CN113592260A - Village hollowing degree evaluation 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 a village to be evaluated; standardizing the evaluation index data, and checking the standardized evaluation index data; performing principal component analysis on the inspected evaluation index data, and extracting principal components of the inspected evaluation index data; constructing a rotation component matrix according to the principal components and the evaluation index data after the inspection, and extracting the constituent indexes of the principal components; 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. By using various multivariate evaluation indexes as evaluation data, the hollowing degree of the village can be reflected more accurately, the evaluation result is more accurate, and the evaluation process is simple, convenient and quick.
Description
Technical Field
The invention relates to the technical field 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 the urban and rural economy in China, the output of rural labor services is increased, the ways of increasing income of rural people are diversified, and the spatial layout of rural residences and the spatial layout of residents at different ages in the village are greatly changed. A large number of new buildings emerge at the periphery of many villages, and old buildings basically have old styles and even are vacant. The phenomenon of settlement of the bustling, cold and idle villages is called the 'hollowing' of the village. The existence of the 'hollowed' village wastes the limited land resources of the country to a great extent, influences the improvement of the living environment quality of rural residents and influences the development of rural economy, so that how to effectively identify the 'hollowed' village is an important research direction.
The existing village 'hollowing' evaluation method mainly adopts unmanned aerial vehicle image identification, mobile phone signal identification or visiting investigation mode identification. However, the existing evaluation method has single evaluation index, poor timeliness and complicated operation.
Disclosure of Invention
The embodiment of the invention provides a village hollowing degree evaluation method which has multiple evaluation indexes, more accurate evaluation results and simple, convenient and quick evaluation process.
The embodiment of the invention provides a village hollowing degree evaluation method, which comprises the following steps:
acquiring evaluation index data of a village to be evaluated;
standardizing the evaluation index data, and checking the standardized evaluation index data;
performing principal component analysis on the inspected evaluation index data, and extracting principal components of the inspected evaluation index data;
constructing a rotation component matrix according to the principal components and the evaluation index data after the inspection, and extracting the constituent indexes of the principal components;
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.
As a preferable mode, the evaluation index data includes: the ratio of the population of the permanent dwellings before and after the spring festival, the area of the residential per capita, the density of public service facilities, the density of enterprises on the scale, the density of three-old reconstruction and the density of cultivated land.
As a preferable mode, the normalizing the evaluation index data and the checking the normalized evaluation index data specifically include:
model normalization by range:
carrying out data standardization on the data of the evaluation index data to obtain standardized evaluation index data Aij；
The evaluation index data A after the normalizationijLoading into an analysis environment of SPSS software;
performing 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 isijIs the normalized result of the ith data in the jth index data, XijFor the ith data in the jth index data, maxXjAnd minXjThe maximum value of all data in the jth index data and the jth index dataMinimum value of all data in (ii), i, j>0。
Further, the performing KMO test and Bartlett sphericity test on the normalized evaluation index data by using a dimension reduction factor analysis tool of SPSS software specifically includes:
calculating a KMO value of the standardized evaluation index data and a P value of the sphericity test by using a dimensionality 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 passes the test; 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 failed;
and when the standardized evaluation index data is not checked, acquiring the evaluation index data of the village to be evaluated again, carrying out standardization processing, and carrying out KMO (KMO) check and Bartlett sphericity check again until the evaluation index data passes the check.
As a preferred mode, the performing principal component analysis on the inspected evaluation index data to obtain a variance interpretation report, and extracting the principal component of the inspected evaluation index data specifically includes:
obtaining a variance interpretation report of the evaluated index data after inspection by using a principal component analysis tool of SPSS software;
and extracting principal components of which the characteristic values are greater than a third preset value in the variance interpretation table.
