KR102679714B1 - A system and method for evaluating the quality of life at different sparial levels - Google Patents

Abstract

A comparative technique between multi-criteria decision-making methods and quality-of-life assessments at different spatial levels is disclosed. A quality of life evaluation method performed by an evaluation system according to an embodiment includes calculating the suitability of quality of life based on a plurality of criteria with respect to spatial data; And it may include a step of evaluating quantitative quality of life at a spatial level by combining each result information regarding the suitability of the calculated quality of life with a multiple decision-making method.

Description

{A SYSTEM AND METHOD FOR EVALUATING THE QUALITY OF LIFE AT DIFFERENT SPARIAL LEVELS}

The explanation below is about techniques for assessing quality of life.

Achieving good urban form has been a problem since the formation of early cities. Humanity's tendency toward urban environments and urbanization has made quality of life more prominent. Quality of life has not been studied simultaneously in different spatial units and on a smaller scale, but maintaining a similar level of quality of life in growing urban centers is becoming increasingly important. Measuring quality of life can be helpful to policy makers and experts for sustainable development in various social aspects, including the economy, environment, decision-making and planning.

In the past, each standard for calculating quality of life was selected, quality of life was measured according to each selected standard, and then integrated with multiple decision-making methods. Regarding quality of life, many conventional technologies use the Analytical Hierarchy Process (AHP) method for data integration, but this may be inefficient, so other methods need to be reviewed.

Reference materials: Korean Patent Publication No. 10-2021-0117242 (September 28, 2021), Korean Patent Publication No. 10-2017-0012608 (February 2, 2017)

It can provide a comparative method and system between multi-criteria decision-making methods and quality of life assessment at various spatial levels.

Quantitative quality of life results at the spatial level by combining the respective resulting information on the adequacy of quality of life calculated according to the socioeconomic dimension, environmental dimension, and accessibility of urban facilities and services to spatial data with multiple decision-making methods. A method and system for evaluating can be provided.

A quality of life evaluation method performed by an evaluation system includes calculating the suitability of quality of life according to a plurality of criteria with respect to spatial data; And it may include a step of evaluating quantitative quality of life at a spatial level by combining each result information regarding the suitability of the calculated quality of life with a multiple decision-making method.

The calculating step may include calculating the suitability of quality of life based on a plurality of criteria including socioeconomic dimension, environmental dimension, and accessibility of urban facilities and services with respect to the spatial data.

The calculating step involves performing factor analysis on the socioeconomic dimension using any one of the method using a correlation matrix or the KMO coefficient and Bartlett test method, and using the performed factor analysis to determine the socioeconomic dimension. It may include calculating the suitability of quality of life.

The calculating step may include calculating the suitability of quality of life for environmental dimensions including greenness, surface temperature, air pollution, and noise pollution.

The calculation step is based on statistical data for a plurality of land uses, including parks, fire stations, gas stations, BRT (Bus Rapid Transit) stations, city bus stations, metro stations, mosques, hospitals and clinics, through network analysis. Obtaining a service area, using an aggregation method to calculate the accessibility of the obtained service area, and calculating the adequacy of quality of life for accessibility to urban facilities and services through a weighted average for each land use. You can.

The evaluating step may include combining each result information regarding the calculated suitability of quality of life with different multiple decision-making methods including SAW, TOPSIS, VIKOR, and ELECTRE.

The evaluating step may include comparing the ranking of zones with different decision-making methods between different spatial levels and decision-making methods.

The evaluating step may include evaluating quantitative quality of life outcomes at three spatial levels: comparing sub-areas of two areas, comparing sub-areas of one area, or comparing two areas.

The evaluation system includes a quality of life suitability calculation unit that calculates the suitability of quality of life based on a plurality of criteria with respect to spatial data; and a quality of life evaluation unit that combines each result information regarding the suitability of the calculated quality of life with a multiple decision-making method to evaluate the quantitative quality of life at a spatial level.

The quality of life suitability calculation unit may calculate the suitability of quality of life based on a plurality of criteria including socioeconomic dimension, environmental dimension, and accessibility of urban facilities and services with respect to the spatial data.

The quality of life suitability calculation unit performs factor analysis on the socioeconomic dimension using any one of the method using a correlation matrix or the KMO coefficient and Bartlett test method, and uses the performed factor analysis to determine social The adequacy of quality of life with economic dimensions can be calculated.

The quality of life suitability calculation unit may include calculating the suitability of the quality of life for environmental dimensions including greenness, surface temperature, air pollution, and noise pollution.

