Method for measuring supply fairness of community pension service facilities

Abstract

The invention discloses a method for measuring the supply fairness of community old-age service facilities, and the method comprises the steps: obtaining the old-age service data of a community, carrying out the calculation and summation of the weights of four indexes, namely house type quality, activity space scale, environment greenness and service scale, and the normalized data, and obtaining an internal quality index; and evaluating the reachability score of the peripheral environment resource by adopting a Gaussian two-step mobile search method to obtain an old-age service supply index, and performing a superposition method on the internal quality index and the old-age service supply index to obtain a comprehensive score of the old-age service facility of each community. The Gini coefficient is used for calculating the comprehensive score accumulation proportion of the old people and the community old-age service facilities, the social environment space supply fairness is obtained, the community old-age service facility supply fairness is evaluated, the difference between communities is effectively reduced, and the community old-age service facilities are concerned and balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of fairness measurement, and in particular to a method for measuring the fairness of community elderly care service facility supply. Background Art

[0003] The existing measurement units of elderly care facilities are mostly based on district-level administrative units, streets or grids, and pay less attention to the fairness of the supply of elderly care facilities among communities. In addition, due to the differences in the distribution of the elderly population and elderly care facilities in the community and the unreasonable layout of various elderly care facilities in various communities, the allocation of elderly care resources in space is uneven. In addition, the differences in the supply of elderly care facilities and spatial environment between different communities have led to more unbalanced resource allocation in disadvantaged communities, which has a negative impact on the quality of life of residents. In the current community elderly care facility supply fairness evaluation system, there is still a large research gap on how to scientifically measure and improve the fairness of community elderly care facilities, especially in disadvantaged communities. In order to effectively reduce the differences between communities and balance the heterogeneity of community elderly care facilities, it is urgent to develop a new measurement method. Summary of the invention

[0004] The purpose of the present invention is to propose a method for measuring the fairness of community elderly care service facilities supply in order to scientifically measure and improve the fairness of community elderly care facilities, so as to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] In one aspect, the present invention comprises the following steps:

[0007] S110: Obtain street view images, and perform calculations on the segmented street view images; perform data analysis based on the street view images, and use Python to obtain POI data around the community. After data cleaning, coordinate conversion and sorting, extract data such as nursing homes, homes for the elderly, welfare homes, sanatoriums, senior apartments and community canteens; extract the quality of housing types using the building type of POI point feature data; use the number of people over 60 years old in the community and the number of beds in nursing homes to obtain the service scale in the form of bed ratio; use OSM road network data to extract traffic road networks according to masks, use ARCMAP to perform topological inspection and correction on road data, and establish a road dataset;

[0008] S120: After cleaning, coordinate conversion and sorting, the data is cleaned, the intrinsic quality data of the four indicators of housing type quality, activity space scale, environmental greenness and service scale are obtained. First, the four element indicators are graded, and then the four indicator data are standardized. After the indicator factor is normalized, the data converted to [0, 1] is obtained. The coefficient of variation method is used to weight the coefficient of variation of the standard deviation of the original data of each indicator to obtain the weight of the intrinsic quality data indicator factor of each community elderly care service facility. Finally, the weight of each indicator factor in the intrinsic quality data of each community elderly care service facility is summed with the normalized data to obtain the intrinsic quality index of the community elderly care service facility.

[0009] S130: Based on ArcGIS, the Gaussian two-step moving search algorithm introduces the Gaussian function to accurately simulate the distance attenuation effect, combines the spatial distance between community residents' houses and community elderly care service facilities and the intensity of elderly care service supply, obtains the elderly care service supply index, and quantitatively describes and evaluates the spatial accessibility of community elderly care service facilities;

[0010] The preferred embodiment of the present invention is further described in detail below in conjunction with Example 1, and the following description is for illustration:

[0011] The above examples are for illustrative purposes only and are not intended to limit the present invention. Any other similar situations shall also fall within the protection scope of the present invention.

[0012] S140: The intrinsic quality index is obtained by summing the weights of the four indicators of housing type quality, activity space scale, environmental greenness, and service scale with the normalized data, and the accessibility score of the peripheral environmental resources is evaluated by the Gaussian two-step moving search method to obtain the elderly care service supply index. The intrinsic quality index and the elderly care service supply index are superimposed to obtain the comprehensive score of each community's elderly care service facilities;

[0013] S150: The Gini coefficient is used to calculate the cumulative proportion of the comprehensive scores of the elderly population and community elderly care service facilities to obtain the fairness of social environmental space supply. The Gini coefficient value is between (0, 1). The larger the G, the more uneven the distribution of elderly care facilities, and the smaller the G, the more even the distribution of elderly care facilities;

[0014] S160: Assess the fairness of the supply of community elderly care service facilities based on the balance of distribution of community elderly care facilities.

[0015] Furthermore, in step S110, DeepLab-v3+ is used to obtain street view images for semantic segmentation and street view image element recognition; semantic labels such as motor vehicle lanes, sidewalks, buildings, plants, and the sky defined in the Cityscapes dataset are used to identify common street elements, and the proportion of street view elements is calculated accordingly;

[0016] Environmental greenness reflects the overall greening environment of the community. The calculation method for environmental greenness of a single street view collection point is as follows:

[0017]

[0018] Where: N represents the number of pictures, C t Represents the number of pixels occupied by each vegetation type (divided into three vegetation types: trees, shrubs and grassland); C total Represents the number of pixels in the i-th image.

[0019] Furthermore, in step S110, the environmental greenness of the community unit is calculated, and the calculation method is as follows:

[0020]

[0021] Furthermore, in step S110, it also includes calculating the spatial activity scale of the elderly care facility, and the calculation method is as follows:

[0022]

[0023] Activity space scale (S i ) is the ratio of the activity area to the total number of beds. The activity area includes the outdoor activity space ((s inside ) and indoor activity space(s outside ), where indoor activity spaces include chess and card rooms, sun rooms, gyms, etc. In addition, the area of ​​standard indoor activity rooms must be larger than 1m 2 / people.

