels 1 km. The optimal emission volume for each is output, and then the total emission volume of is calculated. Before traveling, tourists usually use electronic maps to plan tour routes. The experiment chooses the Tencent map and Gaode map as the control group; the proposed algorithm, Tencent map and Gaode map are represented as PRA, TCA and GDA. The same experimental performance is carried out on the control group, and the results are shown in Table 6. The is the first road interval from the starting point to the first scenic spot. The other is the road interval between two scenic spots. The is the total emission volume. Figure 6 shows the emission volume and total emission volume of the three algorithms under the conditions of different cluster quantities, and the difference value comparison among the three algorithms for the emission volume. Figure 6a-e are the emission volume and total emission volume of the three algorithms under the conditions of cluster quantities . The blue columns relate to PRA, the orange columns relate to TCA, and the gray columns relate to GDA. Figure 6f-j are the difference value comparisons among the three algorithms of the emission volume under the conditions of cluster quantities . The blue columns relate to PRA, the orange columns relate to TCA, and the gray columns relate to GDA.

Experimental Results Analysis
By confirming the experimental environment and collecting experimental data, the proposed algorithm is used to carried out the experiment and output the results. The experimental results are analyzed for the aspects of clusters and related visualization, optimal scenic spots and tour routes, and algorithm comparison.
(1) The analysis of the clusters and related visualization results The proposed algorithm is used to form the scenic water spot clusters. Table 2 data show each scenic spot's average dissimilarity, in which the values have relatively large disparities. This shows that scenic spots have discrepancies in the classification and attribute; in addition, they have different functions to meet tourists' needs. The smaller the average dissimilarity, the closer the scenic spot will approach the cluster center. Meanwhile, the average dissimilarity also reflects the correlation between two scenic spots. The smaller the average dissimilarity, the closer the scenic spot's comprehensive attributes will approach other scenic spots. According to the cluster quantity, the related quantity of center points is calculated and output. The experimental results conform to the clustering rules.
Analyze the Table 3 data. When the cluster quantities are different, the output center points are also different. When the cluster quantity and center points are confirmed, the clusters are presently confirmed, and each one has different scenic spots. This shows that when the preconditions are different, scenic spots will be absorbed into different clusters, which will influence the selection of the optimal scenic spots and planning of the optimal tour routes. In Table 3, the scenic spots that are absorbed into the same cluster must have the smallest objective function values with the center point. This shows that when the center point meets tourists' needs, the scenic spots that are close to the center point have strong correlation to the center point's functions and are thus are recommended to the tourists.
Analyze the Figure 5 cluster visualization results. When the cluster quantity is , the cluster is the initial cluster . It contains all the scenic spots.
When the cluster quantity is , the center points are and . The cluster and have different distributions and form two kinds of structure trees and spatial ranges.
is relatively dispersed, while is relatively concentrated.
When the cluster quantity is , the center points are , and . The cluster , and have different distributions and form three kinds of structure trees and spatial ranges.
is relatively dispersed, while and are relatively concentrated.
When the cluster quantity is , the center points are , , and . , and are relatively concentrated. By analyzing the clustering results, the following conclusions could be obtained. First, when the smart machine sets different cluster numbers, the clustering results would be greatly different. The more the cluster number is, the more accurate the clustering results on the scenic water spots' attributes will be, the more likely to extract the scenic spots that match tourists' needs. Thus, the confirming of the cluster number is critical, it should not be too large or too small. Second, when the clustering numbers are different, the recommended specific scenic water spots are different. When extracting the scenic spots, the S smart machine will preferentially choose the one that best matches tourists' needs in each cluster. Thus the recommended scenic spots and the finally confirmed tour routes would be different. Third, according to the visualization results, most of the clusters display the shape of centralized distribution, which interprets that the spatial attributes play a critical role in the clustering process. It conforms to the tourism activity law since the urban geospatial environment provides the basic conditions, and the scenic water spots attributes and spatial attributes are both the decisive factors for planning the low-carbon tour routes.
(2) The analysis of the optimal scenic spots and tour routes results The results in Table 4 show that the objective function values of the scenic spots are totally different. The minimum value is 0.0877, relating to the scenic spot . This shows that its capacities to meet tourists' needs are the strongest. The maximum value is 2.5119, relating to the scenic spot . This shows that its capacities to meet tourists' needs are the weakest. Based on the Table 3 clustering results, each cluster's scenic spots are sequenced in ascending order. The tourist confirms a tour sequence according to their interests and the recommended optimal scenic spots, as shown in Table 5. As to the Table  5 data, when the clusters are different, the recommended optimal scenic spots and tour sequences are totally different. When the tourist confirms a tour sequence, the smart machine will plan an optimal traveling route, which generates the lowest exhaust emissions.

