Urban low-efficiency land identification and evaluation method and device, and storage medium

Abstract

The invention discloses an urban low-efficiency land identification and evaluation method, which comprises the steps of S1, constructing a low-efficiency land evaluation index system, and forming a multi-layer structure of a target layer, a criterion layer and an index layer; s2, collecting multi-source data; s3, calculating the index value of each evaluation index; s4, determining the weight of each evaluation index by using a Delphi method; s5, carrying out standardization processing on the collected index data, and carrying out dimensionless processing on the index data by adopting an efficacy coefficient method to eliminate dimensional differences among different evaluation indexes; s6, comprehensive evaluation and identification of the low-efficiency land are carried out, the comprehensive evaluation index of each land block is calculated based on the standardized index data and the determined weight, and the category and the low-efficiency degree of the low-efficiency land are identified; and S7, performing spatial association on evaluation results by using a geographic information system, generating a visual map of the low-efficiency land, and displaying spatial distribution and low-efficiency degree of the low-efficiency land. According to the invention, accurate identification and scientific evaluation of the urban inefficient land are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of land identification, and in particular to a method for identifying and evaluating low-efficiency urban land. Background Art

[0002] With the deepening of my country's urbanization process, the efficient use of land resources has gradually become a core issue in urban and rural development. However, the problem of inefficient land use in cities and towns has become increasingly prominent. In urban industrial, mining, residential, village and historical land use types, scattered layout, unreasonable planning and low resource utilization efficiency have led to serious waste of land resources, restricting the process of urban space optimization and redevelopment.

[0003] Under the current technical background, some studies have attempted to introduce geographic information system technology and data analysis tools to identify inefficient land use, but the existing technology still has the following outstanding problems:

[0004] First, there is a lack of a systematic and comprehensive framework for identifying and evaluating inefficient land. Existing methods usually focus on the evaluation of a single category or a single dimension, which makes it difficult to fully reflect the causes and current status of inefficient land. They fail to form a multi-dimensional indicator system from policy, space to efficiency, resulting in identification results that are not comprehensive and scientific enough.

[0005] Second, the data source is single and the evaluation results lack objectivity. The traditional method relies on a single data source and ignores the integrated application of multi-source data such as geological disaster risk points, housing quality, and tax data, and is unable to accurately quantify the resource utilization level and potential risks of inefficient land.

[0006] Third, the weight determination method is highly subjective and lacks an intelligent optimization mechanism. Existing methods usually determine the importance of each indicator through simple empirical judgment or fixed weights, and fail to combine expert opinions with actual data for dynamic optimization and cluster analysis, resulting in insufficient scientificity and rationality in the setting of indicator weights.

[0007] Fourth, the results of inefficient land identification are difficult to visualize and quantify. Currently, the evaluation results of inefficient land are mostly presented in the form of text or statistical data, lacking intuitive spatial distribution display and unable to effectively support the decision-making needs of urban space planning and redevelopment.

[0008] In summary, the existing technologies have significant deficiencies in the systematic nature of low-efficiency land identification and evaluation, data objectivity, scientific weighting and result visualization, and are difficult to meet the actual needs of modern urban land resource management. Therefore, there is an urgent need for a systematic urban low-efficiency land identification and evaluation method based on multi-source data and combining the three dimensions of policy, space and efficiency to comprehensively and scientifically identify the types and degree of low-efficiency land, and provide decision support for urban land redevelopment and spatial optimization. Summary of the invention

[0009] One purpose of the present invention is to propose a method for identifying and evaluating low-efficiency urban land, which realizes accurate identification and scientific evaluation of low-efficiency urban land.

[0010] A method for identifying and evaluating low-efficiency urban land according to an embodiment of the present invention comprises the following steps:

[0011] S1. Construct an evaluation index system for inefficient land use, forming a multi-level structure of target layer, criterion layer and indicator layer. The criterion layer includes inefficient industrial and mining land, inefficient urban land, inefficient village land and historical land, and the indicator layer includes policy dimension, spatial dimension and efficiency dimension.

[0012] S2. Collect multi-source data, including industrial block line data, geological disaster risk point data, land use planning data and housing use survey data, and enter the data into the geographic information system platform;

[0013] S3. Calculate the index values ​​of various evaluation indicators, and quantify the policy compliance, spatial adaptability and land use efficiency evaluation indicators according to the calculation methods of each indicator;

[0014] S4. Use the Delphi method to determine the weight of each evaluation indicator, conduct multiple rounds of comparative analysis based on expert opinions, and clarify the importance of each indicator;

[0015] S5. Standardize the collected indicator data and use the efficacy coefficient method to make the indicator data dimensionless to eliminate the dimensional differences between different evaluation indicators;

[0016] S6. Conduct comprehensive evaluation and identification of inefficient land use, calculate the comprehensive evaluation index of each plot based on the standardized indicator data and determined weights, and identify the types and degree of inefficient land use;

[0017] S7. Use geographic information systems to spatially correlate the evaluation results and generate a visual map of inefficient land to show the spatial distribution and degree of inefficiency of inefficient land.

