A method for identifying urban ventilation corridors based on air flow cost

Abstract

The present invention discloses a method for identifying urban ventilation corridors based on air flow costs. The method comprises the following steps: first, evaluating ventilation influencing factors based on real building-related data; second, standardizing the influencing factors according to their effects; third, calculating the information entropy and weights of the influencing factors; fourth, calculating the ventilation impact index and summarizing it as an air flow cost value; and fifth, calculating the ventilation cost using the minimum ventilation cost path method and visualizing it through color coding to identify urban ventilation corridors. This method addresses the numerous limitations and challenges of existing technologies, such as data accuracy, computing resources, and model validation, achieving air flow cost calculation and ventilation corridor identification that are unaffected by subjective factors. Empirical evidence demonstrates that this method, based on building data, extracts ventilation influencing factors and can calculate air flow costs and identify ventilation corridors within cities. This method can provide a relatively intuitive, accurate, and easy-to-use method for calculating air flow costs and identifying ventilation corridors for studying the ventilation environment of megacities, optimizing building spaces, improving the urban heat island effect, controlling pollution transmission, and improving residential comfort.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and in particular to an urban ventilation corridor identification method supported by air flow costs. Background Art

[0002] Rapid urbanization has led to increased surface roughness, particularly within urban street canyons, which impairs air circulation. Ventilation corridors are a crucial element in urban planning, designed to draw in natural wind from outside the city and promote natural air flow through densely built-up areas. This helps reduce air pollution, mitigate the urban heat island effect, and improve the environmental quality of densely populated urban areas. In recent years, ventilation corridor planning methods integrated with geographic information systems (GIS) have gained increasing attention. Leveraging the powerful data processing and analysis capabilities of GIS, optimal ventilation corridor locations can be precisely determined, thereby optimizing urban planning schemes. However, current technologies still face challenges in data accuracy, computational resources, and model validation, particularly in practical urban planning and design applications. Furthermore, most studies focus on idealized building layouts, while relatively few studies have examined the impact of ventilation corridors on actual urban forms and megacities.

[0003] All these situations indicate that there are still deficiencies in exploring the relationship between building layout and ventilation efficiency, especially in using urban models based on realistic building forms and layouts to achieve effective unification and practical application at different scales (such as a single block to the entire city). Summary of the Invention

[0004] This paper focuses on the many limitations and challenges existing in the existing technology, such as limited data types, computing power constraints, and differences between idealized models and actual urban forms. It provides an urban ventilation corridor identification method supported by air flow costs. The technical problems solved include measuring the impact of building layout on air circulation potential and identifying ventilation corridors.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] An urban ventilation corridor identification method supported by air flow cost includes five parts: calculation of building ventilation influencing factors, standardized data processing of ventilation influencing factors, calculation of information entropy and weight determination of ventilation influencing factors, calculation of air flow cost under the influence of buildings, and identification of urban ventilation corridors.

[0007] The steps for calculating the building ventilation influencing factor are as follows:

[0008] S1: Calculate the proximity index and the Euclidean average distance between buildings to evaluate their physical distances;

[0009] S2: measures the connectivity between the building and the surrounding space, reflecting the proportion of open space;

[0010] S3: Calculate windward surface density, cutoff average height, and three-dimensional building density to evaluate the spatial structure of the building complex;

[0011] The steps for standardizing the data processing of ventilation influencing factors are as follows:

[0012] S4: Based on the effect of ventilation improvement or deterioration, the influencing factors are divided into positive and negative;

[0013] S5: Apply minimum-maximum standardization to positive factors and reverse minimum-maximum standardization to negative factors to ensure that the numerical distribution range of factors is consistent;

[0014] The information entropy calculation and weight determination of ventilation influencing factors are as follows:

[0015] S6: Determine the information entropy of each ventilation influencing factor to evaluate its variability under different settings;

[0016] S7: Calculate the weight of each factor based on information entropy to comprehensively evaluate its impact on ventilation;

[0017] The steps for calculating the air flow cost under the influence of buildings are as follows:

[0018] S8: Calculate the ventilation impact index of each factor by combining the ventilation impact factors and their weights;

[0019] S9: Combine all ventilation impact indices and define the overall air movement cost value;

[0020] Identification of urban ventilation corridors. The steps are as follows:

[0021] S10: Set two non-overlapping points near the city boundary as the entrance and exit of the air duct;

[0022] S11: Calculate the ventilation cost from each grid to these two points to determine the resistance value of air flow;

[0023] S12: Summarize the resistance values of each grid and determine the total ventilation cost of each grid;

[0024] S13: Visualize the ventilation cost of each grid through color coding to identify potential urban ventilation corridors. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are merely schematic diagrams of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0026] Figure 1 A flowchart for the invention technology;

[0027] Figure 2 Air flow cost data distribution map;

[0028] Figure 3 This is a schematic diagram of the minimum ventilation cost path method;

[0029] Figure 4 This is the calculation result map of ventilation cost distance;

[0030] Figure 5 This is the ventilation corridor identification result diagram; DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] The following is the calculation part of the building ventilation influencing factor:

[0033] Step 1: Collect building data within the evaluation range and set the building and space boundary data to the same projection coordinate system to achieve spatial matching

[0034] Step 2: Define a grid within the evaluation area and set the grid to the same coordinate system as other data, using the grid as the minimum calculation unit.