(3) The analysis of the algorithm comparison results
Under the same condition of the tour sequence, the exhaust emission volumes of the three algorithms are different, as shown in Table 6 and Figure 6. When analyzing the Table  6 data and Figure 6a-e, the exhaust emission volume is directly proportional to the traveling distance. As to the arbitrary cluster quantity condition, for the same algorithm, the exhaust emission volumes are different at each interval. When , PRA, TCA and GDA all have the highest exhaust emission volume in and lowest one in . . As to each interval and the whole tour sequence, the exhaust emission volume of the PRA is always lower than TCA and GDA. Figure 6f-j show the exhaust emission volume difference values between TCA and PRA, GDA and PRA. The difference values fluctuate along the interval , namely the algorithms have different performance at different intervals, which creates great differences in the whole tour route. When , and , the GDA exhaust emission is the highest. When and , the TCA exhaust emission is the highest. For arbitrary , the PRA exhaust emission is always the lowest.
It can be seen from the comparison results that the proposed algorithm has great advantages. First, the proposed algorithm applies the parallel search mode to find the optimal result, which could greatly improve the algorithm's efficiency and determine the optimal result in a short time. Thus, the proposed algorithm is the optimal one. Second, when searching the vehicle traveling paths in the downtown area, the experimental group algorithms tend to choose the main roads and neglect the secondary roads, since they are convenient for vehicles. Thus, the searched route might not be the optimal path. Third, the proposed algorithm not only has advantages in searching the optimal paths but also produces the lowest exhaust emission for low-carbon traveling, since it provides the shortest traveling distance, resulting in the lowest volume of vehicle energy. Fourth, since the proposed algorithm has the above-mentioned advantages, it could be directly used as the embedded algorithm to develop the smart recommendation system, helping to provide one-stop recommendation services in scenic spots and low-carbon tour routes, while the experimental group algorithms do not have the functions of smart recommendation of scenic spots and low-carbon tour routes. Tourists have to confirm the scenic spots by themselves and then obtain the planned path between two scenic spots. Thus, in the aspect of application convenience, the proposed algorithm also has advantages.
(4) Discussion of water protection The experimental environment is Chengdu city as well as its urban scenic water spots, and the traveling tool is one 1.6 L swept volume motor vehicle. The experiment shows that the motor vehicle traveling along the route created by the proposed algorithm will generate the lowest exhaust emission, lower than that of the routes created by the electronic maps. According to statistics, over 200 million tourists visit Chengdu every year. If every tourist takes a motor vehicle to travel in the city along the proposed tour sequences designed in the experiment, the algorithm will decrease the exhaust emission volume by more than 100,000 tons. Motor vehicle exhaust contains carbon monoxide, carbon dioxide and nitrogen oxide, which pollute the atmosphere, air, and water resources and can cause greenhouse effects and harm human health. The proposed algorithm provides a method to plan optimal traveling routes, which can effectively reduce the motor vehicle exhaust emission volume, control the pollution of the atmospheric environment and water resources, and realize low-carbon tourism and protect urban ecosystems. As to the proposed algorithm, some managerial insights are provided. First, it could be used to develop a low-carbon driving management APP, which could plan the traveling routes with the lowest exhaust emission when the starting point and the terminal point are confirmed. Meanwhile, it could provide real-time traffic conditions and manage the whole trip for self-driving tourists and thus could reduce environmental pollution. Second, on important festivals and vacation days, it could be used to manage the traffic flow and effectively control the total volume of self-driving vehicles, helping to relieve traffic pressure. It is also useful for increasing the public transportation frequency, to encourage tourists to travel by public transportation and reduce the use of vehicles. Third, the tour routes planned by the proposed algorithm could be highlighted on the electronic maps or published as a paper version and provided to tourism management t or traffic management departments. On important festivals or vacation days, when the tourist travel flow is large, the highlighted routes could be managed to relieve traffic congestion.