As a preferable mode, the extracting of the composition index of each principal component from the checked evaluation index data by the rotation component matrix specifically includes:
constructing a rotation component matrix of the inspected evaluation index data about the principal component to obtain a load value of each evaluation index of the inspected 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 preferable mode, the calculating a comprehensive score of the hollowing degree of the village to be evaluated through the rotation component matrix and the component score coefficient matrix specifically includes:
extracting the scores of all the constituent indexes through a component score coefficient matrix;
by calculating individual principal component scores
Calculating a comprehensive score of the hollowing degree of the village to be evaluated according to the main component scores
Wherein, CkIs the score of the kth principal component corresponding to N constituent indexes, amFor the score of the component score coefficient matrix corresponding to the m-th constituent index, XmData corresponding to the mth composition index; c is the comprehensive score of the hollowing degree of the village to be evaluated, betakIs the characteristic value of the kth principal component, I is the number of the principal components, I is more than or equal to k and more than or equal to 0, m, N>0。
As a preferable mode, the method further includes:
and utilizing arcgis software to connect the comprehensive score of the hollowing degree of the village to be evaluated into a vector file of the village by utilizing an attribute table, grading the hollowing degree, and drawing a vector diagram of the hollowing of the village.
The invention provides a village hollowing degree evaluation method, which comprises the steps of obtaining evaluation index data of a village to be evaluated; standardizing the evaluation index data, and checking the standardized evaluation index data; performing principal component analysis on the inspected evaluation index data, and extracting principal components of the inspected evaluation index data; constructing a rotation component matrix according to the principal components and the evaluation index data after the inspection, and extracting the constituent indexes of the principal components; 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. By using various multivariate evaluation indexes as evaluation data, the hollowing degree of the village can be reflected more accurately, the evaluation result is more accurate, and the evaluation process is simple, convenient and quick.
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 technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present 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, obtaining evaluation index data of a village to be evaluated;
s102, carrying out standardization processing on the evaluation index data, and checking the standardized evaluation index data;
s103, performing principal component analysis on the inspected evaluation index data, and extracting principal components of the inspected evaluation index data;
s104, constructing a rotation component matrix according to the principal components and the evaluation index data after the inspection, and extracting the constituent indexes of the principal components;
and S105, calculating a comprehensive score of the hollowing degree of the village to be evaluated through the rotation component matrix and the component score coefficient matrix.
When the embodiment is implemented, the evaluation indexes which are related to the hollowing of the villages and have larger influence factors are preferentially acquired as evaluation data, so that the hollowing degree of the villages can be reflected more accurately;
standardizing the evaluation index data, inspecting the standardized data, and checking the correlation and independence among the evaluation index data to avoid the phenomenon that the evaluation of the voidage of the villages has large errors due to the weak correlation among the evaluation index data of the selected areas;
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 rotation component matrix according to the principal components and the evaluation index data after the inspection, and extracting the constituent indexes of the principal components; 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 realize the evaluation of 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 a village to be evaluated; standardizing the evaluation index data, and checking the standardized evaluation index data; performing principal component analysis on the inspected evaluation index data, and extracting principal components of the inspected evaluation index data; constructing a rotation component matrix according to the principal components and the evaluation index data after the inspection, and extracting the constituent indexes of the principal components; 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. By using various multivariate evaluation indexes as evaluation data, the hollowing degree of the village can be reflected more accurately, the evaluation result is more accurate, and the evaluation process is simple, convenient and quick.
In another embodiment provided by the present invention, the evaluation index data includes: the ratio of the population of the permanent dwellings before and after the spring festival, the area of the residential per capita, the density of public service facilities, the density of enterprises on the scale, the density of three-old reconstruction and the density of cultivated land.
In the specific implementation of the embodiment, the village hollowing is embodied in three aspects: population hollowing, industrial hollowing and economic hollowing, in particular to the aspects of large loss of village population, low agricultural production, wasteland, housing vacancy, low-efficiency utilization of public resources and the like. The method comprises the steps of combining representativeness of evaluation indexes and data acquirability, comprehensively researching actual conditions of areas, and constructing an evaluation index system of the village voidage from six indexes including population, industry and economy, wherein the population indexes comprise the ratio of the population of the regular lives before and after the spring festival and the area of the residential area per capita, the industry indexes comprise the regular enterprise density and the cultivated land density, and the economy indexes comprise the public service facility density and the three old transformation density.
