CN116596407A - Street determining method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a street determining method, a street determining device, electronic equipment and a storage medium, and relates to the field of building design, wherein the method comprises the following steps: obtaining street data corresponding to street data indexes in the street to be detected; the street data index comprises traffic data, environment data, block quality data and block vitality data; obtaining a street health degree result of a street to be detected according to a preset street health degree algorithm and street data; the street health degree algorithm is generated according to the weight of the street data index; the weight of the street data index is obtained by utilizing a factor analysis method, a structural equation model is constructed based on the latent variable, and the weight is obtained through the structural equation model; based on the street health results, the target street is determined. Interaction of street data indexes and latent variables can be effectively evaluated through the structural equation model, and accuracy of street health evaluation is improved. And determining the street which meets the requirements of the user according to the street health degree result.
Description
Technical Field
The present application relates to the field of building design, and in particular, to a street determining method, apparatus, electronic device, and storage medium.
Background
The problems of respiratory diseases caused by air pollution, potential safety hazards caused by the unhealthy urban street facilities, traffic accidents and the like seriously threaten the health of residents. The street is used as a carrier of various functions of the city and is closely related to the life of residents, but in the prior art, the analysis of the factors affecting the health of the street is lacking, so that the street meeting the requirements of users cannot be accurately determined.
Disclosure of Invention
The embodiment of the application aims at a street determining method, a device, electronic equipment and a storage medium, and aims at accurately obtaining a street meeting the requirement of a user by accurately and quantitatively analyzing the health degree of the street and determining a target street based on a street health degree result.
In a first aspect, an embodiment of the present application provides a street determining method, including: obtaining street data corresponding to street data indexes in the street to be detected; the street data indexes comprise traffic data, environment data, block quality data and block vitality data; obtaining a street health degree result of the street to be detected according to a preset street health degree algorithm and the street data; wherein the street health degree algorithm is generated according to the weight of the street data index; the weight of the street data index is obtained by utilizing a factor analysis method, a structural equation model is constructed based on the latent variable, and the weight is obtained through the structural equation model; and determining a target street based on the street health result.
In the implementation process, interaction of street data indexes and latent variables can be effectively evaluated through the structural equation model, so that the method has scientific effectiveness and improves the accuracy of street health evaluation. The method solves the problems of inaccurate evaluation caused by insufficient street data index and latent variable basis and poor interpretation in the existing evaluation system. And determining streets which better meet the requirements of users according to the street health degree results, meeting the requirements of the users and improving the experience of the users.
Alternatively, in embodiments of the application, the factor analysis method comprises a exploratory factor analysis method; before obtaining the street health result of the street to be detected according to the preset street health algorithm and the street data, the method further comprises the following steps: acquiring a preset health index, and analyzing the health index by a exploratory factor analysis method to obtain a latent variable; obtaining street data indexes based on the latent variables; obtaining the association relation between the latent variable and the street data index through a verification factor analysis method; a structural equation model is generated based on the latent variables, the street data index, and the association relationship.
In the implementation process, the exploratory factor analysis method is utilized to realize the induction of the influence elements of the healthy street, the latent variables required by the structural equation are obtained, and the structural equation model of the healthy street is constructed through the verification factor analysis. By selecting proper latent variables and street data indexes to construct a structural equation model, the relationship between the road and the health is intuitively reflected, the calculation accuracy of the street health degree result is improved, and references and supports are provided for scientific quantitative design and optimization of the healthy street and improvement of the health level of residents.
Optionally, in an embodiment of the present application, after generating the structural equation model based on the latent variable, the street data index and the association relationship, the method further includes: obtaining a non-standardized path coefficient value between the street data index and the latent variable through a structural equation model; and carrying out normalization processing on the street data index based on the non-normalized path coefficient value to obtain the weight of the street data index.
In the implementation process, the weight of each street data index is obtained through the structural equation model, so that the accuracy of the weight value is improved, and the health degree result is further improved.
Optionally, in an embodiment of the present application, after generating the structural equation model based on the latent variable, the street data index and the association relationship, the method further includes: obtaining a verification parameter value of a structural equation; the check parameter value comprises at least one of a chi-square degree of freedom ratio, an approximate error square root and a model increment adaptation degree; and carrying out fitness test on the structural equation based on the verification parameter value.
In the implementation process, the fitness test is carried out on the structural equation model, and the structural equation model is corrected under the condition that the verification parameter value does not accord with the standard, so that the accuracy of the structural equation model of the healthy street is improved.
Optionally, in an embodiment of the present application, after obtaining the street data index based on the latent variable, the method further includes: collecting influence degree data of street data indexes on residents; and calculating the credibility and the effectiveness of the influence degree data, and correcting the street data index based on the credibility and the effectiveness.
In the implementation process, the influence degree data of the street data indexes on residents is collected, and the credibility and the effectiveness of the influence degree data are calculated, so that the street data indexes have higher basis, the reliability and the stability degree of the street data indexes are improved, and the accuracy of street health assessment is improved.