Comparison with the Algorithms in the Literature
Compared with the methods and algorithms in the literature, the proposed algorithm has some novelties. Ref. [5] confirmed the popular urban scenic spots by mining the number of check-ins. In the aspect of data accuracy, the proposed algorithm is superior. It directly confirms tourists' interest labels to match scenic water spots' attributes, which is more elaborate and accurate. Ref. [6] studied an ant colony algorithm for better performance of planning the vehicle route, with emphasis on the algorithm optimization and improvement. By comparison, the novelty of the proposed algorithm is different. It combines the shortest path searching with low-carbon traveling, water tourism and environmental protection. Its aim is to set up the mode of low-carbon water tourism and reduce the emission of greenhouse gases. Ref. [9] studied a route planning system for intelligent driving; the proposed algorithm is not used for intelligent driving but for low-carbon driving, whose service targets are ordinary vehicles, not intelligent vehicles. Ref. [17] studied how to choose the proper intelligent driving mode for drivers using intelligent driving systems. Its aim was to reduce exhaust emissions by avoiding bad driving habits. However, intelligent vehicles are not widely produced and used. The vast majority of vehicles cannot reduce the exhaust emissions by controlling the driving modes. Thus, the novelty of the proposed algorithm is to reduce the travel distances, control the energy consumption and finally reduce the exhaust emissions of ordinary vehicles. Ref. [22] set up a scenic spot recommendation system using the visiting frequency of different tourists, which involved scenic spot recommendations. By comparison, the novelty of the proposed algorithm is recommending scenic spots by matching tourists' interests. In addition to recommending scenic spots, it also plans low-carbon tour routes.

Conclusions
Water tourism is different from other types of tourism. It is closely related to ecosystems. Planning water tourism activities and related tour routes should protect the environment. Based on energy conservation and emission reduction as well as low-carbon tourism, this paper sets up a low-carbon tour route algorithm for urban scenic water spots based on an improved DIANA clustering model. Taking urban water scenery tourism as the research context, the current problems and research status of water tourism are analyzed. An urban scenic water spot clustering model based on an improved DIANA algorithm is proposed. It quantifies the scenic water spot classifications and attributes into specific data based on the ecological tourism mode. The classifications and attributes are set as the parameters to set up the clustering algorithm, and the scenic water spot clusters are obtained. This is the precondition for the smart machine to recommend the optimal scenic spots. Based on the clustering algorithm, by analyzing the requirements of the tourists in ecological tourism, the water tour route algorithm based on the optimal ECER model is set up, in which the tourists' interest data is mined to set up the water tourism space model. The confirmed scenic spots are arranged by the tourists to form the interested tour sequence and the traveling routes. The experiment shows that the proposed algorithm is feasible and has advantages for energy conservation and emission reduction as well as low-carbon tourism over frequently used electronic maps.
Aiming at low-carbon tourism and ecological travel, this paper proposes a feasible method, which could be embedded into a smart recommendation system to provide optimal scenic spots and plan tour routes. On the aspect of theoretical and methodological contributions, first, this paper proposes an improved DIANA scenic water spot clustering algorithm, which breaks down the limitations of spatial distance and sets the specific attributes of scenic water spots as the key parameters. It also optimizes the clustering algorithm and enhances the algorithm accuracy. Thus, the proposed algorithm has made contributions to the clustering theory. In addition, it combines the clustering method with the low-carbon idea, which contributes to urban ecosystem protection. Second, as to the scenic water spot tourists, this paper sets up a quantitative model for scenic water spot interests. Different from the mode of data mining from big data, the proposed algorithm directly confirms tourists' interest labels and matches the best scenic spots. Third, this paper innovatively sets up an algorithm model that can reduce the total exhaust emissions by searching the vehicles' shortest traveling path while meeting the needs of self-driving tourists. Thus, the proposed algorithm is a comprehensive method that combines the tourists' interests with low-carbon goals, which simultaneously satisfies tourists' needs and protects urban water ecosystems. Thus, it makes a contribution to ecotourism construction. Fourth, the proposed algorithm can be directly used as the embedded algorithm for the smart recommendation system. Especially for self-driving tourists, the proposed algorithm can reduce the energy consumption and exhaust emission to protect urban water resources. In addition, as an urban tour route planning algorithm, the proposed algorithm's core aim is to realize a low-carbon footprint and ensure environmental protection. It could be used by tourism management or traffic management departments. During important festivals or vacation days, when the tourist travel flow is large, the low-carbon tour routes could be managed to relieve traffic congestion.
In further research, the following elements of water tourism could be studied. First, the importance of scenic water spots to urban ecosystems is great, and people's activities have a great impact on scenic water spots. Thus, deep mining the tourists' activities in scenic water spots and conducting research on the relationship between tourists' activities and scenic water spots' impact on urban ecosystems should be considered, with more precise attributes and interest labels designed and more elaborate clusters created. Second, more elaborate research on tourists' activities could be performed, in which the activities could be classified to determine their different impacts on related scenic water spots. This aims to determine a more accurate recommendation algorithm and provide more precise and elaborate scenic spots for specific groups of tourists. Third, in the paper, motor vehicles such as taxis and private cars are set as the research objects and tools. In future study, additional transportation modes could be studied. According to the tourists' needs, multiple collocations of transportation modes can be provided for tourists to further optimize the exhaust emissions, to satisfy different travel demands, and finally to minimize the damage to urban water resources and ecosystems.