CN114493346B - Rural industry centralized layout method, system, device and storage medium - Google Patents
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Abstract
The invention relates to the technical field of information processing, in particular to a rural industry centralized layout method, a rural industry centralized layout system, a rural industry centralized layout device and a storage medium. According to the rural industry clustering layout method, the rural industry clustering layout method is constructed and composed of the industry space self-correlation clustering identification, the industry correlation identification, the spatial clustering rural industry cluster and the correlation industry cluster type clustering layout, so that the rule of the endogenous rural industry under the natural environment condition can be objectively reflected, the restriction, the cooperation and the correlation cluster relation in the rural industry space distribution can be found, the development layout of the rural industry can follow the endogenous rule between the local industry and the natural environment and the correlation cluster relation spontaneously formed in the space between the local industries based on the endogenous rule, a plurality of correlation industry clustering type one-to-many layouts can be formed in the same space, and the local characteristic rural industry cluster with the maximized industry scale effect and competitiveness can be formed.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a rural industry centralized layout method, a rural industry centralized layout system, a rural industry centralized layout device and a storage medium.
Background
The current mainstream industry aggregation layout method is to take scale and benefit as the measurement standards, measure the aggregation level among various industries through corresponding indexes, and take the industry with high aggregation level as the aggregation industry for layout. For example, the proportion of a certain industry in the whole country is measured by an index of 'industrial space concentration rate', and the industry with high proportion is selected; measuring the proportion of a plurality of industries in a certain region comprehensively in the national range by using a regional industry concentration index, and extracting a plurality of industries with high proportion comprehensively to perform industry aggregation; measuring the specialized level and the clustering degree of the industry by using the index of the 'zone entropy', and judging the possibility of industrial clustering; and measuring the industrial aggregation degree and the like by the Herstella-Herhman index through enterprises and occupancy.
However, these methods only analyze the geographic concentration ratio from the absolute value of the industry, and do not consider the spatial proximity of the industry, the restriction or cooperation relationship of the industry in the spatial distribution, and ignore the endogenous rules between the local industry and the natural environments such as the geographical features and climate, and the association cluster relationship spontaneously formed in space between the local industries based on the endogenous rules. Although a space statistical method mainly based on space autocorrelation analysis is developed in recent years, a space Moran index is used to identify the space aggregation of industries by measuring the degree of correlation difference of industries in a space adjacent area, and the defect that the method for measuring the industry aggregation level by an index ignores the space proximity is overcome, the problem that the industries have restriction, cooperation and associated cluster relation identification in space distribution is not solved, and the result is limited to a one-to-one aggregated industry layout mode that only a single aggregated industry is located in one space, but cannot form a one-to-many rural industry aggregated layout of multiple associated industry cluster types in the same space.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a country industry clustering layout method, a system, a device and a storage medium, which can enable the development layout of the country industry to follow the endogenous law between local industry and natural environment and the association cluster relation formed spontaneously in space between local industry based on the endogenous law, form a plurality of association industry clustering type 'one-to-many' layouts in the same space, and form a local characteristic advantageous country industry cluster which maximizes the industrial scale effect and the competitiveness when applied.
In a first aspect, the present invention provides a rural industrial centralized layout method, including:
acquiring yield data, product price data, spatial distribution data and natural environment data of each country industry;
calculating to obtain the output value data of each country industry according to the output data and the product price data of each country industry;
carrying out spatial autocorrelation analysis on the production value data of each country industry by using a global Moran index measurement tool of spatial autocorrelation to judge each country industry with spatial clustering;
according to the spatial distribution data of each rural industry, the aggregation degree of each spatial integrated rural industry in each administrative rural space unit is identified by using a spatial autocorrelation local Molan index measurement tool, and the endogenous rule information between the naturally occurring and natural environment of each spatial integrated rural industry is judged by combining the natural environment data;
carrying out Pearson correlation analysis on the output value data of each spatial clustering village industry to obtain a Pearson correlation coefficient between any two spatial clustering village industries, and judging the industrial correlation between the two spatial clustering village industries according to the Pearson correlation coefficient;
clustering the rural industries with the spatial clustering property according to the industrial relevance to obtain clustering results of the rural industries;
calculating the contribution degree of each country industry in the clustering result of the associated country industry according to the production value data, and selecting each associated country industry in the same cluster to form an associated industry cluster according to the set contribution degree condition, the industry association condition and the endogenous rule information condition between the cluster and the natural environment;
and (4) positioning the associated industrial cluster to the corresponding administrative village to complete the clustered layout of the associated industrial cluster.
