Low-efficiency land use identification method

Abstract

The invention provides a low-efficiency land identification method. The low-efficiency land identification method comprises the following steps: obtaining, checking and preliminarily processing multi-source data; constructing land use performance evaluation based on the multi-source data; index weights are determined through a principal component analysis method, and land use absolute performance is obtained through calculation; and through neighborhood analysis, identifying grids which are obviously lower than the performance average value of the same dimension at the periphery, and judging that the grids are low-efficiency land in relative performance. According to the method, the absolute performance of the urban land is measured based on multi-source data and principal component analysis, domain analysis is performed in ArcGIS through a Python code, the low-efficiency land in the city is identified through the relative performance, and a new technical tool is provided for redevelopment work of the low-efficiency urban and rural construction land under the background of territorial space refinement treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of land performance evaluation, and particularly relates to a method for identifying inefficient land use. Background Art

[0002] As a non-renewable and scarce resource, land resources are crucial for the sustainable development of the economy and society. The traditional "increment-driven" urbanization path overemphasizes the relationship between land supply and economic growth, resulting in the generation of a large amount of inefficient land use, which has a negative impact on the ecological environment and agricultural production. As China's economic and social development enters a new stage, urban and rural development will pay more attention to the model of "equal emphasis on stock quality improvement and transformation and incremental structure adjustment", emphasizing the high-quality development of urban and rural spaces. The identification and redevelopment technologies of inefficient land use have received extensive attention. Traditional identification of inefficient urban and rural construction land is based on quantitative evaluation of land use performance, but does not consider the importance of spatial location in performance, which is not conducive to proposing refined governance strategies, and thus leads to unreasonable resource allocation and the lack of fairness in the land development process.

[0003] In the existing patent application CN202311725139.8, a method and device for identifying inefficient land use are disclosed. The method includes constructing an evaluation index library for identifying inefficient land use; based on the evaluation index library for identifying inefficient land use, obtaining various index data, and determining the weight value and the score value of the distribution characteristics of each index data; comparing the various index data of the land to be identified with the various index data of the evaluation index library for identifying inefficient land use; when any of the various index data of the land to be identified is consistent with the various index data of the evaluation index library for identifying inefficient land use, obtaining the corresponding weight value and the score value of the corresponding distribution characteristics, and predicting the detection value of the land to be identified; if the detection value is less than or equal to the preset threshold, preliminarily determining the land to be identified as inefficient land use; using a white list mechanism to conduct a secondary verification of inefficient land use, and using a black list mechanism to conduct a secondary verification of the land to be determined. Through one-time identification and secondary verification, the present application forms a final inefficient land use library, having the effect of accurately identifying inefficient land use.

[0004] However, in the above-mentioned disclosed document, regarding considering the location value and spatial factors of land, achieving a better reflection of the matching between land use efficiency and its spatial location value, and reflecting certain spatial structure evidence, it is impossible to form a more dimension-rich performance evaluation result. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for identifying inefficient land use that overcomes the problem of insufficient refined identification of micro plots based on quantitative evaluation of land use performance.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A method for identifying inefficient land use, which is used to identify inefficient land use in cities, includes the following steps:

[0008] Step S1: Acquisition and processing of multi-source data;

[0009] Step S2: Construct an evaluation index system for land use performance and calculate the absolute land use performance evaluation results;

[0010] Step S3: Calculate the relative land use performance evaluation results through neighborhood analysis based on the absolute land use performance evaluation results;

[0011] Step S4: Identify inefficient land use through the overlay analysis of the absolute land use performance evaluation results and the relative land use performance evaluation results.

[0012] Preferably, in the step S1, the acquisition and processing of the multi-source data include the following steps:

[0013] Step S101: Obtain land use data, and the land use data includes classification based on "Urban Land Classification and Planning Construction Land Standards" (GB 50137-2011) in terms of "rural-urban land classification" and "urban construction land classification";

[0014] Step S102: Obtain residential population data and employment population data, and identify the residential population data and employment population data based on mobile phone signaling data;

[0015] Step S103: Obtain POI data through the API interface of the Amap Open Platform, generate a vector point set file based on the point class data in the POI data, and reclassify the POI data through the vector point set file to divide the POI data into major land use function categories;

[0016] Step S104: Use the fishnet tool of ArcGIS to generate a grid of 100m×100m cells, and perform an intersection tabulation analysis by overlaying the grid with the land patch layer, and count the number of spatial grids through the spatial join tool; call ArcEngine and use NET programming to implement the length statistics and cutting density calculation within the unit area of the entire region.