[0024] Furthermore, in step S110, the environmental greenness, housing type quality, activity space scale, and service scale indicators are also included for grading and classification, and the calculation method is as follows:

[0025] Environmental greenness level classification:

[0026] Level 1:

[0027] Level 2:

[0028] Level 3:

[0029] Level 4:

[0030] Level 5:

[0031] Among them, G Min is the minimum value of environmental greenness, G Max is the maximum value of environmental greenness;

[0032] Housing type quality grade classification: 4 points for single-family buildings, 3 points for courtyard buildings, 2 points for single-family houses, 1 point for temporary housing, and higher scores for elderly care facilities of different types;

[0033] Activity space scale classification:

[0034] Level 1:

[0035] Level 2:

[0036] Level 3:

[0037] Level 4:

[0038] Level 5:

[0039] Among them, S Min is the minimum scale of the activity space, S Max is the maximum size of the activity space;

[0040] Service scale classification: 1 point if there are less than 20 nursing beds per 1,000 elderly people; 2 points if there are more than 20 but less than 50 nursing beds per 1,000 elderly people; 3 points if there are more than 50 nursing beds per 1,000 elderly people;

[0041] Among them, service scale = number of beds / number of people over 60 years old in the community.

[0042] Furthermore, in step S120, the four indicators of environmental greenness, housing type quality, activity space scale, and service scale belong to different dimensions and are not related to each other and cannot be calculated. Therefore, in order to establish the connection between the data, the data is standardized; different indicators have different importance in the comprehensive evaluation, so the importance of various comprehensive evaluation indicators is quantified to obtain a more accurate comprehensive evaluation result; the calculation method is as follows:

[0043] First, the index factors are normalized, that is, the linear function normalization formula is used to convert the original data into a numerical range of [0, 1] through geometric scaling to eliminate the influence of different unit dimensions; the formula is as follows:

[0044]

[0045] Where Y norm represents the normalized result, Y represents the original data value of different dimensions, and Y max Represents the maximum value in the original data set, Y min Represents the minimum value data in the original data set;

[0046] Secondly, the coefficient of variation method is used to weight each indicator based on the coefficient of variation of the standard deviation of the original data of each indicator; the study takes the arithmetic mean of the same indicator factor in the intrinsic quality data of each community elderly care service facility as the weight of the indicator factor, and the calculation formula is as follows:

[0047]

[0048] In the formula, α i Represents the standard deviation of the original data value of the i-th indicator, Represents the arithmetic mean of the original data value of the i-th indicator, C i Represents the coefficient of variation of the original data value of the i-th indicator, W i represents the weight of the i-th coefficient of variation value;

[0049] Finally, the result of the sum of the weights of each indicator factor in the intrinsic quality data of each community elderly care service facility and the normalized data is taken as the intrinsic quality index of the community elderly care service facility. The calculation formula is as follows:

[0050] V i =W1×a i +W2×b i +W3×c i +W4×d i

[0051] Where V i represents the intrinsic quality index of community elderly care service facility i, W1 represents the environmental greenness weight of community elderly care service facility i, W2 represents the housing type quality weight of community elderly care service facility i, W3 represents the activity space scale weight of community elderly care service facility i, W4 represents the service scale weight of community elderly care service facility i, and a i represents the normalized value of the greenness of the community elderly care service facility i, b i represents the normalized value of the quality of housing type i in the community elderly care service facility, c i represents the normalized value of the activity space scale of community elderly care service facility i, d i Represents the normalized value of the service scale of community elderly care service facility i.

[0052] Furthermore, in step S130, based on the concept of the 15-minute living circle, the ArcGIS-based Gaussian two-step mobile search algorithm is used, and the spatial distance between community residents' residences and community elderly care service facilities, as well as the supply intensity and distribution of the elderly population, is combined to quantitatively describe and evaluate the spatial accessibility of community elderly care service facilities, and obtain the elderly care service supply index. The calculation formula is as follows:

[0053] For the elderly care service facility point j, search for the demand scale of each community within its search radius d0, that is, the number of elderly people in the community, and calculate the supply-demand ratio R of the elderly care service facility point j， The calculation method is as follows:

[0054]

[0055] Where: P k is within the spatial scope of j (d kj ≤d0) the population of street k; d kj is the spatial distance from community center k to community center j; S j is the capacity of j, expressed as area; G(d kj ,d0) is the Gaussian equation taking into account the spatial friction problem, and the calculation method is as follows:

[0056]

[0057] Where: For each residential area K, search for the elderly care service facilities j within its search radius, and compare the supply and demand ratio of these elderly care service facilities R j Weighted to get the elderly care service supply index A of each community i , the calculation method is as follows:

[0058]

[0059] Where: R i Indicates the spatial scope of region i (d kj ≤d0) supply ratio of elderly care service facilities j;

[0060] First, taking community elderly care service facilities as the center, the supply-demand ratio of the service capacity of the facilities and the elderly population in the living circle is calculated;

[0061] Secondly, taking the residential area as the center, the accessibility of elderly service facilities within the search radius is measured to obtain the fairness of accessibility of elderly service facilities within the range.

[0062] Furthermore, in step S140, the weights of the four indicators of housing type quality, activity space scale, environmental greenness, and service scale are summed with the normalized data to obtain the intrinsic quality index, and the elderly care service supply index is obtained. The intrinsic quality index and the elderly care service supply index are superimposed to obtain the comprehensive score of each community elderly care service facility. The calculation formula is as follows:

[0063]

[0064] Where: S i is the comprehensive level of elderly care service facilities in community i, A ijLet \(A_{ij}\) be the accessibility level of the elderly care service facility type \(j\) (\(j = 1, 2, 3, 4\)) for community \(i\), and \(V_{j}\) j be the intrinsic quality index of the service facility of type \(j\) (\(0 \lt V_{j}\) j \lt 1\)).

[0065] Furthermore, in step S150, by constructing the Lorenz curve of the community elderly care facilities and calculating the corresponding Gini coefficient, the concentration degree of the layout of the community elderly care facilities is analyzed; the Lorenz curve reflects the relationship between the cumulative proportion of the regional population and the proportion of the available elderly care service facility resources; the Gini coefficient is obtained by plotting the Lorenz curve based on the cumulative proportion of the population and the comprehensive score of the community elderly care service facilities, and the fairness degree of the distribution of the elderly care facilities is judged according to the degree of bending of the curve. The specific calculation formula is as follows:

[0066]

[0067] In the formula, \(G\) is the Gini coefficient, which reflects the balance degree of the distribution of the community elderly care facilities and can be derived from the Lorenz curve: \(q_{k}\) k is the cumulative proportion of the community population variable, \(k = 0,\cdots,n\); \(S_{k}\) k is the cumulative proportion of the comprehensive score of the community elderly care service facilities, and \(n\) is the total number of communities; the value of the Gini coefficient is between \((0, 1)\). The larger \(G\) is, the more unbalanced the distribution area of the elderly care facilities is, and the smaller \(G\) is, the more uniform the regional distribution of the elderly care facilities is;

[0068] When \(G\leq0.2\), it means the distribution is absolutely average; when \(0.2 \lt G\leq0.3\), it means relatively uniform; when \(0.3 \lt G\leq0.4\), it means relatively reasonable; when \(0.4 \lt G\leq0.5\), it means there is a large gap; when \(G\gt0.5\), it means there is a huge gap.