The calculation step is based on statistical data for a plurality of land uses, including parks, fire stations, gas stations, BRT (Bus Rapid Transit) stations, city bus stations, metro stations, mosques, hospitals and clinics, through network analysis. It is possible to obtain a service area, use an aggregation method to calculate the accessibility of the obtained service area, and calculate the suitability of quality of life for accessibility to urban facilities and services through a weighted average for each land use.

The quality of life evaluation unit combines each result information on the suitability of the calculated quality of life with different multiple decision-making methods including SAW, TOPSIS, VIKOR, and ELECTRE, and combines them between different spatial levels and decision-making methods. You can compare the rankings of districts using different decision-making methods.

The quality of life evaluation unit may evaluate quantitative quality of life results at three spatial levels, such as comparing sub-zones of two zones, comparing sub-zones of one zone, or comparing two zones.

Comparison between multi-criteria decision-making methods and quality-of-life assessments at different spatial levels makes it possible to quantitatively assess quality of life from an objective perspective.

1 is a diagram illustrating an operation of measuring objective quality of life using a multiple decision-making method in an evaluation system according to an embodiment.
FIG. 2 is a diagram illustrating an operation of combining each result information on the suitability of quality of life calculated according to a plurality of dimensions with a multiple decision-making method in an evaluation system according to an embodiment.
Figure 3 is a block diagram for explaining the configuration of an evaluation system according to an embodiment.
Figure 4 is a flowchart illustrating a method of evaluating quality of life in an evaluation system according to an embodiment.
Figure 5 is a diagram showing a map of the city of Tehran, according to one embodiment.
Figure 6 is a diagram showing the status of the socioeconomic dimension, according to one embodiment.
Figure 7 is a diagram showing a green color display state, according to one embodiment.
Figure 8 is a diagram showing the surface temperature display state according to one embodiment.
Figure 9 is a diagram showing an air pollution display state in one embodiment.
Figure 10 is a diagram showing a noise pollution display state in one embodiment.
Figure 11 is a diagram showing the state of the environmental dimension, according to one embodiment.
Figure 12 is a diagram showing the accessibility status of city facilities and services, according to one embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating an operation of measuring objective quality of life using a multiple decision-making method in an evaluation system according to an embodiment.

The evaluation system can evaluate quality of life from an objective perspective. To objectively evaluate quality of life, socioeconomic dimensions, environmental dimensions, and accessibility to urban facilities and services must be considered. For this purpose, for example, census data, satellite photos, land use layout maps, traffic control information, etc. can be used.

Research on objective approaches to quality of life and multiple decision-making methods can be conducted. Multiple decision-making methods used to objectively assess quality of life and criteria to be considered for assessing quality of life may be included. To this end, a preliminary study could be conducted to provide a general summary of the criteria and methods for calculating quality of life. Then, the most appropriate criteria and methods can be selected.

Afterwards, preprocessing can be completed. Data collection may be performed in conjunction with preprocessing, and data preprocessing and components may be organized using factor analysis as data preparation for the collected data is completed. Socioeconomic dimensions of quality of life can be measured using factor analysis. The suitability of data for factor analysis can be assessed using the KMO coefficient and the Bartlett test. The environmental dimension can be measured using four indicators: (1) greenness index, (2) surface temperature index, (3) air pollution index, and (4) noise pollution index. Accessibility of city facilities and services includes (1) parks, (2) fire stations, (3) gas stations, (4) bus rapid transit (BRT) stations, (5) city bus stations, (6) metro stations, and (7) mosques. It can be measured using 9 land use data: temples, (8) hospitals and clinics.

Quality of life was measured in terms of each criterion, and the results could be combined with multiple decision-making methods, including SAW, TOPSIS, VIKOR, and ELECTRE. Different decision-making methods and the ranking of areas in different spatial units can be compared. At this time, quality of life can be evaluated at three spatial levels. The first level compares subzones of two zones, the second level compares subzones of one zone, and the third level compares two zones. Comparisons can be made between integration methods at each of these three spatial levels. Correlations between integration methods can be calculated. Additionally, the stability index of the integrated method can be presented.

FIG. 2 is a diagram illustrating an operation of combining each result information on the suitability of quality of life calculated according to a plurality of dimensions with a multiple decision-making method in an evaluation system according to an embodiment.

The evaluation system 100 may calculate the suitability of quality of life based on a plurality of criteria for spatial data. Spatial data may include census data, satellite imagery, land use maps, and traffic control information for specific areas (cities/regions/districts, etc.). The evaluation system 100 can quantitatively evaluate quality of life at a spatial level by combining each result information about the calculated suitability of quality of life with multiple decision-making methods. In other words, numerical analysis can be strengthened by comparing several multiple decision-making methods.

