Inheritance and Innovation of Regional Culture in Urban Landscape Planning and Design

With the rapid development of economy and society, urban landscape construction has also entered a stage of rapid development. Under the impact of informationization and economic globalization, more and more urban landscapes have become homogenized, and the forest of high-rise buildings and endless traffic have become the standard of the city [1-3]. The original features and traces of the city have been gradually erased, and the phenomenon of landscape homogenization has become more and more serious. Urban landscape is an important part of the city, an important place to satisfy citizens’ daily interaction, recreation and physical leisure activities, and an important carrier of urban style and urban impression [4]. Ecologically healthy, diverse and rich urban park landscape environment with cultural connotations highlights the unique taste of a city, makes people feel pleasant and pleasant, and improves the sense of identity and belonging to the city [5-6].

Regional culture is a culture with local characteristics gradually accumulated and formed in the process of local social development, which is a trace of the development of this region and has non-replicability [7]. Cultural elements are the soul of the city personality, regional culture into the urban landscape construction, shaping the characteristic landscape, improve the phenomenon of homogenization of the urban landscape, forming a unique flavor of the urban landscape appearance [8-10]. The regional culture gradually formed in people’s production and life has both local characteristics and rich artistic connotation, involving all aspects of people’s lives, and is easily recognized and accepted by the general public [11-13]. The regional culture, which embodies the spirit of the city and carries the urban civilization, is integrated into the urban landscape without any trace, so that people can feel the humanistic atmosphere and cultural precipitation in the unique regional environmental atmosphere, and spread and promote the local culture [14-16]. Therefore, it is particularly important to apply the unique urban regional culture in urban garden construction to shape a city’s unique cultural quality and regain urban memory [17-18].

This study develops the expression of regional culture in urban garden landscape design from three aspects: retaining regional cultural characteristics, extracting cultural elements and reproducing historical culture. And according to the characteristics of urban garden landscape planning program, the evaluation of urban garden landscape planning program is evaluated in 4 aspects: innovation, artistry, science, and economy. And 21 evaluation indicators were established according to the influencing factors of each of these 4 aspects. Using the hierarchical analysis method, the weight of each index is calculated and consistency test is performed. Then according to the fuzzy comprehensive evaluation method, the entropy weight method was used to get the information entropy of each landscape evaluation index and its weight. Finally, using the combination assignment method, the comprehensive weight value of each evaluation index is calculated, so that the determination of the weight of the evaluation indexes of urban landscape is more rationalized. And based on the comprehensive evaluation cloud model of uncertainty, the landscape design before and after the integration of regional culture is evaluated by the comprehensive cloud.

2

Urban Landscape Design Methods and Evaluation Models

2.1

Expression of regional culture in urban garden landscape design

1)

Preserve regional cultural characteristics

The landscape design of urban gardens must be based on the premise of inheriting regional culture, retaining its essence, discarding its dregs, and carrying out reasonable inheritance of regional culture. The culture of the Chinese nation is profound and profound, with a long history, and there are many excellent traditional cultural landscapes left in the world. Therefore, for the traditional landscapes that remain intact and have high cultural value, it is necessary to inherit their design skills, highlight the regional cultural characteristics, and restore the original appearance of history for the public to the maximum extent possible. China has a vast territory and a large number of nationalities, so the regional culture is a diversity of development, in the process of designing the landscape, must be the characteristics of the region and landscape to make practical use of the regional culture of the organic integration of landscape design, so that the landscape is more full of vitality, to realize the “living” cultural heritage.

2)

Extraction of cultural elements

Regional culture has its own unique typical elements, when designing landscape, we should collect and organize the typical elements of local culture, and apply them in the design. This type of approach is suitable for application in landscape architecture with simple spatial structure, however, the selected elements must best represent the regional characteristics, and can be expressed in diversified forms.

3)

Reproduction of history and culture

In the protection of culture at the same time, the culture should also be inherited and promoted, if only the protection as the only requirement, in a long time, the original cultural heritage will be slowly lost. So not only to protect the culture, but also with the help of the landscape so that the public see the restored historical and cultural landscape, so that the public in the process of feeling the charm of the landscape, to appreciate the rich cultural meaning. Therefore, landscape designers only to the local history and culture to develop a further understanding of the history can better restore the history, evoke historical memory, in the original style and form of landscape architecture to simulate, but also with the help of sculpture and other artistic techniques.

