Urban optimization construction method based on healthy city evaluation results in subtropical areas

Abstract

The present invention discloses a city optimization construction method based on the evaluation results of healthy cities in subtropical areas, comprising: constructing a subtropical healthy city evaluation index system based on existing healthy city evaluation indicators and subtropical city health influencing factors; dividing a target city area into multiple grids according to a scale; collecting data corresponding to the subtropical healthy city evaluation index system in the target city area, and calculating the values ​​of several indicators of each grid according to the data; standardizing the values ​​of several indicators and determining the weight of each indicator; calculating the healthy city comprehensive evaluation value of each grid based on the values ​​and weights of various indicators after the standardization of each grid; identifying grids whose healthy city comprehensive evaluation values ​​are less than an evaluation threshold to form several areas to be optimized, and optimizing the areas to be optimized, so as to establish a healthy city evaluation system for subtropical areas, and thus construct cities in subtropical areas.

Description

Technical Field

[0001] The present invention relates to the field of healthy cities, and in particular to a city optimization construction method based on healthy city evaluation results in subtropical areas. Background Art

[0002] The World Health Organization European Healthy Cities Network (WHO-EHCN, WHO European Healthy Cities Network) proposed a set of 53 healthy city indicators (HCIs: Healthy City Indicators), covering "health" (including traditional mortality index), health services, environment and socio-economic conditions, which were later streamlined to 32 and the definitions were improved. my country has carried out healthy city evaluation work from the national to the local level. At the national level, the "National Healthy City Evaluation Index System (2018 Edition)" was promulgated in 2018. The indicator system includes five first-level indicators, 20 second-level indicators and 42 third-level indicators, including healthy environment, healthy society, healthy culture, health services and healthy population.

[0003] In the existing technology, many provinces and cities in my country have established healthy city evaluation index systems. The establishment time of the index systems is distributed between 2005 and 2022. The dimensions of the index systems of each city are between 3 and 9, and the number of indicators is between 26 and 221. However, in general, most of the evaluation indicators are expanded and extended on the basis of the "National Healthy City Evaluation Index System (2018 Edition)", and are mainly based on universal indicators. They do not take into account the differences in health influencing factors in various regions and cannot reflect regional characteristics. Therefore, the evaluation of health risks faced by cities under specific geographical conditions is not comprehensive enough, especially there is no healthy city evaluation index system for subtropical regions, which makes it difficult to optimize the construction of cities in subtropical regions based on relevant evaluation results. Summary of the invention

[0004] In order to solve the above technical problems, an embodiment of the present invention proposes a city optimization construction method based on the evaluation results of healthy cities in subtropical regions, which can establish a healthy city evaluation index system for subtropical regions, and further optimize the construction of cities in subtropical regions based on relevant evaluation results.

[0005] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a city optimization construction method based on the evaluation results of healthy cities in subtropical areas, comprising:

[0006] Based on the preset existing healthy city evaluation indicators and subtropical city health influencing factors, a subtropical healthy city evaluation indicator system is constructed;

[0007] Divide the target urban area into multiple grids according to the preset scale;

[0008] Collecting data corresponding to the subtropical healthy city evaluation index system in the target city area, and calculating the values ​​of several indicators of each grid according to the data;

[0009] Standardizing the values ​​of several of the indicators and determining the weight of each of the indicators;

[0010] Calculate the healthy city comprehensive evaluation value of each grid based on the values ​​of each indicator after the standardization process and the weights of each grid;

[0011] The grids whose comprehensive evaluation values ​​of the healthy city are less than a preset evaluation threshold are identified to form a plurality of areas to be optimized, and the areas to be optimized are optimized; wherein each area to be optimized is composed of at least one identified grid.

[0012] Furthermore, the existing healthy city evaluation indicators include urban environmental exposure risk assessment and active health activity space assessment; the subtropical city health influencing factors include urban waterlogging disaster indicators and heat island effect indicators.

[0013] Furthermore, the data include: water quality chemical oxygen demand data, air monitoring data, waterlogging impact data, temperature data, noise data, base station data, building data, area of ​​ecological land, park and square land, sports facilities data, medical facilities data and elderly care service facilities data;

[0014] The calculating the values ​​of several indicators of each grid according to the data specifically includes:

[0015] Perform Kriging interpolation calculation based on the water quality chemical oxygen demand data to obtain a water quality pollution index value for each of the grids;

[0016] Perform Kriging interpolation calculation based on the air monitoring data to obtain the air pollution index value of each grid;

[0017] Determine the construction land area affected by waterlogging and the total construction land area in the grid from the waterlogging impact data to calculate the waterlogging disaster index value of each grid; wherein the waterlogging disaster index value is equal to the ratio of the construction land area affected by waterlogging to the total construction land area in the grid;

[0018] Performing Kriging interpolation calculation based on the temperature data to obtain a heat island effect index value for each grid;

[0019] Determine a noise pollution spatial distribution map according to the noise data, and determine a noise overlap area between the noise pollution spatial distribution map and a preset residential land area, and for each of the grids, calculate an area ratio of the noise overlap area to the residential land area as a noise pollution index value;

[0020] Determine the number of resident users in each of the grids according to the base station data, and calculate, for each of the grids, a ratio of the number of resident users to the area of ​​the grid as a population density index value;