In the embodiment, a comparison of the quality of life in districts 6 and 13 of Tehran, Iran will be described as an example with reference to FIG. 5 . Referring to Figure 5, it shows a map of the city of Tehran, showing the city's district units and sub-districts of districts 6 and 13. For example, to assess quality of life at the spatial level, data from the 2011 census from the Iranian Statistical Center, satellite images from Landsat 8, land use layout maps, and traffic control information from Tehran can be used.

In detail, the evaluation system can calculate the adequacy of quality of life according to a plurality of criteria (110) including socioeconomic dimension (101), environmental dimension (102), and accessibility to urban facilities and services (103).

Figure 6 is a diagram showing the status of the socioeconomic dimension in one embodiment. Quality of life has an important relationship with socioeconomic variables. For example, census data may be used to calculate socioeconomic variables. At this time, some of the socioeconomic variables may be selected. As socioeconomic variables are selected from census data, the percentage of the selected variable relative to other variables can be calculated. Factor analysis can then be performed to analyze the data set. Factor analysis is a statistical method that aims to reduce the amount of data and determine the most important and effective variables in the analysis, as well as to find hidden structures within the data. This method requires examining the suitability of the data for input into factor analysis. As a method of selecting appropriate variables, factor analysis on the socioeconomic dimension is performed using a correlation matrix, KMO coefficient, and Bartlett test method, and the performed factor analysis is used to determine the socioeconomic dimension. Quality of life suitability can be calculated. The method of using a correlation matrix shows whether a relationship exists between variables by using census data to calculate a correlation matrix between variables, forming clusters with no correlated variables and those with significant correlations with other variables. Variables without are excluded from the analysis. This method of using a correlation matrix determines how some variable is related to a small number of factors (unobserved variables). The KMO coefficient and Bartlett test method uses the MO coefficient and Bartlett test. KMO always fluctuates between 0 and 1. If the KMO value is less than 0.5, it is not suitable for factor analysis. If it is between 0.5 and 0.7, caution should be taken in factor analysis. If the value is greater than 0.7, the data correlation may be judged to be suitable for factor analysis. Bartlett's test tests the hypothesis that the observed correlation matrix belongs to a population with uncorrelated variables. Bartlett's test is the minimum condition for performing factor analysis. Next, we can extract the component that explains the maximum variance in the data. In embodiments, principal component analysis may be used. Here, eigenvalue criteria can be used to determine components, and components with eigenvalues greater than 1 are considered important components. Then, the matrix of components and variables is interpreted. The values of the matrix represent the relationships between variables and components, known as factor loadings. Ranges can be set to express and interpret the strength of the relationship between variables and components. For example, a load of 0.71 or more can be classified as excellent, a load of 0.63-0.71 as very good, a load of 0.55-0.63 as good, a load of 0.45-0.55 as relatively good, and a load of 0.33-0.45 as inadequate. The final indicator of the socioeconomic dimension of quality of life can be obtained after standardization of each component based on Equation 1.

Equation 1:

From here, is the final index of the socioeconomic dimension of quality of life, n is the number of components, F _i is the specific component i, and W _i is the proportion of poor acids explained by the ith component. Referring to Figure 6, the results of factor analysis can be normalized to values between 0 and 1 and divided into five equal ranges. A range close to 1 can be considered the best state, and a range close to 0 can be considered the worst state. It can be seen that all blocks of very good quality in terms of socioeconomics are located in District 6. Blocks of inadequate quality are located in area 13. Most of the very poor quality blocks are located in zone 6 in terms of area, but there are more in zone 13 in terms of number. Accordingly, it can be concluded that the quality of life in socioeconomic terms is better in District 6 than in District 13.

Figure 11 is a diagram showing the state of the environmental dimension, according to one embodiment.

Indicators of the environmental dimension are spatial-temporal. In the case of air pollution, monthly average measurements over a specific period may be used, and in the case of temperature, greenness and noise emissions, temporal changes may be ignored. The environmental dimension can be obtained through the results of the following four indicators. For example, the results of the environmental dimension of quality of life can be normalized to a value between 0 and 1 and divided into five equal ranges. As shown in Figure 11, a range close to 1 can be considered the best condition, and a range close to 0 can be considered the worst condition. Accordingly, it can be concluded that the quality of blocks in District 13 is very excellent. It can be seen that good, medium, inadequate and very poor quality blocks are generally present in Zone 6. Accordingly, it can be concluded that the quality of life in environmental terms is better in District 13.

Figure 7 is a diagram showing a green color display state, according to one embodiment. Satellite imagery can be used to calculate greenness. NDVI greenness index can be obtained from OLI bands 4 and 5. Band 4 operates in the infrared (NIR) range of the spectrum, with an NDVI range between -1 and +1. The closer it is to +1, the denser the green color becomes. Equation 2 can be used to calculate the greenness index. Referring to Figure 7, it can be seen that green space in the northern area of District 6 is generally concentrated, but is distributed at the eastern edge of District 13, far from residential areas.

