CN113592260B - Village hollowing degree assessment method - Google Patents
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Abstract
The invention discloses a village hollowing degree evaluation method, which comprises the steps of obtaining evaluation index data of villages to be evaluated; performing standardization processing on the evaluation index data, and checking the standardized evaluation index data; performing principal component analysis on the checked evaluation index data to extract principal components of the checked evaluation index data; constructing a rotating component matrix according to the main components and the checked evaluation index data, and extracting the composition index of each main component; and calculating the comprehensive score of the degree of hollowness of the village to be evaluated through the rotation component matrix and the component score coefficient matrix. The village hollowing degree can be reflected more accurately by using various multiple evaluation indexes as evaluation data, the evaluation result is more accurate, and the evaluation process is simple, convenient and rapid.
Description
Technical Field
The invention relates to the technical fields of geography, economics, sociology, urban and rural planning, land management, remote sensing discipline and new rural construction research, in particular to a village hollowing degree evaluation method.
Background
With the further development of urban and rural economy in China, rural labor output is increased, rural people increase income, and meanwhile, the space layout of rural residences and the space layout of residents with different age levels in villages are greatly changed. Many villages are around a large number of new buildings, but the villages are basically old in style and even idle old houses. This phenomenon of bust construction, cold clearing and massive idle settlement in villages is known as "hollowing" of villages. The existence of the hollow village wastes limited land resources of the country to a great extent, influences the improvement of the living environment quality of rural residents and the development of rural economy, so that how to effectively identify the hollow village is also an important research direction.
The existing village 'hollowing' assessment method is mainly identified by unmanned aerial vehicle image identification, mobile phone signal identification or interview investigation. However, the existing evaluation method has single evaluation index, poor timeliness and complex operation.
Disclosure of Invention
The embodiment of the invention provides a village hollowing degree evaluation method, which is multiple in evaluation index, more accurate in evaluation result and simple and quick in evaluation process.
The embodiment of the invention provides a village hollowing degree assessment method, which comprises the following steps:
acquiring evaluation index data of villages to be evaluated;
performing standardization processing on the evaluation index data, and checking the standardized evaluation index data;
performing principal component analysis on the checked evaluation index data to extract principal components of the checked evaluation index data;
constructing a rotating component matrix according to the main components and the checked evaluation index data, and extracting the composition index of each main component;
and calculating the comprehensive score of the degree of hollowness of the village to be evaluated through the rotation component matrix and the component score coefficient matrix.
As a preferred mode, the evaluation index data includes: the ratio of the resident population before and after the spring festival, the area of the residence per person, the density of public service facilities, the density of regular enterprises, the density of three old transformation and the density of cultivated land.
As a preferable mode, the normalizing the evaluation index data and checking the normalized evaluation index data specifically includes:
by a standard deviation model:data normalization is carried out on the data of the evaluation index data to obtain normalized evaluation index data A ij ；
The normalized evaluation index data A ij Loading into an analysis environment of SPSS software;
performing KMO (KMO) test and Bartlett sphericity test on the standardized evaluation index data by using a dimension reduction factor analysis tool of SPSS software;
wherein A is ij X is the standardized result of the ith data in the jth index data ij Is the ith data in the jth index data, maxX j And minX j Respectively, the maximum value of all data in the jth index data and the minimum value of all data in the jth index data, i, j>0。
Further, the dimension reduction factor analysis tool using SPSS software performs KMO test and Bartlett sphericity test on the normalized evaluation index data, and specifically includes:
calculating a KMO value and a P value of sphericity test of the standardized evaluation index data by using a dimension reduction factor analysis tool of SPSS software;
when the KMO value is not smaller than a first preset value and the P value of the sphericity test is smaller than a second preset value, the standardized evaluation index data test passes; when the KMO value is smaller than the first preset value or the P value of the sphericity test is not smaller than the second preset value, the standardized evaluation index data test is not passed;
and when the standardized evaluation index data fails to pass the inspection, acquiring the evaluation index data of the village to be evaluated again, performing standardized processing, and then performing KMO inspection and Bartlett sphericity inspection again until the evaluation index data passes the inspection.
As a preferred mode, the principal component analysis is performed on the tested evaluation index data to obtain a variance interpretation report, and the principal component of the tested evaluation index data is extracted, which specifically includes:
using a principal component analysis tool of SPSS software to obtain a variance interpretation report of the checked evaluation index data;
and extracting principal components with characteristic values larger than a third preset value from the variance interpretation table.