[0017] Preferably, in the step S2, the construction of the evaluation index system for land use performance and the calculation of the absolute land use performance evaluation results include the following steps:

[0018] Step S201: Comprehensively consider principles such as data availability, scientificity, and whether it can comprehensively reflect land use performance, construct an index system, and establish three-level index data of target layer A, criterion layer B, and index layer C;

[0019] Step S202: Measure the distribution characteristics of the objects in the spatial grid, standardize the index data through min-max standardization, and match it with the land patch layer data;

[0020] Step S203: Calculate the covariance matrix between the index data through principal component analysis, perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, and extract the principal components according to the eigenvalue magnitudes;

[0021] Step S204: Multiply the original index data by the eigenvectors of the principal components to obtain the contribution of each index data to each principal component, that is, the principal component load. According to the principal component load, assign the contribution of each index data to each principal component as the weight to the index layer C, perform overlay analysis and regress to the target layer A to obtain the absolute land use performance evaluation result.

[0022] Preferably, the objects include population, enterprises, commerce, and public service activities.

[0023] Preferably, in the step S3, the obtaining of the relative land use performance evaluation result through neighborhood analysis includes the following steps:

[0024] Step S301: Load the absolute land use performance evaluation result of the land use in ArcGIS, and perform neighborhood analysis through the ZonalStatistics spatial analysis tool; for each spatial grid where the object is located, define the surrounding neighborhood range as the adjacent eight spatial grids; then perform attribute analysis on the objects in the neighborhood and statistically calculate the average value of the absolute land use performance evaluation result of the spatial grid.

[0025] Step S302: Compare the average value of the absolute land use performance evaluation result of each spatial grid with the average value of the absolute land use performance evaluation results of the adjacent eight spatial grids, perform traversal calculation, and identify the spatial grids that are significantly lower than the average performance value of the same dimension in the surrounding area to obtain the relative land use performance result.

[0026] Preferably, the overlay analysis of the absolute land use performance evaluation result and the relative land use performance evaluation result to identify inefficient land use includes the following steps:

[0027] Step S401: Divide the absolute land use performance evaluation result and the relative land use performance evaluation result into three parts to obtain high, medium, and low three types of results;

[0028] Step S402: Through the overlay analysis of the absolute land use performance evaluation results and the relative land use performance evaluation results, obtain the nine-category land use performance overlay analysis evaluation results, including high-high, high-medium, high-low, medium-high, medium-medium, medium-low, low-high, low-medium, and low-low respectively;

[0029] Step S403: Visualize and arrange the land use performance overlay analysis evaluation results in a proportion matrix;

[0030] Step S404: Verify the land use performance overlay analysis evaluation results through on-site research.

[0031] Based on multi-source data and principal component analysis, this invention measures the absolute performance of urban land use. Then, through Python code, it conducts domain analysis in ArcGIS, identifies inefficient land in the city through relative performance, and provides a new technical tool for the redevelopment of inefficient urban and rural construction land under the background of refined governance of territorial space.

[0032] Based on the absolute performance evaluation of other related inventions, this invention further considers the location value and spatial factors of land through a spatial neighborhood analysis tool, and invents a relative performance evaluation method. Through case studies, relative performance can better reflect the matching between land use efficiency and its spatial location value, and reflect certain spatial structure evidence; the overlay analysis of absolute performance and relative performance can form a more comprehensive performance evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of a method for identifying inefficient land in this embodiment;

[0034] Figure 2 It is for the absolute land use performance evaluation development intensity dimension (taking the development intensity dimension as an example) of urban and rural construction land in Shanghai in this embodiment;

[0035] Figure 3 It is for the relative land use performance evaluation development intensity dimension of urban and rural construction land in Shanghai in this embodiment; (taking the development intensity dimension as an example);

[0036] Figure 4 It is the overlay evaluation result of the absolute and relative performance of urban and rural construction land in Shanghai in this embodiment (taking the development intensity dimension as an example). DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0038] Figure 1This is a flowchart of a method for identifying inefficient land use in this embodiment.

[0039] As Figure 1 shown, this is a method for identifying inefficient land use disclosed in this embodiment, which is used to identify inefficient land use in the city and includes the following steps:

[0040] Step S1: Acquisition and processing of multi-source data. The acquisition and processing of multi-source data include the following steps:

[0041] Step S101: Obtain land use data. The land use data includes classification based on "Rural-Urban Land Classification" and "Urban Construction Land Classification" in the "Standard for Classification of Urban Land Use and Planning Construction Land" (GB 50137-2011).