[0021] Acquire building area data and construction land area data with number of floors information from the building data, and calculate the gross volume ratio of the unit construction land of each grid as a building density index value based on the building area data and the construction land area data;

[0022] Calculate the ratio of the area of ​​the ecological land of each grid to the area of ​​the grid as an ecosystem indicator value; wherein the area of ​​the ecological land is the sum of the areas of the nature reserve and the ecological corridor;

[0023] An open buffer zone is established based on the park square ground, and an open overlapping area between the open buffer zone and the residential land area is determined, and an area ratio of the open overlapping area to the residential land area of ​​each grid is calculated as an open space index value;

[0024] Establishing a sports facility buffer zone according to the sports facility data, determining the sports facility overlap area between the sports facility buffer zone and the residential land area, and calculating the area ratio of the sports facility overlap area to the residential land area of ​​each grid as a sports facility index value;

[0025] Establishing a medical buffer zone according to the medical facility data, determining the medical overlapping area between the medical buffer zone and the residential land area, and calculating the area ratio of the medical overlapping area to the residential land area of ​​each grid as the medical and health service facility index value;

[0026] A senior care buffer zone is established based on the senior care service facility data, and the senior care overlapping area between the senior care buffer zone and the residential land area is determined. The area ratio of the senior care overlapping area to the residential land area of ​​each grid is calculated as the senior care service facility index value.

[0027] Furthermore, the standardization of the values ​​of the several indicators specifically includes:

[0028] Dividing the plurality of indicators into a positive indicator set and a negative indicator set;

[0029] The value of each indicator in the negative indicator set is positively processed; the positive processing is specifically: Among them, x1 is the value of each indicator in the negative indicator set, x′ is x1 after positive processing, and x 1,max is the maximum value in x1, x 1,min is the minimum value in x1;

[0030] The value of each indicator in the positive indicator set and the negative indicator set after positive processing is normalized; the normalization processing is specifically as follows: Among them, x2 is the value of each indicator in the positive indicator set and the negative indicator set after positive processing, x" is the normalized x2, x 2,max is the maximum value in x2, x 2,min is the minimum value in x2.

[0031] Furthermore, the weight of each indicator is determined as follows:

[0032] The weight of each of the indicators is calculated by the CRITIC weight method.

[0033] Furthermore, the comprehensive evaluation value of a healthy city includes the evaluation results of the urban environmental exposure risk and the evaluation results of the promotion of active health activities;

[0034] The urban environmental exposure risk assessment result is calculated by the following formula:

[0035]

[0036] The evaluation result of promoting active health activities is calculated by the following formula:

[0037]

[0038] Among them, W i1 is the urban environmental exposure risk assessment result of grid i, W i2 is the evaluation result of promoting active health activities in grid i, λ k is the weight of the kth indicator, y ik is the value of the kth index in grid i.

[0039] Further, the identifying the grids whose healthy city comprehensive evaluation values ​​are less than a preset evaluation threshold to form a plurality of areas to be optimized, and optimizing the areas to be optimized, specifically includes:

[0040] Determining a first type corresponding to each of the grids based on the urban environmental exposure risk assessment result and the active health promotion activity assessment result;

[0041] According to the principle of the wooden barrel theory, the first type corresponding to each grid is reclassified into a second type; wherein the second type includes the first type area, the second type area, the third type area and the fourth type area of ​​the urban health environment;

[0042] For each of the grids, determining a supply strategy for the grid according to the corresponding second type;

[0043] assigning a value to each of the grids according to the second type corresponding to each of the grids;

[0044] A hot and cold spot analysis is performed on each of the grids after the assignment to obtain the hot and cold spot type of each of the grids to determine a number of areas to be optimized.

[0045] Furthermore, it also includes:

[0046] Combining a plurality of the grids into a plurality of analysis units according to preset administrative divisions;

[0047] Identify the key optimization indicators of each of the analysis units, and optimize the construction of each of the analysis units according to the key optimization indicators of each of the analysis units.

[0048] Furthermore, the identifying of the key optimization index of each analysis unit and optimizing the construction of each analysis unit according to the key optimization index of each analysis unit specifically includes:

[0049] Calculating the mean of each index after standardization in each analysis unit;

[0050] Based on the mean, the threshold deviation of each indicator of each analysis unit is calculated:

[0051] The maximum threshold deviation in each of the analysis units is determined as a regional restriction factor; wherein the regional restriction factor is used to indicate the main restriction factor when performing optimization construction.

[0052] Furthermore, the scale is determined based on the daily activity range of people in the target urban area.