The loss condition of the population of the village is reflected by the ratio of the population of the residents before and after the spring festival, the outflow of the population of the rural areas is mainly the population of the outdoor workers, the more serious the loss of the population of the village is, the lower the population bearing function of the village is, and the less the population of the residents is after the special time of the traditional family-keeping reunion in the spring festival is removed. The average residential area is represented by a relative value of the residential area and the constant population of the village, and when the average residential area is larger, the relative housing vacancy rate is higher, and the village housing utilization rate is lower. The on-scale enterprise density is the second three-industry situation of the village, generally speaking, the higher the on-scale enterprise density is, the better the second three-industry foundation of the village is, more work posts can be provided, the economic condition is better, and the population concentration of the village is higher. The cultivated land density is the ratio of the basic farmland area to the area of a house base, and mainly measures the cultivated land resource condition of the village, and the cultivated land is used as the most basic living resource of the village, so that the cultivated land has great influence on the exertion of the function of the village and the agricultural development of the village. The public clothes facility density reflects the public clothes matching perfection of the villages, and the bigger the public clothes facility density is, the better the living environment of the villages is, so that the lower the hollow rate of the villages is reflected. Three old transformation density rural house and facilities in danger longer in partial age are transformed and repaired according to the standard, when the village hollow rate is larger, the old vacant house is more, and the old transformation demand is larger.
The specific evaluation index system is shown in table 1:
table 1 evaluation index data system table
| Evaluation index | Index calculation |
| Population ratio of permanent population before and after spring festival | Perennial population/perennial population |
| Area of residential area per capita | Rural homestead area/constant population of town residence |
| Public service facility density | Public service facility area/area of rural residential base of town residence |
| Density of enterprises on scale | On-scale enterprise quantity/construction land area |
| Modified density of three old | Area of san old pattern spot/area of land for construction |
| Density of cultivated land | Basic farmland area/area of rural residential base of town house |
The data of the area standing population is obtained by inquiring the signaling data of the mobile phone.
The statistical time period is one month before 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 judgment that the number of days is more than or equal to 15 in the statistical time period is the area permanent population.
The area of the rural home base of the town house in the per-capita residential area is obtained by extracting data with the names of the land type of the rural home base and the land used by the town house from the three-tone data of the local area, and the permanent population is the average value of the permanent population which is two months before and after the spring festival.
The public service facility area is obtained through the local digital current situation image project data, and the data source is various government functional departments.
The regional on-scale enterprise data is obtained through local scientific and technical industrial business and an informationized local gate, and the regional on-scale enterprise data and village boundaries are processed in the arcgis through a space connecting tool and a field mapping tool to obtain the number of on-scale enterprises in each village.
The construction land area is obtained by extracting construction land data from the local third-tone (second-tone) data.
The areas of the three old patterns and the basic farmland area are provided for regional planning and natural resource bureaus.
In addition, the evaluation index data provided in the present embodiment is the six data described above, but in the specific implementation, data reflecting the state of the population, industry, and economy of the village can be used as the hollowed evaluation index data.
By acquiring evaluation index data of evaluation data covering various aspects of population hollowing, industrial hollowing and economic hollowing, the method can evaluate the village hollowness in a diversified manner, and the evaluation result is more accurate.