[0069] Furthermore, in step S160, according to the balance degree of the distribution of the community elderly care facilities, the supply fairness of the community elderly care service facilities is evaluated.

[0070] In the second aspect, the embodiments of the present application further provide an electronic device, including:

[0071] a processor; and a memory arranged to store computer-executable instructions, and the executable instructions, when executed, cause the processor to execute the method steps described in the first aspect.

[0072] In the third aspect, the embodiments of the present application further provide a computer-readable storage medium, and the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device is caused to execute the method steps described in the first aspect.

[0073] The method and system for measuring fairness of community elderly care service facility supply provided by the present invention have the following advantages over the prior art:

[0074] 1) The existing measurement units of elderly care facilities are mostly based on district-level administrative units, streets or grids, and less attention is paid to the fairness of the supply of elderly care facilities between communities. In addition, due to the differences in the distribution of the elderly population and elderly care facilities in the community and the unreasonable layout of each elderly care facility in each community, the elderly care resources are unevenly allocated in space. The measurement method of the fairness of the supply of community elderly care service facilities provided by the present invention focuses on the fairness of the supply of community elderly care service facilities. This focus can more comprehensively consider the balanced distribution of elderly care service facilities between different communities, thereby helping to reduce environmental differences between communities. The method uses street view images and deep learning methods for data analysis to evaluate the environmental greenness of community elderly care facilities. The Gaussian two-step mobile search algorithm based on ArcGIS evaluates the accessibility of peripheral environmental resources, comprehensively considers the weight of spatial distance and the smoothing effect of the Gaussian function, quantitatively describes and evaluates the spatial accessibility of community elderly care facilities, and improves the accuracy of accessibility analysis. The introduction of the Gini coefficient to evaluate the comprehensive accessibility score of the supply can quantify the fairness of the supply of community elderly care facilities.

[0075] 2) The core principle of the method for measuring the fairness of the supply of community elderly care service facilities provided by the present invention is to focus on the fairness of the distribution of community elderly care service facilities, taking into account the differences between different communities, especially disadvantaged communities. The principle of fairness is an important difference between this method and traditional measurement methods. Pattern recognition and deep learning methods are used to extract vegetation information from each street view, calculate the green view rate of the pictures according to each direction and angle, calculate the overall environmental greenness, and grade and assign points to the quality of house types, activity space scale, and service scale, so as to obtain the intrinsic environmental quality score. Through Gaussian two-step mobile search, the accessibility of peripheral environmental resources is evaluated, and the distance between community residences and community elderly care service facilities, the capacity of community elderly care service facilities, and the smoothing effect of Gaussian functions are considered to more truly reflect the accessibility of community elderly care service facilities. The present invention fills the research gap in the current community elderly care facility evaluation system, effectively measures and improves the fairness of community elderly care facilities, especially in disadvantaged communities. By introducing fairness evaluation, comprehensive measurement and modern technology, it aims to measure and improve the fairness of community elderly care facilities and effectively reduce the differences between communities. Through more comprehensive, objective and scientific measurement methods, more scientific support is provided for urban planning decisions. In particular, it breaks through the previous research scale of using statistical data based on districts and counties and other administrative units, and refines the measurement scale of elderly service facilities and the distribution of the elderly population to the community level, making the evaluation results of elderly service facilities in terms of both supply and demand more accurate and intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of a method for measuring fairness of community elderly care service facility supply according to the present invention;

[0077] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0078] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0079] like Figure 1 As shown, the invention aims at providing certain reference opinions for scientifically measuring and improving the fairness of community elderly care facilities in the fairness evaluation system of community elderly care facilities supply, and for the healthy and coordinated development of community elderly care facilities and the future spatial layout and optimization. By cleaning, coordinate conversion and sorting the acquired data, the intrinsic quality data of the four indicators of housing type quality, activity space scale, environmental greenness and service scale are obtained. The four element indicators are first graded and normalized, and weighted by the coefficient of variation method. The weights of each indicator factor in the intrinsic quality data of each community elderly care service facility are summed with the normalized data operation to obtain the intrinsic quality index of the community elderly care service facility; the Gaussian two-step mobile search algorithm method is introduced to evaluate the accessibility score of peripheral environmental resources and obtain the elderly care service supply index; the intrinsic quality index and the elderly care service supply index are combined to obtain the comprehensive score of each community elderly care service facility by superposition method; at the same time, the Gini coefficient is used to obtain the fairness of social environmental space supply, evaluate the fairness of community elderly care service facility supply, effectively reduce the differences between communities, and pay attention to and balance the community elderly care facility issues.

[0080] The purpose of the present invention is to propose a method for measuring the fairness of the supply of community elderly care service facilities. In order to scientifically measure and improve the fairness of community elderly care facilities in the fairness evaluation system of the supply of community elderly care facilities, the intrinsic quality data of four indicators, namely, housing type quality, activity space scale, environmental greenness, and service scale, are obtained by cleaning, coordinate conversion, and sorting the acquired data. The four element indicators are first classified and normalized, and weighted by the coefficient of variation method. The weight of each indicator factor in the intrinsic quality data of each community elderly care service facility is summed with the normalized data to obtain the intrinsic quality index of the community elderly care service facility; a Gaussian two-step moving search algorithm method is introduced to evaluate the accessibility score of peripheral environmental resources to obtain the elderly care service supply index; the intrinsic quality index and the elderly care service supply index are combined to obtain the comprehensive score of each community elderly care service facility by superposition method; at the same time, the Gini coefficient is used to obtain the fairness of social environmental space supply, evaluate the fairness of community elderly care service facility supply, effectively reduce the differences between communities, and pay attention to and balance the problems of community elderly care facilities.