As a preferable mode, the extracting the constituent index of each principal component from the inspected evaluation index data by rotating the component matrix specifically includes:
constructing a rotation component matrix of the tested evaluation index data about the principal components to obtain a load value of each evaluation index of the tested evaluation index data relative to each principal component;
and the evaluation index with the load value larger than the fourth preset value is a composition index, and the corresponding relation of each composition index of each main component is recorded.
As a preferred manner, the calculating the comprehensive score of the degree of hollowness of the village to be evaluated by the rotation component matrix and the component score coefficient matrix specifically includes:
extracting the score of each constituent index through a component score coefficient matrix;
by calculating the score of each principal component
Calculating a composite score of the degree of hollowness of the village to be evaluated according to the main component scores
Wherein C is k A score of the kth principal component, the principal component corresponding to N constituent indexes, a m Score of component score coefficient matrix corresponding to mth constituent index, X m Data corresponding to the mth composition index; c is the composite score of the degree of hollowness of the village to be evaluated, beta k Is the characteristic value of the kth main component, I is the number of the main components, I is not less than k is not less than 0, m, N>0。
As a preferred mode, the method further comprises:
and connecting the comprehensive score of the degree of hollowing of the village to be evaluated into a vector file of the village by using arcgis software, grading the degree of hollowing, and drawing a hollow vector diagram of the village.
The invention provides a village hollowing degree evaluation method, which comprises the steps of obtaining evaluation index data of villages to be evaluated; performing standardization processing on the evaluation index data, and checking the standardized evaluation index data; performing principal component analysis on the checked evaluation index data to extract principal components of the checked evaluation index data; constructing a rotating component matrix according to the main components and the checked evaluation index data, and extracting the composition index of each main component; and calculating the comprehensive score of the degree of hollowness of the village to be evaluated through the rotation component matrix and the component score coefficient matrix. The village hollowing degree can be reflected more accurately by using various multiple evaluation indexes as evaluation data, the evaluation result is more accurate, and the evaluation process is simple, convenient and rapid.
Drawings
Fig. 1 is a schematic flow chart of a village hollowing degree evaluation method according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a schematic flow chart of a village hollowing degree evaluation method according to an embodiment of the present invention includes steps S101 to S105:
s101, acquiring evaluation index data of villages to be evaluated;
s102, carrying out standardized processing on the evaluation index data, and checking the standardized evaluation index data;
s103, performing principal component analysis on the checked evaluation index data, and extracting principal components of the checked evaluation index data;
s104, constructing a rotating component matrix according to the main components and the checked evaluation index data, and extracting the composition index of each main component;
s105, calculating the comprehensive score of the degree of hollowness of the village to be evaluated through the rotation component matrix and the component score coefficient matrix.
When the embodiment is implemented, the evaluation index which is related to the hollowing of villages and has larger influence factors is preferentially obtained as evaluation data, so that the hollowing degree of villages can be reflected more accurately;
the evaluation index data are standardized, and the standardized data are checked, so that the correlation and independence among the evaluation index data of the selected area are checked, and the occurrence of a large error in the evaluation of the air rate of the village is avoided;
the main component analysis is carried out on the checked data, the evaluation index data are divided into different main components according to the correlation degree among different evaluation index data, the hollowing degree is evaluated according to the different main components, and the evaluation result is more accurate;
constructing a rotating component matrix according to the main components and the checked evaluation index data, and extracting the composition index of each main component; and calculating the comprehensive score of the hollowing degree of the village to be evaluated through the rotation component matrix and the component score coefficient matrix, so as to evaluate the hollowing degree of the village.
The invention provides a village hollowing degree evaluation method, which comprises the steps of obtaining evaluation index data of villages to be evaluated; performing standardization processing on the evaluation index data, and checking the standardized evaluation index data; performing principal component analysis on the checked evaluation index data to extract principal components of the checked evaluation index data; constructing a rotating component matrix according to the main components and the checked evaluation index data, and extracting the composition index of each main component; and calculating the comprehensive score of the degree of hollowness of the village to be evaluated through the rotation component matrix and the component score coefficient matrix. The village hollowing degree can be reflected more accurately by using various multiple evaluation indexes as evaluation data, the evaluation result is more accurate, and the evaluation process is simple, convenient and rapid.