[0018] Optionally, the S1 specifically includes the following steps:

[0019] S11. Determine the overall framework of the low-efficiency land evaluation index system. The target layer is the low-efficiency land identification and evaluation system. The criterion layer includes low-efficiency industrial and mining land, low-efficiency urban land, low-efficiency village land and historical land. The index layer includes policy dimension, spatial dimension and efficiency dimension.

[0020] S12. Under the policy dimension, select indicators related to national policies and local planning for land use types at different criteria levels;

[0021] S13, in the spatial dimension, analyze spatial adaptability based on multi-source data;

[0022] S14. Construct a quantitative evaluation model under the efficiency dimension to identify the resource utilization efficiency of land.

[0023] Optionally, the indicators related to national policies and local planning specifically include:

[0024] Inefficient industrial and mining land, based on industrial block line data, determines areas that do not comply with industrial policies;

[0025] Inefficient urban land: Based on the master plan and urban renewal data, identify urban land that does not meet planning control requirements;

[0026] Inefficient village land use, combined with geological disaster risk point data, identifies village land with disaster risks but not yet managed;

[0027] For historical land use, based on land approval and expropriation data, identify land use with incomplete procedures or prominent historical problems.

[0028] Optionally, the spatial adaptability specifically includes:

[0029] Inefficient industrial and mining land, obtain control planning data, and identify areas that are inconsistent with the current planning layout;

[0030] Inefficient urban land use, through the analysis of spatial imbalance areas through urban renewal scope data and old community renovation data;

[0031] Inefficient village land use, combining geological hazard distribution data and spatial layout data to identify areas with scattered spaces and hidden dangers;

[0032] Historical land use, areas where spatial use has become invalid or layout is misplaced, as determined based on land survey data.

[0033] Optionally, the resource utilization efficiency of the land specifically includes:

[0034] Inefficient industrial and mining land, the output efficiency per unit area of ​​land is calculated based on tax data;

[0035] Inefficient urban land use, combined with housing quality certification data to identify dangerous houses or areas with poor building quality;

[0036] Inefficient village land use, calculate housing vacancy rate and idle rate based on housing use survey data;

[0037] For historical land use, the utilization of inefficient resources is analyzed through land efficiency data.

[0038] Optionally, S3 specifically includes the following steps:

[0039] S31. Collect multi-source data required for low-efficiency land evaluation, the multi-source data including:

[0040] Industrial block line data of the area is used to identify the indicator values ​​of inefficient industrial and mining land in policy compliance;

[0041] Data on geological disaster risk points are used to quantify the risk indicators of inefficient village land and historical land under the policy dimension;

[0042] Land use planning data, including regulatory detailed planning data, master planning data, and urban renewal data, are used to analyze the index values ​​of inefficient urban land and inefficient village land in spatial adaptability;

[0043] Housing use survey data, including housing quality, vacancy rate and use efficiency data, is used to calculate the resource utilization efficiency of each plot in the efficiency dimension;

[0044] S32. Based on the collected multi-source data and combined with the calculation method of the inefficient land use evaluation index, various indicators under the three dimensions of policy compliance, spatial adaptability and land use efficiency are quantified:

[0045] Policy compliance, based on the industrial block line data and geological disaster risk point data of the area, calculate the policy compliance index value P x :

[0046]

[0047] Among them, C i is the policy compliance judgment value of the i-th plot, R i is the land parcel policy correction coefficient, n is the number of land parcels to be evaluated;

[0048] Spatial adaptability: Based on land use planning data, calculate the spatial adaptability index value S x :

[0049]

[0050] Among them, W j is the weight of the j-th spatial index, A j is the actual value of the j-th spatial index, A max and A min are the maximum and minimum values ​​of the spatial index, respectively, and m is the number of spatial indexes;

[0051] Land efficiency: Based on the housing use survey data, calculate the land efficiency index value F x :

[0052]

[0053] Among them, Qk is the resource utilization rate of the kth plot, E k is the economic output value of a unit plot, and p is the total number of plots to be evaluated.

[0054] Optionally, the S4 specifically includes the following steps:

[0055] S41. Select multiple experts in the fields of land planning, economic management and geographic information systems to form a multidisciplinary expert group E = {e1, e2, …, e n},1≤k≤n, where e k represents the kth expert, n is the number of experts;

[0056] S42. Form an indicator scoring matrix template based on specific indicators in the policy dimension, spatial dimension and efficiency dimension S i,k , where i is the index number, S i,k Score the importance of the i-th indicator by the k-th expert, with the score range R = [1,5];

[0057] S43, summarizing the experts' scores on the importance of each indicator to form an initial expert scoring matrix S:

[0058]

[0059] Among them, m is the number of evaluation indicators, n is the number of experts;