Optionally, in an embodiment of the present application, obtaining street data corresponding to a street data index in a street to be detected includes: acquiring actual measurement data of a street to be detected through a data acquisition device; the data acquisition device comprises an image acquisition device, a noise acquisition device and an air quality detection device; and obtaining street data according to the pre-established evaluation standard and the actual measurement data.
In the implementation process, the following steps are adopted: the data acquisition device is used for acquiring actual measurement data of the street to be detected, and converting the actual measurement data into street data with unified standards according to the pre-established evaluation standards, so that the accuracy of street health evaluation is improved.
Optionally, in an embodiment of the present application, the street health algorithm includes:
wherein ,for street health result, ++>For street data index>For street data index->Is used in the weight of the total weight of the (c),for street data index->Corresponding street data.
In the implementation process, the accuracy of calculating the street health degree is improved through formula calculation corresponding to the street health degree algorithm, and the problem that the calculation of the street health degree is inaccurate due to the fact that factors affecting the street health degree are difficult to quantify is solved.
In a second aspect, an embodiment of the present application further provides a street determining apparatus, including: the acquisition module is used for acquiring street data corresponding to the street data index in the street to be detected; the street data indexes comprise traffic data, environment data, block quality data and block vitality data; the degree result module is used for obtaining the street health degree result of the street to be detected according to a preset street health degree algorithm and the street data; wherein the street health degree algorithm is generated according to the weight of the street data index; the weight of the street data index is obtained by utilizing a factor analysis method, a structural equation model is constructed based on the latent variable, and the weight is obtained through the structural equation model; and the determining module is used for determining a target street based on the street health degree result.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor and a memory storing machine-readable instructions executable by the processor to perform the method as described above when executed by the processor.
In a fourth aspect, embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method described above.
The street determining method, the street determining device, the electronic equipment and the storage medium can effectively evaluate the interaction between the street data index and the latent variable through the structural equation model, have scientific effectiveness and improve the accuracy of street health evaluation. The method solves the problems of inaccurate evaluation caused by insufficient street data index and latent variable basis and poor interpretation in the existing evaluation system. The health influence degree of street health elements on residents in urban roads is deeply analyzed through the structural equation model, so that healthy street design can be scientifically carried out, and the healthy quality of a neighborhood can be improved. And the street meeting the user requirements can be determined according to the street health degree result, the requirements of the user are met, and the user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a street determining method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a preset health indicator according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a calculation model of a structural equation according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a model normalized path coefficient provided by an embodiment of the present application;
FIG. 5 is a flow chart of factor analysis according to an embodiment of the present application;
FIG. 6 is a schematic flow chart of constructing a structural equation model according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a street determining apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the technical scheme of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, and are not intended to limit the scope of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
In the description of embodiments of the present application, the technical terms "first," "second," and the like are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless otherwise specifically defined.
Referring to fig. 1, a flowchart of a street determining method according to an embodiment of the present application is shown. The street determining method provided by the embodiment of the application can be applied to electronic equipment, and the electronic equipment can comprise a terminal and a server; the terminal can be a smart phone, a tablet computer, a personal digital assistant (Personal Digital Assitant, PDA) and the like; the server may be an application server or a Web server. The street determining method may include the steps of:
Step S110: obtaining street data corresponding to street data indexes in the street to be detected; street data metrics include traffic data, environmental data, block quality data, and block vitality data.
Step S120: obtaining a street health degree result of a street to be detected according to a preset street health degree algorithm and street data; the street health degree algorithm is generated according to the weight of the street data index; the weight of the street data index is obtained by utilizing a factor analysis method, a structural equation model is constructed based on the latent variable, and the weight is obtained through the structural equation model.
Step S130: based on the street health results, the target street is determined.
In step S110: the concept of "healthy street" claims to improve natural resources, social resources, environment, etc. of the street, and to provide more healthy elements for the residents to improve the health of the residents. Street data metrics are used to describe factors affecting street health, and may include traffic data, environmental data, block quality data, and block vitality data, among others. The street data index may be obtained by factor analysis of a preset health index or by a related specification of the street design.
The street data corresponding to each street data index in the street to be detected can be acquired through a data acquisition device, and the data acquisition device can be an image acquisition device, a noise acquisition device, an air quality detection device and the like. And analyzing the collected original data and the like to obtain street data.
In step S120: the street health degree algorithm is used for calculating a street health degree result of a street, and the street health degree result can be obtained by inputting street data into the street health degree algorithm. The street health result may be a score, probability, or evaluation word that characterizes street health.
The street health degree algorithm is generated according to the weights of the street data indexes, and specifically may be a linear weighting algorithm, for example, each street data index has a corresponding weight, and the collected street data and the corresponding street data index are subjected to linear weighting to obtain a street health degree result. The street health algorithm may also be a weighted average algorithm or the like.
The obtaining mode of the weight of the street data index may be: and analyzing the preset health index by using a factor analysis method to obtain the latent variable. The latent variables screened through exploratory factor analysis have scientificity, uniqueness and higher interpretability. In street health evaluation, the latent variable relates to factors which are difficult to directly measure, such as whether residents perceive good or bad, whether the environment is healthy or not, whether traffic is convenient or not, and the like.