In yet another embodiment provided by the present invention, the evaluation index data includes: the ratio of the resident population before and after the spring festival, the area of the residence per person, the density of public service facilities, the density of regular enterprises, the density of three old transformation and the density of cultivated land.
In the implementation of this embodiment, the hollowing out of villages is reflected in three aspects: the method has the advantages of hollow population, hollow industry and economic hollow economy, and is particularly suitable for the aspects of massive loss of village population, low fans in agricultural production, waste of cultivated land, empty houses, low-efficiency utilization of public resources and the like. And combining the representativeness of the evaluation indexes and the availability of data, and comprehensively researching the actual situation of the area, and constructing an evaluation index system of the village air-core rate from six indexes of population, industry and economy, wherein the population index comprises the ratio of the resident population to the resident population before and after the spring festival and the area of the residence per person, the industry index comprises the regular enterprise density and the cultivated land density, and the economic index comprises the public and service facility density and the three old reconstruction density.
The ratio of the resident population before and after the spring festival reflects the loss condition of the village population, the rural population flows out mainly to the outside attendant population, the more serious the village population loss is, the population bearing function of the village is reduced, the special time of the traditional family gathering in the spring festival is removed, and the resident population is less. The average residence area is represented by the relative value of the residence area and the resident population of villages, when the average residence area is larger, the relative house vacancy rate is higher, and the house utilization rate of villages is lower. In the second and third industry conditions of villages, generally, the higher the rule of the enterprise density, the better the foundation of the village second and third industry, so as to provide more working posts, better economic conditions and higher population concentration of the villages. The cultivated land density is the ratio of the basic farmland area to the residential land area, the cultivated land resource condition of villages is mainly measured, and the cultivated land is used as the most basic living resource of the villages, so that the village functions and the agricultural development of the villages are greatly influenced. The public facility density represents the matching perfection of the public service of villages, and the higher the public facility density is, the better the living environment of the villages is, so that the lower the hollow rate of the villages is likely to be. The old and partly old houses and facilities in rural areas with different ages are modified and repaired according to the standard, and when the hollow heart rate of villages is larger, the old and empty houses are more, and the old and empty houses are more and the old requirements are larger.
The specific evaluation index system is shown in table 1:
table 1 evaluation index data System Table
| Evaluation index | Index calculation |
| Ratio of front and rear resident population in spring festival | Population for life before/after year |
| Area of average residence | Urban residential rural residence area/resident population |
| Public service facility density | Public service facility area/town residential rural residential area |
| On-gauge enterprise density | On-site enterprise quantity/construction land area |
| Density of three old modifications | Area of three old pattern spots/area for construction |
| Density of cultivated land | Basic farmland area/town residential rural residence area |
The regional resident population data is obtained by querying the mobile phone signaling data.
The statistical time period is one month before the spring festival and one month after the spring festival, the residence time in the area between 0 and 6 points per day is longer than 3 hours, and the regional resident population is judged to be the regional resident population in the statistical time period, wherein the number of days is more than or equal to 15 days.
The rural residential land area of the urban residents in the area of the people's average residence is obtained by extracting land class names from the three-tone data of the local area, namely rural residential land and urban residential land data, and the resident population is an average value of resident population of two months before and after the spring festival.
The public service facility area is obtained through the digital current map project data of the local area, and the data sources are all government functional departments.
The enterprise data on the whole region rule is obtained through local region scientific and industrial business and informatization local gate, and the boundary between the enterprise data on the whole region rule and villages is processed in arcgis through a space connection tool and a field mapping tool to obtain the number of enterprises on the rule of each village.
The construction land area is obtained by extracting construction land data from local area three-tone (two-tone) data.
The area of the three old pattern spots and the basic farmland area are provided for regional planning and natural resource bureau.
It should be noted that, the above six evaluation index data are provided in this embodiment, but in practical implementation, data that can reflect the status of village population, industry and economy can be used as the hollow evaluation index data.
By acquiring the evaluation index data covering the evaluation data of the aspects of population hollowing, industry hollowing and economy hollowing, the air-core rate of villages can be evaluated in a diversified manner, and the evaluation result is more accurate.