In another embodiment of the present invention, the normalizing the evaluation index data and checking the standardized evaluation index data specifically includes:
model normalization by range:
carrying out data standardization on the data of the evaluation index data to obtain standardized evaluation index data Aij；
The evaluation index data A after the normalizationijLoading into an analysis environment of SPSS software;
performing 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 isijIs the normalized result of the ith data in the jth index data, XijFor the ith data in the jth index data, maxXjAnd minXjRespectively 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 specific implementation of the present embodiment, the estimation index data of the village is first subjected to range standardization, and the range standardization model:
carrying out data standardization on the data of the evaluation index data to obtain standardized evaluation index data Aij；
The evaluation index data A after the normalizationijLoading the SPSS software into an analysis environment, and analyzing data by using the SPSS software;
performing 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 isijIs the normalized result of the ith data in the jth index data, XijFor the ith data in the jth index data, maxXjAnd minXjRespectively 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。
The evaluation index data is subjected to standardization processing, so that the unified standard of each evaluation index is realized, the evaluation of the hollowing degree can be directly performed subsequently, and errors of the evaluation results caused by the data are reduced; detecting the correlation among different evaluation indexes through KMO detection, and detecting the independence of the different evaluation indexes through Bartlett sphericity; and the indexes with stronger correlation and independence are adopted to evaluate the hollowing degree, so that the evaluation result has higher accuracy.
Further, in this embodiment, the performing KMO test and Bartlett sphericity test on the normalized evaluation index data by using the dimension reduction factor analysis tool of the SPSS software specifically includes:
calculating a KMO value of the standardized evaluation index data and a P value of the sphericity test by using a dimensionality 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 passes the test; 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 failed;
and when the standardized evaluation index data is not checked, acquiring the evaluation index data of the village to be evaluated again, carrying out standardization processing, and carrying out KMO (KMO) check and Bartlett sphericity check again until the evaluation index data passes the check.
In a specific implementation of this embodiment, the method includes calculating, by using a dimensionality reduction factor analysis tool of SPSS software, a KMO value and a P value of sphericity inspection of the normalized evaluation index data;
when the KMO value is less 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 subjected to factor analysis, the closer the KMO value is to 1, the stronger the correlation among variables, the weaker the partial correlation, and the better the effect of factor analysis.
The P value sig of the Bartlett sphericity test is less than 0.05, which shows that the selected evaluation index data are in spherical distribution, all variables are mutually independent to a certain degree, and 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 in spherical distribution, and all variables are mutually independent to a certain extent, so that the selected evaluation index data can be used for evaluating the hollowing degree.
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 evaluating the hollowing degree, so that the evaluation index data of the village to be evaluated needs to be obtained again, standardized, and subjected to the KMO test and the Bartlett sphericity test again until the test is passed.
It should be noted that the first preset value and the second preset value can be set according to the actual hollowing evaluation criteria, in this embodiment, the first preset value is 0.5, and the second preset value is 0.05, but this embodiment is only a preferred embodiment of the present invention, and does not constitute a limitation to the present invention.
By performing KMO (KMO) test and Bartlett sphericity test on the evaluation index data and selecting the evaluation index with stronger correlation and independence, the unified standard of different villages to be evaluated is realized, and evaluation errors caused by too low correlation and poor independence among the evaluation indexes are avoided.
In another embodiment of the present invention, the performing principal component analysis on the checked evaluation index data to obtain a variance interpretation report, and extracting the principal component of the checked evaluation index data specifically includes:
obtaining a variance interpretation report of the evaluated index data after inspection by using a principal component analysis tool of SPSS software;
and extracting principal components of which the characteristic values are greater than a third preset value in the variance interpretation table.
In the specific implementation of this embodiment, a principal component analysis tool of the SPSS software is used to obtain a variance interpretation report of the evaluation index data, extract the number of principal components having a characteristic value greater than 1, and store the characteristic values of the principal components, where the principal components are independent of each other and can represent most of information in the evaluation index data.
It should be noted that the third preset value can be set according to the actual hollowing evaluation criterion, and in this embodiment, the third preset value is 1, but this embodiment is only a preferred embodiment of the present invention, and does not constitute a limitation to the present invention.
In another embodiment of the present invention, the extracting the constituent index of each principal component from the checked evaluation index data by rotating the component matrix specifically includes:
constructing a rotation component matrix of the inspected evaluation index data about the principal component to obtain a load value of each evaluation index of the inspected 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 specific implementation of this embodiment, the evaluation index data can be better summarized by using the rotation component matrix, the rotation component matrix includes the degree of association between each evaluation index and the principal component, that is, the load value, and the evaluation index in which the load value of each principal component in the rotation component matrix is greater than 0.7 is extracted as the constituent index of the corresponding principal component, and the constituent index of each principal component is recorded.