In one possible design, the production data includes a production for a set year, the product price data includes a product price for the set year, and the calculating of the production value data for the rural industry from the production data and the product price data for the rural industry includes: and multiplying the yield of the set year of the rural industry by the product price corresponding to the set year to obtain the yield value of the rural industry in the year, and using the yield value as the yield value data of the rural industry.
In one possible design, the global morn index measurement tool using spatial autocorrelation performs spatial autocorrelation analysis on the country industry production value data to determine country industries with spatial congregation, including:
invoking a Spatial Autocorrelation module in an arcpy module library through python, respectively inputting the production value data of each country industry for calculation, and obtaining Moran's I index values, z scores and p values corresponding to each country industry;
representing the spatial clustering or discrete state of each rural industry by using a Moran's I index value, checking whether the Moran's I index value is effective or not by using a z-score and a p-value, and judging that the corresponding rural industry has spatial clustering when the p-value is smaller than a first threshold value, the z-score is larger than a second threshold value, and the Moran's I index value is positive and larger than a third threshold value.
In one possible design, the identifying, according to spatial distribution data of each rural industry, aggregation degrees of each spatial centralized rural industry in each rural space unit by using a spatial autocorrelation local morn index measurement tool, and determining endogenous law information between each spatial centralized rural industry spontaneously existing and natural environment by combining natural environment data includes:
calling a Cluster and Outlier Analysis module in an arcpy module library through python, respectively inputting the output value data of each spatial clustering village industry for calculation, and obtaining corresponding Local Moran's I index values, z scores and p values in each administrative village spatial unit according to the spatial distribution data of each spatial clustering village industry;
when the Local Moran's I index value is positive, the p value is less than a fourth threshold value, and the z score is greater than a fifth threshold value, extracting the corresponding Local Moran's I index value;
and comparing the extracted Local Moran's I index value with corresponding natural environment data, and judging whether the corresponding spatial clustering village industry spontaneously exists in the corresponding administrative village space unit and the endogenous rule between the corresponding administrative village space unit and the natural environment according to a set rule to obtain endogenous rule information.
In one possible design, when the Pearson correlation coefficient is negative, a constraint relationship between two spatially clustered rural industries is represented, when the Pearson correlation coefficient is positive, a collaborative relationship between two spatially clustered rural industries is represented, and an absolute value of the Pearson correlation coefficient represents the strength of the constraint or collaborative relationship between the two spatially clustered rural industries.
In one possible design, the clustering the spatially aggregated rural industries according to the industry relevance to obtain a clustering result of the relevant rural industries comprises:
calling a spatialconstrained multivariable Clustering module in an arcpy module library through python, adopting a spatial constraint multi-element Clustering method, taking an administrative village as a spatial constraint condition, substituting into production value data of the spatial Clustering village industry meeting the set industry relevance condition, estimating the Clustering number, measuring the Clustering effectiveness under different Clustering numbers by adopting Calinski-Harabasz pseudo-F statistic, estimating the optimal Clustering number, and outputting the Clustering result of the associated village industry under the optimal Clustering number.
In one possible design, the contribution degree condition is that the contribution degree reaches a sixth threshold, the industry relevance condition is a collaborative relationship, the collaborative strength is greater than a seventh threshold, and the endogenous law information condition with the natural environment is that the endogenous law exists under the same natural environment condition.
In a second aspect, the present invention provides a rural industrial centralized layout system, which includes an obtaining unit, a calculating unit, a first determining unit, a second determining unit, a third determining unit, a clustering unit, a building unit and a layout unit, wherein:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring yield data, product price data, space distribution data and natural environment data of each country industry;
the calculating unit is used for calculating and obtaining the output value data of each country industry according to the output data and the product price data of each country industry;
the first judgment unit is used for carrying out spatial autocorrelation analysis on the production value data of each country industry by using a spatial autocorrelation global Moran index measurement tool to judge each country industry with spatial clustering;
the second judgment unit is used for identifying the aggregation degree of each spatial clustering rural industry in each administrative space unit by using a spatial autocorrelation local Molan index measurement tool according to spatial distribution data of each rural industry, and judging endogenous law information between the naturally occurring and natural environment of each spatial clustering rural industry by overlapping natural environment data;
the third judging unit is used for carrying out Pearson correlation analysis on the production value data of each spatial clustering village industry to obtain a Pearson correlation coefficient between any two spatial clustering village industries and judging the industrial relevance between the two spatial clustering village industries according to the Pearson correlation coefficient;
the clustering unit is used for clustering the rural industries with the spatial clustering property according to the industrial relevance to obtain clustering results of the rural industries;
the building unit is used for calculating the contribution degree of each country industry in the clustering result of the associated country industry according to the production value data, and selecting each associated country industry in the same cluster to form an associated industry cluster according to the set contribution degree condition, the industry association condition and the endogenous rule information condition with the natural environment;
and the layout unit is used for positioning the associated industrial clusters to the corresponding administrative villages to complete the clustered layout of the associated industrial clusters.