In still another embodiment of the present invention, the normalizing the evaluation index data and checking the normalized evaluation index data specifically includes:
by a standard deviation model:data normalization is carried out on the data of the evaluation index data to obtain normalized evaluation index data A ij ；
The normalized evaluation index data A ij Loading into an analysis environment of SPSS software;
performing KMO (KMO) test and Bartlett sphericity test on the standardized evaluation index data by using a dimension reduction factor analysis tool of SPSS software;
wherein A is ij X is the standardized result of the ith data in the jth index data ij Is the ith data in the jth index data, maxX j And minX j Respectively, the maximum value of all data in the jth index data and the minimum value of all data in the jth index data, i, j>0。
In the embodiment, the village evaluation index data is first enteredLine range normalization by a range normalization model:data normalization is carried out on the data of the evaluation index data to obtain normalized evaluation index data A ij ；
The normalized evaluation index data A ij Loading the data into an analysis environment of SPSS software, and using the SPSS software to analyze the data;
performing KMO (KMO) test and Bartlett sphericity test on the standardized evaluation index data by using an analysis-dimension reduction-factor analysis tool in SPSS software;
wherein A is ij X is the standardized result of the ith data in the jth index data ij Is the ith data in the jth index data, maxX j And minX j Respectively, the maximum value of all data in the jth index data and the minimum value of all data in the jth index data, i, j>0。
By carrying out standardized processing on the evaluation index data, unified standards of all evaluation indexes are realized, and the method can be directly used for subsequent evaluation of the hollowing degree, so that errors of the data on an evaluation result are reduced; detecting the correlation between different evaluation indexes through KMO test, and adopting Bartlett sphericity to test the independence of the different evaluation indexes; and the degree of hollowing is evaluated by adopting indexes with stronger correlation and independence, and the accuracy of an evaluation result is higher.
Further, in this embodiment, the dimension-reduction factor analysis tool using SPSS software performs KMO test and Bartlett sphericity test on the normalized evaluation index data, and specifically includes:
calculating a KMO value and a P value of sphericity test of the standardized evaluation index data by using a dimension reduction factor analysis tool of SPSS software;
when the KMO value is not smaller than a first preset value and the P value of the sphericity test is smaller than a second preset value, the standardized evaluation index data test passes; when the KMO value is smaller than the first preset value or the P value of the sphericity test is not smaller than the second preset value, the standardized evaluation index data test is not passed;
and when the standardized evaluation index data fails to pass the inspection, acquiring the evaluation index data of the village to be evaluated again, performing standardized processing, and then performing KMO inspection and Bartlett sphericity inspection again until the evaluation index data passes the inspection.
When the embodiment is implemented, the dimension reduction factor analysis tool of the SPSS software is used for calculating the KMO value and the P value of sphericity test of the standardized evaluation index data;
when the KMO value is smaller than 0.5, the selected evaluation index data is not suitable for factor analysis, and an index system needs to be reconstructed; when the KMO value is not less than 0.5, the selected evaluation index data can be used for factor analysis, and the closer the KMO value is to 1, the stronger the correlation among variables, the weaker the bias correlation, and the better the factor analysis effect.
The P value sig of Bartlett sphericity test is less than 0.05, which shows that the selected evaluation index data are spherically distributed, and each variable is mutually independent to a certain extent, so that the factor analysis effect is good.
In specific implementation, the KMO value is 0.589, factor analysis can be performed, the P value sig of sphericity test is less than 0.05, the data are spherically distributed, and each variable is mutually independent to a certain extent, so that the evaluation index data selected at this time can be used for evaluating the degree of hollowing.
When the KMO value is not less than 0.5 or the P value sig of the Bartlett sphericity test is not less than 0.05, the selected evaluation index data is not suitable for the evaluation of the hollowness degree, so that the evaluation index data of villages to be evaluated need to be acquired again, standardized processing is carried out, and then the KMO test and the Bartlett sphericity test are carried out again until the evaluation index data pass the test.
It should be noted that the first preset value and the second preset value may be set according to the actual hollowing evaluation standard, and in this embodiment, the first preset value takes 0.5 and the second preset value takes 0.05, but this embodiment is only a preferred embodiment of the present invention, and does not limit the present invention.
Through KMO test and Bartlett sphericity test on the evaluation index data, the evaluation indexes with strong correlation and independence are selected, unified standards of different villages to be evaluated are realized, and evaluation errors caused by low correlation and poor independence among the evaluation indexes are avoided.
