Climate comfort improvement method based on equivalent weight

Abstract

The invention discloses a climate comfort improvement method based on an equivalent weight, and relates to the technical field of climate comfort, in particular to the climate comfort improvement method based on the equivalent weight. Comprising the following steps of confirming and classifying influence factors, performing primary processing on the influence factors, performing partition statistics and grading on the influence factors, and performing improved climate comfort index calculation based on equivalent weight. According to the method, on the basis of the general thermal climate index, the equivalent weight is adopted to fuse various humanity elements, and the climate comfort is improved, so that the method is more accurate in the aspect of urban livability evaluation. Besides, different influence factors can be fused according to actual requirements, so that the analysis result is more intuitive and practical, and a direction can be provided for development and application of urban livability evaluation and climate comfort and a development direction can be provided for the fields of urban planning and the like.

Description

Technical Field

[0001] The invention relates to the technical field of climate comfort, and in particular to a climate comfort improvement method based on equal weights. Background Art

[0002] Climate comfort is an important reference factor for people when choosing a living environment. It is a biometeorological indicator that evaluates the comfort of humans under different climatic conditions from a meteorological perspective based on the principle of heat exchange between the human body and the near-ground atmosphere. It occupies a vital and fundamental position in urban environmental meteorological services and is an important influencing factor in scientifically guiding travel and evaluating the living environment. It is widely used in many fields such as meteorological services, public health warnings, urban planning, tourism and entertainment.

[0003] In the more than 100-year history of climate comfort, there have been many names, such as human comfort, climate comfort, biometeorological index, human heat stress index, etc., and more than 160 climate comfort evaluation models have been proposed. At present, the more commonly used climate comfort models can be summarized as empirical models and mechanism models. Empirical models are generally linear equations about temperature, humidity and wind speed, such as the common effective temperature (ET), wet bulb globe temperature index (WBGT), temperature and humidity index (THI) for hot environments, and wind chill index (WCI) for cold environments. These indexes consider relatively few factors and the calculation process is relatively simple, so they are widely spread in practical applications, but they are only applicable to areas with the same climate type. The comfort indexes representing different climate zones and even different seasons are different, so the empirical model has certain temporal and spatial limitations. In order to overcome this problem, scientists have proposed a mechanism model based on the theory of human heat balance, which comprehensively considers the influence of various factors such as environmental factors, human metabolic heat dissipation and clothing thermal resistance. PMV, PET, SET, etc. are the most commonly used mechanism model indexes.

[0004] Evaluation factors for livable cities include the city's ecological environment and cultural environment, which can meet the production and living needs of residents. Although existing models have made progress in the temporal and spatial limitations of empirical models, they are still insufficient in evaluating livable cities. How to integrate multiple important factors such as hospital accessibility, school accessibility, scenic spot accessibility, housing prices and air quality index is still a challenge. Summary of the invention

[0005] The object of the present invention is to provide a method for improving climate comfort based on equal weights to overcome the above-mentioned defects in the prior art.

[0006] A method for improving climate comfort based on equal weights includes the following steps:

[0007] S1. Confirm and classify influencing factors: First, determine the influencing factors for improving climate comfort, and then divide them into two types of data: data using accessibility analysis methods and data using spatial interpolation methods based on the type of data and how the data affects urban livability;

[0008] S2. Preliminary processing of influencing factors: After determining the data processing method, accessibility analysis and spatial interpolation processing are carried out. The processed data lays the foundation for subsequent zoning statistics;

[0009] S3. Zoning statistics and classification of influencing factors: Based on the initially processed data, zoning statistics are performed, and then the average value is graded equally for subsequent indicator fusion;

[0010] S4. Calculation of improved climate comfort index based on equal weights: Use equal weights to calculate the improved climate comfort index and use it to evaluate livable cities.

[0011] Preferably, the step S1 is divided into the following steps:

[0012] S1.1. Determine the influencing factors: Starting from the factors of pollution and noise, leisure activities, living facilities, human environment, travel convenience and natural environment, which affect the livability of a city, we selected several important factors, including hospital accessibility, school accessibility, scenic spot accessibility, housing prices and air quality index;

[0013] S1.2. Classification of influencing factors: The influencing factors for improving climate comfort include point data, surface data and raster data. The analysis methods of different factors need to be determined according to the needs to ensure that the processed data has an impact on livable cities. For data that is subjectively believed to be more accessible the closer it is to a place, such as schools, hospitals and scenic spots, accessibility analysis is performed. For data that can only obtain partial point information, such as housing prices and air quality index, spatial interpolation processing is performed to obtain a spatial interpolation map of the entire research range.