In a third aspect, the present invention provides another rural industrial centralized layout apparatus, including:
a memory to store instructions;
a processor configured to read the instructions stored in the memory and execute the method of any of the first aspects according to the instructions.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon instructions which, when run on a computer, cause the computer to perform the method of any of the first aspects described above.
In a fifth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of any of the first aspects above.
The beneficial effects of the invention are as follows:
according to the rural industry clustering layout method, the rural industry clustering layout method is constructed and composed of the industry space self-correlation clustering identification, the industry correlation identification, the spatial clustering rural industry cluster and the correlation industry cluster type clustering layout, so that the rule of the endogenous rural industry under the natural environment condition can be objectively reflected, the restriction, the cooperation and the correlation cluster relation in the rural industry space distribution can be found, the development layout of the rural industry can follow the endogenous rule between the local industry and the natural environment and the correlation cluster relation spontaneously formed in the space between the local industries based on the endogenous rule, a plurality of correlation industry clustering type one-to-many layouts can be formed in the same space, and the local characteristic rural industry cluster with the maximized industry scale effect and competitiveness can be formed. The problems that the spatial proximity of the industries and the restriction or cooperative relationship of the industries in spatial distribution are not considered, the endogenous rules between the country industry and natural environments such as geographical features, climates and the like and the associated cluster relationship spontaneously formed in space between local industries based on the endogenous rules are ignored and the like in the planning method for layout by taking the scale and the benefit as the measurement standards, measuring the aggregation level among the industries through corresponding indexes and taking the industry with high aggregation level as the aggregation industry are solved, and the 'one-to-one' layout mode that the layout result of the country industry is limited to only one space and only one single aggregation industry is changed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the process steps of the present invention;
FIG. 2 is a schematic flow diagram of the process of the present invention;
FIG. 3 is a schematic diagram of the system of the present invention;
FIG. 4 is a schematic view of the apparatus of the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It should be understood that the terms first, second, etc. are used solely for distinguishing between descriptions and are not intended to indicate or imply relative importance. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments of the present invention.
In the following description, specific details are provided to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, systems may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring example embodiments.
Example 1:
the embodiment provides a rural industry centralized layout method, as shown in fig. 1 and fig. 2, the method includes the following steps:
s101, acquiring yield data, product price data, spatial distribution data and natural environment data of each country industry.
In specific implementation, data collection of local areas can be performed firstly, such as collection of topographic maps, meteorological observation data, farmland soil force evaluation results, agricultural production annual reports, animal husbandry production annual reports, forestry production annual reports, aquaculture annual reports, statistical annual rings, land utilization survey, forest resource survey and other data. And then extracting required yield data, product price data, spatial distribution data and natural environment data of each country industry from the collected data. If data such as elevation, gradient and the like are extracted from a topographic map, natural environment data such as landform subareas, annual average air temperature, annual average precipitation, accumulated temperature and the like are extracted from meteorological observation and farmland and land force evaluation results; extracting country industry types from agricultural production annual newspapers, extracting various types of country industry spaces from forest resource surveys, land utilization surveys and other data according to plant or land utilization forms dependent on various types of country industry, such as dominant tree species distribution in forest resource surveys, extracting forestry spatial distribution data, extracting grassland distribution data from land utilization surveys, corresponding to livestock husbandry spatial distribution data, extracting cultivation pit and pond distribution data from land utilization surveys, corresponding to aquaculture industry spatial distribution data, extracting administrative division spatial data from land utilization surveys, obtaining each administrative district spatial data, and obtaining a series of country industry spatial distribution data; extracting various country industry yield data from the data of agricultural production annual newspaper, animal husbandry annual newspaper, forestry production annual newspaper, aquaculture annual newspaper, statistical annual book and the like; and extracting price data of various country industry products from the statistical yearbook for subsequent analysis and processing.