In another embodiment of the present invention, the principal component analysis is performed on the tested evaluation index data to obtain a variance interpretation report, and the principal component extraction of the tested evaluation index data specifically includes:
using a principal component analysis tool of SPSS software to obtain a variance interpretation report of the checked evaluation index data;
and extracting principal components with characteristic values larger than a third preset value from the variance interpretation table.
When the embodiment is implemented, the variance interpretation report of the evaluation index data can be obtained by using the principal component analysis tool of the SPSS software, the number of principal components with the characteristic value larger than 1 is extracted, the characteristic values of the principal components are stored, and the principal components are mutually independent and can represent most of information in the evaluation index data.
It should be noted that the third preset value may be set according to the actual hollowing evaluation standard, and in this embodiment, the third preset value takes 1, but this embodiment is only a preferred embodiment of the present invention, and is not limited to this embodiment.
In still another embodiment of the present invention, the extracting, by rotating the component matrix, the constituent index of each of the principal components from the examined evaluation index data specifically includes:
constructing a rotation component matrix of the tested evaluation index data about the principal components to obtain a load value of each evaluation index of the tested evaluation index data relative to each principal component;
and the evaluation index with the load value larger than the fourth preset value is a composition index, and the corresponding relation of each composition index of each main component is recorded.
In the implementation of this embodiment, the rotation component matrix is used to better induce the evaluation index data, where the rotation component matrix includes the association degree between each evaluation index and the main component, that is, the load value, and the evaluation index with the load value of each main component greater than 0.7 in the rotation component matrix is extracted as the composition index of the corresponding main component, and the composition index of each main component is recorded.
It should be noted that, the fourth preset value may be set according to the actual hollowing evaluation standard, and in this embodiment, the fourth preset value takes 0.7, but this embodiment is only a preferred embodiment of the present invention, and is not limited to this embodiment.
The constituent indexes of each main component are extracted through rotating the component matrix, the corresponding relation between the constituent indexes and the main components is formed, the main components are evaluated through each constituent index, the hollowing of villages to be evaluated is evaluated through all the main components, and the evaluation process is simple.
In yet another embodiment of the present invention, the calculating, by using the rotation component matrix and the component score coefficient matrix, a composite score of the degree of hollowness of the village to be evaluated specifically includes:
the score of each constituent index can be extracted through the component score coefficient matrix;
by calculating the score of each principal component
Calculating a composite score of the degree of hollowness of the village to be evaluated according to the main component scores
Wherein C is k A score of the kth principal component, the principal component corresponding to N constituent indexes, a m Score of component score coefficient matrix corresponding to mth constituent index, X m Data corresponding to the mth composition index; c is the composite score of the degree of hollowness of the village to be evaluated, beta k Is the characteristic value of the kth main component, I is the number of the main components, I is not less than k is not less than 0, m, N>0。
In the implementation of this embodiment, the score of each principal component is calculated by rotating the component matrix and the component score matrix, and then the score of each principal component is counted to calculate a comprehensive score of the degree of hollowness of the village to be evaluated, so as to obtain an evaluation result. And the evaluation flow agrees, errors are reduced, and the evaluation result is more accurate.
In yet another embodiment provided by the present invention, the method further comprises:
and connecting the comprehensive score of the degree of hollowing of the village to be evaluated into a vector file of the village by using arcgis software, grading the degree of hollowing, and drawing a hollow vector diagram of the village.
When the embodiment is implemented, the obtained comprehensive score is connected to the village vector file by using the attribute table in arcgis software, the hollow degree score is graded, and a corresponding map is drawn, so that the vectorization degree of the village can be intuitively expressed.
By linking the composite score of the village to be evaluated to the vector file of the village, visualization of the degree of hollowing of the village can be achieved.