[0014] Preferably, the step S2 is divided into the following steps:

[0015] S2.1, Road network data processing: Process the road network data within the research scope, obtain the road network centerline data, then perform interruption and intersection processing, and perform topology inspection and traffic network construction to obtain the processed traffic network data;

[0016] S2.2. Check and organize the influencing factor data: Use multiple data on hospitals, schools, scenic spots, housing prices and air quality index to check and organize these data, remove outliers, ensure data quality, and combine the processed traffic network data to conduct service area analysis in network analysis;

[0017] S2.3, accessibility analysis and processing: In ArcGIS software, the processed transportation network data and the data of school, hospital and scenic spot facilities are combined, and the service area analysis method in network analysis is used to solve and generate the service area range of each facility point, and obtain the accessibility data within the research scope;

[0018] S2.4, spatial interpolation processing: According to the characteristics of the study area and the uneven spatial distribution of the influencing factor data, the inverse distance weighted interpolation method is selected to collect the coordinates of the known points in the study area and the corresponding air quality index, general thermal climate index and housing price data. The collected data are organized into a table form, including the point number, X coordinate, Y coordinate and variable value. The inverse distance weighted interpolation method in ArcGIS software is used. After completing the interpolation calculation, the interpolation result is verified to ensure the accuracy and reliability of the interpolation result. Finally, the interpolation result is visualized to intuitively observe the spatial distribution of the data.

[0019] Preferably, the step S3 is divided into the following steps:

[0020] S3.1. Regional statistics of influencing factors: Anhui Province was divided into 16 sub-regions according to the city-level administrative regions. For each sub-region, the preliminary processed data of hospital accessibility, primary school accessibility, secondary school accessibility, scenic spot accessibility, general thermal climate index, air quality index and housing price were statistically analyzed, and the average value of each influencing factor in each city was calculated;

[0021] S3.2. Determination of grading standards: After completing the zoning statistics, the average values ​​of hospital accessibility, primary school accessibility, middle school accessibility, scenic spot accessibility, general thermal climate index, air quality index and housing prices in each city in Anhui Province are graded equally. The value range of all data average values ​​is divided into 5 intervals from the minimum to the maximum value. Each interval represents a grade, and the grade is 5, the highest, and then decreases in order;

[0022] Preferably, the step S4 is divided into the following steps:

[0023] S4.1, equal weight setting: clearly give the same weight to each influencing factor. When calculating the improved climate comfort index, do not consider the difference in the actual impact of each influencing factor, but give them the same weight value. Use 7 types of data, and the weight of each data is 1 / 7;

[0024] S4.2, Index calculation: Through the above processing steps, the level value of the factors in each city in Anhui Province is obtained. According to the set equal weight, the value of the influencing factor is multiplied by the weight of each data, and the sum is calculated to obtain the improved climate comfort index of each city in Anhui Province;

[0025] S4.3. Evaluation of livable cities: Use the calculated improved climate comfort index to evaluate cities in Anhui Province.

[0026] The beneficial effects achieved by the present invention are:

[0027] 1. The method of this application can enhance the accuracy of livable city evaluation: by integrating the climate comfort improvement of multiple human factors based on equal weights, the accuracy of cities evaluated based on climate comfort is improved. Simple calculation: using the climate comfort improvement with equal weights, there is no need for a complicated weight determination process. Expanded application scope: This method is not only applicable to livable city evaluation, but can also be extended to multiple fields such as environmental assessment and urban planning.

[0028] 2. The technical solution proposed in the present invention is based on equal weights and integrates multiple data such as hospital accessibility, school accessibility, scenic spot accessibility, housing prices and air quality index, which improves the accuracy of the evaluation of climate comfort and livable cities. It has broad application prospects and significant practical value.