[0081] Implementation method 1, see Figure 1 , showing a flow chart of a method for measuring the fairness of community elderly care service facility supply according to the present invention.

[0082] S110: Generate street view collection points according to intrinsic quality data, collect images according to street view collection points, obtain street view images, and calculate the segmented street view images; perform data analysis according to street view images, use python to obtain POI data around the community, and extract data such as nursing homes, homes for the elderly, welfare homes, sanatoriums, senior apartments and community canteens after data cleaning, coordinate conversion and sorting; extract house type quality using building type of POI point feature data; use the number of people over 60 years old in the community and the number of beds in nursing homes to obtain service scale in the form of bed ratio; use OSM road network data to extract traffic road network according to mask, use ARCMAP to perform topological inspection and correction on road data, and establish road data set.

[0083] S120: After data cleaning, coordinate conversion and sorting, the intrinsic quality data of four indicators, namely, housing type quality, activity space scale, environmental greenness and service scale, are obtained. First, the four element indicators are graded, and then the four indicator data are standardized. After the indicator factors are normalized, the data converted to [0, 1] are obtained. The coefficient of variation method is used to weight the coefficient of variation of the standard deviation of the original data of each indicator to obtain the weight of the intrinsic quality data indicator factor of each community elderly care service facility. Finally, the weight of each indicator factor in the intrinsic quality data of each community elderly care service facility is summed with the normalized data to obtain the intrinsic quality index of the community elderly care service facility.

[0084] S130: Based on ArcGIS, the Gaussian two-step moving search algorithm introduces the Gaussian function to accurately simulate the distance attenuation effect, combines the spatial distance between community residents' houses and community elderly care service facilities and the intensity of elderly care service supply, obtains the elderly care service supply index, and quantitatively describes and evaluates the spatial accessibility of community elderly care service facilities;

[0085] S140: The intrinsic quality index is obtained by summing the weights of the four indicators of housing type quality, activity space scale, environmental greenness, and service scale with the normalized data, and the accessibility score of the peripheral environmental resources is evaluated using the Gaussian two-step moving search method to obtain the elderly care service supply index. The intrinsic quality index and the elderly care service supply index are superimposed to obtain the comprehensive score of each community's elderly care service facilities.

[0086] S150: The Gini coefficient is used to calculate the cumulative proportion of the comprehensive scores of the elderly population and community elderly care service facilities to obtain the fairness of social environmental space supply. The Gini coefficient value is between (O, 1). The larger the G, the more uneven the distribution of elderly care facilities, and the smaller the G, the more even the regional distribution of elderly care facilities.

[0087] S160: Assess the fairness of the supply of community elderly care service facilities based on the balance of distribution of community elderly care facilities.

[0088] The method described in this embodiment comprehensively considers the intrinsic environmental quality of community elderly care facilities and the accessibility of peripheral environmental resources, and more comprehensively evaluates the supply of community elderly care facilities. Data analysis is performed using street view images and deep learning methods to evaluate the environmental greenness of community elderly care facilities. The Gaussian two-step mobile search algorithm based on ArcGIS evaluates the accessibility of peripheral environmental resources, comprehensively considers the weight of spatial distance and the smoothing effect of the Gaussian function, quantitatively describes and evaluates the spatial accessibility of community elderly care facilities, and improves the accuracy of accessibility analysis. The introduction of the Gini coefficient to evaluate the comprehensive accessibility score of the supply can quantify the fairness of the supply of community elderly care facilities.

[0089] The method described in this embodiment adopts pattern recognition and deep learning methods to extract vegetation information from each street view, calculate the green view rate of the picture according to each direction and angle, calculate the overall environmental greenness, and grade and assign points to the quality of the house type, the scale of the activity space, and the scale of the service, so as to obtain the intrinsic environmental quality score. Through the Gaussian two-step mobile search, the accessibility of the peripheral environmental resources is evaluated, and the distance between the community residence and the community elderly care service facilities, the capacity of the community elderly care service facilities, and the smoothing effect of the Gaussian function are considered to more truly reflect the accessibility of the community elderly care service facilities. The fairness of the supply of community elderly care service facilities is evaluated by the Gini coefficient, an indicator of the degree of inequality. To solve the problem that the existing measurement methods pay insufficient attention to the fairness of the supply of community elderly care service facilities, by comprehensively considering the intrinsic quality of the community and the accessibility of the peripheral environmental resources, and introducing the Gini coefficient for fairness assessment, a scientific basis is provided for community planning and improvement, and its spatial characteristics are identified, providing a reference plan for the planning and construction of future elderly care service facilities.

[0090] Implementation method 2: This implementation method further limits the method for measuring fairness of community elderly care facility supply described in implementation method 1. Step S110 includes:

[0091] DeepLab-v3+ is used to obtain street view images for semantic segmentation and street view image element recognition. Semantic labels such as motor vehicle lanes, sidewalks, buildings, plants, and sky defined in the Cityscapes dataset are used to identify common street elements, and the proportion of street view elements is calculated accordingly. Environmental greenness reflects the overall greening environment of the community. The calculation method for environmental greenness of a single street view collection point is as follows:

[0092]

[0093] Where: N represents the number of pictures, C t Represents the number of pixels occupied by each vegetation type (divided into three vegetation types: trees, shrubs and grassland); C total Represents the number of pixels in the i-th image.

[0094] It also includes the calculation of the environmental greenness of the community unit, which is calculated as follows:

[0095] In this implementation, DeepLab-v3+ is used to obtain street view images for semantic segmentation, and high-precision image segmentation results can be obtained, which helps to accurately extract various elements in street view images. Semantic labels such as motor lanes, sidewalks, buildings, plants, and the sky defined in the Cityscapes dataset are used to identify common street elements, and the proportion of street view elements is calculated based on this, making the measurement results more objective. This helps to eliminate subjective factors and improve the accuracy and scientificity of the measurement.

[0096] This implementation uses the DeepLab V3+ model to obtain street view images for semantic segmentation. The model performs well in image segmentation tasks. Through image segmentation, different elements in street view images are separated to provide a basis for environmental greenness calculation. By calculating the ratio of the number of tree pixels to the total number of pixels, the vegetation condition in the community, that is, the environmental greenness, is reflected. By calculating the number of pixels of computer driving lanes, sidewalks, buildings, plants, the sky, etc., on the one hand, it can reflect the space utilization in the community, that is, the spatial density, and on the other hand, it can also reflect the surrounding situation of the community environment. By calculating the color component value of the pixel, the overall visual contrast of the image, that is, the color difference in different areas, is reflected. By calculating the indicators of different elements, the fairness of the supply of community elderly care facilities can be evaluated. According to these quantitative indicators, targeted measures are taken to improve the environment of community elderly care facilities and promote fair space supply.