Equation 2:

Figure 8 is a diagram showing the surface temperature display state according to one embodiment. The radiance values of the two TIR sensor bands 10 and 11 can be extracted from the satellite image according to Equation 3.

Equation 3:

From here, is the spectral luminosity (top of the atmosphere); , multiplicative rebalancing coefficient for each band in the metadata (RADIANCE_MULT_BAND_x, x is the band number), is the quantified and corrected standard product pixel value (digital number), is the additional scaling factor for each band of the metadata (RADIANCE_ADD_BAND_x, x is the band number). metadata file , can be linked to the satellite imagery used to calculate greenness.

Then, the vegetation ratio ( ) can be derived using Equation 4.

Equation 4:

In Equation 4, NDVI, NDVI _min , and NDVI _max are the minimum and maximum greenness indices, respectively.

The ground surface emissivity (E) can be derived using Equation 5.

Equation 5:

Finally, ground surface temperature (LST) can be calculated using Equation 6 and Equation 7.

Equation 6:

Here, BT is the top of the atmospheric brightness temperature, k ₁ is the heat conversion constant for each band of the metadata (K1_CONSTANT_BAND_x, x is the heat band number), and k ₂ is the heat conversion constant for each band of the metadata (K2_CONSTANT_BAND_x, x is the heat band number. ), and the thermal conversion constants (for thermal bands 10 and 11 of the satellite image metadata file) and is the spectral radiance (top of the atmosphere).

Equation 7:

Here, BT is the top of the ambient brightness temperature, is the wavelength of the emission luminosity, is 14,380, and E is the ground surface emissivity. Referring to Figure 8, it can be seen that the surface temperature in the northern area of Zone 6 and the surface temperature in the central area of Zone 13 are higher.

Figure 9 is a diagram showing an air pollution display state in one embodiment. Air quality management center data, especially the air quality index (AQI) including CO, O ₃ , NO ₂ , SO ₂ , Pm10, and Pm2.5 pollutants, can be used to calculate air pollution. Quality control stations in specific and surrounding areas may include multiple stations. An average Air Quality Index (AQI) value can be calculated for each station. For example, the average AQI value for January 2017 can be calculated for each station. The area covered by multiple stations is placed within a polygon to enable interpolation and determination of the amount of pollution for each point. Referring to Figure 9, it can be seen that air pollution in the northwest area of Zone 6 is concentrated and decreases toward the southeast, while air pollution in Zone 13 is higher in the central part and decreases toward the southwest of Zone 13.

Figure 10 is a diagram showing a noise pollution display state in one embodiment. Land use data, including education, health care, residential, recreational, commercial, and commercial residential, can be used to calculate noise pollution. Additionally, road network layers provided by traffic control companies, such as highways, major roads, and adjacent roads, may be used. Population density can also be calculated from residential blocks. Finally, all layers can be converted to raster format and normalized between 0 and 1.

Referring to Table 1 where the hierarchical weights of the classes are taken, each layer can be multiplied by its weight and the noise pollution class can be calculated according to the available data.

Table 1: Weights of criteria and sub-criteria in noise pollution map

Referring to Figure 10, it can be seen that noise pollution in District 6 is concentrated in the north and southwest, and that Zone 13 has higher noise pollution in the periphery, except for small parts in the center and west.

Figure 12 is a diagram showing the accessibility status of city facilities and services, according to one embodiment.

Depending on the road network layer of the traffic control company, the service area is obtained at the level of each block of statistical data by land use using network analysis, and an aggregation method can be used to calculate the accessibility of the obtained service area. Finally, each land use can be weighted average according to Equation 8. The reason for using this method is that it is simple. There are two important points here. First, accessibility levels were calculated at the level of very small spatial units (statistical blocks). Second, the service area of different land uses is not limited to the official areas of the city (sub-districts and districts), i.e. it takes into account the influence of other areas on accessibility. As a result, the value of accessibility criteria will be more accurate.

Equation 8:

Where, D is the weighted population average of sub-district access, n is the number of sub-districts, y is the population of each block, k is the population of each sub-district, is the number of accesses for each land use.

Referring to Figure 12, the results of measuring the accessibility of city facilities and services can be divided into five equal ranges by normalizing the values between 0 and 1. The range closest to 1 can be considered the best condition, and the range closest to 0 can be considered the worst condition. It can be seen that there are more very good quality blocks and very unsuitable quality blocks in Zone 13, and that there are more good, medium, and unsuitable quality blocks in Zone 6. Accordingly, it can be concluded that the quality of life in terms of accessibility to city facilities and services is better in District 13.