[0060] S44, the initial rating matrix S is standardized to obtain a standardized rating matrix S', and K-means clustering algorithm is used to determine K rating distribution clusters for the standardized rating matrix S', and the rating values ​​that deviate from the mean by more than two standard deviations in the clustering results are eliminated:

[0061] C i ={S′ i,k ∣|S′ i,k -μ i |≤2σ i};

[0062] Among them, C i is the effective scoring set of the i-th indicator, μ i is the mean score of the i-th indicator, σ i is the score standard deviation of the i-th indicator;

[0063] S45. Valid score set C based on cluster analysis i Calculate the average importance score M of each indicator i :

[0064]

[0065] Among them, |Ci | is the number of ratings in the valid rating set;

[0066] S46. Calculate the standard deviation σ of the scoring results of each indicator i , used to judge the consistency of ratings:

[0067]

[0068] If σ i ≤T, T is the set consistency threshold, the scoring results are considered consistent; if σ i >T, the scoring result of indicator i will be fed back to the expert group and re-scored until the consistency requirement is met;

[0069] S47, average importance score M based on consistency verification i , calculate the final weight W of each evaluation index i ;

[0070] S48, the calculated weights of each evaluation index W i Applied to policy compliance index value P x , spatial adaptability index value S x and land use efficiency index F x The calculation of low-efficiency land three-dimensional index value matrix M is formed x :

[0071] M x = {W p ·P x ,W s ·S x ,W f ·F x};

[0072] Among them, W p , W s , W f are the weights of policy compliance, spatial adaptability and land use efficiency, respectively.

[0073] Optionally, the S6 specifically includes the following steps:

[0074] S61. Based on the three-dimensional index value matrix of inefficient land use, combined with the policy compliance index value, spatial adaptability index value and land use efficiency index value, the comprehensive evaluation index I of the xth plot is calculated by the index weight. x :

[0075]

[0076] Among them, I x is the comprehensive evaluation index of the xth plot, W d Represents the weight of each dimension, Mx,d represents the index value of the xth plot in the dth dimension, where d = 1, 2, and 3 correspond to policy compliance, spatial adaptability, and land use efficiency, respectively;

[0077] S62. Setting the efficient land use threshold T h , low-efficiency land use threshold T l , the comprehensive evaluation index I of the xth plot x Compare with the threshold to identify the land use category L of the plot x :

[0078]

[0079] S63. For the identified inefficient land use, the inefficiency level D x To quantify:

[0080]

[0081] Among them, D x is the inefficiency level of the xth plot, and its value range is [0,1]. The larger the value, the higher the inefficiency level.

[0082] S64. Spatially associate the three-dimensional index value matrix, land use category and inefficiency process of the xth plot, and visualize them through the geographic information system platform:

[0083] Mark the low-efficiency land use categories of each plot on the map, distinguishing high-efficiency land, medium-efficiency land and low-efficiency land;

[0084] Visualize the spatial distribution of inefficient land use by using color gradients or legends to indicate the degree of inefficiency;

[0085] Output the three-dimensional indicator values ​​and comprehensive evaluation index corresponding to each block.

[0086] A computer-readable storage medium stores a computer program, which implements the steps of the embodiment method when executed by a processor.

[0087] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of an embodiment method when executing the computer program.

[0088] The beneficial effects of the present invention are:

[0089] (1) The present invention introduces a weight optimization mechanism that combines an intelligent clustering method with a multi-round Delphi method, performs cluster analysis on expert rating data, removes rating data that deviates greatly from the mean, ensures the accuracy and consistency of weight distribution, and dynamically adjusts indicator weights through rating consistency verification and feedback iteration. This overcomes the problem in the prior art that weight determination relies on subjective experience and the results are not scientific enough, and ensures the rationality and objectivity of the evaluation indicator weights.

[0090] (2) The present invention constructs a systematic indicator system based on the three dimensions of policy compliance, spatial adaptability and land use efficiency, and quantifies multi-source data into evaluation index values ​​to form a complete three-dimensional indicator value matrix for inefficient land use. On this basis, the indicator weights are used to calculate the comprehensive evaluation index of each plot to scientifically evaluate the inefficiency of the plot. Compared with the existing technology that focuses on a single dimension or relies on qualitative analysis, the present invention realizes multi-dimensional quantitative evaluation and accurate identification of inefficient land categories, providing a clear decision-making basis for the optimal allocation of land resources.

[0091] (3) The present invention uses a geographic information system platform to associate the comprehensive evaluation results of inefficient land with spatial data, generate a spatial distribution map of inefficient land, and display the inefficiency category and degree of inefficiency of each plot through color gradients and legends, intuitively reflecting the spatial distribution characteristics of inefficient land, helping relevant departments to quickly identify problem areas and formulate targeted redevelopment and optimization strategies, thus making up for the lack of intuitive operability of evaluation results in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0093] Figure 1 The present invention provides a flow chart of a method for identifying and evaluating low-efficiency urban land. DETAILED DESCRIPTION

[0094] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0095] refer to Figure 1 , a method for identifying and evaluating low-efficiency urban land, comprising the following steps:

[0096] S1. Construct an evaluation index system for inefficient land use, forming a multi-level structure of target layer, criterion layer and indicator layer. The criterion layer includes inefficient industrial and mining land, inefficient urban land, inefficient village land and historical land, and the indicator layer includes policy dimension, spatial dimension and efficiency dimension.