[0097] Implementation method 3: This implementation method further limits the method for measuring fairness of community elderly care facility supply described in implementation method 1. Step S110 includes:

[0098] The environmental greenness, housing type quality, activity space scale, and service scale indicators are graded and classified, and the calculation method is as follows:

[0099] Environmental greenness level classification:

[0100] Level 1:

[0101] Level 2:

[0102] Level 3:

[0103] Level 4:

[0104] Level 5:

[0105] Among them, G Min is the minimum value of environmental greenness, G Max is the maximum value of environmental greenness;

[0106] Housing type quality grade classification: 4 points for single-family buildings, 3 points for courtyard buildings, 2 points for single-family houses, 1 point for shared temporary buildings, and higher scores will be given to elderly care facilities of different types.

[0107] Activity space scale classification:

[0108] Level 1:

[0109] Level 2:

[0110] Level 3:

[0111] Level 4:

[0112] Level 5:

[0113] Calculate the spatial activity scale of elderly care facilities as follows:

[0114]

[0115] Among them, S Min is the minimum scale of the activity space, S Max The activity space scale is the maximum value of the activity space scale; the activity space scale is the ratio of the activity area to the total number of beds. The activity area includes outdoor activity space and indoor activity space, among which the indoor activity space includes chess and card rooms, sun rooms, gyms, etc. In addition, the area of ​​standard indoor activity rooms must be greater than 1m 2 / people.

[0116] Service scale level classification: 1 point if there are less than 20 nursing beds per thousand elderly people; 2 points if there are more than 20 but less than 50 nursing beds per thousand elderly people; 3 points if there are more than 50 nursing beds per thousand elderly people.

[0117] Service scale = number of beds / number of people over 60 years old in the community

[0118] This implementation method makes the supply fairness assessment of community elderly care facilities clearer and easier to understand by grading the quality of housing types, activity space scale, environmental greenness, and service scale. Different levels represent different levels of environmental characteristics, which helps to intuitively understand the status of community elderly care facilities. Through the setting of levels, the assessment of the fairness of community space environment supply becomes more quantitative, making the assessment easier to compare and explain.

[0119] Implementation method 4: This implementation method further limits the method for measuring fairness of community elderly care facility supply described in implementation method 1. Step S120 includes:

[0120] The four indicators of environmental greenness, housing type quality, activity space scale, and service scale belong to different dimensions and cannot be calculated without any connection between them. Therefore, in order to establish the connection between the data, the data is standardized. Different indicators have different importance in the comprehensive evaluation, so the importance of various comprehensive evaluation indicators is quantified to obtain more accurate comprehensive evaluation results. The calculation method is as follows:

[0121] First, the index factors are normalized, that is, the linear function normalization formula is used to convert the original data into a numerical range of [0, 1] through geometric scaling to eliminate the influence of different unit dimensions. The following is the public statement:

[0122]

[0123] Where Y norm represents the normalized result, Y represents the original data value of different dimensions, and Y max Represents the maximum value in the original data set, Y min Represents the minimum value in the original data set.

[0124] Table 1 Comparison of quantitative data on the importance of comprehensive evaluation indicators

[0125]

[0126] As shown in Table 1, the indoor activity area and outdoor activity area reflect the activity space provided by the elderly care facilities for the elderly. Larger activity space usually means more activities and better quality of life; the diversity of housing types (such as courtyard buildings, single-family houses, etc.) may affect the living experience of the elderly and the operational efficiency of the facilities. Different types of houses may require different maintenance and operating costs, and these factors will also affect the fairness of services; the size of the community's permanent population and the number of people over 60 years old: These data help us understand the proportion of the elderly in the community and whether the elderly care service facilities can meet the needs of the elderly population. If a community has a high proportion of elderly people, but the number of beds in the elderly care service facilities is insufficient, it may indicate that there is unfairness in the supply of elderly care services in the community; the bed size is an important indicator for measuring the service capacity of elderly care service facilities. The ratio of the number of beds to the elderly population can reflect the supply and demand relationship of services, and then evaluate the fairness of services. The design and operation of elderly care service facilities should take into account basic, inclusive, shared and systematic aspects to ensure that all elderly people can obtain basic elderly care services.

[0127] Secondly, the coefficient of variation method is used to weight each indicator by the coefficient of variation of the standard deviation of the original data of each indicator. The study takes the arithmetic mean of the same indicator factor in the intrinsic quality data of each community elderly care service facility as the weight of the indicator factor, and the calculation formula is as follows:

[0128]

[0129] In the formula, α i Represents the standard deviation of the original data value of the i-th indicator, Represents the arithmetic mean of the original data value of the i-th indicator, C i Represents the coefficient of variation of the original data value of the i-th indicator, W i Represents the weight of the i-th coefficient of variation value.

[0130] Finally, the weight of each indicator factor in the intrinsic quality data of each community elderly care service facility is calculated and summed with the normalized data to serve as the intrinsic quality index of the community elderly care service facility. The calculation formula is as follows:

[0131] V i =W1×a i +W2×b i +W3×c i +W4×d i

[0132] Where V i represents the intrinsic quality index of community elderly care service facility i, W1 represents the environmental greenness weight of community elderly care service facility i, W2 represents the housing type quality weight of community elderly care service facility i, W3 represents the activity space scale weight of community elderly care service facility i, W4 represents the service scale weight of community elderly care service facility i, and a i represents the normalized value of the greenness of the community elderly care service facility i, b i represents the normalized value of the quality of housing type i in the community elderly care service facility, c i represents the normalized value of the activity space scale of community elderly care service facility i, d i Represents the normalized value of the service scale of community elderly care service facility i.

[0133] This implementation method obtains the intrinsic quality data of four indicators, namely, housing type quality, activity space scale, environmental greenness, and service scale, by cleaning, coordinate conversion, and sorting the data. However, the above four indicator dimensions belong to different dimensions and are not related to each other and cannot be calculated. Therefore, in order to establish the connection between the data, it is necessary to standardize the data to eliminate the impact of the differences in dimensions and orders of magnitude of different indicators, so as to provide a quantitative basis for subsequent comprehensive evaluation and analysis.