And constructing a structural equation model based on the latent variables. The structural equation model comprises a regression analysis method and a factor analysis method, and is a mathematical statistics method for analyzing a variable relation through a covariance matrix, and comprises a measurement model and a structural model. The structural equation model can effectively evaluate complex multivariate relations between street data indexes and latent variables. And finally, obtaining the weight of the street data index through a structural equation model.
In step S130: after the health result of each candidate street is obtained, the target street can be determined from the candidate streets according to the requirements of the user. Specifically, for example, if the obtained user requirement is to find a street suitable for rest walk, a street with a higher street health degree represented by a street health degree result is selected as a target street, so that the street with serious air pollution, unhealthy street facilities or traffic accidents is prevented from being used as the target street, the health of residents is threatened, and the experience of the user is improved.
If the user demand is to search the street to be optimized, selecting the street with the lower street health degree as the target street according to the street health degree result, so that the user can perform health optimization on the target street to obtain a healthier street. For example, the health influence degree of street health elements on residents in urban roads is deeply analyzed through a structural equation model, so that healthy street design can be scientifically carried out, and the health quality of a neighborhood can be improved.
In the implementation process, interaction of street data indexes and latent variables can be effectively evaluated through the structural equation model, so that the method has scientific effectiveness and improves the accuracy of street health evaluation. The method solves the problems of inaccurate evaluation caused by insufficient street data index and latent variable basis and poor interpretation in the existing evaluation system. And determining streets which better meet the requirements of the users according to the street health degree results, and meeting the requirements of the users. And the accuracy of determining the target street is improved, so that the user experience is improved.
Alternatively, in embodiments of the application, the factor analysis method comprises a exploratory factor analysis method; before obtaining the street health result of the street to be detected according to the preset street health algorithm and the street data, the method further comprises the following steps: acquiring a preset health index, and analyzing the health index by a exploratory factor analysis method to obtain a latent variable; obtaining street data indexes based on the latent variables; obtaining the association relation between the latent variable and the street data index through a verification factor analysis method; a structural equation model is generated based on the latent variables, the street data index, and the association relationship.
Please refer to fig. 2, which illustrates a schematic diagram of a preset health indicator provided by an embodiment of the present application.
In the specific implementation process: existing indexes for street evaluation can be used as preset health indexes. As shown in fig. 2, the preset health index may include feel friendly, easy to cross street, shade rest, rest place, noise-tolerant, riding-friendly, feel safe, sightseeing view, feel relaxed, and air freshening. The preset health index has the problems that partial concepts repeatedly cross and direct measurement is difficult. Therefore, the embodiment of the application analyzes the exploratory factor of the 10 health indexes, and reduces the dimension of the health indexes, thereby obtaining the latent variable of constructing the structural equation by the healthy street.
And selecting the streets basically meeting the requirements of the healthy streets as sample streets to conduct exploratory factor analysis, so that the street data indexes of the healthy streets are extracted. In factor analysis, what is first done is to convert intuitive, concrete reality condition data into an abstract data matrix with numerical values as metrics. For variables which cannot be counted in numerical values, the variables are classified and expressed in numerical values according to the actual value level of the variables and combined with the application in the real scene. The average and deviation tables of the health index of the sample streets are collated by SPSS software.
The adequacy test was performed using the butt Lei Qiuzhuang causal relationship test (BarlettTest of Sphericity) and the KMO (Kaiser-Meyer-Olkin) test. The Butt Lei Qiuzhuang causal relationship test and the KMO test are used for indicating that the correlation exists between the variables, wherein the closer the KMO value is 1, the stronger the correlation between the variables is, the KMO value is 0, and the weaker the correlation between the variables is. The preset indicators with weak correlation to the health indicators can be deleted.
The variance of the health index is extracted through a principal component analysis method, the variance value can reflect the accurate expression degree of the variable which can be expressed by the health index, and if the variance value is larger than 0.5, the expression of the variable by the health index is more reasonable. The indicator that the variance value is less than the preset variance threshold may be deleted.
And calculating a component matrix of the health index, wherein the component matrix is used for expressing the influence degree of the variable on each factor, and the factors can be classified according to the magnitude of the numerical value, namely the higher the numerical value is, the stronger the relevance is. Rotating the component matrix, namely analyzing the corresponding relation between each factor and the variable by observing the factor load coefficient value, deleting unreasonable variables, and repeating the process for a plurality of times until the corresponding relation between all the variables and the factors is basically consistent with the expectation. The matrix rotation has converged after 6 iterations, and further analysis of the component matrix from the principal component analysis can be performed, see table 1. Table 1 shows the component matrix after latent variable rotation.