[0035] Step 3: Calculate the Nearest Neighbor Index (NNI) and the Euclidean Nearest Neighbor Distance (NNI) of each building for each grid.

[0036] Average Distance, ENNAD), the calculation formulas are:

[0037]

[0038] in, represents the average nearest neighbor distance between observed buildings, is the expected average nearest neighbor distance based on the total number of buildings and the grid area.

[0039]

[0040] Where N is the total number of buildings in the grid, d i is the Euclidean distance between the i-th building and its nearest neighbor.

[0041] Step 4: Calculate the spatial connectivity index (CI) of buildings within each grid using the following formula:

[0042]

[0043] Among them, A s is the area of open space in the grid not occupied by buildings, and A is the area of the grid.

[0044] Step 5: Calculate the frontal area index (FAI), truncated mean height (TMH), and three-dimensional building density (3DBD) within each grid using the following formulas:

[0045]

[0046] Among them, A t is the total windward projection area of all buildings in the grid, A l is the area of the overlapping part of the projected area, and A is the area of the grid.

[0047]

[0048] Among them, H i is the height of the i-th building, and N is the number of buildings in the grid.

[0049]

[0050] Where N represents the total number of buildings in the grid, A k represents the horizontal area of the kth building, H k represents the height of the kth building, A is the area of the grid, H c A value representing a certain building height that can be included in the grid.

[0051] The following is the standardization data processing part of ventilation influencing factors:

[0052] Step 1: Based on their effect on ventilation, factors are divided into positive and negative factors. An increase in the value of a positive factor means improved ventilation, while an increase in the value of a negative factor means worsening ventilation.

[0053] Step 2: Use min-max normalization on positive factors to ensure that the value range is 0 to 1. Use reverse min-max normalization on negative factors so that larger original values are converted to smaller normalized values.

[0054] The following is the calculation of information entropy and weight determination of ventilation influencing factors:

[0055] Step 1: Calculate the information entropy of each ventilation influencing factor to evaluate the degree of change of each factor under different settings. The calculation formula is as follows:

[0056]

[0057] Among them, p ij represents the normalized value of the jth factor in the ith setting, n is the number of settings, E j is the information entropy of the j-th factor.

[0058] Step 2: Based on the information entropy results, determine the weight of each factor. The weight reflects the importance of the factor in the overall evaluation, and the calculation formula is as follows:

[0059]

[0060] Among them, ω j is the weight of the jth factor, and m is the total number of factors. Factors with lower information entropy have higher weights because they show greater variability in different scenarios and have a greater impact on the comprehensive evaluation results.

[0061] The following is the calculation part of the air flow cost under the influence of buildings:

[0062] Step 1: Calculate the ventilation impact index of each grid independently. For each grid, multiply the value of each ventilation impact factor by its corresponding weight value to calculate the ventilation impact index of the grid. The calculation formula is as follows:

[0063]

[0064] Among them, V ij represents the ventilation impact index of grid j, F ij represents the ventilation influence factor i in grid j, W i represents the weight of factor i.

[0065] Step 2: Calculate the air flow cost value for each grid. Based on the ventilation impact index, this is used as the air flow cost value for that grid. This step does not involve the accumulation or comparison of data across grids.

[0066] The following is part of the identification of urban ventilation corridors:

[0067] Step 1: Determine the inlet N and outlet M of the urban ventilation path. These two points are located near the regional boundary and do not want to be adjacent to each other.

[0068] Step 2: Select an additional random point S, ensuring that it does not overlap with points N or M. Calculate the ventilation cost from N and M to S.

[0069] Step 3: Accumulate the ventilation costs from S to N and M to obtain the ventilation cost index of point N. Traverse the entire area and calculate the ventilation cost for all points. The calculation formula is as follows:

[0070] COST s→N|M =COST s→N +COST s→Μ

[0071] Step 4: Use visualization technology to display the ventilation costs of each point in the area and screen out areas with ventilation costs below a certain threshold. After visual interpretation, these areas are identified as urban ventilation corridors.

[0072] Finally, Xi'an, Shaanxi Province, located in the southern part of the Guanzhong Plain, was selected as the experimental object. Building height raster data and building outline vector data were selected, and the grid size was set to 100m×100m. The method of this patent was used to calculate the urban air flow cost value and identify ventilation corridors. The air flow cost calculation results of the experimental object are shown in the figure below. Figure 2 As shown in the figure, the calculation principle of ventilation cost distance used in the experiment is as follows Figure 3 As shown, the experimental subjects calculated the ventilation cost distance. Figure 4 As shown, the final ventilation corridor results of the experimental object are Figure 5 shown.