It should be noted that the fourth preset value can be set according to the actual hollowing evaluation criterion, and in this embodiment, the fourth preset value is 0.7, but this embodiment is only a preferred embodiment of the present invention, and does not constitute a limitation to the present invention.
The principal component is evaluated through each constituent index, hollowing of the village to be evaluated is evaluated through all the principal components, and the evaluation process is simple and convenient.
In 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 hollowing degree of the village to be evaluated specifically includes:
the scores of all the constituent indexes can be extracted through the component score coefficient matrix;
by calculating individual principal component scores
Calculating a comprehensive score of the hollowing degree of the village to be evaluated according to the main component scores
Wherein, CkIs the score of the kth principal component corresponding to N constituent indexes, amFor the score of the component score coefficient matrix corresponding to the m-th constituent index, XmData corresponding to the mth composition index; c is the comprehensive score of the hollowing degree of the village to be evaluated, betakIs the characteristic value of the kth principal component, I is the number of the principal components, I is more than or equal to k and more than or equal to 0, m, N>0。
In the specific implementation of the embodiment, the scores of the principal components are calculated by rotating the component matrix and the component score matrix, and then the scores of the principal components are counted to calculate the comprehensive score of the hollowing degree of the village to be evaluated, so that the evaluation result is obtained. The evaluation process agrees, the error is reduced, and the evaluation result is more accurate.
In another embodiment provided by the present invention, the method further comprises:
and utilizing arcgis software to connect the comprehensive score of the hollowing degree of the village to be evaluated into a vector file of the village by utilizing an attribute table, grading the hollowing degree, and drawing a vector diagram of the hollowing of the village.
In the specific implementation of the embodiment, the obtained comprehensive score is connected to a village vector file by using an attribute table in arcgis software, the score of the degree of hollowing is graded, a corresponding map is drawn, and the vectorization degree of the village can be visually expressed.
By connecting the integrated score of the village to be evaluated to the vector file of the village, the visualization of the hollowing degree of the village can be realized.
The invention provides a village hollowing degree evaluation method, which comprises the steps of obtaining evaluation index data of a village to be evaluated; standardizing the evaluation index data, and checking the standardized evaluation index data; performing principal component analysis on the inspected evaluation index data, and extracting principal components of the inspected evaluation index data; constructing a rotation component matrix according to the principal components and the evaluation index data after the inspection, and extracting the constituent indexes of the principal components; 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. By using various multivariate evaluation indexes as evaluation data, the hollowing degree of the village can be reflected more accurately, the evaluation result is more accurate, and the evaluation process is simple, convenient and quick.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (8)
1. A village hollowing degree evaluation method is characterized by comprising the following steps:
acquiring evaluation index data of a village to be evaluated;
standardizing the evaluation index data, and checking the standardized evaluation index data;
performing principal component analysis on the inspected evaluation index data, and extracting principal components of the inspected evaluation index data;
constructing a rotation component matrix according to the principal components and the evaluation index data after the inspection, and extracting the constituent indexes of the principal components;
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.
2. The village hollowing degree evaluation method according to claim 1, wherein said evaluation index data includes: the ratio of the population of the permanent dwellings before and after the spring festival, the area of the residential per capita, the density of public service facilities, the density of enterprises on the scale, the density of three-old reconstruction and the density of cultivated land.