| 1 | 2 | 3 | 4 | |
| Feel friendly | 0.941 | 0.258 | 0.088 | 0.115 |
| Easy to cross street | 0.634 | 0.194 | -0.229 | 0.543 |
| Rest for shading | -0.109 | 0.012 | 0.931 | -0.047 |
| Stop-and-rest place | 0.854 | 0.324 | 0.122 | -0.362 |
| Noise can be received | 0.313 | 0.899 | 0.013 | 0.126 |
| Lefuzhi riding | 0.937 | 0.270 | 0.079 | 0.120 |
| Feel safe | 0.960 | 0.146 | -0.017 | 0.104 |
| Sightseeing view | 0.502 | 0.225 | 0.746 | -0.067 |
| Feel relaxed | 0.934 | 0.212 | 0.132 | 0.142 |
| Air cleaning | 0.196 | 0.914 | 0.136 | -0.039 |
TABLE 1 component matrix after latent variable rotation
When 4 factors are selected, the accumulated rotation load square is 91.837%, which shows that the 4 factors are extracted with extremely strong interpretation force. Respectively extracting the maximum absolute values: first column, maximum values of 0.960, 0.941, 0.937, 0.934, corresponding to feeling safe, friendly, pleasurable to riding, relaxed, all of which can affect street quality; second, the maximum values of 0.914 and 0.899 correspond to air cleaning and noise tolerance, and the reduction factors are all influencing the healthy environment; the third column, the maximum value is 0.931, 0.746, correspond to the rest and sightseeing view, and influence the vigor of the street to a certain extent; and the fourth column has maximum values of 0.543 and 0.362, and is corresponding to places easy to cross streets and stop and rest, and both the places affect the convenience of traffic. Therefore, the method can improve street quality, build a healthy environment, build a vitality block and bring traffic convenience as latent variables in the structural equation model.
The structural equation model is composed of two parts, namely a latent variable and an observable variable. The observable variable is the street data index. Building a healthy street can bring about four effects of improving street quality, building a healthy environment, building a vitality block and bringing about convenience and quickness in traffic. Street data metrics of the model may be used to describe corresponding latent variables. Four latent variables are used as references, street quality, environment, activities and traffic are used as measuring objects, and street data indexes are selected by combining relevant specifications of China on street design.
For example, for improving street quality, ensuring physiological health through hardware facilities is a safety foundation for street life, and is required to have continuous and complete walking environment, safe street crossing facilities, perfect street illumination, and safe motor vehicle running speed for reducing bus station conflict, so that physical activity is promoted, personal safety is ensured, and people in the street enjoy an equally safe trip environment.
For building a healthy environment, environmental factors are first considered and guaranteed in order to create a healthy street, including air pollution effects and noise pollution effects.
For building the vitality block, increasing social interaction is an important way for building the vitality block, and requires public space, sufficient rest facilities, sound guide marks, sound barrier-free facilities and open leisure greenbelts for the street interaction, thereby improving social participation, promoting interaction activities and increasing social inclusion.
For bringing convenience in transportation, a slow-going transportation system mainly comprising walking and bicycle can optimize resident travel and improve physical health, and transportation is a key for promoting sustainable transportation travel modes such as walking, riding and the like. Reasonable adjacent crossing distance, standard sidewalk and non-motor vehicle lane width, sufficient bicycle parking space, shared bicycle high coverage rate and ordered motor vehicle parking can all promote traffic to a certain extent convenient, and the street of the healthy trip of making more easily.
Please refer to fig. 3, which illustrates a schematic diagram of a structural equation calculation model provided in an embodiment of the present application.
Through the consideration factors of each latent variable, the determined street data indexes are shown in fig. 3, wherein the street data indexes comprise traffic data, environment data, block quality data and block vitality data; the traffic data comprises a non-motor vehicle lane distance, a sidewalk distance, a bicycle parking, a shared single-vehicle coverage, an adjacent intersection distance and a motor vehicle parking; environmental data includes motor vehicle noise and air quality; the block quality data includes street lighting, motor vehicle speed, street crossing facilities, bus station conflicts, pavement quality and road continuity; the neighborhood vitality data includes stop-and-break facilities, public space walking pitch, unobstructed trips, indication signs, and forest coverage.
After the latent variable and the street data index are determined, the latent variable and the street data index are connected through verification factor analysis, and a structural equation model of the healthy street is built. Latent variables in the hypothesis are verified through a verification factor analysis method, and street data indexes are corresponding to the latent variables. Unlike exploratory factor analysis, verifiable factor analysis is a process of checking whether a model is consistent with an actual dataset using sample data for known theoretical and prior knowledge. The calculation result shows that the variable can be reasonably expressed by a common factor and has significance.
And importing 19 data reflecting variables into an initial measurement model, carrying out normalization processing on each variable through an operation model, calculating parameters such as the number of comprehensive samples, the degree of freedom of the samples and the like, and adopting an AMOS to establish a structural equation model for healthy street evaluation.
In the implementation process, the exploratory factor analysis method is utilized to realize the induction of the influence elements of the healthy street, the latent variables required by the structural equation are obtained, and the structural equation model of the healthy street is constructed through the verification factor analysis. By selecting proper latent variables and street data indexes to construct a structural equation model, the relationship between the road and the health is intuitively reflected, the calculation accuracy of the street health degree result is improved, and references and supports are provided for scientific quantitative design and optimization of the healthy street and improvement of the health level of residents.