2.2

Fuzzy integrated evaluation method

Fuzzy comprehensive evaluation (FCE) is a method of evaluation based on fuzzy mathematics and applying the principle of fuzzy relationship synthesis, which quantifies some factors that are not easy to be quantified, as well as comprehensively evaluates the objects affected by multiple factors.The specific steps include:(1) Determine the factors of the evaluation object and the domain of the evaluation hierarchy; (2) Conduct a single-factor evaluation and establish a fuzzy relationship matrix; (3) Determine the fuzzy weight vector of evaluation factors; (4) fuzzy evaluation of factors; (5) comprehensive evaluation results analysis.

2.2.1

Fundamentals of fuzzy comprehensive evaluation

Set the set of factors that have an influence on the variable being evaluated as U = {u₁,u₂,Λ,u_m} , then describe the different states of the factors of each kind as n , and then set the set of decisions as V = {v₁,v₂,Λ,v_n} . Among them, the fuzzy sets of focus are divided into two categories, the degree of public recognition occupied by the set of indicator factors U and the fuzzy correlations of U × V . The former is concretized in the set U by the fuzzy weight vector A = {a₁,a₂,Λ,a_m}; while the latter is programmed with different fuzzy matrices R as m × n . Then, the appropriate fuzzy way is taken to operate the two different types of sets, resulting in the fuzzy subset B = {b₁,b₂,Λ,b_n} ∈ V . Therefore, the fuzzy comprehensive evaluation of the fuzzy weight vector $\underset{~}{A} = {a_{1}, a_{2}, Λ, a_{m}} \in F (U)$ $\mathop{A}\limits_{ \tilde {}}= \left\{ {{a_1},{a_2},\Lambda ,{a_m}} \right\} \in F(U)$ is sought, as well as the fuzzy transformations f will be the set U to V , i.e., each factor indicator u_i(i = 1,2,Λ,m) is made to make the separate discriminations f(u_i) = (r₁₁,r_i2,Λ,r_in) ∈ F(V) , and then finish the Matrix construction R = |r_ij|_m×n ∈ (U × V) , where r_ij denotes the factor indicator u_i with a rating degree of v_j . Finally, a comprehensive integrated evaluation $\underset{˜}{B} = {b_{1}, b_{2}, Λ, b_{n}} \in F (V)$ is obtained, where b_i denotes the rating degree of the subject, i.e., the degree of commitment of b_j to B .

2.2.2

Indicator weights

The weighting method based on entropy index is a combination of qualitative and quantitative methods, which combines subjective experience and evaluation results with quantitative indicators for quantification. According to the regulations of relevant national departments, combined with the characteristics of the main project of distribution automation master station, the current evaluation index system is reasonably determined, the weights of the project indexes are calculated, and a complete evaluation hierarchical vector model is established. Through the definition of entropy and related knowledge, it is known that the size of entropy indicates the existence of a discrete probability distribution. When the entropy is maximum and equal to 1, it indicates that the probability distribution is uniform; when the entropy is minimum and equal to 0, it indicates that there is only one random value. If the probability value is the indicator weight value, the distribution of each weight of the indicator system can be calculated from the entropy value.

Based on the above description, the entropy value of the assessment indicator system is calculated according to the entropy calculation formula, and it is used as a formula to measure the effective measurement of its weight distribution, as shown in formula (1): (1) $H = - \frac{1}{\log k} \sum_{k = 1}^{k} w_{t} \log w_{t}$

By using the above formula, the overall distribution of the weight distribution of the evaluation indicator system can be calculated, and the weight distribution calculated through the entropy weight method can be grasped as a whole, and the desired effect of the evaluator has been realized. However, the actual comprehensive evaluation indicators are generally based on different attributes, and the secondary indicators corresponding to each indicator also involve weight distribution. This must be accomplished by measuring the weights at multiple levels, analyzing each sub-indicator, and then calculating upward to obtain an overall distribution of weights for the evaluation indicator system, as shown in Figure 1.