The evaluation system can quantitatively evaluate quality of life at a spatial level (130) by combining each result information on the calculated suitability of quality of life with a multiple decision-making method (120).

The multiple decision-making method will be described in detail.

VIKOR method

The VIKOR method was developed for multi-choice optimization of complex systems, ranking and selecting a set of options and finding a consensus solution to a problem with contradictory criteria that can help decision makers reach a final decision. Focusing on making decisions. The agreed upon solution here is the closest possible solution to the ideal solution. Agreement is achieved by mutual concessions. This methodology is a multiple pseudo-ranking index based on a certain proximity to an ideal solution. The VIKOR method is a useful tool in multiple decision making, especially in situations where the decision maker cannot express priorities at the beginning of the system design.

The VIKOR method focuses on ranking and selecting from a set of different options and determining a compromise solution for problems with incompatible criteria. In this method, the decision maker can reach a final decision. A compromise may be closest to the ideal answer, and a compromise is an agreement on two-way exchange. The advantage of the VIKOR method is that it can determine a compromise that reflects the attitudes of most decision makers. The advantage of using the VIKOR method in the examples is that it uses valid statistics and data rather than relying solely on personal judgment.

The steps of the VIKOR method are as follows:

Highest in all criteria based on Equation 9 and Equation 10 and worst The value of is determined.

Equation 9:

Equation 10:

S _i and R _i can be calculated using Equation 11 and Equation 12. is the weight of the criteria that determines relative importance.

Equation 11:

Equation 12:

The Q _i value can be calculated using Equation 13.

Equation 13:

The v value is the weight for applying the maximum group tool strategy (Equations 14 and 15).

Equation 14:

Equation 15:

At this stage, the alternatives can be ranked. For this purpose, the values are sorted in descending order, with lower values indicating more alternatives are desirable.

TOPSIS method

The basic logic of the TOPSIS method (a method of arranging preferences by similarity to strange solutions) is to define ideal positive and ideal negative solutions based on the shortest distance to the strange solution. The ideal positive and negative solutions are hypothetical solutions where all values of the exponents are similar to the maximum and minimum exponent values in the database, respectively.

Simply put, an ideal positive solution is a combination of the best available values of the criteria, and an ideal negative solution is a combination of the worst available values. In practice, TOPSIS can be used to solve selection and evaluation in problems with a limited number of options. The TOPSIS method is based on distance measurements, similar to the VIKOR method. This technique allows types of indicators to be included in the model in terms of their positive or negative impact on the decision objective, and the weight and importance of each indicator can be entered into the model. Quantitative and qualitative criteria are also included simultaneously in the evaluation and a significant number of criteria and options are considered. This method is easy and quick to apply, is a compensatory method, and the weight of all options and criteria is involved in decision making.

The steps of the TOPSIS method can be summarized as follows.

The decision matrix becomes a normalized matrix using Equation 16.

Equation 16:

Using equation 17 A weighted normalized decision matrix can be obtained. In this step, the normalized matrix is multiplied by the diagonal matrix of weights.

Equation 17:

At this stage, the ideal positive solution and the ideal negative solution can be determined using Equation 18 and Equation 19.

Equation 18:

Equation 19:

The distance between the ideal positive solution and the ideal negative solution for each criterion can be derived using Equation 20 and Equation 21.

Equation 20:

Equation 21:

The relative proximity (Ci) of the criterion to the optimal solution can be determined using Equation 22. The criteria may be ranked based on the descending order of C _i .

Equation 22:

SAW method

The SAW method is one of the most frequently used techniques to solve spatial decision-making problems. The decision maker directly assigns relative importance (weight) to each attribute. Then, the importance weight assigned to each alternative (site unit) is multiplied by the scale value given to the alternative attribute for that attribute and the total product attributes are summed to obtain the total score. The alternative with the highest overall score is selected.

The calculations of this method are simple and can be performed without the help of complex computer programs. The SAW method is the best-known and most adopted multiple decision-making method model that considers only the weights and additive form of the criteria. The advantage of the SAW method is that it is simple and allows for more precise evaluation because it is based on predetermined criteria and preference weights. Unlike other methods, the SAW method does not have positive or negative factors in selecting the optimal option. The decision function of this decision-making technique is linear and feature collectivity is guaranteed.

The SAW method can be applied in the following order:

The decision matrix can be quantified.

Linear normalization of the decision matrix values can be performed based on Equation 23 and Equation 24. If the exponent has a positive side, Equation 23 is used, and if the exponent has a negative side, Equation 24 is used.

Equation 23:

Equation 24:

The optimal option (S _i ) can be selected using Equation 25.

Equation 25:

Here, S _i is the final weight of each factor, is the weight of each criterion and is the normalized value of each criterion variable. am. As S _i is higher than other criteria, the weight of that criterion can be simply selected.