And S102, calculating to obtain the yield value data of each country industry according to the yield data and the product price data of each country industry.
In specific implementation, in order to facilitate the use of subsequent analysis layout, the yield data and the product price data of the rural industry can be correspondingly converted into the yield data of the rural industry, so that the data comparability is enhanced. The yield data comprises the yield of a set year, the product price data comprises the product price of the set year, and the process of calculating and obtaining the yield value data of the country industry according to the yield data and the product price data of the country industry comprises the step of multiplying the yield of the set year of the country industry by the product price of the corresponding set year to obtain the yield value of the country industry in the year, wherein the yield value data is used as the yield value data of the country industry.
S103, carrying out spatial autocorrelation analysis on the country industry production value data by using a spatial autocorrelation global Moran index measurement tool, and judging each country industry with spatial clustering.
In specific implementation, a global Moran index measurement tool of spatial autocorrelation is used for carrying out spatial autocorrelation analysis on the country industry production value data so as to distinguish the country industry type with spatial clustering, and the specific process comprises the following steps:
calling a Spatial Autocorrelation module in an arcpy (site package) module library through python (computer programming language), respectively inputting the output value data of each rural industry for calculation, and obtaining Moran's I index values, z scores and p values corresponding to each rural industry;
representing the spatial aggregation or dispersion state of each rural industry by using Moran's I index values, wherein the result shows that the index values are positive and represent industrial spatial aggregation distribution, the index values are negative and represent industrial spatial dispersion distribution, and the index values are zero and represent industrial spatial random distribution; meanwhile, the numerical value of the absolute value of the index value indicates the degree of aggregation or dispersion, the larger the numerical value is, the higher the degree is, and the lower the degree is otherwise;
and (3) checking whether the Moran's I index value is effective or not by using the z-score and the p-value, and when the p-value is smaller than a first threshold, the z-score is larger than a second threshold, and the Moran's I index value is positive and larger than a third threshold, judging that the corresponding rural industry has space clustering performance, wherein when the p-value is smaller than 0.1 and the z-score > +1.65, the calculated confidence coefficient of the Moran's I index value of a certain rural industry is high and effective, and the rural industry has remarkable space clustering performance.
The Spatial Autocorrelation (Global Moran's I) module respectively inputs various rural industry production value data and sequentially outputs Moran's I index values, z scores and p values corresponding to various rural industries. The Moran index (Moran's I) is one of important indexes for measuring the correlation of geographic space objects, wherein the Moran's I is the aggregation which is used for representing the spatial distribution positions of the geographic objects, and has positive spatial correlation, and the larger the Moran's I is, the stronger the spatial correlation is; otherwise, the geographic objects are distributed discretely, the spatial correlation is negative, and the smaller the Moran' sI is, the larger the spatial difference is; when Moran's I equals zero, the geographic objects exhibit a spatially random distribution:
wherein Moran's I is the Molan value of village I and village j; n is the sample size; x is a radical of a fluorine atom i And x j The observed values of village i and village j are obtained;
is the sample average; w is a ij The most commonly used Queen first-order adjacency matrix (spatial boundaries or vertices are 1 connected and not 0 connected). The results of examining the Moran's I index values for validity with z-score and p-value are presented as: 1. the p-value was not statistically significant (null, meaning that the index value was highly random, not believed); 2. the p-value is statistically significant, and z-score is positive and>+1.65 (valid, indicating significant clustering distribution of industrial space); 3. the p-value is statistically significant, and z-score is negative and<1.65 (valid, representing industry space significance scatter distribution).
S104, according to the spatial distribution data of each rural industry, the aggregation degree of each spatial clustering rural industry in each administrative rural space unit is identified by using a spatial autocorrelation local Molan index measurement tool, and endogenous rule information between the spontaneous existence of each spatial clustering rural industry and the natural environment is judged by overlapping the natural environment data.