[0073] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown in this embodiment, but is intended to be construed in the widest manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying urban ventilation corridors based on air flow costs, including calculation of building ventilation influencing factors, standardized data processing of ventilation influencing factors, calculation of information entropy and weight determination of ventilation influencing factors, calculation of air flow costs under building influence, and identification of urban ventilation corridors. The steps for calculating the building ventilation influencing factor are as follows: S1: Calculate the proximity index and the Euclidean average distance between buildings to evaluate their physical distances; S2: measures the connectivity between the building and its surrounding space, reflecting its open space ratio; S3: Calculate windward surface density, cutoff mean height, and three-dimensional building density to assess the spatial structure of the building complex; The steps for standardizing the data processing of ventilation influencing factors are as follows: S4: Based on the effect of ventilation improvement or deterioration, the influencing factors are divided into positive and negative; S5: Apply minimum-maximum standardization to positive factors and reverse standardization to negative factors to ensure the consistency of factor values; The information entropy calculation and weight determination of ventilation influencing factors are as follows: S6: Determine the information entropy of each ventilation influencing factor to evaluate its variability under different settings; S7: Calculate the weight of each factor based on information entropy to comprehensively evaluate its impact on ventilation; The steps for calculating the air flow cost under the influence of buildings are as follows: S8: Calculate the ventilation impact index of each factor by combining the ventilation impact factors and their weights; S9: Combine all ventilation impact indices and define the overall air movement cost value; Identification of urban ventilation corridors. The steps are as follows: S10: Set two non-overlapping points near the city boundary as the entrance and exit of the air duct; S11: Calculate the ventilation cost from each grid to these two points to determine the resistance value of air flow; S12: Summarize the resistance values of each grid and determine the total ventilation cost of each grid; S13: Visualize the ventilation cost of each grid through color coding to identify potential urban ventilation corridors; The following is the calculation part of the building ventilation influencing factor: Step 1: Collect building data within the evaluation range and set the building and space boundary data to the same projection coordinate system to achieve spatial matching Step 2: Define a grid within the evaluation area and set the grid to the same coordinate system as other data, using the grid as the minimum calculation unit; Step 3: Calculate the Nearest Neighbor Index (NNI) and the Euclidean Nearest Neighbor Distance (NNI) of each building for each grid. Average Distance, ENNAD), the calculation formulas are: in, represents the average nearest neighbor distance between observed buildings, is the expected average nearest neighbor distance based on the total number of buildings and the grid area; Where N is the total number of buildings in the grid, d i is the Euclidean distance between the i-th building and its nearest neighbor; Step 4: Calculate the spatial connectivity index (CI) of buildings within each grid using the following formula: Among them, A s is the area of open space not occupied by buildings within the grid, and A is the area of the grid; Step 5: Calculate the frontal area index (FAI), truncated mean height (TMH), and three-dimensional building density (3DBD) within each grid using the following formulas: Among them, A t is the total windward projection area of all buildings in the grid, A l is the area of the overlapping part of the projected area, and A is the area of the grid; Among them, H i is the height of the i-th building, N is the number of buildings in the grid; Where N represents the total number of buildings in the grid, A k represents the horizontal area of the kth building, H k represents the height of the kth building, A is the area of the grid, H c A value representing a certain building height that can be included in the grid.

2. The urban ventilation corridor identification method based on air flow cost according to claim 1 is characterized in that: In steps S1, S2, and S3, the real location and layout of buildings in three-dimensional space are considered, and then the analysis and calculation of relevant attributes of buildings in the city are realized.

3. The urban ventilation corridor identification method based on air flow cost according to claim 1 is characterized in that: In steps S4 and S5, the ventilation effects are differentiated into positive factors and negative factors according to the influencing factors, and standardized accordingly.

4. The urban ventilation corridor identification method based on air flow cost according to claim 1 is characterized in that: In steps S6, S7, and S8, the entropy method is used to avoid subjective influences, consider the relationships between influencing factors, and generate an objective comprehensive evaluation value.

5. The urban ventilation corridor identification method based on air flow cost according to claim 1 is characterized in that: In step S10, the air inlets and outlets near the city boundary can be located at any two non-overlapping positions, and the shapes of the air inlets can be point-shaped, line-shaped, or surface-shaped according to different scenarios and needs.

6. The urban ventilation corridor identification method based on air flow cost according to claim 1 is characterized in that: Calculating the sum of the distances from any point to the two air outlets avoids the limitation of the ordinary minimum cost path method that can only extract a straight line with a narrow width, and can intuitively generate ventilation paths for all locations.

7. The urban ventilation corridor identification method based on air flow cost according to claim 1 is characterized in that: In step S13, color coding can be in the form of grayscale, gradient, or color band. The visualization process is performed in GIS software. The main methods for identifying urban ventilation corridors are image comparison and visual interpretation.

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