[0053] In summary, the present invention has the following beneficial effects:

[0054] By adopting the embodiment of the present invention, the geographical and spatial factors of the subtropical region are taken into consideration, and a healthy city evaluation index system for the subtropical region is established, and indicators related to the heat island effect and waterlogging disasters are added, which provides an example for carrying out healthy city evaluation in a specific region; a healthy environment evaluation method at the urban space level is established, and the spatial distribution of indicators is calculated through spatial analysis methods such as buffer zone analysis, spatial interpolation method, and kernel density analysis. The healthy city evaluation results are obtained through spatial superposition, and hot and cold spot analysis is used to identify areas with low healthy city evaluation values, forming a healthy city evaluation method based on the urban space perspective, which can support the analysis of the spatial differentiation characteristics of the urban health environment; the threshold deviation method is used to identify key health environment issues of spatial units of different scales, so that the evaluation results have practical application significance for optimizing the construction of healthy cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of an embodiment of a city optimization construction method based on the evaluation results of healthy cities in subtropical areas provided by the present invention;

[0056] Figure 2 is a schematic diagram of an embodiment of grid division provided by the present invention;

[0057] Figure 3 It is a flow chart of another embodiment of a city optimization construction method based on the evaluation results of healthy cities in subtropical areas provided by the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0060] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0061] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0062] See also Figure 1 , is a flow chart of an embodiment of a method for optimizing urban construction based on the evaluation results of healthy cities in subtropical areas provided by the present invention, the method comprising steps S1 to S6, which are specifically as follows:

[0063] S1, based on the preset existing healthy city evaluation indicators and subtropical city health influencing factors, construct a subtropical healthy city evaluation indicator system;

[0064] S2, divide the target urban area into multiple grids according to the preset scale;

[0065] S3, collecting data corresponding to the subtropical healthy city evaluation index system in the target city area, and calculating the values ​​of several indicators of each grid according to the data;

[0066] S4, standardizing the values ​​of several of the indicators and determining the weight of each of the indicators;

[0067] S5, calculating the healthy city comprehensive evaluation value of each grid based on the values ​​of the various indicators after the standardization processing and the weights of each grid;

[0068] S6, identifying the grids whose comprehensive evaluation values ​​of the healthy city are less than a preset evaluation threshold to form a plurality of areas to be optimized, and optimizing the areas to be optimized; wherein each area to be optimized is composed of at least one identified grid.

[0069] As an improvement of the above scheme, the existing healthy city evaluation indicators include urban environmental exposure risk assessment and active health activity space assessment; the subtropical city health influencing factors include urban waterlogging disaster indicators and heat island effect indicators.

[0070] Specifically, in combination with this embodiment, in step S1, based on existing research at home and abroad, the scope of healthy city evaluation indicators is very broad. The healthy city evaluation from the urban environment perspective is mainly evaluated from two aspects: urban environmental exposure risks and promotion of active health activities. Among them, the urban environmental exposure risk evaluation indicators mainly include water pollution, air pollution, noise pollution, population density, etc., and the promotion of active health activities evaluation indicators mainly include ecosystems, open spaces, sports facilities, medical and health service facilities, etc.

[0071] Analyze the environmental exposure risks of subtropical cities. Subtropical regions are hot and rainy in summer. High temperatures can easily cause heat stroke, threaten cardiovascular health, respiratory health, and intestinal health, induce related diseases and even cause death. Heavy rains lead to frequent waterlogging, which has an adverse effect on human health.

[0072] Therefore, this embodiment adds two indicators, waterlogging disaster and heat island effect, to the existing healthy city evaluation index system, and constructs an evaluation index system of 5 primary indicators and 12 secondary indicators covering two dimensions of urban environmental exposure risk assessment and active health activity space assessment. The constructed subtropical healthy city evaluation index system is shown in Table 1 below:

[0073]

[0074]

[0075] Table 1

[0076] As an improvement of the above scheme, the data include: water quality chemical oxygen demand data, air monitoring data, waterlogging impact data, temperature data, noise data, base station data, building data, ecological land area, park and square land area, sports facilities data, medical facilities data and elderly care service facilities data;

[0077] The calculating the values ​​of several indicators of each grid according to the data specifically includes:

[0078] Specifically, spatial analysis methods such as buffer analysis, spatial interpolation, and kernel density analysis are used, with 1000m*1000m grids as units to calculate the values ​​of various indicators and obtain the spatial distribution of the corresponding indicators.

[0079] Perform Kriging interpolation calculation based on the water quality chemical oxygen demand data to obtain a water quality pollution index value for each of the grids;

[0080] An optional embodiment, Kriging interpolation method, also known as spatial local interpolation method, is the most widely used and optimal interpolation method in geostatistics. It is based on the original sample point data and the semivariogram model, and takes unbiased optimal estimation for the area where the sample points have not been selected. In the study area, the measured value of n sample points of a regionalized variable is known to be Z(x i )(i=1,2,3……,n), by calculating the weighting coefficient λ i , and then calculate the predicted value Z at the waiting point x0 by weighted average * (x0), that is

[0081]

[0082] According to the "Surface Water Environmental Quality Standard" (GB3838-2002), Class I and Class II water quality requires the chemical oxygen demand to be less than or equal to 15. Therefore, the chemical oxygen demand less than or equal to 15 is defined as the high-quality section, and greater than 15 is defined as the polluted section.

[0083] Calculate the proportion of polluted sections in each grid to the total water surface as the water quality pollution index value of the grid, that is,

[0084]

[0085] In the formula, y i1 is the water pollution index value of the i-th grid, A i is the surface water area of ​​the ith grid, A′ i is the surface water area with chemical oxygen demand greater than 15 in the i-th grid.

[0086] Perform Kriging interpolation calculation based on the air monitoring data to obtain the air pollution index value of each grid;

[0087] An optional embodiment uses the Kriging spatial interpolation method (Formula 1) based on the PM2.5 monitoring point data to obtain a spatial distribution grid map of urban air quality covering the entire city.