Please refer to fig. 4, which illustrates a schematic diagram of a model normalization path coefficient according to an embodiment of the present application.
Optionally, in an embodiment of the present application, after generating the structural equation model based on the latent variable, the street data index and the association relationship, the method further includes: obtaining a non-standardized path coefficient value between the street data index and the latent variable through a structural equation model; and carrying out normalization processing on the street data index based on the non-normalized path coefficient value to obtain the weight of the street data index.
In the specific implementation process: the non-normalized path coefficients of the street data index and latent variables are shown in fig. 4. The meaning of the non-standardized path coefficient value is that the independent variable changes by one standard deviation, the change amount of the independent variable is not more than 1, and the model assumption is reasonable. In fig. 4, the non-normalized path coefficient value between traffic convenience and pavement spacing is ".77", representing 0.77.
The model results show that the 4 latent variables reach the significance level, namely 'improving the street quality, constructing the healthy environment, bringing traffic convenience and building the vitality neighborhood' are all established, and the healthy street is positively correlated and has significant influence. And taking the non-standardized path coefficient as calculation data of the weights of the street data indexes, carrying out normalization processing on all the street data indexes as a whole, and calculating the weights of the street data indexes in the respective categories and the weights of the street data indexes in the whole.
The weights include intra-group weights, inter-group weights, and total weights. The weights in the group represent the weights of street data indexes in the corresponding latent variable groups; the weight between groups represents the weight occupied by each latent variable group; the total weight characterizes the weight that the street data index occupies in influencing the street health degree result.
For one embodiment, please refer to table 2, table 2 is a street data index weight calculation table.
Table 2 street data index weight calculation table
The weights between groups represent latent variables of every 1 unit increase, i.e., bringing traffic convenience, improving street quality, building vitality blocks, and building health environments, the street health levels are increased by 0.258, 0.293, 0.231, and 0.217 units, respectively. The total weight represents the street data index increased by 1 unit, and the street health degree is correspondingly increased by the weight unit corresponding to the street data index.
In the implementation process, the weight of each street data index is obtained through the structural equation model, so that the accuracy of the weight value is improved, and the health degree result is further improved.
Optionally, in an embodiment of the present application, after generating the structural equation model based on the latent variable, the street data index and the association relationship, the method further includes: obtaining a verification parameter value of a structural equation; the check parameter value comprises at least one of a chi-square degree of freedom ratio, an approximate error square root and a model increment adaptation degree; and carrying out fitness test on the structural equation based on the verification parameter value.
In the specific implementation process: in order to ensure the accuracy of the structural equation model of the healthy street, the structural equation needs to be further subjected to fitness test. The fitness test between the model and the sample data outputs a verification parameter value comprising at least one of a chi-squared degree of freedom ratio (CMIN/DF), a square root of approximation error, and a model delta fitness. The chi-square degree of freedom ratio is used for representing data suitability, and the lower the chi-square degree of freedom ratio measurement value is, the more data suitability is proved. And comparing the chi-square degree of freedom ratio, the approximate error square root and the model increment adaptation degree with corresponding preset standards, and if the chi-square degree of freedom ratio, the approximate error square root and the model increment adaptation degree do not accord with the standards, correcting the structural equation.
In the implementation process, the fitness test is carried out on the structural equation model, and the structural equation model is corrected under the condition that the verification parameter value does not accord with the standard, so that the accuracy of the structural equation model of the healthy street is improved.
Optionally, in an embodiment of the present application, after obtaining the street data index based on the latent variable, the method further includes: collecting influence degree data of street data indexes on residents; and calculating the credibility and the effectiveness of the influence degree data, and correcting the street data index based on the credibility and the effectiveness.
In the specific implementation process: the influence degree data of the street data index on residents is used for representing the acceptance degree of the residents on the generalized street health index. Specifically, questionnaires can be adopted to collect the influence degree of each healthy street evaluation index on urban residents by adopting a 5-level Likett scale method. After the valid questionnaires are arranged, the confidence and the effectiveness of the questionnaires are checked by using the SPSS, if the confidence and the effectiveness of the questionnaire result meet the standards, the street data indexes are combined for factor analysis, the street data indexes corresponding to the latent variables are obtained, and a healthy street evaluation model is built by using a structural equation.
The credibility is an index reflecting the authenticity degree of the measured element, and can reflect the consistency, reliability and stability of the result obtained by the tested content. The confidence coefficient method, namely Cronbach's alpha confidence, can be specifically adopted for the confidence calculation. And comparing the obtained Cronbach's alpha value with a confidence evaluation standard to judge whether the street data index needs to be corrected. The effectiveness calculation mode can adopt a KMO test and a Bartlett sphere test. And if the credibility and the validity of the influence degree data meet the corresponding evaluation standards, the subsequent step of constructing the structural equation model can be performed. If the credibility or the validity does not accord with the corresponding evaluation standard, the street data index needs to be corrected.