The hierarchical weighting distribution of the evaluation index system can be used to understand the distribution of weights, and the form of categorization at different levels will affect the results of the comprehensive evaluation index. According to the relevant theory and measurement formula, the entropy range is 0 ≤ H ≤ 1 , which is discussed in three cases:

1)

If H is close to 1

According to the definition of entropy, it can be known that the maximum value of entropy is 1 when the weight of each index in the evaluation index system is equal.Assuming that there are k evaluation indexes in the evaluation index system, the entropy value should be equal to the value of metrics in the index system if the weight of each evaluation index is kept equal, i.e. w_i = 1 / k_i , that is to say, the entropy value should be calculated to be equal to the value of metrics in the index system: (2) $H = - \frac{1}{\log k} \times (k \times \frac{1}{k}) \times (- \log k) = 1 (k \neq 0)$

When H = 1 , it means that the indexes of the evaluation indicators in the evaluation index system are the same; when H is close to 1, it means that in the index evaluation system used, there is a situation in which the indexes of each indicator are close to the same. In fact, this is rarely the case in the process of evaluating the index system. In general, in the evaluation index system, the evaluation indicators are measured close to 1 time, i.e. each indicator is more relevant at the time of setting. The independence of each indicator is not obvious, indicating that the evaluation indicator system is unreasonable.

2)

If H is close to 0

When the weight of one evaluation index is equal to 1 and the weight of other remaining indexes is 0, the entropy is minimized and equal to zero. Assuming that there are k evaluation indicators in the evaluation index system: (3) $H = - \frac{1}{\log k} \log_{k} 1 = 0 (k \neq 0)$

When H = 0 , it is important to have very large weights in the evaluation index system. When H approaches 0 it can be concluded that the index system has a small number of evaluation indicators with large weights. Evaluating other less important indicators can also lead to large errors in the evaluation results; this is rare in the process of evaluating the index system. In general, in the evaluation index system, when the measurement results of the evaluation indicators are close to 0, that is, when setting each indicator, only a small number of indicators are relevant to the system. The other remaining indicators are largely irrelevant to the system, which indicates that the evaluation indicator system is not reasonable.

3)

If H is between 0 and 1

when H is between 0 and 1, i.e., it is not significantly close to 0 and it is clearly not close to 1. Therefore, this distribution of weights for the system of assessment indicators is very reasonable. Indicators in this case not only have the advantage of the hierarchy, but they are more or less related to the indicator system, but not in the same degree of proximity of contact. This also reflects the characteristics of the hierarchy, which can be quantified using the entropy method. From the results of the above analysis and discussion, the design of the evaluation indicator system is reasonable when 0 ≤ H ≤ 1 .

2.3

Improved fuzzy comprehensive evaluation method

According to the basic situation of urban landscape planning and design and the theoretical basis of comprehensive evaluation method, the entropy weight fuzzy multilevel evaluation model was constructed to evaluate the project.

2.3.1

Selection of evaluation criteria

When planning and designing the urban garden landscape, it is necessary to consider all aspects of the elements, consult and discuss with the relevant people, and select a set of scientific and reasonable evaluation criteria in order to improve the objectivity and accuracy of the evaluation. According to the relevant standards, collect the plan objectively and reasonably, and collect the relevant data, and finally normalize and normalize the scoring results in order to obtain the degree of affiliation d , and the formula for the degree of affiliation d is as follows: (4) $d = \frac{m}{M}$

In equation (4), m represents the number of indicators selected by the experts and M represents the total number of experts.

In the process of determining appropriate evaluation standards, 10 experts in related fields were invited to evaluate the scores according to the level. Since China has not yet formed a scientific, standardized, unified and complete evaluation system for urban landscape planning and design, it is necessary to establish appropriate pre-project evaluation standards by referring to other industry standards.

2.3.2

Constructing an assessment matrix

Based on the above evaluation criteria, the following evaluation matrix is constructed based on the affiliation degree d of the expert rating results: (5) $R = (\begin{array}{r} r_{11} & Λ & r_{1 n} \\ M & O & M \\ r_{m 1} & Λ & r_{m n} \end{array})$

2.3.3

Determination of weights using the entropy weighting method

The entropy value of the i rd evaluation indicator in the pre-assessment problem of urban landscape projects with m evaluation indicator and n evaluation indicators is: (6) $H_{i} = - \frac{1}{\log m} \sum_{m = 1}^{m} r_{i j} \log r_{i j} (i = 1, 2, Λ, m; j = 1, 2, Λ, n)$

Thereby then, for the i st indicator, its entropy weight is w_i , which can be defined as: (7) $w_{i} = \frac{1 - H_{i}}{\sum_{i = 1}^{m} (1 - H_{i})}$

2.3.4

Determination of weights for higher-level indicators

Using the hierarchical asymptotic method, the metrics at each level can be weighted and averaged, and then gradually standardized. Based on the principle of entropy weighting method, it is known that the greater the weight of the metrics, the smaller the entropy value.