[0029] 3. Based on the universal thermal climate index, the invention uses equal weights to integrate multiple human factors to improve climate comfort and make it more accurate in evaluating urban livability. In addition, the invention can integrate different influencing factors according to actual needs, making the analysis results more intuitive and practical, and can provide directions for the development and application of urban livability evaluation, climate comfort, and urban planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a general flow chart of the present invention as a whole.

[0031] Figure 2 The figure is a flow chart of the accessibility analysis of the present invention (taking hospital accessibility analysis as an example).

[0032] Figure 3 This is the original climate comfort index grading diagram of the present invention.

[0033] Figure 4 This is a diagram of climate comfort improvement results based on equal weights according to the present invention. DETAILED DESCRIPTION

[0034] The specific implementation modes of the present invention are further explained in detail below by describing the embodiments with reference to the accompanying drawings, so as to help those skilled in the art to have a more complete, accurate and in-depth understanding of the concept and technical solution of the present invention.

[0035] like Figure 1-4 As shown, the present invention provides a method for improving climate comfort based on equal weights, and the specific steps are as follows:

[0036] S1, classification of influencing factors: In this step, first determine the influencing factors on the improved climate comfort, and then conduct preliminary classification; S2, preliminary processing of influencing factors: perform necessary preliminary processing of the data, including accessibility analysis and spatial interpolation; S3, zoning statistics and grading of influencing factors: based on the preliminary processed data, perform zoning statistics, and then perform equal grading of the average value for subsequent indicator fusion; S4, calculation of improved climate comfort index based on equal weights: use equal weights to calculate the improved climate comfort index and use it to evaluate livable cities.

[0037] S1, classification of influencing factors: S1 is the process of classifying the influencing factors for improving climate comfort. First, the influencing factors for improving climate comfort are determined. Then, based on the type of data and the way the data affects the livability of the city, they are divided into two types of data: data using accessibility analysis methods and data using spatial interpolation methods.

[0038] Before processing the data of each impact factor, the impact factor classification in the S1 stage is a crucial step, which lays the foundation for the entire analysis process. It can be broken down into the following steps:

[0039] S1.1 Determine influencing factors: The patent starts from the pollution and noise factors, leisure activity factors, living facilities factors, human environment factors, travel convenience factors and natural environment factors that mainly affect the livability of the city, and selects several important factors such as hospital accessibility, school accessibility, scenic spot accessibility, housing prices and air quality index.

[0040] S1.2 Classification of influencing factors: These influencing factors for improving climate comfort include point data, surface data, and raster data. The analysis methods of different factors need to be determined according to the needs to ensure that the processed data has an impact on livable cities. For data that is subjectively considered to be more accessible and more livable the closer it is to, such as schools, hospitals, and scenic spots, accessibility analysis is performed, while for data that can only obtain partial point information, such as housing prices and air quality index, spatial interpolation processing is performed to obtain a spatial interpolation map of the entire research range, providing ideas for subsequent data processing.

[0041] S2, preliminary processing of influencing factors: after determining the data processing method, accessibility analysis and spatial interpolation processing are carried out. The processed data lays the foundation for subsequent zoning statistics.

[0042] S2.1 Road network data processing: Process the road network data within the research scope, obtain the road network centerline data, then perform interruption and intersection processing, and perform topology inspection and traffic network construction to obtain processed traffic network data. This step is the basis for subsequent accessibility analysis, and the processing results of the road network data determine the accuracy of the subsequent accessibility analysis results.

[0043] S2.2 Checking and collating influencing factor data: The patent uses multiple data on hospitals, schools, scenic spots, housing prices and air quality index. These data are checked and sorted to remove outliers to ensure data quality. The data is combined with processed traffic network data to conduct service area analysis in network analysis.

[0044] S2.3 Accessibility analysis and processing: The focus of the study is the convenience of residents' daily travel, so the method uses time indicators to measure accessibility. In ArcGIS software, the service area analysis method in network analysis is used in combination with the processed traffic network data and the data of school, hospital and scenic spot facilities. According to the analysis requirements, the analysis parameters are set: multiple interruption values ​​are set to generate service areas of different ranges, for example, multiple interruption values ​​of 5 minutes, 10 minutes, 30 minutes, and 45 minutes are set; the driving direction is specified to leave the facility point; and the trimming surface is selected. Finally, the solution is performed to generate the service area range of each facility point and obtain the accessibility data within the research scope.