[0097] S2. Collect multi-source data, including industrial block line data, geological disaster risk point data, land use planning data and housing use survey data, and enter the data into the geographic information system platform;

[0098] S3. Calculate the index values ​​of various evaluation indicators, and quantify the policy compliance, spatial adaptability and land use efficiency evaluation indicators according to the calculation methods of each indicator;

[0099] S4. Use the Delphi method to determine the weight of each evaluation indicator, conduct multiple rounds of comparative analysis based on expert opinions, and clarify the importance of each indicator;

[0100] S5. Standardize the collected indicator data and use the efficacy coefficient method to make the indicator data dimensionless to eliminate the dimensional differences between different evaluation indicators;

[0101] S6. Conduct comprehensive evaluation and identification of inefficient land use, calculate the comprehensive evaluation index of each plot based on the standardized indicator data and determined weights, and identify the types and degree of inefficient land use;

[0102] S7. Use geographic information systems to spatially correlate the evaluation results and generate a visual map of inefficient land to show the spatial distribution and degree of inefficiency of inefficient land.

[0103] In this implementation, S1 specifically includes the following steps:

[0104] S11. Determine the overall framework of the low-efficiency land evaluation index system. The target layer is the low-efficiency land identification and evaluation system. The criterion layer includes low-efficiency industrial and mining land, low-efficiency urban land, low-efficiency village land and historical land. The index layer includes policy dimension, spatial dimension and efficiency dimension.

[0105] S12. Under the policy dimension, select indicators related to national policies and local planning for land use types at different criteria levels;

[0106] S13, in the spatial dimension, analyze spatial adaptability based on multi-source data;

[0107] S14. Construct a quantitative evaluation model under the efficiency dimension to identify the resource utilization efficiency of land.

[0108] In this implementation, the indicators related to national policies and local planning specifically include:

[0109] Inefficient industrial and mining land, based on industrial block line data, determines areas that do not comply with industrial policies;

[0110] Inefficient urban land: Based on the master plan and urban renewal data, identify urban land that does not meet planning control requirements;

[0111] Inefficient village land use, combined with geological disaster risk point data, identifies village land with disaster risks but not yet managed;

[0112] For historical land use, based on land approval and expropriation data, identify land use with incomplete procedures or prominent historical problems.

[0113] In this implementation, spatial adaptability specifically includes:

[0114] Inefficient industrial and mining land, obtain control planning data, and identify areas that are inconsistent with the current planning layout;

[0115] Inefficient urban land use, through the analysis of spatial imbalance areas through urban renewal scope data and old community renovation data;

[0116] Inefficient village land use, combining geological hazard distribution data and spatial layout data to identify areas with scattered spaces and hidden dangers;

[0117] Historical land use, areas where spatial use has become invalid or layout is misplaced, as determined based on land survey data.

[0118] In this implementation, the land resource utilization efficiency specifically includes:

[0119] Inefficient industrial and mining land, the output efficiency per unit area of ​​land is calculated based on tax data;

[0120] Inefficient urban land use, combined with housing quality certification data to identify dangerous houses or areas with poor building quality;

[0121] Inefficient village land use, calculate housing vacancy rate and idle rate based on housing use survey data;

[0122] For historical land use, the utilization of inefficient resources is analyzed through land efficiency data.

[0123] In this implementation, S3 specifically includes the following steps:

[0124] S31. Collect multi-source data required for low-efficiency land evaluation, including:

[0125] Industrial block line data of the area is used to identify the indicator values ​​of inefficient industrial and mining land in policy compliance;

[0126] Data on geological disaster risk points are used to quantify the risk indicators of inefficient village land and historical land under the policy dimension;

[0127] Land use planning data, including regulatory detailed planning data, master planning data, and urban renewal data, are used to analyze the index values ​​of inefficient urban land and inefficient village land in spatial adaptability;

[0128] Housing use survey data, including housing quality, vacancy rate and use efficiency data, is used to calculate the resource utilization efficiency of each plot in the efficiency dimension;

[0129] S32. Based on the collected multi-source data and combined with the calculation method of the inefficient land use evaluation index, various indicators under the three dimensions of policy compliance, spatial adaptability and land use efficiency are quantified:

[0130] Policy compliance, based on the industrial block line data and geological disaster risk point data of the area, calculate the policy compliance index value P x :

[0131]

[0132] Among them, C i is the policy compliance judgment value of the i-th plot, R i is the land parcel policy correction coefficient, n is the number of land parcels to be evaluated;

[0133] Spatial adaptability: Based on land use planning data, calculate the spatial adaptability index value S x :

[0134]

[0135] Among them, W j is the weight of the j-th spatial index, A j is the actual value of the j-th spatial index, A max and A min are the maximum and minimum values ​​of the spatial index, respectively, and m is the number of spatial indexes;

[0136] Land efficiency: Based on the housing use survey data, calculate the land efficiency index value F x :

[0137]

[0138] Among them, Q k is the resource utilization rate of the kth plot, E k is the economic output value of a unit plot, and p is the total number of plots to be evaluated.