[0088] Calculate the mean value of all grids in each grid as the air pollution index value of the grid, that is,

[0089]

[0090] In the formula, y i2 is the air pollution index value of the i-th grid, B ij is the PM2.5 value of the jth grid in the ith grid, and n is the number of grids in the ith grid.

[0091] Determine the construction land area affected by waterlogging and the total construction land area in the grid from the waterlogging impact data to calculate the waterlogging disaster index value of each grid; wherein the waterlogging disaster index value is equal to the ratio of the construction land area affected by waterlogging to the total construction land area in the grid;

[0092] An optional implementation method, waterlogging disaster: create a 500-meter buffer zone for the rainstorm waterlogging point, superimpose the construction land data, and obtain the spatial distribution map of the construction land affected by waterlogging. Calculate the ratio of the construction land affected by waterlogging to the total construction land in the grid as the waterlogging disaster index value of the grid, that is,

[0093]

[0094] In the formula, y i3 is the waterlogging disaster index value of the i-th grid, c i is the construction land area in the ith grid, c′ i It is the construction land area covered by the 500-meter buffer zone of the rainstorm waterlogging point in the i-th grid.

[0095] Performing Kriging interpolation calculation based on the temperature data to obtain a heat island effect index value for each grid;

[0096] An optional embodiment uses the Kriging spatial interpolation method (Formula 1) based on the average temperature of each street from 2011 to 2020 provided by the meteorological department to obtain a spatial distribution map of the average urban temperature covering the entire city.

[0097] Calculate the average value of the average temperature from 2011 to 2020 in each grid as the heat island effect index value of the grid, that is,

[0098]

[0099] In the formula, y i4 is the heat island effect index value of the ith grid, D ij is the average temperature value from 2011 to 2020 of the jth grid in the ith grid, and n is the number of grids in the ith grid.

[0100] Determine a noise pollution spatial distribution map according to the noise data, and determine a noise overlap area between the noise pollution spatial distribution map and a preset residential land area, and for each of the grids, calculate an area ratio of the noise overlap area to the residential land area as a noise pollution index value;

[0101] An optional embodiment, according to the "Urban Area Environmental Noise Standard" (GB3096-93), establishes a buffer zone with a maximum impact distance of 200 meters for urban expressways and a maximum impact distance of 50 meters for main roads to obtain a spatial distribution map of noise pollution.

[0102] The noise pollution spatial distribution map is superimposed on the residential land to obtain the residential land area affected by noise in each grid. The ratio of the residential land area to the total residential land area of ​​each grid is calculated as the noise pollution index value of each grid, that is,

[0103]

[0104] In the formula, y i5 is the waterlogging disaster index value of the i-th grid, E i is the residential area in the ith grid, E′ i It is the residential land area covered by the 200-meter buffer zone of the urban expressway or the 50-meter buffer zone of the main road in the i-th grid.

[0105] Determine the number of resident users in each of the grids according to the base station data, and calculate, for each of the grids, a ratio of the number of resident users to the area of ​​the grid as a population density index value;

[0106] An optional embodiment is to count the sum of the number of permanent users of the mobile communication operator base stations in each grid and divide it by the grid area to serve as the population density index of each grid, that is,

[0107]

[0108] In the formula, y i6 is the population density index value of the i-th grid, F′ ij is the number of permanent users of the jth base station in the i-th grid, n is the number of base stations in the i-th grid, F i is the area of ​​the ith grid.

[0109] Acquire building area data and construction land area data with number of floors information from the building data, and calculate the gross volume ratio of the unit construction land of each grid as a building density index value based on the building area data and the construction land area data;

[0110] An optional embodiment uses the building area data with the number of floors and the construction land area data to calculate the gross plot ratio of the unit construction land in each grid as the building density index, that is,

[0111]

[0112] In the formula, y ii is the building density index value of the i-th grid, G′ ij is the building area of ​​the g-th building in the i-th grid, n is the number of buildings in the i-th grid, G i is the construction land area of ​​the ith grid.

[0113] Calculate the ratio of the area of ​​the ecological land of each grid to the area of ​​the grid as an ecosystem indicator value; wherein the area of ​​the ecological land is the sum of the areas of the nature reserve and the ecological corridor;

[0114] An optional embodiment is to calculate the sum of the areas of nature reserves and ecological corridors in each grid, divide it by the area of ​​each grid, and use the obtained value as the ecosystem indicator, that is,

[0115]

[0116] In the formula, y i8 is the ecosystem indicator value of the i-th grid, h i is the area of ​​the nature reserve in the ith grid, h i ′ is the area of ​​the ecological corridor in the i-th grid, H i is the area of ​​the ith grid.

[0117] An open buffer zone is established based on the park square ground, and an open overlapping area between the open buffer zone and the residential land area is determined, and an area ratio of the open overlapping area to the residential land area of ​​each grid is calculated as an open space index value;

[0118] An optional implementation example is to establish a buffer zone based on the park square ground and a service radius of 500 meters, and calculate the proportion of residential land in each grid covered by the buffer zone as the open space index, that is,

[0119]

[0120] In the formula, y i9 is the open space index value of the i-th grid, I i ′ is the residential land area within the 500-meter buffer zone of the park square in the i-th grid, I i is the residential land area in the i-th grid.