The invention provides a village hollowing degree evaluation method, which comprises the steps of obtaining evaluation index data of villages to be evaluated; performing standardization processing on the evaluation index data, and checking the standardized evaluation index data; performing principal component analysis on the checked evaluation index data to extract principal components of the checked evaluation index data; constructing a rotating component matrix according to the main components and the checked evaluation index data, and extracting the composition index of each main component; and calculating the comprehensive score of the degree of hollowness of the village to be evaluated through the rotation component matrix and the component score coefficient matrix. The village hollowing degree can be reflected more accurately by using various multiple evaluation indexes as evaluation data, the evaluation result is more accurate, and the evaluation process is simple, convenient and rapid.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (6)
1. A village hollowing degree evaluation method, comprising:
acquiring evaluation index data of villages to be evaluated;
performing standardization processing on the evaluation index data, and checking the standardized evaluation index data;
performing principal component analysis on the checked evaluation index data to extract principal components of the checked evaluation index data;
constructing a rotating component matrix according to the main components and the checked evaluation index data, and extracting the composition index of each main component;
calculating the comprehensive score of the degree of hollowing of the village to be evaluated through the rotation component matrix and the component score coefficient matrix;
the calculating, by the rotation component matrix and the component score coefficient matrix, a composite score of the degree of hollowness of the village to be evaluated specifically includes:
extracting the score of each constituent index through a component score coefficient matrix;
by calculating the respective principal componentsScore of；
Calculating a composite score of the degree of hollowness of the village to be evaluated according to the main component scores；
Wherein,C k is the firstkA score of each principal component corresponding toNThe number of the constituent indexes is equal to the number of the constituent indexes,a m the score of the component score coefficient matrix corresponding to the mth constituent index,X m data corresponding to the mth composition index; c is the composite score of the degree of hollowness of the village to be evaluated,β k is the characteristic value of the kth main component, I is the number of the main components, I is not less than k is not less than 0, m, N>0；
The evaluation index data includes: the ratio of the resident population before and after the spring festival, the area of the residence per person, the density of public service facilities, the density of regular enterprises, the density of three old transformation and the density of cultivated land.
2. The village hollowness assessment method according to claim 1, wherein the normalizing the assessment index data and checking the normalized assessment index data comprises:
by a standard deviation model:data normalization is carried out on the data of the evaluation index data to obtain normalized evaluation index dataA ij ；
The normalized evaluation index dataA ij Loading into an analysis environment of SPSS software;
performing KMO (KMO) test and Bartlett sphericity test on the standardized evaluation index data by using a dimension reduction factor analysis tool of SPSS software;
wherein,A ij is the firstjThe first index dataiThe result of the normalization of the individual data,X ij is the firstjThe first index dataiThe data of the plurality of data,maxX j andminX j respectively the firstjMaximum value sum of all data in the individual index datajThe minimum value of all data in the individual index data,i，j>0。
3. the village hollowness assessment method according to claim 2, wherein the standardized assessment index data is subjected to KMO test and Bartlett sphericity test by using a dimension reduction factor analysis tool of SPSS software, and the method specifically comprises:
calculating the KMO value and the P value of sphericity test of the standardized evaluation index data by using a dimension reduction factor analysis tool of SPSS software;
when the KMO value is not smaller than a first preset value and the P value of the sphericity test is smaller than a second preset value, the standardized evaluation index data test passes; when the KMO value is smaller than the first preset value or the P value of the sphericity test is not smaller than the second preset value, the standardized evaluation index data test is not passed;
and when the standardized evaluation index data fails to pass the inspection, acquiring the evaluation index data of the village to be evaluated again, performing standardized processing, and then performing KMO inspection and Bartlett sphericity inspection again until the evaluation index data passes the inspection.
4. The village hollowing degree evaluation method according to claim 1, wherein the principal component analysis is performed on the inspected evaluation index data to obtain a variance interpretation report, and the principal component of the inspected evaluation index data is extracted, specifically comprising:
using a principal component analysis tool of SPSS software to obtain a variance interpretation report of the checked evaluation index data;
and extracting principal components with characteristic values larger than a third preset value from the variance interpretation table.
5. The village hollowing degree evaluation method as recited in claim 1, wherein the extracting the constituent index of each of the principal components from the examined evaluation index data by rotating the component matrix comprises:
constructing a rotation component matrix of the tested evaluation index data about the principal components to obtain a load value of each evaluation index of the tested evaluation index data relative to each principal component;
and the evaluation index with the load value larger than the fourth preset value is a composition index, and the corresponding relation of each composition index of each main component is recorded.
6. The village hollowness assessment method according to claim 1, wherein the method further comprises:
and connecting the comprehensive score of the degree of hollowing of the village to be evaluated into a vector file of the village by using arcgis software, grading the degree of hollowing, and drawing a hollow vector diagram of the village.
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