[0139] In this implementation, S4 specifically includes the following steps:

[0140] S41. Select multiple experts in the fields of land planning, economic management and geographic information systems to form a multidisciplinary expert group E = {e1, e2, …, e n},1≤k≤n, where e k represents the kth expert, n is the number of experts;

[0141] S42. Form an indicator scoring matrix template based on specific indicators in the policy dimension, spatial dimension and efficiency dimension S i,k , where i is the index number, S i,k Score the importance of the i-th indicator by the k-th expert, with the score range R = [1,5];

[0142] S43, summarizing the experts' scores on the importance of each indicator to form an initial expert scoring matrix S:

[0143]

[0144] Among them, m is the number of evaluation indicators, n is the number of experts;

[0145] S44, the initial rating matrix S is standardized to obtain a standardized rating matrix S', and K-means clustering algorithm is used to determine K rating distribution clusters for the standardized rating matrix S', and the rating values ​​that deviate from the mean by more than two standard deviations in the clustering results are eliminated:

[0146] C i ={S′ i,k ∣|S′ i,k -μ i |≤2σ i};

[0147] Among them, C i is the effective scoring set of the i-th indicator, μ i is the mean score of the i-th indicator, σ i is the score standard deviation of the i-th indicator;

[0148] S45. Valid score set C based on cluster analysis i Calculate the average importance score M of each indicator i :

[0149]

[0150] Among them, |C i | is the number of ratings in the valid rating set;

[0151] S46. Calculate the standard deviation σ of the scoring results of each indicator i , used to judge the consistency of ratings:

[0152]

[0153] If σ i ≤T, T is the set consistency threshold, the scoring results are considered consistent; if σ i >T, the scoring result of indicator i will be fed back to the expert group and re-scored until the consistency requirement is met;

[0154] S47, average importance score M based on consistency verification i , calculate the final weight W of each evaluation index i ;

[0155] S48, the calculated weights of each evaluation index W i Applied to policy compliance index value P x , spatial adaptability index value S x and land use efficiency index F x The calculation of low-efficiency land three-dimensional index value matrix M is formed x :

[0156] M x = {W p ·P x ,W s ·S x ,W f ·F x};

[0157] Among them, W p , W s , W f are the weights of policy compliance, spatial adaptability and land use efficiency, respectively.

[0158] In this implementation, S6 specifically includes the following steps:

[0159] S61. Based on the three-dimensional index value matrix of inefficient land use, combined with the policy compliance index value, spatial adaptability index value and land use efficiency index value, the comprehensive evaluation index I of the xth plot is calculated by the index weight. x :

[0160]

[0161] Among them, I x is the comprehensive evaluation index of the xth plot, W d Represents the weight of each dimension, M x,d represents the index value of the xth plot in the dth dimension, where d = 1, 2, and 3 correspond to policy compliance, spatial adaptability, and land use efficiency, respectively;

[0162] S62. Setting the efficient land use threshold T h , low-efficiency land use threshold T l , the comprehensive evaluation index I of the xth plot x Compare with the threshold to identify the land use category L of the plot x :

[0163]

[0164] S63. For the identified inefficient land use, the inefficiency level D x To quantify:

[0165]

[0166] Among them, D x is the inefficiency level of the xth plot, and its value range is [0,1]. The larger the value, the higher the inefficiency level.

[0167] S64. Spatially associate the three-dimensional index value matrix, land use category and inefficiency process of the xth plot, and visualize them through the geographic information system platform:

[0168] Mark the low-efficiency land use categories of each plot on the map, distinguishing high-efficiency land, medium-efficiency land and low-efficiency land;

[0169] Visualize the spatial distribution of inefficient land use by using color gradients or legends to indicate the degree of inefficiency;

[0170] Output the three-dimensional indicator values ​​and comprehensive evaluation index corresponding to each block.

[0171] A computer-readable storage medium stores a computer program, which implements the steps of the embodiment method when executed by a processor.

[0172] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of an embodiment method when executing the computer program.

[0173] Embodiment 1:

[0174] This embodiment takes the identification and evaluation of typical urban inefficient land in a provincial capital city in the east as the application scenario. In recent years, with the acceleration of urbanization, the city's land use problems have become increasingly prominent, and a large number of inefficient lands have appeared in the town, such as idle land in old industrial areas, dysfunctional residential land in towns, scattered village land space, and lagging planning of historical land plots. These lands have low utilization efficiency and serious waste of resources, and scientific identification and evaluation methods are urgently needed to achieve the optimization and redevelopment of urban space.