During specific implementation, the aggregation degree of each spatial clustering village industry in each administrative village space unit can be identified by using a spatial autocorrelation local Moran index measurement tool, natural environment data is superposed, and the endogenous law of the spontaneous existence of the village industry with the spatial clustering and the natural environment is analyzed, wherein the specific process comprises the following steps:
calling Cluster and Outlier Analysis modules in an arcpy module library through python (computer programming language), respectively inputting output value data of each spatial integrated rural industry for calculation, and obtaining corresponding Local Moran's I index value, z score and p value in each administrative spatial unit according to spatial distribution data of each spatial integrated rural industry;
when the Local Moran's I index value is positive, the p value is smaller than a fourth threshold value, and the z score is larger than a fifth threshold value, such as the p value is less than 0.05, and the z score > +1.96, extracting the corresponding Local Moran's I index value; and comparing the extracted Local Moran's I index value with corresponding natural environment data, and judging whether the corresponding spatial clustering village industry spontaneously exists in the corresponding administrative village space unit and the endogenous rule between the corresponding administrative village space unit and the natural environment according to a set rule to obtain endogenous rule information. For example, if a spatial clustering village industry has high Local Moran's I index values in spatial units of certain administrative villages and has the same elevation, gradient and average air temperature conditions, it can be determined that the spatial clustering village industry is in the natural environment condition, and the spatial clustering village industry is not suitable for layout when the spatial clustering village industry leaves the natural environment condition, i.e. the spatial clustering village industry and the natural environment condition spontaneously have endogenous laws.
The Cluster and Outlier Analysis (Anselin Local Moran's I) module respectively inputs production value data of various spatial clustering rural industries and sequentially outputs Local Moran's I index values, z scores, p values and COType fields of all rural spatial units in the various spatial clustering rural industries. Due to the consideration of spatial heterogeneity, the Local spatial autocorrelation statistic (LISA) can analyze spatial instability and Local spatial clustering relation, and can measure the spatial clustering, dispersion and random distribution pattern of Local system, wherein the Local Moran index (Local Moran's I) is most widely applied:
describing the spatial Local state of the clustering industry of each village by using Local Moran's I index values, wherein the result shows that the index value is positive to indicate that the village is one part of the cluster, the index value is negative to indicate that the village is an abnormal value, and the index value is zero to indicate that the village is in a random state; wherein, local Moran's I is the Local Molan value of village I and village j; n is the number of samples in the sample,
is the sample average; w is a ij To the most commonly used Queen first-order adjacency momentsThe matrix (spatial boundaries or vertices are 1 and not 0).
The z-score and p-value were used to test whether the Local Moran's I index value was valid, and the results are shown as: 1. the p-value was not statistically significant (null, meaning that the index value was highly random, not believed); 2. p-value is statistically significant, and z-score is positive and > +1.65 (valid, indicating that the village is in significant clustering with neighboring villages); 3. the p-value was statistically significant, and the z-score was negative and < -1.65 (valid, indicating that the village was in an abnormal state). Statistical significance is generally indicated in the analysis by p < 0.1.
The COType field is used for visualizing the Local Moran's I index value with the confidence level of more than 95% (namely z score is < -1.96 or > +1.96 and p value is less than 0.05), and the results are shown as High-High (representing Local region High value aggregation), low-Low (representing Local region Low value aggregation), low-High (representing Local region Low value is surrounded by High value to surround the exception) and High-Low (representing Local region High value is surrounded by Low value to surround the exception).
And S105, carrying out Pearson correlation analysis on the production value data of each spatial clustering village industry to obtain a Pearson correlation coefficient between any two spatial clustering village industries, and judging the industrial correlation between the two spatial clustering village industries according to the Pearson correlation coefficient.
In specific implementation, the Pearson correlation coefficient is negative, which represents that a constraint relationship exists between two spatially clustered country industries, the Pearson correlation coefficient is positive, which represents that a collaborative relationship exists between two spatially clustered country industries, the absolute value of the Pearson correlation coefficient represents the strength of the constraint or collaborative relationship between the two spatially clustered country industries, and the greater the absolute value of the Pearson correlation coefficient is, the greater the strength of the constraint or collaborative relationship is.
And S106, clustering the rural industries with the spatial clustering property according to the industrial relevance to obtain a clustering result of the rural industries.