[0134] In this implementation, the indicator factors are first normalized to obtain data converted to [0, 1]. Secondly, the coefficient of variation method is used to weight the coefficient of variation of the standard deviation of the original data of each indicator to obtain the weight of the indicator factor of the intrinsic quality data of each community elderly care service facility. Finally, the weight of each indicator factor in the intrinsic quality data of each community elderly care service facility is summed with the normalized data to obtain the intrinsic quality index of the community elderly care service facility.

[0135] Implementation mode 5: This implementation mode further limits the method for measuring fairness of community elderly care facility supply described in implementation mode 1. Step S130 includes:

[0136] According to the concept of 15-minute living circle, the ArcGIS-based Gaussian two-step moving search algorithm (G2SFCA) was used to quantitatively describe and evaluate the spatial accessibility of community elderly care service facilities, combining the spatial distance between community residents' residences and community elderly care service facilities, the supply intensity and the distribution of the elderly population, and the elderly care service supply index was obtained. The calculation formula is as follows:

[0137] For the elderly care service facility point j, search for the demand scale of each community within its search radius d0, that is, the population of the elderly community, and calculate the supply-demand ratio R of the elderly care service facility point j , the calculation method is as follows:

[0138] Where: P k is within the spatial scope of j (d kj ≤d0) the population of street k; d kj From the community k center

[0139] Where: For each residential area K, search for public service facilities j within its search radius, and compare the supply and demand ratio of these elderly care service facilities R j Weighted to get the elderly care service supply index A of each community i , the calculation method is as follows:

[0140]

[0141] Where: R i Indicates the spatial scope of region i (d kj ≤d0) supply ratio of elderly care service facilities j.

[0142] This implementation method is evaluated based on the scope of the 15-minute living circle of residents. The average travel speed of residents of different age levels in the city within 15 minutes is 4 to 6 kilometers per hour, and the travel coverage area is about 3-5 square kilometers. A 15-minute walk can reach a network of urban community living circles that are suitable for living, working, and traveling. The Gaussian two-step mobile search method can be used to measure the size of the accessibility value between the starting points using the supply and demand relationship between the starting points, and the accuracy of the final accessibility value can be improved by additionally introducing a distance decay function and a time limit parameter. The accessibility scores of different types of resources are combined to obtain the total score of the elderly care facility evaluation for each community. This helps to intuitively compare the fairness of the supply of elderly care facilities between different communities and provide a comprehensive reference for relevant planning departments.

[0143] In this implementation, 15 minutes is used as a benchmark. Within an acceptable walking range, all services needed by a person in daily life are solved, and eating, drinking and playing are all done in the living circle, especially for children, the elderly and the disabled. The selection of this range is based on actual life needs, making the evaluation closer to the actual situation. Modern urban residential areas are formed through comprehensive planning, reasonable layout, comprehensive development and supporting construction. The biggest feature of residential areas is that the residential function is single, the area is relatively closed and independent, and the residents live together. All facilities inside and outside the community are designed and built for the convenience of community residents. Unlike other spatial accessibility measurement methods, the Gaussian two-step moving search method can use the supply and demand relationship between the starting points to measure the size of the accessibility value between the starting points, and can improve the accuracy of the final accessibility value by additionally introducing distance decay functions and time limit parameters. By considering multiple resource types and the scope of the 15-minute living circle, the purpose is to comprehensively evaluate the fairness of the supply of community elderly care facilities. This method better reflects the various types of resources that residents can obtain in a short period of time, which helps to discover potential inequality problems. It helps to guide urban planning, resource allocation and policy making to improve the overall quality of life in the community.

[0144] Implementation method 6: This implementation method further limits the method for measuring fairness of community elderly care facility supply described in implementation method 1. Step S140 includes:

[0145] The intrinsic quality index is obtained by summing the weights of the four indicators of housing type quality, activity space scale, environmental greenness, and service scale with the normalized data, and the accessibility score of the peripheral environmental resources evaluated by the Gaussian two-step moving search method is used to obtain the elderly care service supply index. The intrinsic quality index and the elderly care service supply index are superimposed to obtain the comprehensive score of each community's elderly care service facilities. The calculation formula is as follows:

[0146]

[0147] Where: Si is the comprehensive level of elderly care service facilities in community i, Aij is the accessibility level of community i to the j (j = 1, 2, 3, 4) type of indicators of elderly care service facilities, and V j is the intrinsic quality index of the j type of service facilities (0 < V j < 1).

[0148] In this embodiment, the intrinsic quality index obtained by calculating the sum of the weights of the four indicators of housing type quality, activity space scale, environmental greenness, and service scale and the normalized data is combined with the accessibility score obtained by using the Gaussian two-step moving search method to evaluate the peripheral environmental resources to obtain the elderly care service supply index. The intrinsic quality index and the elderly care service supply index are used by the superposition method to obtain the comprehensive score of the elderly care service facilities in each community. The comprehensive score can reflect the accessibility level of the elderly care service facilities in the community as a whole, understand the influencing factors of the elderly care service facilities in the community, promote the aging-friendly construction of urban planning, and provide a reference for improving the construction of various facilities in the community.

[0149] Embodiment 7. This embodiment further limits a method for measuring the supply fairness of community elderly care facilities described in Embodiment 1. The step S150 includes:

[0150] By constructing the Lorenz curve of the community elderly care facilities and calculating the corresponding Gini coefficient, the concentration degree of the layout of the community elderly care facilities is analyzed. The Lorenz curve reflects the relationship between the cumulative proportion of the regional population and the proportion of the available elderly care service facility resources. The Gini coefficient is to draw the Lorenz curve through the cumulative proportion of the population and the comprehensive score of the community elderly care service facilities, and judge the fairness degree of the community elderly care service facilities according to the bending degree of the curve. The specific calculation formula is as follows:

[0151]

[0152] In the formula, G is the Gini coefficient, which reflects the balance degree of the distribution of community elderly care facilities and can be derived from the Lorenz curve. q k is the cumulative proportion of the community population variable, k = 0,..., n; S k is the cumulative proportion of the comprehensive accessibility score of the community elderly care service facilities, and n is the total number of communities. The value of the Gini coefficient is between (0, 1). The larger G is, the more unbalanced the distribution area of the elderly care facilities is, and the smaller G is, the more uniform the distribution area of the elderly care facilities is. Usually, G ≤ 0.2 means absolute average distribution, 0.2 < G ≤ 0.3 means relatively uniform, 0.3 < G ≤ 0.4 means relatively reasonable, 0.4 < G ≤ 0.5 means a large gap, and above 0.5 means a huge gap.