[0175] In order to solve the problems of insufficient evaluation system, strong subjectivity in weight determination and lack of quantification and visualization of identification results in the prior art, a pilot project for low-efficiency land identification and evaluation was carried out in a certain area of ​​the city based on the urban low-efficiency land identification and evaluation method of the present invention.

[0176] Through collaboration with local natural resources management departments, statistics bureaus, housing and urban-rural development departments, and emergency management departments, relevant data in the region were collected, including:

[0177] District industrial block line data: including old industrial areas, enterprise layout lines, and spatial distribution data of idle industrial and mining enterprises;

[0178] Data on geological disaster risk points: 30 geological disaster risk points were identified, mainly concentrated in some village lands around towns;

[0179] Land use planning data: including control detailed planning data, overall planning style control range data, and urban renewal area data;

[0180] Tax data: covering the tax situation of 1,200 plots of land, of which 40% of the plots have an average tax per mu lower than the city average;

[0181] Housing quality data: Through housing safety inspections, 90 Class C and Class D dangerous buildings were found in residential areas;

[0182] Survey data on housing vacancy rate and usage: The survey shows that 40% of village land has houses that have been vacant for a long time, and the vacancy rate of some plots exceeds 50%.

[0183] After standardization and spatial analysis, the data is entered into the geographic information system platform, forming a multi-source data base for inefficient land use.

[0184] This embodiment establishes an evaluation index system for inefficient land use based on three dimensions: policy compliance, spatial adaptability, and land use efficiency:

[0185] Policy dimension: including compliance with industrial block lines, compliance with planning control requirements, and indicators of geological disaster hazards;

[0186] Spatial dimension: including rationality of land use layout, building quality and spatial adaptability;

[0187] Efficiency dimension: including tax output efficiency, housing vacancy rate, and building utilization rate.

[0188] The weight of each indicator is determined through the Delphi method combined with the intelligent clustering algorithm, and ultimately a scientific and reasonable weight distribution result is formed.

[0189] Based on the above weights and standardized data, the comprehensive evaluation index of each plot was calculated. A three-dimensional indicator value matrix was used in the specific calculation, and the comprehensive evaluation index of different plots was identified by combining the policy compliance, spatial adaptability and land use efficiency of the plot.

[0190] The results show:

[0191] Inefficient industrial and mining land: A total of 23 inefficient industrial and mining plots were identified in the region, with an average tax revenue of less than 20,000 yuan per mu per year, and the industrial block lines were inconsistent with the planned layout;

[0192] Inefficient urban land: 15 inefficient urban residential plots were identified, which had poor construction quality and a vacancy rate of more than 30%, including 11 Class C dangerous buildings;

[0193] Inefficient village land use: A total of 42 inefficient village land uses were identified, of which 26 plots had geological disaster risks and the average housing vacancy rate reached 47%;

[0194] Historical land use: 12 historical land plots with incomplete procedures or delayed planning were identified, and their spatial use was out of line with actual functions.

[0195] Through the GIS platform, the identification results of inefficient land are visualized, a spatial distribution map of inefficient land is generated, and the degree of inefficiency of each plot is marked with a color gradient, clearly showing the spatial distribution of different types of inefficient land. These results provide intuitive data support and decision-making basis for relevant government departments.

[0196] In order to verify the effectiveness of the method of the present invention, the method of the present invention was compared with the traditional method. The specific results are as follows: the accuracy of identifying inefficient land in the region reached 92.5%, which was 14.3 percentage points higher than the traditional manual survey method, and the recognition efficiency was significantly improved. It only took 10 days to complete the identification of the entire region, while the traditional method took 30 days. In addition, the multi-dimensional indicator system and weight optimization mechanism of the method of the present invention can comprehensively reflect the causes and current status of various types of inefficient land, and achieve accurate identification and quantitative evaluation.

[0197] Through the application of this embodiment, the present invention effectively solves the problems of insufficient systematicness in the identification and evaluation of low-efficiency urban land, strong subjectivity in weights, and lack of quantification and visualization of results, significantly improves the recognition accuracy and decision-making support capabilities, and provides a scientific basis and data support for urban space optimization and land redevelopment.

[0198] The present invention introduces a weight optimization mechanism that combines an intelligent clustering method with a multi-round Delphi method, performs cluster analysis on expert scoring data, eliminates scoring data that deviates greatly from the mean, ensures the accuracy and consistency of weight distribution, and dynamically adjusts indicator weights through scoring consistency verification and feedback iteration, overcoming the problems in the prior art that weight determination relies on subjective experience and the results are not scientific enough, thereby ensuring the rationality and objectivity of the evaluation indicator weights.