In specific implementation, a spatialconstrained multivariable Clustering module in an arcpy module library is called through python (computer programming language), a space constraint multi-Clustering method is applied, administrative village region data is used as a space constraint condition, production value data of the spatial Clustering village industry is input, the number of clusters is estimated to be 10-20, calinski-Harabasz pseudo-F statistics is adopted to measure Clustering effectiveness under different Clustering numbers, clustering is better if the statistics are larger, clustering is not good if the statistics are larger, the optimal Clustering number is estimated, associated countryside industry Clustering results under the optimal Clustering number are output, and the automatically clustered and associated countryside industry clusters are used as clusters. Calinski-Harabasz pseudo-F statistic is used for measuring the clustering effectiveness by reflecting the ratio of the variance between clusters and the variance in clusters, namely the ratio of the similarity in clusters and the difference between clusters, the larger the statistic is, the larger the similarity in clusters is, the smaller the difference between clusters is, the better the clustering state is, thereby evaluating the optimal cluster number
S107, calculating contribution degrees of all country industries in the clustering result of the associated country industries according to the production value data, and selecting all the associated country industries in the same cluster to form an associated industry cluster according to set contribution degree conditions, industry association conditions and endogenous rule information conditions between the cluster and the natural environment.
In specific implementation, the contribution degrees of different country industries in the country industry clustering result are calculated by calling a pandas module through python (computer programming language), the contribution degree in the same cluster reaches a sixth threshold value, the industry association is a collaborative relationship, the collaborative strength is greater than a seventh threshold value, and the country industries with the endogenous laws under the same natural environment condition are used as the associated industry cluster.
And S108, positioning the associated industrial clusters to the corresponding administrative villages to complete the clustered layout of the associated industrial clusters.
And finally, the associated industrial clusters are located in the corresponding administrative villages, so that the clustered layout of the associated industrial clusters can be efficiently completed. By establishing a set of rural industry clustering layout method system which is driven by related data such as natural data and industry development and applies algorithms such as spatial autocorrelation, correlation and spatial constraint clustering and is formed by industrial spatial autocorrelation clustering identification, industrial relevance identification, spatial clustering rural industry clusters and associated industry cluster type clustering layout, the effects of complying with regional endogenous association cluster rules, spatially gathering characteristic superior industry clusters and exerting maximized local rural industry scale effect and competitiveness in rural industry layout planning are achieved.
Example 2:
the present embodiment provides a rural industry centralized layout system, as shown in fig. 3, including an obtaining unit, a calculating unit, a first determining unit, a second determining unit, a third determining unit, a clustering unit, a building unit, and a layout unit, wherein:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring yield data, product price data, space distribution data and natural environment data of each country industry;
the calculating unit is used for calculating and obtaining the output value data of each country industry according to the output data and the product price data of each country industry;
the first judgment unit is used for carrying out spatial autocorrelation analysis on the country industry production value data by using a spatial autocorrelation global Moran index measurement tool to judge the country industry with spatial congregation;
the second judgment unit is used for identifying the aggregation degree of each spatial clustering rural industry in each administrative space unit by using a spatial autocorrelation local Molan index measurement tool according to spatial distribution data of each rural industry, and judging endogenous law information between the naturally occurring and natural environment of each spatial clustering rural industry by overlapping natural environment data;
a third judging unit, configured to perform Pearson correlation analysis on the production value data of each spatial clustered rural industry, obtain Pearson correlation coefficients between any two spatial clustered rural industries, and judge an industrial correlation between the two spatial clustered rural industries according to the Pearson correlation coefficients;
the clustering unit is used for clustering the rural industries with the spatial clustering property according to the industrial relevance to obtain clustering results of the rural industries;
the building unit is used for calculating the contribution degree of each country industry in the clustering result of the associated country industry according to the production value data, and selecting each associated country industry in the same cluster to form an associated industry cluster according to the set contribution degree condition, the industry association condition and the endogenous rule information condition with the natural environment;
and the layout unit is used for positioning the associated industrial cluster to the corresponding administrative village to complete the clustered layout of the associated industrial cluster.
Example 3:
the embodiment provides a rural industry centralized layout device, as shown in fig. 4, at a hardware level, including:
a memory to store instructions;
and the processor is used for reading the instructions stored in the memory and executing the rural industry clustering layout method in the embodiment 1 according to the instructions.
Optionally, the apparatus further comprises an internal bus and a communication interface. The processor, the memory, and the communication interface may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
The Memory may include, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Flash Memory (Flash Memory), a First In First Out (FIFO), a First In Last Out (FILO), and/or the like. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
Example 4:
the present embodiment provides a computer-readable storage medium having stored thereon instructions that, when executed on a computer, cause the computer to execute the rural industrial clustering layout method of embodiment 1. The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, floppy disks, optical disks, hard disks, flash memories, flash disks and/or Memory sticks (Memory sticks), etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems.