[0153] This implementation uses the Gini coefficient as a measurement indicator to construct the Lorenz curve of community elderly care facilities and calculate the corresponding Gini coefficient to analyze the degree of concentration of the layout of community elderly care facilities. The Gini coefficient is a statistical method to measure the distribution of inequality. It is often used to assess the degree of inequality in income, wealth, etc. in a group. It can also be used to analyze broader issues of distribution fairness, including the field of urban planning. The value range of the Gini coefficient is between 0 and 1, where 0 represents complete equality and 1 represents complete inequality. Here, the degree of inequality in the fairness of the supply of community elderly care facilities is introduced. By comparing the resource scores of elderly care facilities, it reflects the balanced and reasonable distribution of elderly care facilities, which helps to understand the differences between communities.

[0154] The step S160 includes:

[0155] According to the degree of balance in the distribution of community elderly care facilities, the fairness of the supply of community elderly care service facilities is evaluated. The purpose of this implementation method is to introduce the Gini coefficient to measure the degree of balance in the scores of elderly care facility resources among different communities. It provides more scientific support for urban planning decisions, especially breaking through the previous research scale of using statistical data based on administrative units such as districts and counties, and refining the measurement scale of elderly care service facilities and the distribution of the elderly population to the community level, making the evaluation results of elderly care service facilities in terms of both supply and demand more accurate and intuitive.

[0156] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0157] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0158] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0159] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for measuring the fairness of community elderly care service facilities supply, characterized by: The steps include: S110: Obtain street view images, and perform calculations on the segmented street view images; perform data analysis based on the street view images, and use Python to obtain POI data around the community. After data cleaning, coordinate conversion and sorting, data on nursing homes, homes for the elderly, welfare homes, sanatoriums, senior citizen apartments and community canteens are extracted; The building type of POI point feature data is used to extract the quality of housing types; the service scale is obtained in the form of bed ratio using the number of people over 60 years old in the community and the number of beds in nursing homes; Use OSM road network data to extract traffic road network by mask, use ARCMAP to check and modify the road data topology, and establish road dataset; S120: After cleaning, coordinate conversion and sorting, the data is cleaned, the intrinsic quality data of the four indicators of housing type quality, activity space scale, environmental greenness and service scale are obtained. First, the four element indicators are graded, and then the four indicator data are standardized. After the indicator factor is normalized, the data converted to [0, 1] is obtained. The coefficient of variation method is used to weight the coefficient of variation of the standard deviation of the original data of each indicator to obtain the weight of the intrinsic quality data indicator factor of each community elderly care service facility. Finally, the weight of each indicator factor in the intrinsic quality data of each community elderly care service facility is summed with the normalized data to obtain the intrinsic quality index of the community elderly care service facility. S130: Based on ArcGIS, the Gaussian two-step moving search algorithm introduces the Gaussian function to accurately simulate the distance attenuation effect, combines the spatial distance between community residents' houses and community elderly care service facilities and the intensity of elderly care service supply, obtains the elderly care service supply index, and quantitatively describes and evaluates the spatial accessibility of community elderly care service facilities; S140: The intrinsic quality index is obtained by summing the weights of the four indicators of housing type quality, activity space scale, environmental greenness, and service scale with the normalized data, and the accessibility score of the peripheral environmental resources is evaluated by the Gaussian two-step moving search method to obtain the elderly care service supply index. The intrinsic quality index and the elderly care service supply index are superimposed to obtain the comprehensive score of each community's elderly care service facilities; S150: The Gini coefficient is used to calculate the cumulative proportion of the comprehensive scores of the elderly population and community elderly care service facilities to obtain the fairness of social environmental space supply. The Gini coefficient value is between (0, 1). The larger the G, the more uneven the distribution of elderly care facilities, and the smaller the G, the more even the distribution of elderly care facilities; S160: Assess the fairness of the supply of community elderly care service facilities based on the balance of distribution of community elderly care facilities.

2. The method for measuring fairness of community elderly care service facility supply according to claim 1 is characterized by: In step S110, DeepLab-v3+ is used to obtain street view images for semantic segmentation and street view image element recognition; semantic labels such as motor vehicle lanes, sidewalks, buildings, plants, and sky defined in the Cityscapes dataset are used to identify common street elements, and the proportion of street view elements is calculated accordingly; Environmental greenness reflects the overall greening environment of the community. The calculation method for environmental greenness of a single street view collection point is as follows: Where: N represents the number of pictures, C t Represents the number of pixels occupied by each vegetation type (divided into three vegetation types: trees, shrubs and grassland); C total Represents the number of pixels in the i-th image. Calculate the environmental greenness of the community unit as follows: Calculate the spatial activity scale of elderly care facilities as follows: Activity space scale (S i ) is the ratio of the activity area to the total number of beds. The activity area includes the outdoor activity space ((s inside ) and indoor activity space(s outside ), where indoor activity spaces include chess and card rooms, sun rooms, gyms, etc. In addition, the area of ​​standard indoor activity rooms must be larger than 1m 2 / people.

3. The method for measuring fairness of community elderly care service facility supply according to claim 1 is characterized by: In step S110, the environmental greenness, housing type quality, activity space scale, and service scale indicators are also graded and classified, and the calculation method is as follows: Environmental greenness level classification: Level 1: Level 2: Level 3: Level 4: Level 5: Among them, G Min is the minimum value of environmental greenness, G Max is the maximum value of environmental greenness; Housing type quality grade classification: 4 points for single-family buildings, 3 points for courtyard buildings, 2 points for single-family houses, 1 point for temporary housing, and higher scores for elderly care facilities of different types; Activity space scale classification: Level 1: Level 2: Level 3: Level 4: Level 5: Among them, S Min is the minimum scale of the activity space, S Max is the maximum size of the activity space; Service scale classification: 1 point if there are less than 20 nursing beds per 1,000 elderly people; 2 points if there are more than 20 but less than 50 nursing beds per 1,000 elderly people; 3 points if there are more than 50 nursing beds per 1,000 elderly people; Among them, service scale = number of beds / number of people over 60 years old in the community.