(8)

I_{i} = \frac{\sum_{i = 1}^{m} (1 - H_{i})}{m}

The representative meaning is: The larger I_i is, the greater the weight of the superior indicator. This is consistent with logical thinking and the calculation of actual project weights. Normalize I_i as follows: (9) $v_{i} = \frac{I_{i}}{\sum_{i}^{m} I_{i}}$

2.3.5

Constructing fuzzy comprehensive evaluation subsets

(10)

B = A * R

Where A denotes the hierarchical weights and R denotes the hierarchical membership matrix.

By the above method, the weights and affiliations of each level are calculated separately to obtain the subset of fuzzy comprehensive evaluation for the corresponding level. From the lower level to the higher level, the final fuzzy comprehensive evaluation is obtained B* . The evaluation results calculated according to the basic principle of maximum membership can be compared with the relevant data and scoring results in the given table. After the comparison, the project can be rated by “excellent, good, fair, poor and bad” to determine whether the project meets the feasibility criteria or not.

2.4

Uncertainty-based integrated evaluation cloud modeling

The cloud model has certain advantages in describing the fuzzy and random indexes in the evaluation system of urban landscape planning and design, which is to transform the values of the indexes to be evaluated n into (x₁,x₂,…x_n) cloud drops with the expected value E_x , entropy E_n and super entropy H_r to realize the quantitative transformation into qualitative analysis.

Suppose G is a quantitative domain expressed as an exact number and C is a qualitative concept on G . If the quantitative value x ∈ G and x is a random realization of the qualitative concept C_x , then the normal random function satisfies $C_{x} ~ N (E_{x}, {(E_{n}^{'})}^{2})$ and $E_{n}^{^{'}} ~ N (E_{n}, H_{e}^{2})$ as follows: (11) $y (x) = e^{- \frac{{(x - E_{F})}^{2}}{2 {(ε_{n})}^{2}}}, y (x) \in [0, 1]$

Where: E_x generation represents the value of the qualitative concept; E_n is a measure of uncertainty about the attribute concept; and H_e is the degree of entropy discretization.

2.4.1

Determination of cloud assessment criteria

In this paper, the entire score range 0-100 is divided into five scoring intervals according to the scoring levels excellent, good, medium, poor and poor, and then the cloud evaluation characteristic parameters (E_x,E_n,H_e) under the criterion are calculated according to the upper and lower bounds of the scoring intervals $[x_{k}^{\min}, x_{k}^{\max}]$ , and the calculation formula is as follows: (12) ${\begin{matrix} E_{x k} = (x_{k}^{\min} + x_{k}^{\max}) / 2 \\ E_{x k} = (x_{k}^{\max} - x_{k}^{\min}) / 6 \\ H_{o k} = e \end{matrix}$

Where: e is a constant, which can be adjusted according to the corresponding standard and actual situation. In this paper, e = 0.5 is selected and the cloud evaluation criteria and cloud evaluation levels under different levels are given as shown in Table 1:

Table 1.

Characteristic parameters of cloud evaluation at different levels

Score interval	Grading scale	Characteristic parameter
[90.100]	Excellent	(95,1.67,0.5)
[75.90]	Good	(82.5,2.5,0.5)
[50.75]	Intermediate	(62.5,4.17,0.5)
[30.50]	Poor	(40,3.33,0.5)
[0,30)	Very poor	(15,5,0.5)