[0199] The present invention constructs a systematic indicator system through the three dimensions of policy compliance, spatial adaptability and land use efficiency, and quantifies multi-source data into evaluation index values ​​to form a complete three-dimensional indicator value matrix for inefficient land use. On this basis, the indicator weights are used to calculate the comprehensive evaluation index of each plot to scientifically evaluate the inefficiency of the plot. Compared with the existing technology that focuses on a single dimension or relies on qualitative analysis, the present invention realizes multi-dimensional quantitative evaluation and accurate identification of inefficient land categories, providing a clear decision-making basis for the optimal allocation of land resources.

[0200] The present invention uses a geographic information system platform to associate the comprehensive evaluation results of inefficient land with spatial data, generate a spatial distribution map of inefficient land, and display the inefficiency category and degree of inefficiency of each plot through color gradients and legends, intuitively reflecting the spatial distribution characteristics of inefficient land, helping relevant departments to quickly identify problem areas and formulate targeted redevelopment and optimization strategies, thus making up for the lack of intuitive operability of evaluation results in the prior art.

[0201] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for identifying and evaluating low-efficiency urban land, characterized in that: The steps include: S1. Construct an evaluation index system for inefficient land use, forming a multi-level structure of target layer, criterion layer and indicator layer. The criterion layer includes inefficient industrial and mining land, inefficient urban land, inefficient village land and historical land, and the indicator layer includes policy dimension, spatial dimension and efficiency dimension. S2. Collect multi-source data, including industrial block line data, geological disaster risk point data, land use planning data and housing use survey data, and enter the data into the geographic information system platform; S3. Calculate the index values ​​of various evaluation indicators, and quantify the policy compliance, spatial adaptability and land use efficiency evaluation indicators according to the calculation methods of each indicator; S4. Use the Delphi method to determine the weight of each evaluation indicator, conduct multiple rounds of comparative analysis based on expert opinions, and clarify the importance of each indicator; S5. Standardize the collected indicator data and use the efficacy coefficient method to make the indicator data dimensionless to eliminate the dimensional differences between different evaluation indicators; S6. Conduct comprehensive evaluation and identification of inefficient land use, calculate the comprehensive evaluation index of each plot based on the standardized indicator data and determined weights, and identify the types and degree of inefficient land use; S7. Use geographic information systems to spatially correlate the evaluation results and generate a visual map of inefficient land to show the spatial distribution and degree of inefficiency of inefficient land.

2. A method for identifying and evaluating low-efficiency urban land according to claim 1, characterized in that: The S1 specifically includes the following steps: S11. Determine the overall framework of the low-efficiency land evaluation index system. The target layer is the low-efficiency land identification and evaluation system. The criterion layer includes low-efficiency industrial and mining land, low-efficiency urban land, low-efficiency village land and historical land. The index layer includes policy dimension, spatial dimension and efficiency dimension. S12. Under the policy dimension, select indicators related to national policies and local planning for land use types at different criteria levels; S13, in the spatial dimension, analyze spatial adaptability based on multi-source data; S14. Construct a quantitative evaluation model under the efficiency dimension to identify the resource utilization efficiency of land.

3. A method for identifying and evaluating low-efficiency urban land according to claim 2, characterized in that: The indicators related to national policies and local planning include: Inefficient industrial and mining land, based on industrial block line data, determines areas that do not comply with industrial policies; Inefficient urban land: Based on the master plan and urban renewal data, identify urban land that does not meet planning control requirements; Inefficient village land use, combined with geological disaster risk point data, identifies village land with disaster risks but not yet managed; For historical land use, based on land approval and expropriation data, identify land use with incomplete procedures or prominent historical problems.

4. The method for identifying and evaluating low-efficiency urban land according to claim 2 is characterized in that: The spatial adaptability specifically includes: Inefficient industrial and mining land, obtain control planning data, and identify areas that are inconsistent with the current planning layout; Inefficient urban land use, through the analysis of spatial imbalance areas through urban renewal scope data and old community renovation data; Inefficient village land use, combining geological hazard distribution data and spatial layout data to identify areas with scattered spaces and hidden dangers; Historical land use, areas where spatial use has become invalid or layout is misplaced, as determined based on land survey data.

5. The method for identifying and evaluating low-efficiency urban land according to claim 2 is characterized in that: The resource utilization efficiency of the land specifically includes: Inefficient industrial and mining land, the output efficiency per unit area of ​​land is calculated based on tax data; Inefficient urban land use, combined with housing quality certification data to identify dangerous houses or areas with poor building quality; Inefficient village land use, calculate housing vacancy rate and idle rate based on housing use survey data; For historical land use, the utilization of inefficient resources is analyzed through land efficiency data.