In the implementation process, the influence degree data of the street data indexes on residents is collected, and the credibility and the effectiveness of the influence degree data are calculated, so that the street data indexes have higher basis, the reliability and the stability degree of the street data indexes are improved, and the accuracy of street health assessment is improved.
Optionally, in an embodiment of the present application, obtaining street data corresponding to a street data index in a street to be detected includes: acquiring actual measurement data of a street to be detected through a data acquisition device; the data acquisition device comprises an image acquisition device, a noise acquisition device and an air quality detection device; and obtaining street data according to the pre-established evaluation standard and the actual measurement data.
In the specific implementation process: if the street health evaluation is to be performed on the street to be detected, a street health result corresponding to the street to be detected is obtained, and street data corresponding to the street data index in the street to be detected is required to be obtained, for example: the image acquisition device is used for acquiring actual measurement data of images and/or videos of the street to be detected, and analyzing the acquired images and/or videos to acquire the actual measurement data corresponding to street data indexes such as pavement distance, bicycle parking, street crossing facilities, motor vehicle speed and the like.
Acquiring actual measurement data of motor vehicle noise of a street to be detected through a noise acquisition device; the noise collection device comprises a noise meter including a sound level meter and the like. Actual measurement data of the air quality of the street to be detected, such as parameters of PM2.5, PM10, meteorological environment and the like, are acquired through the air quality detection device.
It can be understood that the street data corresponding to some street data indexes can also be obtained by inquiring in a preset database, for example, the name of the street to be detected can be input in the database, and the street data stored in the database in advance, such as the data of the distance between adjacent intersections, the indication identifier of the street and the like, can be obtained.
The method comprises the steps of obtaining actual measurement data, and matching the actual measurement data in a pre-established evaluation standard to obtain street data. The evaluation criteria pre-store the correspondence between the actual measured data and the street data in each street data index. The street data can be represented in a hierarchical manner, in a percent score manner, or in a percent manner. It is understood that the grades, percentile scores and percentiles may be converted to each other.
Specifically, for example, the actual measured data of the noise of the motor vehicle is obtained as 50 db. In the evaluation standard, the noise of the motor vehicle is less than 55 dB, and the street data is 3; the noise of the motor vehicle is more than or equal to 55 dB and less than 65 dB, and the street data is 2; the noise of the motor vehicle is more than or equal to 65 dB and less than 75 dB, and the street data is 1; the motor vehicle noise is greater than 75 db and the street data is 0. After matching, the actual measured data of the motor vehicle noise is 50 db, which accords with the standard that the motor vehicle noise is less than 55 db in the evaluation standard and the street data is 3", so that the street data corresponding to the motor vehicle noise is 3.
For another example, the actual measurement data of the street-crossing facility is obtained as the street-crossing facility is imperfect, a part of the intersections are not provided with safety guards, and the existing part involves irrational. The evaluation criteria for the street-crossing facility were: the pedestrian crossing facilities, the safety guardrails and other facilities are complete, the setting positions are reasonable, and the street data is 3; the pedestrian crossing facilities, the safety guardrails and other facilities are complete, but part of the facilities are unreasonable, and the street data is 2; the arrangement of pedestrian crossing facilities, safety guardrails and the like is incomplete, part of the arrangement is unreasonable, and street data is 1; serious defects such as pedestrian crossing facilities and safety guardrails exist, the reasonability exists, and the street data is 0. After matching, the actual measurement data of the street crossing facilities accords with the standard that the arrangement of pedestrian street crossing facilities, safety guardrails and the like is incomplete, part of the data is unreasonable, and the street data is 1, so that the corresponding street data of the street crossing facilities is 1.
In the implementation process, the following steps are adopted: the data acquisition device is used for acquiring actual measurement data of the street to be detected, and converting the actual measurement data into street data with unified standards according to the pre-established evaluation standards, so that the accuracy of street health evaluation is improved.
Optionally, in an embodiment of the present application, the street health algorithm includes:
wherein ,for street health result, ++>For street data index>For street data index->Is a sum of (2)The weight of the material to be weighed,for street data index->Corresponding street data.
Street data indexIs>The calculation mode of (a) can be as follows: />
wherein ,for street data index->Is>Group weight for street data index j, +.>Inter-group weight for street data index j, < +.>Is a latent variable.
As one implementation, in calculating the street health result according to the above formula, if the street data adopts a hierarchical manner, the street data can be converted into a percentage score according to the set total number of levels. For example, the street data in the embodiment of the present application includes four levels of 0,1, 2 and 3, and after conversion to the percentile score, the 0 percentile score is 0, the 1 percentile score is 100/3, the 2 percentile score is 200/3, and the 3 percentile score is 100.
Referring to Table 2 for the correspondence between the latent variable and the street data index, when the latent variable i is 1, the latent variable is convenient for transportation; when the latent variable is 2, the latent variable is for improving the street quality; when the latent variable is 3, the latent variable is the created vitality neighborhood; when the latent variable is 4, the latent variable is the construction health environment.