2.4.2

Computation of evaluation clouds at the normative, programmatic level

In this paper, m expert is invited to rate n indicators at the program level, and the standard evaluation matrix ${\tilde{D}}_{m \times n}^{C}$ is constructed in the form of triangular fuzzy numbers, and $D_{m \times n}^{C}$ is obtained by using optimistic index defuzzification as follows: (13) $\tilde{D} = [\begin{matrix} {\tilde{d}}_{11} & {\tilde{d}}_{12} & \dots & {\tilde{d}}_{1 n} \\ {\tilde{d}}_{21} & {\tilde{d}}_{22} & \dots & {\tilde{d}}_{2 n} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ {\tilde{d}}_{m 1} & {\tilde{d}}_{m 2} & \dots & {\tilde{d}}_{m n} \end{matrix}] \overset{Deblurring}{\to} D = [\begin{matrix} d_{n} & d_{12} & \dots & d_{1 n} \\ d_{21} & d_{22} & \dots & d_{2 n} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ d_{m 1} & d_{m 2} & \dots & d_{m n} \end{matrix}]$

Where: ${\tilde{d}}_{m m}$ represents the scoring result of the m nd expert on the n rd indicator and ${\tilde{d}}_{m n} = (l_{m n}, m_{m n}, u_{m n})$ .

According to the scoring results, the evaluation cloud digital features of the i th indicator of the program layer can be obtained $R_{i}^{C} (E_{x i}^{C}, E_{n i}^{C}, H_{e i}^{C})$ , and the calculation formula is as follows: (14) ${\begin{matrix} E_{x i}^{C} = \frac{1}{m} \sum_{p = 1}^{m} d_{p i} \\ E_{n i}^{C} = \sqrt{\frac{π}{2} \times \frac{1}{m} \sum_{p = 1}^{n} | d_{p i} - E_{x i}^{c} |} \\ H_{e i}^{C} = \sqrt{| S_{i}^{2} - {(E_{n i}^{c})}^{2} |} \end{matrix}$

Where, d_pi represents the rating given to the i rd indicator by the p nd expert, and p = 1,2,…,m , i = 1,2,…,n . The evaluation of the f th indicator of the guideline layer cloud digital feature $R_{f}^{B} (E_{x f}^{B}, E_{n f}^{B}, H_{o f}^{B})$ is calculated as follows: (15) ${\begin{matrix} E_{x f}^{B} = \frac{{\sum_{i = 1}^{n} E_{x j}}^{C} ω_{f i}^{C}}{{\sum_{i = 1}^{n} ω_{f i}}^{C}} \\ E_{n f}^{B} = \frac{{\sum_{i = 1}^{n} E_{n i}}^{c} {(ω_{f i}^{c})}^{2}}{\sum_{i = 1}^{n} {(ω_{f j}^{c})}^{2}} \\ H_{e f}^{B} = \frac{{\sum_{i = 1}^{n} H_{e}}^{c} {(ω_{f i}^{c})}^{2}}{\sum_{i = 1}^{n} {(ω_{f i}^{c})}^{2}} \end{matrix}$

where ω_fi denotes the weight value of the i rd program level indicator that directly corresponds to

the f nd indicator of the criterion level, and i = 1,2,….n , f = 1,2,….n .

2.4.3

Comprehensive evaluation of cloud computing

In order to reflect the overall evaluation effect of the comprehensive evaluation of urban landscape planning and design, according to the formula below can be calculated to get the comprehensive evaluation cloud digital features R(E_x,E_n,H_e) : (16) ${\begin{matrix} E_{x} = \sum_{i = 1}^{n} E_{x i} ω_{i}^{c} \\ E_{n} = \sqrt{\sum_{i = 1}^{n} {(E_{n i}^{c})}^{2} ω_{i}^{c}} \\ H_{e} = \sum_{i = 1}^{n} H_{e i} ω_{i}^{c} \end{matrix}$

3

Research on the evaluation of urban garden landscape planning

3.1

Selection of evaluation index system for urban garden landscape planning program

The evaluation of urban garden landscape planning program is the evaluation of innovation, artistry, science and economy of the landscape elements in the environmental expansion of landscape planning, the organizational relationship between the landscape elements and the surrounding environment, and the creation of landscape elements.

According to the hierarchical analysis method, “target-indicator hierarchy” is chosen to establish the evaluation index system of urban garden landscape planning program. According to the characteristics of the urban garden landscape planning program, the evaluation of the urban garden landscape planning program is evaluated in four aspects: innovation, artistry, science and economy. And the above four aspects have their own influencing factors constitute the index factor. The evaluation index system of urban garden landscape planning program is shown in Figure 2.