3. The village hollowing degree evaluation method according to claim 1, wherein the step of normalizing the evaluation index data and checking the normalized evaluation index data includes:
model normalization by range:
carrying out data standardization on the data of the evaluation index data to obtain standardized evaluation index data Aij；
The evaluation index data A after the normalizationijLoading into an analysis environment of SPSS software;
performing 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 isijIs the normalized result of the ith data in the jth index data, XijFor the ith data in the jth index data, maxXjAnd minXjRespectively 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。
4. The method as claimed in claim 3, wherein the KMO test and the Bartlett sphericity test are performed on the normalized evaluation index data using a dimensionality reduction factor analysis tool of the SPSS software, and specifically includes:
calculating a KMO value of the standardized evaluation index data and a P value of the sphericity test by using a dimensionality 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 passes the test; 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 failed;
and when the standardized evaluation index data is not checked, acquiring the evaluation index data of the village to be evaluated again, carrying out standardization processing, and carrying out KMO (KMO) check and Bartlett sphericity check again until the evaluation index data passes the check.
5. The village hollowing degree evaluation method according to claim 1, wherein the step of performing principal component analysis on the inspected evaluation index data to obtain a variance interpretation report and extracting principal components of the inspected evaluation index data comprises:
obtaining a variance interpretation report of the evaluated index data after inspection by using a principal component analysis tool of SPSS software;
and extracting principal components of which the characteristic values are greater than a third preset value in the variance interpretation table.
6. The village hollowing degree evaluation method according to claim 1, wherein said extracting a configuration index of each of said principal components from the evaluation index data after the examination by using a rotation component matrix, specifically comprises:
constructing a rotation component matrix of the inspected evaluation index data about the principal component to obtain a load value of each evaluation index of the inspected 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.
7. The method according to claim 6, wherein said calculating a composite score of the nullification degree of the village to be evaluated from the rotation component matrix and the component score coefficient matrix comprises:
extracting the scores of all the constituent indexes through a component score coefficient matrix;
by calculating individual principal component scores
Calculating a comprehensive score of the hollowing degree of the village to be evaluated according to the main component scores
Wherein, CkIs the score of the kth principal component corresponding to N constituent indexes, amFor the score of the component score coefficient matrix corresponding to the m-th constituent index, XmData corresponding to the mth composition index; c is the comprehensive score of the hollowing degree of the village to be evaluated, betakIs the characteristic value of the kth principal component, I is the number of the principal components, I is more than or equal to k and more than or equal to 0, m, N>0。
8. The village hollowing degree evaluation method according to claim 1, wherein said method further comprises:
and utilizing arcgis software to connect the comprehensive score of the hollowing degree of the village to be evaluated into a vector file of the village by utilizing an attribute table, grading the hollowing degree, and drawing a vector diagram of the hollowing of the village.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115209351A (en) * | 2022-09-16 | 2022-10-18 | 智慧足迹数据科技有限公司 | Hollow village identification method, device, equipment and storage medium based on signaling data |
| CN115456371A (en) * | 2022-08-26 | 2022-12-09 | 广东省城乡规划设计研究院有限责任公司 | GIS-based rural inefficient construction land evaluation method and platform |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008070956A (en) * | 2006-09-12 | 2008-03-27 | Ntt Data Corp | Evaluation device, evaluation method and evaluation program |