6. The method for identifying and evaluating low-efficiency urban land according to claim 1 is characterized in that: The S3 specifically includes the following steps: S31. Collect multi-source data required for low-efficiency land evaluation, the multi-source data including: Industrial block line data of the area is used to identify the indicator values ​​of inefficient industrial and mining land in policy compliance; Data on geological disaster risk points are used to quantify the risk indicators of inefficient village land and historical land under the policy dimension; Land use planning data, including regulatory detailed planning data, master planning data, and urban renewal data, are used to analyze the index values ​​of inefficient urban land and inefficient village land in spatial adaptability; Housing use survey data, including housing quality, vacancy rate and use efficiency data, is used to calculate the resource utilization efficiency of each plot in the efficiency dimension; S32. Based on the collected multi-source data and combined with the calculation method of the inefficient land use evaluation index, various indicators under the three dimensions of policy compliance, spatial adaptability and land use efficiency are quantified: Policy compliance, based on the industrial block line data and geological disaster risk point data of the area, calculate the policy compliance index value P x : Among them, C i is the policy compliance judgment value of the i-th plot, R i is the land parcel policy correction coefficient, n is the number of land parcels to be evaluated; Spatial adaptability: Based on land use planning data, calculate the spatial adaptability index value S x : Among them, W j is the weight of the j-th spatial index, A j is the actual value of the j-th spatial index, A max and A min are the maximum and minimum values ​​of the spatial index, respectively, and m is the number of spatial indexes; Land efficiency: Based on the housing use survey data, calculate the land efficiency index value F x : Among them, Q k is the resource utilization rate of the kth plot, E k is the economic output value of a unit plot, and p is the total number of plots to be evaluated.

7. The method for identifying and evaluating low-efficiency urban land according to claim 1 is characterized in that: The S4 specifically comprises the following steps: S41. Select multiple experts in the fields of land planning, economic management and geographic information systems to form a multidisciplinary expert group E = {e1, e2, …, e n },1≤k≤n, where e k represents the kth expert, n is the number of experts; S42. Form an indicator scoring matrix template based on specific indicators in the policy dimension, spatial dimension and efficiency dimension S i,k , where i is the index number, S i,k Score the importance of the i-th indicator by the k-th expert, with the score range R = [1,5]; S43, summarizing the experts' importance scores for each indicator to form an initial expert scoring matrix S: Among them, m is the number of evaluation indicators, n is the number of experts; S44, the initial rating matrix S is standardized to obtain a standardized rating matrix S', and K-means clustering algorithm is used to determine K rating distribution clusters for the standardized rating matrix S', and the rating values ​​that deviate from the mean by more than two standard deviations in the clustering results are eliminated: C i ={S′ i,k ∣|S′ i,k -m i |≤2σ i }; Among them, C i is the effective scoring set of the i-th indicator, μ i is the mean score of the i-th indicator, σ i is the score standard deviation of the i-th indicator; S45. Valid score set C based on cluster analysis i Calculate the average importance score M of each indicator i : Among them, |C i | is the number of ratings in the valid rating set; S46. Calculate the standard deviation σ of the scoring results of each indicator i , used to judge the consistency of ratings: If σ i ≤T, T is the set consistency threshold, the scoring results are considered consistent; if σ i >T, the scoring result of indicator i will be fed back to the expert group and re-scored until the consistency requirement is met; S47, average importance score M based on consistency verification i , calculate the final weight W of each evaluation index i ; S48, the calculated weights of each evaluation index W i Applied to policy compliance index value P x , spatial adaptability index value S x and land use efficiency index F x The calculation of low-efficiency land three-dimensional index value matrix M is formed x : M x ={W p ·P x ,W s ·S x ,W f ·F x }; Among them, W p , W s , W f are the weights of policy compliance, spatial adaptability and land use efficiency, respectively.

8. The method for identifying and evaluating low-efficiency urban land according to claim 1 is characterized in that: The S6 specifically comprises the following steps: S61. Based on the three-dimensional index value matrix of inefficient land use, combined with the policy compliance index value, spatial adaptability index value and land use efficiency index value, the comprehensive evaluation index I of the xth plot is calculated by the index weight. x : Among them, I x is the comprehensive evaluation index of the xth plot, W d Represents the weight of each dimension, M x,d represents the index value of the xth plot in the dth dimension, where d = 1, 2, and 3 correspond to policy compliance, spatial adaptability, and land use efficiency, respectively; S62. Setting the efficient land use threshold T h , low-efficiency land use threshold T l , the comprehensive evaluation index I of the xth plot x Compare with the threshold to identify the land use category L of the plot x : S63. For the identified inefficient land use, the inefficiency level D x To quantify: Among them, D x is the inefficiency level of the xth plot, and its value range is [0,1]. The larger the value, the higher the inefficiency level. S64. Spatially associate the three-dimensional index value matrix, land use category and inefficiency process of the xth plot, and visualize them through the geographic information system platform: Mark the low-efficiency land use categories of each plot on the map, distinguishing high-efficiency land, medium-efficiency land and low-efficiency land; Visualize the spatial distribution of inefficient land use by using color gradients or legends to indicate the degree of inefficiency; Output the three-dimensional indicator values ​​and comprehensive evaluation index corresponding to each block.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 8 when executed by a processor.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 9 are implemented.

Images