3.1.1

Innovative

As an excellent urban garden landscape planning program, there must be a certain degree of innovation. The evaluation is mainly carried out in the following 3 aspects:

1)

The innovation of the planning concept

Look at the landscape planning program in the conception, whether the intention of originality, borrowed ideas and the existing site with the perfect degree of how to combine, as well as landscape architecture, the application of new materials and new engineering technology, graphic presentation and other aspects of innovation.

2)

Landscape compatibility

Look at the landscape planning program’s conception, intention and planning site, the integration of the surrounding environment, the degree of coordination.

3)

Overall effect

Landscape planning program as an organic whole, whether the elements are interconnected, echo, each with its own characteristics, linked into a whole. So that after the completion of the landscape can become a fascinating place of recreation.

3.1.2

Artistic

The evaluation was conducted in the following 3 main areas.

1)

Visual aesthetics

Planned landscape in the landscape space, plant configuration from the human visual image of the requirements of feeling, according to the laws of aesthetics, the use of space virtual, real scenery, whether to create a pleasing garden landscape. Whether the graphic performance has visual aesthetics.

2)

The art of space creation

Whether the planned landscape space, such as the opposite view, axis, nodes, paths, visual corridors, opening and closing of space, etc., is artistic, as well as whether there is artistry in the closing and closing of landscape space and the enclosure of space.

3)

Artistry of cultural landscape performance

See whether the landscape planning program has regional characteristics, sense of the times, whether historical relics and sites are protected, and how rich the humanistic landscape is.

3.1.3

Scientific

The evaluation is mainly carried out in the following 4 aspects.

1)

Current national technical specifications

The main point is to see how well the landscape planning program complies with the current national technical specifications.

2)

Functionality

Considering the users’ needs, whether the functional zoning is reasonable, whether the various facilities and flow lines meet the requirements of humanization and popularization, and whether sufficient sites and spatial facilities for the majority of people are designed according to the number of users, gender, age, occupation, living habits, interests and needs. At the same time should also consider special requirements, such as the design of the pool, is an ornamental pool or also used as a fire pool, if it is a fire pool, it should always maintain sufficient fire water and set up the appropriate equipment and piping.

3)

Ecological

Mainly from the biodiversity, improve the microclimate, health, protective functions, green amount of green vision rate and other aspects to be evaluated.

4)

Systematic

It is mainly evaluated from the systematic nature of landscape space, landscape sequence, road transportation and comprehensive pipeline.

3.1.4

Economy

This evaluation was conducted in 3 main areas: (1) the difficulty of construction, (2) the cost of landscape creation, and (3) the cost of maintenance.

3.2

Evaluation index system of urban garden landscape planning program

3.2.1

Factor system of evaluation indicators

According to the characteristics of the urban landscape and the principle of index selection, the evaluation index system starts from the three functions of production, life and ecology of the urban landscape, constructs a multi-level evaluation system with “ecological-living-ecological space” as the project layer, reflects the characteristics of the “environment-society-economy” composite system in the study area as a whole, selects the indicators and makes a pre-index set based on the current situation characteristics of the study area, and then adopts the expert scoring method to solicit and inquire about the opinions of experts through questionnaire survey, and finally determines the comprehensive evaluation index model of urban landscape landscape.

By collecting and organizing the questionnaires, calculating the mean, median and plural as the parameters to show the importance of the indicators, calculating the standard deviation of the indicators and the coefficient of variation to show the degree of dispersion of the expert’s opinion, and the coefficient of variation and the concentration of the expert’s opinion are inversely proportional to each other, and the statistical results are shown in Table 2.

Table 2.