[0045] S2.4 Spatial interpolation processing: According to the characteristics of the study area and the uneven spatial distribution of the influencing factor data, the inverse distance weighted interpolation method is selected. The coordinates of the known points in the study area and the corresponding air quality index, universal thermal climate index (UTCI) and housing price data are collected, and the collected data are organized into a table form, including the point number, X coordinate, Y coordinate and variable value. The inverse distance weighted interpolation method in ArcGIS software is used. After completing the interpolation calculation, the interpolation results are verified to ensure the accuracy and reliability of the interpolation results. Finally, the interpolation results are visualized to intuitively observe the spatial distribution of the data.

[0046] S3, Regional statistics and classification of impact factors:

[0047] S3.1 Regional statistics of influencing factors: The patent divides Anhui Province into 16 sub-regions according to city-level administrative regions. For each sub-region, statistical analysis is conducted on the preliminary processed hospital accessibility, primary school accessibility, middle school accessibility, scenic spot accessibility, Universal Thermal Climate Index (UTCI), air quality index and housing price data, and the average value of each influencing factor for each city is calculated to provide basic data for subsequent classification and indicator fusion.

[0048] S3.2 Determination of grading standards: After completing the zoning statistics, the average values ​​of hospital accessibility, primary school accessibility, middle school accessibility, scenic spot accessibility, universal thermal climate index (UTCI), air quality index and housing prices in each city in Anhui Province are graded equally. The range of the average values ​​of all data is divided into 5 intervals from the minimum to the maximum. Each interval represents a level, and the level is 5, which is the highest, and then decreases in sequence. This grading method helps to more intuitively compare the relative levels of the same influencing factors in different sub-regions, and prepares for the subsequent fusion of influencing factors of different levels with other factors to calculate the improved climate comfort index.

[0049] S4, Improved climate comfort index calculation based on equal weights:

[0050] S4.1 Equal weight setting: clearly assign the same weight to each influencing factor. That is to say, when calculating the improved climate comfort index, the difference in the actual impact of each influencing factor is not considered, but the same weight value is assigned to them. The patent uses 7 types of data, and the weight of each data is 1 / 7.

[0051] S4.2 Index calculation: Through the above processing steps, the grade value of the factors in each city in Anhui Province is obtained. According to the set equal weight, the value of the influencing factor is multiplied by the weight of each data, and the sum is added to obtain the improved climate comfort index of each city in Anhui Province.

[0052] S4.3 Evaluation of livable cities: Use the calculated improved climate comfort index to evaluate cities in Anhui Province. The higher the index value, the higher the climate comfort of the area, and the more suitable it is for living. The differences in climate comfort between cities can be compared, providing a reference for urban planning, population distribution, tourism development, etc., so as to further improve the livability of cities.

[0053] On November 28, 2020, the Livable Cities and Urban-Rural Governance Professional Committee of the Chinese National Architecture Research Association released the "2020 China Livable and Business-Friendly Cities List", and Hefei and Wuhu in Anhui Province were listed. In the evaluation of livable cities in Anhui Province using the original climate comfort index, Hefei and Wuhu were ranked 3 and 2, which were in the middle and not at the top, which was inconsistent with the results of the "2020 China Livable and Business-Friendly Cities List". In the evaluation of the livable cities in Anhui Province using the improved climate comfort index, Hefei ranked first and Wuhu ranked second. The evaluation results are more consistent with the facts and have strong feasibility.

[0054] The innovation of this application is that it uses equal weights to integrate multiple human factors on the basis of the general thermal climate index, improves climate comfort, and makes it more accurate in evaluating urban livability. In addition, the invention can integrate different influencing factors according to actual needs, making the analysis results more intuitive and practical, and can provide direction for the development and application of urban livability evaluation, climate comfort, and urban planning.

[0055] The above-described embodiments of the present invention do not constitute a limitation on the protection scope of the present invention. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A method for improving climate comfort based on equal weights, characterized in that: The following steps are involved: S1. Confirm and classify influencing factors: First, determine the influencing factors for improving climate comfort, and then divide them into two types of data: data using accessibility analysis methods and data using spatial interpolation methods based on the type of data and how the data affects urban livability; S2. Preliminary processing of influencing factors: After determining the data processing method, accessibility analysis and spatial interpolation processing are carried out. The processed data lays the foundation for subsequent zoning statistics; S3. Zoning statistics and classification of influencing factors: Based on the initially processed data, zoning statistics are performed, and then the average value is graded equally for subsequent indicator fusion; S4. Calculation of improved climate comfort index based on equal weights: Use equal weights to calculate the improved climate comfort index and use it to evaluate livable cities.