Illustratively, when the street data index j is equal to 1, the street data index is the non-motor vehicle lane spacing; when the street data index j is equal to 2, the street data index is the pavement distance; when the street data index j is equal to 3, the street data index is that the bicycle is parked; when the street data index j is equal to 4, the street data index is the shared bicycle coverage.
In an alternative embodiment, the total weight of the street data index of the street to be detected is calculated by comparing the total weight of the street data index with the total weight of the street data indexStreet data corresponding to street data index +.>Multiplying the street data indexes to obtain a score of the street data index; if the score of the street data index is smaller than the preset threshold value of the street data index, optimizing the reception data index in the street to be detected.
Specifically, for example, the street data corresponding to the street facilities in the street data index is 1, the total weight of the street facilities is 0.050, the score of the street facilities is calculated to be 0.050, if the preset threshold of the street facilities is 0.1, the score of the street facilities is smaller than the preset threshold of the street facilities, and the street facilities in the street to be detected can be optimized.
The preset threshold of the street data index can be set according to actual requirements. By calculating the score of the street crossing facilities, the targeted optimization of the street to be detected is realized, so that the health degree of the street to be detected is improved.
In the implementation process, the accuracy of calculating the street health degree is improved through formula calculation corresponding to the street health degree algorithm, and the problem that the calculation of the street health degree is inaccurate due to the fact that factors affecting the street health degree are difficult to quantify is solved.
Please refer to fig. 5, which illustrates a flow chart of factor analysis according to an embodiment of the present application.
The method comprises the steps of carrying out initial scoring on healthy streets by combing a plurality of road sections in a plurality of streets of a certain area, and selecting a plurality of streets with highest initial scoring as streets basically meeting the requirements of the healthy streets as study objects. And acquiring a factor measurement statistical table according to the initial scores of the selected streets by using data analysis software, wherein the factor measurement statistical table comprises an average value, a standard deviation, a KMO detection value and a Bartlett sphere test value of the factors. And then, carrying out factor analysis to extract a common factor, and obtaining the latent variable for constructing the structural equation model.
Please refer to fig. 6, which is a schematic diagram illustrating a flow chart for constructing a structural equation model according to an embodiment of the present application.
And selecting a healthy street with higher health degree for field investigation and data acquisition, and performing exploratory factor analysis on the acquired data to obtain a latent variable. An observable index of a healthy street, namely a street data index, is obtained based on the latent variable inertia. By involving the questionnaire and obtaining the credibility and validity of the questionnaire, it is determined whether the generalized observable index needs to be corrected. After the latent variable and the observable index are determined, the association relation between the observable index and the latent variable is analyzed through the verification factor, a structural equation is constructed based on the observable index, the latent variable and the association relation, and the structural equation is adjusted by utilizing structural equation fitness verification.
Based on the obtained structural equation, model standardization parameter calculation is carried out, weight corresponding to each observable index is obtained, and based on the weight corresponding to the observable index and street data of the street to be detected, the health degree of the street to be detected can be evaluated.
Referring to fig. 7, a schematic structural diagram of a street determining apparatus according to an embodiment of the present application is shown; the embodiment of the application provides a street determining device 200, which comprises:
An obtaining module 210, configured to obtain street data corresponding to a street data index in a street to be detected; the street data index comprises traffic data, environment data, block quality data and block vitality data;
the degree result module 220 is configured to obtain a street health degree result of the street to be detected according to a preset street health degree algorithm and street data; the street health degree algorithm is generated according to the weight of the street data index; the weight of the street data index is obtained by utilizing a factor analysis method, a structural equation model is constructed based on the latent variable, and the weight is obtained through the structural equation model;
a determining module 230 is configured to determine a target street based on the street health result.
Alternatively, in an embodiment of the application, the factor analysis method includes a exploratory factor analysis method; the street determining apparatus further includes: the structural equation construction module is used for acquiring a preset health index, and analyzing the health index through a exploratory factor analysis method to acquire a latent variable; obtaining street data indexes based on the latent variables; obtaining the association relation between the latent variable and the street data index through a verification factor analysis method; a structural equation model is generated based on the latent variables, the street data index, and the association relationship.
Optionally, in an embodiment of the present application, the street determining apparatus further includes: the weight obtaining module is used for obtaining a non-standardized path coefficient value between the street data index and the latent variable through the structural equation model; and carrying out normalization processing on the street data index based on the non-normalized path coefficient value to obtain the weight of the street data index.
Optionally, in an embodiment of the present application, the street determining apparatus further includes: the matching degree checking module is used for acquiring a checking parameter value of the structural equation; the check parameter value comprises at least one of a chi-square degree of freedom ratio, an approximate error square root and a model increment adaptation degree; and carrying out adaptation degree inspection on the structural equation based on the verification parameter value.
Optionally, in an embodiment of the present application, the street determining device, the correction module is configured to collect data of influence degree of the street data index on the residents; and calculating the credibility and the validity of the influence degree data, and correcting the street data index based on the credibility and the validity.