[0121] Establishing a sports facility buffer zone according to the sports facility data, determining the sports facility overlap area between the sports facility buffer zone and the residential land area, and calculating the area ratio of the sports facility overlap area to the residential land area of ​​each grid as a sports facility index value;

[0122] An optional implementation method is to divide sports facilities into three categories: high-level facilities, district-level facilities, and grassroots-level facilities. Buffer zones are established with service radii of 6,000 meters, 4,000 meters, and 500 meters, respectively. The proportion of residential land in each grid covered by the buffer zone is calculated to obtain the sports facility index value of each grid, that is,

[0123]

[0124] In the formula, y i10 is the sports facilities index value of the i-th grid, J i ′ is the residential area within the sports facility buffer zone in the i-th grid, J i is the residential land area in the i-th grid.

[0125] Establishing a medical buffer zone according to the medical facility data, determining the medical overlapping area between the medical buffer zone and the residential land area, and calculating the area ratio of the medical overlapping area to the residential land area of ​​each grid as the medical and health service facility index value;

[0126] An optional implementation method is to divide medical facilities into two categories: hospitals and primary medical and health facilities, with service radii of 1000 meters and 500 meters respectively, to establish a buffer zone, calculate the proportion of residential land in each grid covered by the buffer zone, and obtain the index value of medical and health service facilities in each grid, that is,

[0127]

[0128] In the formula, y i11 is the index value of medical and health service facilities in the ith grid, K i ′ is the residential land area within the buffer zone of medical and health service facilities in the i-th grid, K i is the residential land area in the i-th grid.

[0129] A senior care buffer zone is established based on the senior care service facility data, and the senior care overlapping area between the senior care buffer zone and the residential land area is determined. The area ratio of the senior care overlapping area to the residential land area of ​​each grid is calculated as the senior care service facility index value.

[0130] An optional implementation example is to establish a buffer zone for elderly care service facilities with a service radius of 1000 meters, calculate the proportion of residential land in each grid covered by the buffer zone, and obtain the elderly care service facility index value of each grid, that is,

[0131]

[0132] In the formula, y i12 is the index value of the elderly care service facilities in the i-th grid, L′ i is the residential land area within the buffer zone of the elderly care service facility in the i-th grid, L i is the residential land area in the i-th grid.

[0133] As an improvement to the above solution, the standardization of the values ​​of the several indicators specifically includes:

[0134] Dividing the plurality of indicators into a positive indicator set and a negative indicator set;

[0135] The value of each indicator in the negative indicator set is positively processed; the positive processing is specifically:

[0136] Among them, x1 is the value of each indicator in the negative indicator set, x′ is x1 after positive processing, and x 1,max is the maximum value in x1, x 1,min is the minimum value in x1;

[0137] The value of each indicator in the positive indicator set and the negative indicator set after positive processing is normalized; the normalization processing is specifically as follows:

[0138] Among them, x2 is the value of each indicator in the positive indicator set and the negative indicator set after positive processing, x″ is the normalized x2, x 2,max is the maximum value in x2, x 2,min is the minimum value in x2.

[0139] Specifically, the indicators are divided into a positive indicator set and a negative indicator set, as shown in Table 2 below:

[0140]

[0141]

[0142] Table 2

[0143] It should be noted that the values ​​of the normalized indicators are unified to the interval [0, 1].

[0144] As an improvement of the above solution, the weight of each indicator is determined as follows:

[0145] The weight of each of the indicators is calculated by the CRITIC weight method.

[0146] For example, the CRITIC weight method is used to calculate the weights of various indicators. The CRITIC weight method is an objective weighting method, and its idea is to use the conflict and information content between indicators to determine their weight values. The higher the correlation coefficient between two indicators, the smaller the conflict; the larger the standard deviation of a single indicator, the greater the amount of information it contains.

[0147] Conflict between indicators ij and the information content of a single indicator C j It is expressed as:

[0148]

[0149] Where r ij is the correlation coefficient between indicators i and j, is the standard deviation of the jth indicator, and n is the number of evaluations. j The larger it is, the more important the indicator is.

[0150] The weight coefficient is expressed as:

[0151]

[0152] Where W j is the weight coefficient of the jth indicator.

[0153] The healthy city evaluation indicators are divided into two categories: urban environmental exposure risks and promotion of active health activities. The indicator weights are calculated separately, and the final weights of each indicator are shown in Tables 3 and 4 below:

[0154]

[0155]

[0156] Table 3

[0157]

[0158] Table 4

[0159] As an improvement of the above scheme, the comprehensive evaluation value of the healthy city includes the evaluation results of the urban environmental exposure risk and the evaluation results of promoting active health activities;

[0160] The urban environmental exposure risk assessment result is calculated by the following formula:

[0161]

[0162] The evaluation result of promoting active health activities is calculated by the following formula:

[0163]

[0164] Among them, W i1 is the urban environmental exposure risk assessment result of grid i, W i2 is the evaluation result of promoting active health activities in grid i, λ k is the weight of the kth indicator, y ik is the value of the kth index in grid i.

[0165] It should be noted that in formula (19):

[0166] The value range of k is [1,7], corresponding to the secondary indicators numbered 1 to 7 in Table 3.

[0167] The evaluation results are all in the interval [0,1]. The closer to 1, the lower the urban environmental exposure risk, and vice versa.