ELECTRE METHOD

The ELECTRE method lies at the border between compensated and non-compensated methods. Simply put, in this way, trade-offs are allowed within the scope of the decision maker's decision. The ELECTRE method is based on pairwise comparisons and uses precedence relationships to rank and sort options or select the best option. Another key feature of this approach is that in decision-making problems, the inaccuracy of existing evaluators can lead to the use of quasi-criteria instead of real criteria and the application of different utility functions to different decision makers. Two main concepts are used to form high-level relationships: congruence measurement and mismatch measurement. A consistency measure based on a match set is a subset of all criteria for which alternative i is no worse than a competing alternative i', whereas a discrepancy measure based on a discrepancy set is a subset of all criteria for which alternative i is worse than a competing alternative i'. The main advantage of the ELECTRE method is that it allows comparison of alternatives even if there is no clear preference for one of them, making it more reliable compared to other methods that are sensitive to the beliefs of the decision maker. The ELECTRE method has advantages such as the concept of superiority and indifference and thresholds for agreement and disagreement.

The steps of the ELECTRE method can be expressed as follows.

The decision matrix is converted to a scale matrix using equation 26.

Equation 26:

At this stage, the weighted normalization matrix (V) can be derived using vector w and equation 27.

Equation 27:

For each reference pair (A _K , A _l ), a match set and a mismatch set can be specified. In the matching set, if the criterion has a positive side, Equation 28 is used, and if the criterion has a negative side, Equation 29 is used.

Equation 28:

Equation 29:

The set of discrepancies also includes criteria for which the A _K criterion is less desirable than A _l . The mathematical expression for the positive standard is 30 and the mathematical expression for the negative standard is 31.

Equation 30:

Equation 31:

At this stage, the square matrix mХm is calculated, and the diameter is a factorless coincidence matrix. Additionally, this matrix element can be derived from the sum of the criteria weights belonging to the matching set (Equation 32).

Equation 32:

Calculate the discrepancy matrix denoted by d _kl . There are also no factors in the original diameter of this matrix, and the other factors are calculated from the weight normalization matrix for the discrepancy set of S _Kl (Equation 33).

Equation 33:

The effective coincidence matrix is determined based on Equation 34. The C _kl value of the coincidence matrix should be compared to a threshold to better determine the likelihood that the criterion will be preferred. The threshold c can be determined using expert opinion and historical information.

Equation 34:

Based on c, the critical value of F is formed with factors 0 and 1 as follows (Equation 35).

Equation 35:

The effective discrepancy matrix d is determined based on Equation 36.

Equation 36:

Then, the Boolean G matrix, known as the effective discrepancy matrix, is formed as follows (Equation 37).

Equation 37:

At this stage, the total dominance matrix is determined according to Equation 38. The total dominance matrix is obtained from the combination of the effective consistency matrix and the effective discordance matrix.

Equation 38:

Options of low importance are removed at this stage. The total dominance matrix E represents the superiority of different criteria over each other.

The evaluation system can use multiple decision-making methods for three-dimensional integration of the result information obtained from each of the socioeconomic dimension (101), environmental dimension (102), and accessibility dimension of urban facilities and services (103). The evaluation system can combine the result information obtained from each of the socioeconomic dimension (101), environmental dimension (102), and accessibility dimension of urban facilities and services (103) with the SAW, VIKOR, TOPSIS, and ELECTRE methods. The rating system can compare the ranking of areas (zones) in different spatial units with other decision-making methods. In this case, sub-zones may be ranked at different spatial levels. Then, rankings and methods can be compared across different spatial levels.

For example, the rating system can rank and compare quality of life at the sub-district level in Tehran's Districts 6 and 13 (24 sub-districts). The rating system can rank and compare quality of life separately at the sub-district level in each district. Rating systems can compare quality of life at district level. Additionally, rating systems can be compared using multiple decision-making methods when calculating quality of life.

Comparisons can be performed at three spatial levels. At the first level, 24 sub-districts can be compared in districts 6 and 13 of Tehran. As a result of the comparison at the first level, the first place can be judged to be sub-zone 9 of district 13. According to the map of the sub-district, while it ranks 4th in terms of sub-district area, it is located almost in the center of District 13, which means it is actually limited by the boundaries of the district and can support different types of access. As a result, it may be ranked first. Ranks 2 through 6 were all located in the eastern and southern parts of District 6, near the city center. At the first level of comparison, it can be concluded that the ranking of sub-districts in District 6 is higher than that of sub-districts in District 13. The second level is comparison between sub-zones of a zone. In both regions, subdistricts closer to the city center rank higher than subdivisions farther from the city center. The third level is an overall comparison of the two districts, and according to the comparison results, it can be seen that District 6 ranked higher. Compared to multiple decision-making methods, Spearman correlation can be calculated by taking into account the contradictions that exist in the rankings in four ways. The results of the correlation table of the three spatial levels show that all decision methods at all levels have positive correlations of more than 0.75. Since the sig value is less than 0.05, this relationship is at the 95% confidence level, which is acceptable. In other words, depending on the method, the rankings of the sub-sections may differ, but in most cases, the rankings appear similar to each other.