Optionally, in the embodiment of the present application, the street determining device, the acquiring module 210 is specifically configured to acquire, by using the data acquisition device, actual measurement data of the street to be detected; the data acquisition device comprises an image acquisition device, a noise acquisition device and an air quality detection device; and obtaining the street data according to a pre-formulated evaluation standard and the actual measurement data.
Optionally, in an embodiment of the present application, the street determining apparatus, the street health algorithm includes:
wherein ,for street health result, ++>For street data index>For street data index->Weight of->For street data index->Corresponding street data.
It should be understood that the apparatus corresponds to the above-mentioned street determining method embodiment, and is capable of executing the steps involved in the above-mentioned method embodiment, and specific functions of the apparatus may be referred to the above description, and detailed descriptions thereof are omitted herein as appropriate to avoid redundancy. The device includes at least one software functional module that can be stored in memory in the form of software or firmware (firmware) or cured in an Operating System (OS) of the device.
Please refer to fig. 8, which illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application. An electronic device 300 provided in an embodiment of the present application includes: a processor 310 and a memory 320, the memory 320 storing machine-readable instructions executable by the processor 310, which when executed by the processor 310 perform the method as described above.
The embodiment of the application also provides a storage medium, wherein a computer program is stored on the storage medium, and the computer program is executed by a processor to execute the method.
The storage medium may be implemented by any type of volatile or nonvolatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In the embodiments of the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The foregoing description is merely an optional implementation of the embodiment of the present application, but the scope of the embodiment of the present application is not limited thereto, and any person skilled in the art may easily think about changes or substitutions within the technical scope of the embodiment of the present application, and the changes or substitutions are covered by the scope of the embodiment of the present application.
Claims (10)
1. A method of street determination, comprising:
obtaining street data corresponding to street data indexes in the street to be detected; the street data indexes comprise traffic data, environment data, block quality data and block vitality data;
obtaining a street health degree result of the street to be detected according to a preset street health degree algorithm and the street data; wherein the street health degree algorithm is generated according to the weight of the street data index; the weight of the street data index is obtained by utilizing a factor analysis method, a structural equation model is constructed based on the latent variable, and the weight is obtained through the structural equation model;
And determining a target street based on the street health result.
2. The method of claim 1, wherein the factor analysis comprises an exploratory factor analysis; before the obtaining the street health result of the to-be-detected street according to the preset street health algorithm and the street data, the method further comprises:
acquiring a preset health index, and analyzing the health index by a exploratory factor analysis method to obtain a latent variable;
obtaining the street data index based on the latent variable;
obtaining the association relation between the latent variable and the street data index through a verification factor analysis method;
the structural equation model is generated based on the latent variables, the street data index, and the association relationship.
3. The method of claim 2, wherein after the generating the structural equation model based on the latent variables, the street data metrics, and the associations, the method further comprises:
obtaining a non-standardized path coefficient value between the street data index and the latent variable through the structural equation model;
And carrying out normalization processing on the street data index based on the non-normalized path coefficient value to obtain the weight of the street data index.
4. The method of claim 2, wherein after the generating the structural equation model based on the latent variables, the street data metrics, and the associations, the method further comprises:
obtaining a verification parameter value of the structural equation; the check parameter value comprises at least one of a chi-square degree of freedom ratio, an approximate error square root and a model increment adaptation degree;
and carrying out adaptation degree inspection on the structural equation based on the verification parameter value.
5. The method of claim 2, wherein after the obtaining the street data index based on the latent variable, the method further comprises:
collecting influence degree data of the street data indexes on residents;
and calculating the credibility and the validity of the influence degree data, and correcting the street data index based on the credibility and the validity.
6. The method of claim 1, wherein the obtaining street data corresponding to the street data index in the street to be detected comprises:
Acquiring actual measurement data of the street to be detected through a data acquisition device; the data acquisition device comprises an image acquisition device, a noise acquisition device and an air quality detection device;
and obtaining the street data according to a pre-formulated evaluation standard and the actual measurement data.
7. The method of any one of claims 1-6, wherein the street health algorithm comprises:
wherein ,for street health result, ++>For street data index>For street data index->Weight of->For street data index->Corresponding street data.
8. A street determining apparatus, comprising:
the acquisition module is used for acquiring street data corresponding to the street data index in the street to be detected; the street data indexes comprise traffic data, environment data, block quality data and block vitality data;
the degree result module is used for obtaining the street health degree result of the street to be detected according to a preset street health degree algorithm and the street data; wherein the street health degree algorithm is generated according to the weight of the street data index; the weight of the street data index is obtained by utilizing a factor analysis method, a structural equation model is constructed based on the latent variable, and the weight is obtained through the structural equation model;
And the determining module is used for determining a target street based on the street health degree result.
9. An electronic device, comprising: a processor and a memory storing machine-readable instructions executable by the processor to perform the method of any one of claims 1 to 7 when executed by the processor.
10. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, performs the method according to any of claims 1 to 7.
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