[0042] Step S102: Obtain residential population data and employment population data, and identify residential population data and employment population data based on mobile phone signaling data.

[0043] Step S103: Obtain POI data through the API interface of the Amap Open Platform, generate a vector point set file based on the point class data in the POI data, and reclassify the POI data through the vector point set file to divide and obtain the major land use functions of the POI data.

[0044] Step S104: Use the fishnet tool of ArcGIS to generate a grid of 100m×100m cells, overlay the grid with the land parcel layer to perform intersection tabulation analysis, and count the number of spatial grids through the spatial join tool; call ArcEngine and use NET programming to implement the length statistics and cutting density calculation within the unit area of the entire region.

[0045] Step S2: Construct an evaluation index system for land use performance and calculate the absolute land use performance evaluation results, including the following steps:

[0046] Step S201: Construct an index system based on the principles of strong data availability and scientificity, weak autocorrelation between data, and the ability to comprehensively reflect land use evaluation content, and establish three-level index data of the target layer A, criterion layer B, and index layer C;

[0047] Step S202: Measure the distribution characteristics of the object in the spatial grid, perform standardization processing on the index data through min-max standardization, and match it with the land parcel layer data.

[0048] In this embodiment, the criterion layer B includes development intensity, economic and social performance, traffic performance, service performance, and structural performance, and the sub-index layer C is as shown in Table 1 below.

[0049] Table 1

[0050]

[0051]

[0052] Step S203: Calculate the covariance matrix between each index data through principal component analysis, perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, and extract the principal components according to the eigenvalue magnitudes.

[0053] Step S204: Multiply the original index data by the eigenvectors of the principal components to obtain the contributions of each index data to each principal component, that is, the principal component loadings. According to the principal component loadings, assign the contributions of each index data to each principal component as weights to the index layer C, perform overlay analysis and regress to the target layer A to obtain the land use absolute performance evaluation result.

[0054] Step S3: Calculate the land use relative performance evaluation result through neighborhood analysis based on the land use absolute performance evaluation result, including the following steps:

[0055] Step S301: Load the land use absolute performance evaluation result in ArcGIS, and perform neighborhood analysis through the Zonal Statistics spatial analysis tool; for each spatial grid where an object is located, define the neighborhood range around it as the eight adjacent spatial grids; then perform attribute analysis on the objects within the neighborhood and statistically calculate the average value of the land use absolute performance evaluation results of the spatial grids.

[0056] Step S302: By comparing the average value of the land use absolute performance evaluation result of each spatial grid with the average values of the land use absolute performance evaluation results of the eight adjacent spatial grids, perform traversal calculation to identify the spatial grids that are significantly lower than the average performance value of the same dimension in the surrounding area, and obtain the land use relative performance result.

[0057] The "relative performance" analysis (Step S3) of this application incorporates the matching degree between the spatial location corresponding value and the land use efficiency into the performance evaluation through the "neighborhood analysis" method. The embodiments show that this step can clearly reflect the urban-rural spatial structure characteristics of the land use efficiency.

[0058] Step S4: Identify inefficient land through the overlay analysis of the land use absolute performance evaluation result and the land use relative performance evaluation result, including the following steps:

[0059] Step S401: Divide the land use absolute performance evaluation result and the land use relative performance evaluation result into three parts to obtain high, medium, and low category results.

[0060] Step S402: Through the overlay analysis of the absolute land use performance evaluation results and the relative land use performance evaluation results, the overlay analysis and evaluation results of the nine types of land use performance are obtained, including high-high, high-medium, high-low, medium-high, medium-medium, medium-low, low-high, low-medium, and low-low respectively;

[0061] Step S403: As Figure 4 shown, visually present the overlay analysis and evaluation results of the land use performance and arrange them in a proportion matrix.

[0062] Step S404: Verify the overlay analysis and evaluation results of the land use performance through on-site investigation.

[0063] In this embodiment, through the acquisition, verification, and preliminary processing of multi-source data; constructing a land use performance evaluation based on multi-source data; determining the index weights through the principal component analysis method and calculating the absolute land use performance; through neighborhood analysis, identifying the grids that are significantly lower than the average performance value of the same dimension in the surrounding area, and determining them as low-efficiency land in the relative performance. After on-site investigation and verification of street view photos, the analysis results are reliable.