Table 2 shows that almost all methods have a weak correlation with the VIKOR method and a high correlation with the ELECTRE method (but not at all levels).

In comparing four different methods, the rankings obtained using ELECTRE are different from the rankings obtained with the other methods. Some identical rankings can be achieved for a small number of alternatives. As a result of comparing the stability of the methods, it can be concluded that the SAW method with the highest value of L = 0.583333 is the most stable, and the VIKOR method with the lowest value of L = 1.583333 is the least stable. These comparisons can lead to better results and more effective solutions. The more relevant and realistic the results, the more accurate decisions and plans can be made. For example, when comparing quality of life at district level, which district should receive more funding to ensure that the lives of people in that district are the same as in other districts, the type of funding and planning may be different when comparing sub-district levels within a district. there is. To achieve this goal, quality of life can first be measured for each of multiple criteria and then integrated with multiple decision-making methods. At all three levels of comparing quality of life, the city center can be judged to be of higher quality than the surrounding areas because there are more people moving there. Since more people require more facilities, city managers can pay more attention to providing urban facilities in their districts. On the other hand, the city center borders other urban areas of Tehran municipality. From this perspective, development and construction sites exist around the district. Therefore, it is reasonable for the city center to have a higher quality of life as it is given more points than the outskirts of the city due to the lack of construction in the outskirts of the city outside Tehran. Land prices on the city's periphery are much cheaper than in central Tehran, so more people live on the city's periphery. Even on the outskirts of the city, many people live in houses that are smaller than those in the city center. Therefore, the city center is better in this regard. This is part of the urban benefits that improve quality of life. District 6 is located in the city center, and District 13 exists on the outskirts of the city. According to the above explanation and the comparison results at level 3, District 6 has a higher quality of life than District 13.

FIG. 3 is a block diagram for explaining the configuration of an evaluation system according to an embodiment, and FIG. 4 is a flowchart for explaining a method of evaluating quality of life in an evaluation system according to an embodiment.

The processor of the evaluation system 100 may include a quality of life suitability calculation unit 310 and a quality of life evaluation unit 320. These processor components may be expressions of different functions performed by the processor according to control instructions provided by program codes stored in the evaluation system. The processor and its components may control the evaluation system to perform steps 410 to 420 included in the quality of life evaluation method of FIG. 4 . At this time, the processor and its components may be implemented to execute instructions according to the code of an operating system included in the memory and the code of at least one program.

The processor may load program code stored in a file of a program for a quality of life evaluation method into memory. For example, when a program is executed in the evaluation system, the processor can control the evaluation system to load program code from the program file into memory under the control of the operating system. At this time, each of the quality of life suitability calculation unit 310 and the quality of life evaluation unit 320 is a processor for executing the subsequent steps 410 to 420 by executing instructions of the corresponding portion of the program code loaded in the memory. may be different functional expressions of .

In step 410, the quality of life suitability calculation unit 310 may calculate the suitability of the quality of life based on a plurality of criteria for spatial data. The quality of life suitability calculation unit 310 may calculate the suitability of quality of life based on a plurality of criteria including socioeconomic dimension, environmental dimension, and accessibility of urban facilities and services with respect to spatial data. The quality of life suitability calculation unit 310 performs factor analysis on the socioeconomic dimension using either a correlation matrix method or a KMO coefficient and Bartlett test method, and uses the performed factor analysis to determine social The adequacy of quality of life with economic dimensions can be calculated. The quality of life suitability calculation unit 310 may calculate the suitability of the quality of life for environmental dimensions including greenness, surface temperature, air pollution, and noise pollution. The quality of life suitability calculation unit 310 provides a plurality of land use statistics including parks, fire stations, gas stations, BRT (Bus Rapid Transit) stations, city bus stations, metro stations, mosques, hospitals, and clinics through network analysis. Obtain a service area based on data, use an aggregation method to calculate the accessibility of the obtained service area, and calculate the suitability of quality of life for accessibility to urban facilities and services through a weighted average for each land use. .