[0168] The quantile method was used to classify the evaluation results. The grids with result values ​​between 0.76-0.94 were low-risk areas for urban environmental exposure, those with values ​​between 0.69-0.75 were medium-risk areas for urban environmental exposure, and those with values ​​between 0.31-0.68 were high-risk areas for urban environmental exposure.

[0169] In formula (20):

[0170] The value range of k is [8,12], corresponding to the secondary indicators numbered 8 to 12 in Table 4.

[0171] The evaluation results are all in the interval [0,1]. The closer to 1, the higher the possibility of promoting active health activities, and vice versa.

[0172] From a people-oriented perspective, the object of evaluation for promoting active health activities should be a grid containing residential land to avoid interference from indicator values ​​in uninhabited areas.

[0173] The quantile method was used to classify the evaluation results. The grids with result values ​​between 0.50-0.94 were high promotion areas of active health activities, the grids with values ​​between 0.28-0.49 were medium promotion areas of active health activities, and the grids with values ​​between 0.00-0.27 were low promotion areas of active health activities.

[0174] As an improvement of the above scheme, the grids whose comprehensive evaluation values ​​of the healthy cities are less than a preset evaluation threshold are identified to form a number of areas to be optimized, and the areas to be optimized are optimized, specifically including:

[0175] Determining a first type corresponding to each of the grids based on the urban environmental exposure risk assessment result and the active health promotion activity assessment result;

[0176] According to the principle of the wooden barrel theory, the first type corresponding to each grid is reclassified into a second type; wherein the second type includes the first type area, the second type area, the third type area and the fourth type area of ​​the urban health environment;

[0177] For each of the grids, determining a supply strategy for the grid according to the corresponding second type;

[0178] assigning a value to each of the grids according to the second type corresponding to each of the grids;

[0179] A hot and cold spot analysis is performed on each of the grids after the assignment to obtain the hot and cold spot type of each of the grids to determine a number of areas to be optimized.

[0180] Specifically, based on the results of urban environmental exposure risk assessment and promotion of active health activities evaluation, the grids are divided into 9 types: “low-high”, “low-medium”, “medium-high”, “medium-medium”, “low-low”, “high-high”, “medium-low”, “high-medium”, and “high-low”.

[0181] Based on the "barrel theory", the above 9 types are reclassified into 4 categories:

[0182] The “low-high” type is a Class I urban health environment area, with low health risks and good health resources;

[0183] The “low-medium”, “medium-high” and “medium-medium” types are Class II urban health environment areas, and it is recommended to investigate health risks and appropriately increase the supply of health resources;

[0184] The three types of urban health environment areas are "low-low", "high-high", "medium-low" and "high-medium", which require general remediation, targeted investigation of health risks and improvement of the supply of supplementary health resources.

[0185] The “high-low” type is the fourth category of urban health environment areas, which require key remediation, comprehensive investigation of health risks, and replenishment of health resource supply.

[0186] Among them, the weights of the evaluation indicators for promoting active health activities in healthy cities are shown in Table 5 below:

[0187]

[0188] Exploratory spatial data analysis (ESDA) was used to explore the clustering and differentiation characteristics of the comprehensive evaluation results of healthy cities.

[0189] Exploratory spatial data analysis aims to reveal similar clustering and differentiation characteristics by exploring the intrinsic relationship of space. The analysis methods can be divided into two categories: global autocorrelation and local autocorrelation. Global autocorrelation generally explores the degree of aggregation or differentiation of the overall space. Research methods include Moran's I index, global G statistics, etc. Local autocorrelation mainly reveals the high-high aggregation and low-low aggregation characteristics of local areas, that is, the formation of "hot spots" and "cold spots". Research methods include local Getis-Ord Gi* index, etc.

[0190] The local Getis-Ord Gi* index was selected to explore the local autocorrelation of various indicators, and the "hot spots" and "cold spots" of various health risks and health resources were identified, providing a basis for the optimized construction of healthy cities.

[0191] The Getis-Ord Gi* calculation formula is as follows:

[0192]

[0193] where x j is the attribute value of feature j, w i,j is the spatial weight between features i and j, n is the total number of features, and:

[0194]

[0195] The z-score is a direct interpretation of the results, so no further calculations are required. For statistically significant z-scores, when the z-score is positive, the higher the value, the tighter the clustering of high values ​​(hot spots); when the z-score is negative, the lower the value, the tighter the clustering of low values ​​(cold spots).

[0196] The first, second, third and fourth category areas were assigned values ​​of 4, 3, 2 and 1 respectively, and then the hot and cold spot analysis was performed to obtain the areas with low health city evaluation. Taking Guangzhou as an example, the areas with low health city evaluation values ​​are concentrated at the junction of Baiyun District and Huadu District, the southern part of Zengcheng District, the northeast, northwest and southwest of Panyu District, and the northwest and southeast of Nansha District.

[0197] As an improvement of the above scheme, it also includes:

[0198] Combining a plurality of the grids into a plurality of analysis units according to preset administrative divisions;

[0199] Identify the key optimization indicators of each of the analysis units, and optimize the construction of each of the analysis units according to the key optimization indicators of each of the analysis units.