| US20090070379A1 (en) * | 2007-09-10 | 2009-03-12 | Rappaport Theodore R | Clearinghouse system, method, and process for inventorying and acquiring infrastructure, monitoring and controlling network performance for enhancement, and providing localized content in communication networks |
| CN105354781A (en) * | 2015-10-19 | 2016-02-24 | 中国科学院遥感与数字地球研究所 | Rural hollowing degree early warning system |
| US20160260059A1 (en) * | 2015-03-02 | 2016-09-08 | Locus Solutions, Llc | Systems and methods for monitoring transported cargo |
| WO2016174143A1 (en) * | 2015-04-29 | 2016-11-03 | W & H Dentalwerk Bürmoos GmbH | Method and device for evaluating the effectiveness of the processing of medical, in particular dental, hollow-body instruments |
| CN106780261A (en) * | 2017-01-12 | 2017-05-31 | 四川省土地统征整理事务中心 | Village evaluating data processing method and its device |
| CN107368958A (en) * | 2017-07-05 | 2017-11-21 | 国网江苏省电力公司电力科学研究院 | Big customer's Indexes of Value Assessment Weight Determination based on PCA |
| CN107506920A (en) * | 2017-07-24 | 2017-12-22 | 南京大学 | Land resource saving utilizes and environment-friendly Integrated Evaluation |
| CN107545380A (en) * | 2017-10-13 | 2018-01-05 | 常州工学院 | Livable City evaluation model based on principal component analysis |
| CN108776868A (en) * | 2018-06-05 | 2018-11-09 | 广东电网有限责任公司电力科学研究院 | A kind of rural area village hollowing appraisal procedure and device based on electricity consumption big data |
| CN109191001A (en) * | 2018-09-21 | 2019-01-11 | 常州工学院 | Evaluation in Education Quality method based on principal component analysis |
| CN112348404A (en) * | 2020-11-26 | 2021-02-09 | 广州市白云区城市规划设计研究所 | Village planning implementation evaluation system |
| CN113034005A (en) * | 2021-03-26 | 2021-06-25 | 浙大城市学院 | Method and system for analyzing space difference influence factors of traditional village population hollowing degree |
-
2021
- 2021-07-15 CN CN202110801333.4A patent/CN113592260B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008070956A (en) * | 2006-09-12 | 2008-03-27 | Ntt Data Corp | Evaluation device, evaluation method and evaluation program |
| US20090070379A1 (en) * | 2007-09-10 | 2009-03-12 | Rappaport Theodore R | Clearinghouse system, method, and process for inventorying and acquiring infrastructure, monitoring and controlling network performance for enhancement, and providing localized content in communication networks |
| US20160260059A1 (en) * | 2015-03-02 | 2016-09-08 | Locus Solutions, Llc | Systems and methods for monitoring transported cargo |
| WO2016174143A1 (en) * | 2015-04-29 | 2016-11-03 | W & H Dentalwerk Bürmoos GmbH | Method and device for evaluating the effectiveness of the processing of medical, in particular dental, hollow-body instruments |
| CN105354781A (en) * | 2015-10-19 | 2016-02-24 | 中国科学院遥感与数字地球研究所 | Rural hollowing degree early warning system |
| CN106780261A (en) * | 2017-01-12 | 2017-05-31 | 四川省土地统征整理事务中心 | Village evaluating data processing method and its device |
| CN107368958A (en) * | 2017-07-05 | 2017-11-21 | 国网江苏省电力公司电力科学研究院 | Big customer's Indexes of Value Assessment Weight Determination based on PCA |
| CN107506920A (en) * | 2017-07-24 | 2017-12-22 | 南京大学 | Land resource saving utilizes and environment-friendly Integrated Evaluation |
| CN107545380A (en) * | 2017-10-13 | 2018-01-05 | 常州工学院 | Livable City evaluation model based on principal component analysis |
| CN108776868A (en) * | 2018-06-05 | 2018-11-09 | 广东电网有限责任公司电力科学研究院 | A kind of rural area village hollowing appraisal procedure and device based on electricity consumption big data |
| CN109191001A (en) * | 2018-09-21 | 2019-01-11 | 常州工学院 | Evaluation in Education Quality method based on principal component analysis |
| CN112348404A (en) * | 2020-11-26 | 2021-02-09 | 广州市白云区城市规划设计研究所 | Village planning implementation evaluation system |
| CN113034005A (en) * | 2021-03-26 | 2021-06-25 | 浙大城市学院 | Method and system for analyzing space difference influence factors of traditional village population hollowing degree |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115456371A (en) * | 2022-08-26 | 2022-12-09 | 广东省城乡规划设计研究院有限责任公司 | GIS-based rural inefficient construction land evaluation method and platform |
| CN115209351A (en) * | 2022-09-16 | 2022-10-18 | 智慧足迹数据科技有限公司 | Hollow village identification method, device, equipment and storage medium based on signaling data |
| CN115209351B (en) * | 2022-09-16 | 2022-12-27 | 智慧足迹数据科技有限公司 | Method, device and equipment for identifying hollow village based on signaling data and storage medium |
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|---|---|
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