4. The method for measuring fairness of community elderly care service facility supply according to claim 1 is characterized by: In step S120, the four indicators of environmental greenness, housing type quality, activity space scale, and service scale belong to different dimensions and cannot be calculated without any connection between them. Therefore, in order to establish the connection between the data, the data is standardized; Different indicators have different importance in the comprehensive evaluation, so the importance of various comprehensive evaluation indicators is quantified to obtain more accurate comprehensive evaluation results; the calculation method is as follows: First, the index factors are normalized, that is, the linear function normalization formula is used to convert the original data into a numerical range of [0, 1] through geometric scaling to eliminate the influence of different unit dimensions; the formula is as follows: Where Y norm represents the normalized result, Y represents the original data value of different dimensions, and Y max Represents the maximum value in the original data set, Y min Represents the minimum value data in the original data set; Secondly, the coefficient of variation method is used to weight each indicator based on the coefficient of variation of the standard deviation of the original data of each indicator; the study takes the arithmetic mean of the same indicator factor in the intrinsic quality data of each community elderly care service facility as the weight of the indicator factor, and the calculation formula is as follows: In the formula, α i Represents the standard deviation of the original data value of the i-th indicator, Represents the arithmetic mean of the original data value of the i-th indicator, C i Represents the coefficient of variation of the original data value of the i-th indicator, W i represents the weight of the i-th coefficient of variation value; Finally, the result of the sum of the weights of each indicator factor in the intrinsic quality data of each community elderly care service facility and the normalized data is taken as the intrinsic quality index of the community elderly care service facility. The calculation formula is as follows: In i =W1×a i +W2×b i +W3×c i +W4×d i Where V i represents the intrinsic quality index of community elderly care service facility i, W1 represents the environmental greenness weight of community elderly care service facility i, W2 represents the housing type quality weight of community elderly care service facility i, W3 represents the activity space scale weight of community elderly care service facility i, W4 represents the service scale weight of community elderly care service facility i, and a i represents the normalized value of the greenness of the community elderly care service facility i, b i represents the normalized value of the quality of housing type i in the community elderly care service facility, c i represents the normalized value of the activity space scale of community elderly care service facility i, d i Represents the normalized value of the service scale of community elderly care service facility i.

5. The method for measuring fairness of community elderly care service facility supply according to claim 1 is characterized by: In step S130, according to the concept of the 15-minute living circle, using the Gaussian two-step moving search algorithm based on ArcGIS, and combining the spatial distance between the residential houses of community residents and community elderly care service facilities, the supply intensity, and the distribution of the elderly population, the spatial accessibility of community elderly care service facilities is quantitatively described and evaluated to obtain the elderly care service supply index. The calculation formula is as follows: For the elderly care service facility point j, search for the demand scale of each community within its search radius d0, that is, the number of elderly people in the community, and calculate the supply-demand ratio R of the elderly care service facility point j， The calculation method is as follows: Where: P k is within the spatial scope of j (d kj ≤d0) the population of street k; d kj is the spatial distance from community center k to community center j; S j is the capacity of j, expressed as area; G(d kj ,d0) is the Gaussian equation taking into account the spatial friction problem, and the calculation method is as follows: Where: For each residential area K, search for the elderly care service facilities j within its search radius, and compare the supply and demand ratio of these elderly care service facilities R j Weighted to get the elderly care service supply index A of each community i , the calculation method is as follows: Where: R i Indicates the spatial scope of region i (d kj ≤d0) supply ratio of elderly care service facilities j; First, taking the community elderly care service facility as the center, calculate the supply-demand ratio between the service capacity of the facility and the elderly population within the living circle; Secondly, taking the residential community as the center, measure the accessibility of elderly care facilities within the search radius to obtain the fairness of the accessibility of elderly care facilities within the range.

6. The method for measuring the supply fairness of community elderly care service facilities according to claim 1, wherein: In step S140, the weights of the four indicators of housing type quality, activity space scale, environmental greenness, and service scale are used to calculate the sum with the normalized data to obtain the intrinsic quality index, and the elderly care service supply index is obtained. The comprehensive score of each community elderly care service facility is obtained by using the superposition method for the intrinsic quality index and the elderly care service supply index. The calculation formula is as follows: Where: S i is the comprehensive level of elderly care service facilities in community i, A ij V is the accessibility level of community i to elderly care service facility j (j = 1, 2, 3, 4) type indicator, j is the intrinsic quality index of type j service facilities (0<V j <1).

7. The method for measuring the supply fairness of community elderly care service facilities according to claim 1, wherein: In step S150, by constructing the Lorenz curve of community elderly care facilities and calculating the corresponding Gini coefficient, analyze the degree of concentration and dispersion of the layout of community elderly care facilities; the Lorenz curve reflects the relationship between the cumulative proportion of the regional population and the proportion of the available elderly care service facility resources; the Gini coefficient is to draw the Lorenz curve through the cumulative proportion of the population and the comprehensive score of community elderly care service facilities, and judge the fairness of the distribution of elderly care facilities according to the degree of curvature of the curve. The specific calculation formula is as follows: In the formula, G is the Gini coefficient, which reflects the degree of balance in the distribution of community elderly care facilities. It can be derived from the Lorenz curve: k is the cumulative proportion of the community population variable, k = 0, ..., n; S k is the cumulative proportion of the comprehensive score of community elderly care service facilities, n is the total number of communities; the Gini coefficient value is between (0, 1), the larger the G, the more uneven the distribution of elderly care facilities, and the smaller the G, the more even the distribution of elderly care facilities; When G ≤ 0.2, it indicates absolutely average distribution; 0.2 < G ≤ 0.3 indicates relatively uniform distribution; 0.3 < G ≤ 0.4 indicates relatively reasonable distribution; 0.4 < G ≤ 0.5 indicates a relatively large gap; G > 0.5 indicates a huge gap.

8. The method for measuring the supply fairness of community elderly care service facilities according to claim 1, wherein: In step S160, evaluate the supply fairness of community elderly care service facilities according to the degree of balance of the distribution of community elderly care facilities.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program executes a method for measuring the supply fairness of a community elderly care service facility according to any one of claims 1-8.

10. A computer device, characterized in that: It includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a method for measuring the supply fairness of a community elderly care service facility according to any one of claims 1-8.

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