[0064] The present invention measures the absolute land use performance of cities based on multi-source data and principal component analysis, and then conducts neighborhood analysis in ArcGIS through Python code, and identifies low-efficiency land in cities through relative performance, providing a new technical tool for the redevelopment of low-efficiency urban and rural construction land under the background of refined governance of territorial space.

[0065] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0066] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0067] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

Claims

1. A method for identifying inefficient land use, for identifying inefficient land use in a city, characterized in that: The following steps are involved: Step S1: Obtain land use data, population data, and POI data; Step S2: construct a land use performance evaluation index system and calculate the absolute land use performance evaluation results; Step S3: Obtaining a relative land use performance evaluation result through neighborhood analysis based on the absolute land use performance evaluation result; Step S4: Identify inefficient land through superposition analysis of the absolute land performance evaluation results and the relative land performance evaluation results.

2. The method for identifying inefficient land use according to claim 1, characterized in that: The step S1 comprises the following steps: Step S101: Acquire land use data, wherein the land use data includes classification based on "urban and rural land classification" and "urban construction land classification" in the "Urban Land Classification and Planning and Construction Land Standard" (GB 50137-2011); Step S102: Acquire resident population data and employed population data, and identify the resident population data and employed population data based on mobile phone signaling data; Step S103: obtaining POI data through the API interface of the AutoNavi open platform, generating a vector point set file based on the point class data in the POI data, reclassifying the POI data through the vector point set file, and obtaining the major land use function categories of the POI data; Step S104: Using the fishing net tool of ArcGIS, a grid of 100m×100m cells is generated, and the grid is superimposed with the land map layer for intersection tabulation analysis, and the number of spatial grids is counted by the spatial connection tool; ArcEngine is called, and NET programming is used to implement length statistics and cutting density calculation within the unit area of ​​the entire area.

3. A method for identifying inefficient land use according to claim 2, characterized in that: In step S2, the construction of the land use performance evaluation index system and the calculation of the land use absolute performance evaluation result include the following steps: Step S201: Considering the principles of data availability, scientificity and whether it can fully reflect land use performance, etc., an indicator system is constructed, and three-level indicator data of target layer A, criterion layer B and indicator layer C are established; Step S202: Calculate the distribution characteristics of the object in the spatial grid, standardize the index data by min-max standardization, and match it with the land patch layer data; Step S203: Calculate the covariance matrix between the indicator data through principal component analysis, perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, and extract principal components according to the eigenvalues; Step S204: Multiply the original indicator data with the eigenvector of the principal component to obtain the contribution of each indicator data to each principal component, that is, the principal component load. According to the principal component load, the contribution of each indicator data to each principal component is assigned to the indicator layer C as a weight, and the overlay analysis is performed and regressed to the target layer A to obtain the absolute performance evaluation result of the land use.

4. A method for identifying low-efficiency land use according to claim 3, characterized in that: The objects mentioned include population, enterprises, commerce and public service activities.

5. The method for identifying low-efficiency land use according to claim 3, characterized in that: In step S3, obtaining the relative performance evaluation result of land use by neighborhood analysis calculation includes the following steps: Step S301: Load the absolute performance evaluation results of land use in ArcGIS, and perform neighborhood analysis through the ZonalStatistics spatial analysis tool; for each spatial grid where the object is located, define the neighborhood range around it as eight adjacent spatial grids; then perform attribute analysis on the objects in the neighborhood, and statistically calculate the average value of the absolute performance evaluation results of the spatial grid; Step S302: By comparing the average value of the absolute land use performance evaluation results of each of the spatial grids with the average value of the absolute land use performance evaluation results of the eight adjacent spatial grids, traversing the calculation, identifying the spatial grid whose performance is significantly lower than the average value of the surrounding same dimension, and obtaining the relative land use performance result.

6. The method for identifying inefficient land use according to claim 1, characterized in that: The superposition analysis of the absolute land use performance evaluation results and the relative land use performance evaluation results to identify inefficient land use includes the following steps: Step S401: Divide the absolute land use performance evaluation result and the relative land use performance evaluation result into three parts to obtain three types of results: high, medium and low; Step S402: by superimposing and analyzing the absolute land use performance evaluation result and the relative land use performance evaluation result, nine types of land use performance superimposed analysis and evaluation results are obtained, including high-high, high-medium, high-low, medium-high, medium-medium, medium-low, low-high, low-medium, and low-low; Step S403: Visualizing and arranging the land use performance superposition analysis and evaluation results in a matrix; Step S404: verifying the land use performance superposition analysis and evaluation results through on-site investigation.

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