The importance of landscape comprehensive evaluation indicators

Index	Mean number	Mid-value	Mode	Standard deviation	Coefficient of variation
1 Landform diversity	4.50	4.80	5.00	0.41	0.08
2 Landform Oddities	4.60	5.00	5.00	0.37	0.07
3 Mountain ecology	4.90	5.00	5.00	0.25	0.05
4 Mountain landscape	2.00	1.80	2.50	0.78	0.58
5 Vegetation coverage	4.80	4.50	4.50	0.47	0.36
6 Natural Plant Diversity	4.60	4.80	5.00	0.38	0.40
7 Water landscape	2.10	1.90	2.50	0.70	0.50
8 Water Ecology	4.80	4.60	5.00	0.32	0.18
9 Traditional site richness	4.70	4.50	5.00	0.28	0.06
10 Popularity of traditional sites	4.90	5.00	5.00	0.19	0.09
11 Intangible cultural richness	4.70	4.90	5.00	0.21	0.05
12 Intangible Cultural Attraction	4.70	4.50	5.00	0.27	0.04
13 Spatial order of settlement	4.90	5.00	5.00	0.17	0.03
14 Local characteristics	4.50	4.50	5.00	0.36	0.09
15 Road accessibility	4.60	5.00	5.00	0.21	0.13
16 Road landscaping comfort	4.70	4.90	5.00	0.25	0.04
17 Agricultural production area	4.50	4.80	5.00	0.28	0.07
18 Characteristics of agricultural landscape	4.70	5.00	5.00	0.21	0.06
19 Agricultural landscape continuity	4.80	5.00	5.00	0.19	0.05
20 Agricultural tourism industry richness	4.90	5.00	5.00	0.15	0.04
21 Leisure agriculture landscape attractiveness	4.30	4.50	5.00	0.28	0.06

The analysis of the calculation results in the above table shows that the coefficient of variation of most indicators is below 0.50, and the experts’ opinions are relatively consistent. All 21 evaluation indicators were selected for screening and determination with reference to the mean, median, plural and standard deviation of each indicator.

3.2.2

AHP-based indicator factor weights

This paper builds a comprehensive evaluation model of urban landscape, through the judgment of ecological, living, productive landscape function proportion and specific factor score index ranking, comprehensive evaluation of the most important features of the region thus judging the appropriate development mode of urban landscape construction, therefore, the evaluation model of the project level indicator weights are equal, i.e., ecological landscape, living landscape, productive landscape, three indicators are weighted 1/3.

Using the hierarchical analysis method, the weights of the indicators are calculated and the consistency test results are shown in Table 3.

Table 3.

Weight value of urban landscape comprehensive evaluation system

Target layer	Project layer	Weight	Factor layer	Weight	Normalized weights	Index layer	Weight	Normalized weights
Urban garden landscape evaluation system A	Ecological landscape B1	1/3	Geographical landscape C1	0.283	0.088	D1	0.694	0.064
			Geographical landscape C1	0.283	0.088	D2	0.306	0.060
			Mountain landscape C2	0.084	0.027	D3	0.713	0.051
			Mountain landscape C2	0.084	0.027	D4	0.287	0.045
			Plant landscape C3	0.056	0.016	D5	0.206	0.054
			Plant landscape C3	0.056	0.016	D6	0.794	0.040
			Hydrologic landscape C4	0.577	0.191	D7	0.154	0.031
			Hydrologic landscape C4	0.577	0.191	D8	0.856	0.029
	Living landscape B2	1/3	Human landscape C5	0.695	0.263	D9	0.403	0.061
						D10	0.125	0.061
						D11	0.356	0.064
						D12	0.116	0.046
			Settlement landscape C6	0.206	0.069	D13	0.284	0.035
			Settlement landscape C6	0.206	0.069	D14	0.716	0.046
			Road view C7	0.089	0.036	D15	0.646	0.045
			Road view C7	0.089	0.036	D16	0.354	0.060
	Productive landscape B3	1/3	Agricultural production landscape C8	0.805	0.224	D17	0.674	0.038
						D18	0.188	0.030
						D19	0.138	0.048
			Agricultural leisure landscape C9	0.195	0.086	D20	0.806	0.065
			Agricultural leisure landscape C9	0.195	0.086	D21	0.194	0.027

3.2.3

Indicator factor weights based on entropy weighting method

According to the fuzzy comprehensive evaluation method applied in urban landscape planning and design proposed in the second chapter, the entropy weight method is used to determine the weights. The information entropy of each evaluation index and its weight can be obtained, as shown in Table 4.

Table 4.