Example 5:
the present embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the rural industries clustering layout method of embodiment 1. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system.
Finally, it should be noted that: the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A rural industry gathering layout method is characterized by comprising the following steps:
acquiring yield data, product price data, spatial distribution data and natural environment data of each country industry;
calculating to obtain the output value data of each country industry according to the output data and the product price data of each country industry;
carrying out spatial autocorrelation analysis on the production value data of each country industry by using a global Moran index measurement tool of spatial autocorrelation to judge each country industry with spatial clustering;
according to the spatial distribution data of each rural industry, identifying the aggregation degree of each spatial clustering rural industry in each administrative rural space unit by using a spatial self-correlated local Molan index measurement tool, and judging endogenous rule information between the spontaneous existence of each spatial clustering rural industry and the natural environment by combining the natural environment data;
carrying out Pearson correlation analysis on the output value data of each spatial clustering village industry to obtain a Pearson correlation coefficient between any two spatial clustering village industries, and judging the industrial correlation between the two spatial clustering village industries according to the Pearson correlation coefficient;
clustering the rural industries with space clustering according to the industrial relevance to obtain a clustering result of the rural industries;
calculating the contribution degree of each country industry in the clustering result of the associated country industry according to the production value data, and selecting each associated country industry in the same cluster to form an associated industry cluster according to the set contribution degree condition, the industry association condition and the endogenous rule information condition between the cluster and the natural environment;
the associated industrial cluster is positioned to the corresponding administrative village to complete the clustered layout of the associated industrial cluster;
the method for judging the country industry with space aggregation by utilizing the spatial autocorrelation global Moran index measurement tool to perform spatial autocorrelation analysis on the country industry production value data comprises the following steps:
invoking a spatialAutocorrlation module in an arcpy module library through python, respectively inputting the output value data of each country industry for calculation, and obtaining Moran's I index values, z scores and p values corresponding to each country industry;
representing the space aggregation or discrete state of each rural industry by using a Moran ' sI index value, checking whether the Moran ' sI index value is effective or not by using a z-score and a p-value, and judging that the corresponding rural industry has space aggregation when the p-value is smaller than a first threshold value, the z-score is larger than a second threshold value, and the Moran's I index value is positive and larger than a third threshold value;
according to the spatial distribution data of each rural industry, the aggregation degree of each spatial clustering rural industry in each administrative village space unit is identified by using a spatial self-correlation local Molan index measurement tool, and endogenous rule information between the spontaneous existence of each spatial clustering rural industry and the natural environment is judged by overlapping the natural environment data, and the method comprises the following steps:
calling a Cluster outside Analysis module in an arcpy module library through python, respectively inputting the output value data of each spatial integrated village industry for calculation, and obtaining a Local Moran's I index value, a z score and a p value corresponding to each administrative village spatial unit according to the spatial distribution data of each spatial integrated village industry;
when the index value of Local Moran's I is positive, the p value is smaller than a fourth threshold value, and the z score is larger than a fifth threshold value, extracting the corresponding index value of Local Moran's I;
and comparing the extracted LocalMoran's I index value with corresponding natural environment data, and judging whether the corresponding spatial clustering village industry spontaneously exists endogenous laws between the endogenous laws and the natural environment in the corresponding administrative village space units according to a set rule to obtain endogenous law information.
2. The centralized layout method for rural industries according to claim 1, wherein the production data comprises production in a set year, the product price data comprises product price in a set year, and the calculation of the production value data of the rural industries according to the production data and the product price data of each rural industry comprises: and multiplying the yield of the set year of the rural industry by the product price corresponding to the set year to obtain the yield value of the rural industry in the year, and using the yield value as the yield value data of the rural industry.
3. The rural industry clustering layout method according to claim 1, wherein the Pearson correlation coefficient is negative and positive to indicate that there is a constraint relationship between two spatially clustered rural industries, and the absolute value of the Pearson correlation coefficient indicates the strength of the constraint or the coordination relationship between the two spatially clustered rural industries.