[0200] As an improvement of the above solution, the key optimization index of each analysis unit is identified, and each analysis unit is optimized according to the key optimization index of each analysis unit, which specifically includes:

[0201] Calculating the mean of each index after standardization in each analysis unit;

[0202] Based on the mean, the threshold deviation of each indicator of each analysis unit is calculated:

[0203] The maximum threshold deviation in each of the analysis units is determined as a regional restriction factor; wherein the regional restriction factor is used to indicate the main restriction factor when performing optimization construction.

[0204] Exemplarily, the district, street or community is used as the analysis unit, and the mean of each indicator in the unit is calculated. The threshold deviation method is used to compare the mean of each indicator of the analysis unit with the mean of the indicator within the municipal area, so as to obtain the indicators that need to be optimized in each district, street or community.

[0205] The specific steps are as follows:

[0206] 1) Calculate the threshold deviation:

[0207]

[0208] Among them, D ij represents the deviation of the single threshold of indicator j in area i; x ij represents the actual value of index j in region i; s j represents the threshold of index j, that is, the mean of each index; w j Represents the weight of indicator j.

[0209] 2) Compare and obtain the maximum threshold deviation of 12 indicators in the same administrative unit:

[0210] D i =MAX(D i1 ,D i2 ,……,D i12 )

[0211] Among them, D i Represents the maximum value of the threshold deviation of all elements in region i. If x ij Within the pre-set threshold range, D ij =0; if x ij If it is not within the threshold range, the maximum deviation degree of the region is calculated according to the formula, and the element with the maximum threshold deviation degree is the regional restrictive element.

[0212] Taking the district-level administrative unit as an example, the threshold deviation of each administrative unit is calculated and the maximum threshold deviation is obtained by comparison. The main health problem in Baiyun and Huangpu is air pollution, the main health problem in Conghua and Huadu is the insufficient coverage of open spaces such as parks and green spaces, the main problem in Panyu, Haizhu, Liwan, Nansha and Yuexiu is the heat island effect, the main problem in Tianhe is the insufficient coverage of the ecological environment, and the main problem in Zengcheng is the insufficient coverage of elderly care service facilities.

[0213] As an improvement of the above solution, the scale is determined based on the daily activity range of people in the target urban area.

[0214] For details, see Figure 2 The goal of the Healthy City Action is to build a healthy living environment. Therefore, the scale of grid unit division mainly considers the daily activity range of urban residents. Referring to the radius of the 15-minute living circle, a 1000m*1000m grid is established as the basic unit for subsequent analysis.

[0215] In summary, the present invention has the following beneficial effects:

[0216] By adopting the embodiment of the present invention, the geographical and spatial factors of the subtropical region are taken into consideration, and a healthy city evaluation index system for the subtropical region is established, and indicators related to the heat island effect and waterlogging disasters are added, which provides an example for carrying out healthy city evaluation in a specific region; a healthy environment evaluation method at the urban space level is established, and the spatial distribution of indicators is calculated through spatial analysis methods such as buffer zone analysis, spatial interpolation method, and kernel density analysis. The healthy city evaluation results are obtained through spatial superposition, and hot and cold spot analysis is used to identify areas with low healthy city evaluation values, forming a healthy city evaluation method based on the urban space perspective, which can support the analysis of the spatial differentiation characteristics of the urban health environment; the threshold deviation method is used to identify key health environment issues of spatial units of different scales, so that the evaluation results have practical application significance for optimizing the construction of healthy cities.

[0217] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary hardware platform, and of course can also be implemented entirely by hardware. Based on such an understanding, all or part of the contribution of the technical solution of the present invention to the background technology can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0218] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing urban construction based on the evaluation results of healthy cities in subtropical areas, characterized in that: include: Based on the preset existing healthy city evaluation indicators and subtropical city health influencing factors, a subtropical healthy city evaluation indicator system is constructed; Divide the target urban area into multiple grids according to the preset scale; Collecting data corresponding to the subtropical healthy city evaluation index system in the target city area, and calculating the values ​​of several indicators of each grid according to the data; Standardizing the values ​​of several of the indicators and determining the weight of each of the indicators; Calculate the healthy city comprehensive evaluation value of each grid based on the values ​​of each indicator after the standardization process and the weights of each grid; Identify the grids whose healthy city comprehensive evaluation values ​​are less than a preset evaluation threshold to form a plurality of areas to be optimized, and optimize the areas to be optimized; wherein each area to be optimized is composed of at least one identified grid; The data include: water quality chemical oxygen demand data, air monitoring data, waterlogging impact data, temperature data, noise data, base station data, building data, ecological land area, park and square land area, sports facilities data, medical facilities data and elderly care service facilities data; The calculating the values ​​of several indicators of each grid according to the data specifically includes: Perform Kriging interpolation calculation based on the water quality chemical oxygen demand data to obtain a water quality pollution index value for each of the grids; Perform Kriging interpolation calculation based on the air monitoring data to obtain the air pollution index value of each grid; Determine the construction land area affected by waterlogging and the total construction land area in the grid from the waterlogging impact data to calculate the waterlogging disaster index value of each grid; wherein the waterlogging disaster index value is equal to the ratio of the construction land area affected by waterlogging to the total construction land area in the grid; Performing Kriging interpolation calculation based on the temperature data to obtain a heat island effect index value for each grid; Determine a noise pollution spatial distribution map according to the noise data, and determine a noise overlap area between the noise pollution spatial distribution map and a preset residential land area, and for each of the grids, calculate an area ratio of the noise overlap area to the residential land area as a noise pollution index value; Determine the number of resident users in each of the grids according to the base station data, and calculate, for each of the grids, a ratio of the number of resident users to the area of ​​the grid as a population density index value; Acquire building area data and construction land area data with number of floors information from the building data, and calculate the gross volume ratio of the unit construction land of each grid as a building density index value based on the building area data and the construction land area data; Calculate the ratio of the area of ​​the ecological land of each grid to the area of ​​the grid as an ecosystem indicator value; wherein the area of ​​the ecological land is the sum of the areas of the nature reserve and the ecological corridor; An open buffer zone is established based on the park square ground, and an open overlapping area between the open buffer zone and the residential land area is determined, and an area ratio of the open overlapping area to the residential land area of ​​each grid is calculated as an open space index value; Establishing a sports facility buffer zone according to the sports facility data, determining the sports facility overlap area between the sports facility buffer zone and the residential land area, and calculating the area ratio of the sports facility overlap area to the residential land area of ​​each grid as a sports facility index value; Establishing a medical buffer zone according to the medical facility data, determining the medical overlapping area between the medical buffer zone and the residential land area, and calculating the area ratio of the medical overlapping area to the residential land area of ​​each grid as the medical and health service facility index value; A senior care buffer zone is established based on the senior care service facility data, and the senior care overlapping area between the senior care buffer zone and the residential land area is determined. The area ratio of the senior care overlapping area to the residential land area of ​​each grid is calculated as the senior care service facility index value.