Information entropy and weight of water landscape evaluation index

Index	Information entropy	Weight
X1 Landform diversity	0.996	0.038
X2 Landform Oddities	0.992	0.069
X3 Mountain ecology	0.995	0.026
X4 Mountain landscape	0.991	0.060
X5 Vegetation coverage	0.998	0.059
X6 Natural Plant Diversity	0.992	0.058
X7 Water landscape	0.992	0.029
X8 Water Ecology	0.997	0.049
X9 Traditional site richness	0.998	0.067
X10 Popularity of traditional sites	0.992	0.067
X11 Intangible cultural richness	0.995	0.049
X12 Intangible Cultural Attraction	0.991	0.056
X13 Spatial order of settlement	0.997	0.037
X14 Local characteristics	0.998	0.028
X15 Road accessibility	0.991	0.032
X16 Road landscaping comfort	0.997	0.035
X17 Agricultural production area	0.995	0.055
X18 Characteristics of agricultural landscape	0.992	0.034
X19 Agricultural landscape continuity	0.990	0.057
X20 Agricultural tourism industry richness	0.996	0.063
X21 Leisure agriculture landscape attractiveness	0.993	0.032

3.2.4

Comprehensive empowerment based on combinatorial empowerment methods

The combination assignment method used in this paper combines the subjective AHP weights with the objective entropy weights, and according to the characteristics of the index model establishment of AHP and entropy weights mentioned above, it can be obtained that the two methods have their own advantages and disadvantages, therefore, in this chapter, the combination assignment method based on the above AHP and entropy weights will be constructed so as to better utilize the advantages of the two and avoid the disadvantages of the two. Thus, it makes the evaluation results of urban landscape more scientific and precise.

The AHP weights and entropy weights are synthesized to get the final weights of the evaluation indicators of urban landscape.

The final weight values of the comprehensive indicators are obtained, as shown in Table 5.

Table 5.

Evaluation index comprehensive weight

Index	AHP weight	Entropy weight method weight	Comprehensive weight
X1	0.064	0.038	0.051
X2	0.060	0.069	0.065
X3	0.051	0.026	0.039
X4	0.045	0.060	0.053
X5	0.054	0.059	0.057
X6	0.040	0.058	0.049
X7	0.031	0.029	0.030
X8	0.029	0.049	0.039
X9	0.061	0.067	0.064
X10	0.061	0.067	0.064
X11	0.064	0.049	0.057
X12	0.046	0.056	0.051
X13	0.035	0.037	0.036
X14	0.046	0.028	0.037
X15	0.045	0.032	0.039
X16	0.060	0.035	0.048
X17	0.038	0.055	0.047
X18	0.030	0.034	0.032
X19	0.048	0.057	0.053
X20	0.065	0.063	0.064
X21	0.027	0.032	0.030

3.3

Comprehensive cloud evaluation results

According to the uncertainty-based comprehensive evaluation cloud model, the comparison of the comprehensive cloud evaluation results of landscape design before and after the integration of regional culture is derived. Figures 3 and 4 show the comprehensive cloud evaluation results before and after regional culture integration, respectively.

It can be seen from Fig. 3 and Fig. 4 that the comprehensive cloud evaluation results of landscape design before the integration of regional culture belong to the range of “medium” and “good”, and the evaluation results of the comprehensive cloud after upgrading are in the range of “good” and “excellent”, indicating that the upgrading effect of regional culture into landscape design is good, and its overall operation effect has been improved.

4

Conclusion

This study establishes a fuzzy comprehensive evaluation model of urban garden landscape planning program by analyzing the evaluation index system of urban garden landscape planning program. It can quantify some qualitative evaluation factors such as the innovation of planning concept, visual aesthetics, artistry of space creation, functionality, etc., which lays a certain foundation for improving the comprehensive quality of urban garden landscape planning program.

At the same time, the comprehensive cloud evaluation results show that the upgrading effect of regional culture integration into garden landscape design is remarkable, and the overall operation is improved.

Funding:

Research and Practice Project on Vocational Education Teaching Reform in Henan Province: Research on the Construction Path of Industry-Education Integration Training Base from the Perspective of a Community of Shared Future (Project Number:S-ZJGG-2023B-064).

Langue:: Anglais

Périodicité:: 1 fois par an
Sujets de la revue:: Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres

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Inheritance and Innovation of Regional Culture in Urban Landscape Planning and Design

Ran Tian

Publié en ligne: 19 mars 2025

Reçu: 09 nov. 2024

Accepté: 19 févr. 2025

DOI: https://doi.org/10.2478/amns-2025-0527

Mots clésRegional culture, Urban garden landscape planning, Fuzzy comprehensive evaluation method, Entropy weight method, Comprehensive evaluation cloud

© 2025 Ran Tian, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Regional culture, Urban garden landscape planning, Fuzzy comprehensive evaluation method, Entropy weight method, Comprehensive evaluation cloud