In step 420, the quality of life evaluation unit 320 may combine each result information regarding the suitability of the calculated quality of life with a multiple decision-making method to quantitatively evaluate the quality of life at the spatial level. The quality of life evaluation unit 320 may combine each result information on the suitability of the calculated quality of life with different multiple decision-making methods including SAW, TOPSIS, VIKOR, and ELECTRE. The quality of life evaluation unit 320 can compare the rankings of areas using different decision-making methods between different spatial levels and decision-making methods. The quality of life evaluation unit 320 can evaluate quantitative quality of life results at three spatial levels: comparing sub-zones of two zones, comparing sub-zones of one zone, or comparing two zones.

The device described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, devices and components described in embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), etc. , may be implemented using one or more general-purpose or special-purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. It can be embodied in . Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

As described above, although the embodiments have been described with limited examples and drawings, various modifications and variations can be made by those skilled in the art from the above description. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (15)

In the quality of life evaluation method performed by the evaluation system,
Calculating suitability of quality of life according to a plurality of criteria with respect to spatial data; and
A step of evaluating quantitative quality of life at a spatial level by combining each result information on the suitability of the calculated quality of life with a multiple decision-making method.
Including,
The calculating step is,
Calculating the suitability of quality of life based on a plurality of criteria including socioeconomic dimension, environmental dimension, and accessibility to urban facilities and services with respect to the spatial data.
Including,
The evaluation step is,
Combining each resulting information on the adequacy of quality of life calculated above with different multiple decision-making methods, comparing the rankings of zones with different decision-making methods between different spatial levels and decision-making methods, and comparing the rankings of the two zones. Assessing quantitative quality of life outcomes at multiple spatial levels, comparing sub-areas, comparing sub-areas of one area, or comparing two areas.
Quality of life assessment method including. delete According to paragraph 1,
The calculating step is,
Perform factor analysis on the socioeconomic dimension using any of the methods using a correlation matrix or the KMO coefficient and Bartlett test method, and determine the suitability of quality of life for the socioeconomic dimension using the factor analysis performed above. Steps to calculate
Quality of life assessment method including. According to paragraph 1,
The calculating step is,
Calculating the adequacy of quality of life with respect to environmental dimensions including greenness, surface temperature, air pollution and noise pollution.
Quality of life assessment method including. According to paragraph 1,
The calculating step is,
Through network analysis, service areas are obtained based on statistical data by multiple land uses, including parks, fire stations, gas stations, BRT (Bus Rapid Transit) stations, city bus stations, metro stations, mosques, hospitals, and clinics; Using an aggregation method to calculate the accessibility of the obtained service area, and calculating the suitability of quality of life for accessibility to urban facilities and services through a weighted average for each land use.
Quality of life assessment method including. According to paragraph 1,
The different multiple decision-making methods include SAW, TOPSIS, VIKOR, and ELECTRE.
How to assess quality of life. delete delete In the evaluation system,
a quality of life suitability calculation unit that calculates the suitability of quality of life based on a plurality of criteria for spatial data; and
A quality of life evaluation unit that evaluates quantitative quality of life at a spatial level by combining each result information on the suitability of the calculated quality of life with multiple decision-making methods.
Including,
The quality of life suitability calculation unit,
For the spatial data, it includes calculating the suitability of quality of life according to a plurality of criteria including socioeconomic dimension, environmental dimension, and accessibility to urban facilities and services,
The quality of life evaluation department,
Combining each resulting information on the adequacy of quality of life calculated above with different multiple decision-making methods, comparing the ranking of zones with different decision-making methods between different spatial levels and decision-making methods, and comparing the rankings of the two zones. Assessing quantitative quality of life outcomes at multiple spatial levels, comparing sub-areas, comparing sub-areas of one area, or comparing two areas.
Evaluation system. delete According to clause 9,
The quality of life suitability calculation unit,
Perform factor analysis on the socioeconomic dimension using either a method using a correlation matrix or the KMO coefficient and Bartlett test method, and determine the suitability of quality of life for the socioeconomic dimension using the factor analysis performed above. to calculate
An evaluation system characterized by: According to clause 9,
The quality of life suitability calculation unit,
Calculate the adequacy of quality of life with respect to environmental dimensions including greenness, surface temperature, air pollution and noise pollution.
An evaluation system characterized by: According to clause 9,
The quality of life suitability calculation unit,
Through network analysis, service areas are obtained based on statistical data by multiple land uses, including parks, fire stations, gas stations, BRT (Bus Rapid Transit) stations, city bus stations, metro stations, mosques, hospitals, and clinics; Using an aggregation method to calculate the accessibility of the obtained service area, and calculating the suitability of quality of life for accessibility to urban facilities and services through a weighted average for each land use
An evaluation system characterized by: According to clause 9,
The different multiple decision-making methods include SAW, TOPSIS, VIKOR, and ELECTRE.
An evaluation system characterized by:
delete