2. The city optimization construction method based on the evaluation results of subtropical healthy cities according to claim 1, characterized in that: The existing healthy city evaluation indicators include urban environmental exposure risk assessment and active health activity space assessment; the subtropical city health influencing factors include urban waterlogging disaster indicators and heat island effect indicators.

3. The city optimization construction method based on the evaluation results of subtropical healthy cities according to claim 1, characterized in that: The standardization of the values ​​of the plurality of indicators specifically includes: Dividing the plurality of indicators into a positive indicator set and a negative indicator set; The value of each indicator in the negative indicator set is positively processed; the positive processing is specifically: Among them, x1 is the value of each indicator in the negative indicator set, x ′ is x1 after forward processing, x 1,max is the maximum value in x1, x 1,min is the minimum value in x1; The value of each indicator in the positive indicator set and the negative indicator set after positive processing is normalized; the normalization processing is specifically as follows: Among them, x2 is the value of each indicator in the positive indicator set and the negative indicator set after positive processing, x″ is the normalized x2, x 2,max is the maximum value in x2, x 2,min is the minimum value in x2.

4. The city optimization construction method based on the evaluation results of subtropical healthy cities according to claim 1, characterized in that: The weight of each indicator is determined as follows: The weight of each of the indicators is calculated by the CRITIC weight method.

5. The city optimization construction method based on the evaluation results of subtropical healthy cities according to claim 1, characterized in that: The comprehensive evaluation value of a healthy city includes the evaluation results of the urban environmental exposure risk and the evaluation results of the promotion of active health activities; The urban environmental exposure risk assessment result is calculated by the following formula: The evaluation result of promoting active health activities is calculated by the following formula: Among them, W i1 is the urban environmental exposure risk assessment result of grid i, W i2 is the evaluation result of promoting active health activities in grid i, λ k is the weight of the kth indicator, y ik is the value of the kth index in grid i.

6. The city optimization construction method based on the evaluation results of subtropical healthy cities according to claim 5, characterized in that: The identifying the grids whose healthy city comprehensive evaluation values ​​are less than a preset evaluation threshold to form a plurality of areas to be optimized, and optimizing the areas to be optimized, specifically includes: Determining a first type corresponding to each of the grids based on the urban environmental exposure risk assessment result and the active health promotion activity assessment result; According to the principle of the wooden barrel theory, the first type corresponding to each grid is reclassified into a second type; wherein the second type includes the first type area, the second type area, the third type area and the fourth type area of ​​the urban health environment; For each of the grids, determining a supply strategy for the grid according to the corresponding second type; assigning a value to each of the grids according to the second type corresponding to each of the grids; A hot and cold spot analysis is performed on each of the grids after the assignment to obtain the hot and cold spot type of each of the grids to determine a number of areas to be optimized.

7. The city optimization construction method based on the evaluation results of subtropical healthy cities according to claim 1, characterized in that: Also includes: Combining a plurality of the grids into a plurality of analysis units according to preset administrative divisions; Identify the key optimization indicators of each of the analysis units, and optimize the construction of each of the analysis units according to the key optimization indicators of each of the analysis units.

8. The method for optimizing urban construction based on the evaluation results of healthy cities in subtropical areas according to claim 7, characterized in that: The identifying of the key optimization index of each analysis unit and optimizing the construction of each analysis unit according to the key optimization index of each analysis unit specifically includes: Calculating the mean of each index after standardization in each analysis unit; Based on the mean, the threshold deviation of each indicator of each analysis unit is calculated: The maximum threshold deviation in each of the analysis units is determined as a regional restriction factor; wherein the regional restriction factor is used to indicate the main restriction factor when performing optimization construction.

9. The method for optimizing urban construction based on the evaluation results of healthy cities in subtropical areas according to claim 1, characterized in that: The scale is determined based on the daily activity range of people in the target urban area.

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