4. The rural industry clustering layout method according to claim 1, wherein the clustering of the spatially clustered rural industries according to the industry relevance to obtain the relevant rural industry clustering result comprises:
calling a spatialconstrained multivariable Clustering module in an arcpy module library through python, adopting a spatial constraint multi-Clustering method, taking an administrative village as a spatial constraint condition, substituting into production value data of the spatial Clustering village industry meeting the set industry relevance condition, estimating the Clustering number, measuring the Clustering effectiveness under different Clustering numbers by adopting Calinski-Harabasz pseudo-F statistic, estimating the optimal Clustering number, and outputting the Clustering result of the associated village industry under the optimal Clustering number.
5. The centralized layout method for rural industries according to claim 3, wherein the contribution degree condition is that the contribution degree reaches a sixth threshold value, the industry association condition is a collaborative relationship, the collaborative strength is greater than a seventh threshold value, and the endogenous rule information condition with the natural environment is that endogenous rules exist under the same natural environment condition.
6. The countryside industry gathering layout system is characterized by comprising an acquisition unit, a calculation unit, a first judgment unit, a second judgment unit, a third judgment unit, a clustering unit, a building unit and a layout unit, wherein:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring yield data, product price data, space distribution data and natural environment data of each country industry;
the calculating unit is used for calculating and obtaining the output value data of each country industry according to the output data and the product price data of each country industry;
the first judgment unit is used for carrying out spatial autocorrelation analysis on the production value data of each country industry by using a global Morland index measurement tool of spatial autocorrelation to judge each country industry with spatial clustering, and is specifically used for calling a spatialAutocorrlation module in an arcpy module library through python, respectively inputting the production value data of each country industry to carry out calculation, and obtaining Moran's I index value, z score and p value corresponding to each country industry; representing the space aggregation or discrete state of each rural industry by using a Moran ' sI index value, checking whether the Moran ' sI index value is effective or not by using a z-score and a p-value, and judging that the corresponding rural industry has space aggregation when the p-value is smaller than a first threshold value, the z-score is larger than a second threshold value, and the Moran's I index value is positive and larger than a third threshold value;
the second judgment unit is used for identifying the aggregation degree of each spatial clustering country industry in each administrative space unit by using a spatial autocorrelation Local Molan index measurement tool according to spatial distribution data of each country industry, and judging endogenous law information between each spatial clustering country industry and a natural environment by combining natural environment data, and is specifically used for calling a Cluster and Outlier Analysis module in an arcpy module library through python, respectively inputting the production value data of each spatial clustering country industry for calculation, and obtaining the corresponding Local Moran's I index value, z score and p value in each administrative space unit according to the spatial distribution data of each spatial clustering country industry; when the index value of Local Moran's I is positive, the p value is smaller than a fourth threshold value, and the z score is larger than a fifth threshold value, extracting the corresponding index value of Local Moran's I; comparing the extracted Local Moran's I index value with corresponding natural environment data, and judging whether the corresponding spatial clustering village industry spontaneously exists in the corresponding administrative village space unit and the endogenous rule between the corresponding administrative village space unit and the natural environment according to a set rule to obtain endogenous rule information;
the third judging unit is used for carrying out Pearson correlation analysis on the production value data of each spatial clustering village industry to obtain a Pearson correlation coefficient between any two spatial clustering village industries and judging the industrial relevance between the two spatial clustering village industries according to the Pearson correlation coefficient;
the clustering unit is used for clustering the rural industries with the spatial clustering property according to the industrial relevance to obtain clustering results of the rural industries;
the building unit is used for calculating the contribution degree of each country industry in the clustering result of the associated country industry according to the production value data, and selecting each associated country industry in the same cluster to form an associated industry cluster according to the set contribution degree condition, the industry association condition and the endogenous rule information condition with the natural environment;
and the layout unit is used for positioning the associated industrial cluster to the corresponding administrative village to complete the clustered layout of the associated industrial cluster.
7. The utility model provides a rural area industry gathers and gathers overall arrangement device which characterized in that includes:
a memory to store instructions;
a processor for reading the instructions stored in the memory and executing the method of any one of claims 1-5 in accordance with the instructions.
8. A computer-readable storage medium having instructions stored thereon, which when executed on a computer, cause the computer to perform the method of any one of claims 1-5.
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| CN112508332A (en) * | 2020-11-03 | 2021-03-16 | 武汉大学 | Gradual rural settlement renovation partitioning method considering multidimensional characteristics |
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