2. A method for improving climate comfort based on equal weights according to claim 1, characterized in that: The step S1 is divided into the following steps: S1.

1. Determine the influencing factors: Starting from the factors of pollution and noise, leisure activities, living facilities, human environment, travel convenience and natural environment, which affect the livability of a city, we selected several important factors, including hospital accessibility, school accessibility, scenic spot accessibility, housing prices and air quality index; S1.

2. Classification of influencing factors: The influencing factors for improving climate comfort include point data, surface data and raster data. The analysis methods of different factors need to be determined according to the needs to ensure that the processed data has an impact on livable cities. For data that is subjectively believed to be more accessible the closer it is to the location, such as schools, hospitals and scenic spots, accessibility analysis is performed. For data that can only obtain partial point information, such as housing prices and air quality index, spatial interpolation processing is performed to obtain a spatial interpolation map of the entire research range.

3. A method for improving climate comfort based on equal weights according to claim 1, characterized in that: The step S2 is divided into the following steps: S2.1, Road network data processing: Process the road network data within the research scope, obtain the road network centerline data, then perform interruption and intersection processing, and perform topology inspection and traffic network construction to obtain the processed traffic network data; S2.

2. Check and organize the influencing factor data: Use multiple data on hospitals, schools, scenic spots, housing prices and air quality index to check and organize these data, remove outliers, ensure data quality, and combine the processed traffic network data to conduct service area analysis in network analysis; S2.3, accessibility analysis and processing: In ArcGIS software, the processed transportation network data and the data of school, hospital and scenic spot facilities are combined, and the service area analysis method in network analysis is used to solve and generate the service area range of each facility point, and obtain the accessibility data within the research scope; S2.4, spatial interpolation processing: According to the characteristics of the study area and the uneven spatial distribution of the influencing factor data, the inverse distance weighted interpolation method is selected to collect the coordinates of the known points in the study area and the corresponding air quality index, general thermal climate index and housing price data. The collected data are organized into a table form, including the point number, X coordinate, Y coordinate and variable value. The inverse distance weighted interpolation method in ArcGIS software is used. After completing the interpolation calculation, the interpolation result is verified to ensure the accuracy and reliability of the interpolation result. Finally, the interpolation result is visualized to intuitively observe the spatial distribution of the data.

4. The method for improving climate comfort based on equal weights according to claim 1, characterized in that: The step S3 is divided into the following steps: S3.

1. Regional statistics of influencing factors: Anhui Province was divided into 16 sub-regions according to the city-level administrative regions. For each sub-region, the preliminary processed data of hospital accessibility, primary school accessibility, secondary school accessibility, scenic spot accessibility, general thermal climate index, air quality index and housing price were statistically analyzed, and the average value of each influencing factor in each city was calculated; S3.

2. Determination of grading standards: After completing the zoning statistics, the average values ​​of hospital accessibility, primary school accessibility, middle school accessibility, scenic spot accessibility, general thermal climate index, air quality index and housing prices in each city in Anhui Province are graded equally. The value range of all data averages is divided into 5 intervals from the minimum to the maximum value. Each interval represents a level, with 5 being the highest and the levels decreasing in order.

5. The method for improving climate comfort based on equal weights according to claim 1, characterized in that: The step S4 is divided into the following steps: S4.1, equal weight setting: clearly give the same weight to each influencing factor. When calculating the improved climate comfort index, do not consider the difference in the actual impact of each influencing factor, but give them the same weight value. Use 7 types of data, and the weight of each data is 1 / 7; S4.2, Index calculation: Through the above processing steps, the level value of the factors in each city in Anhui Province is obtained. According to the set equal weight, the value of the influencing factor is multiplied by the weight of each data, and the sum is calculated to obtain the improved climate comfort index of each city in Anhui Province; S4.

3. Evaluation of livable cities: Use the calculated improved climate comfort index to evaluate cities in Anhui Province.

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