A method for urban renewal resource evaluation, identification and spatial division

Abstract

The application provides a town renewal resource evaluation identification and spatial division method, and belongs to the technical field of urban and rural planning and construction. The method takes a geographic information system as a spatial analysis basis, takes a hierarchical index system and an objective weighting method as evaluation support, and combines multi-source data collection, gridding analysis and spatial statistical methods to form a complete process from data import, grid establishment, type identification, index evaluation to spatial unit division. The application forms a complete technical process from multi-source data collection, index weighting, renewal demand identification to spatial unit division through objective, automatic and parameterized technical means. The method helps to improve the scientificity, stability and operability of town renewal resource evaluation identification, can provide technical support for planning compilation and implementation management of different types of town renewal areas, and has good novelty, creativity and industrial application value.

Description

Technical Field

[0001] This invention belongs to the field of urban and rural planning and construction technology, specifically relating to a method for evaluating, identifying, and spatially dividing urban renewal resources. This method combines Geographic Information System (GIS), Analytic Hierarchy Process (AHP), big data analysis, and spatial statistics techniques to assess the renewal needs of existing urban space, identify potential renewal resources, and perform reasonable type classification and spatial division. Background Technology

[0002] Modern urban and rural planning encompasses a wide range of aspects, including spatial layout, land use, infrastructure development, environmental protection, and socio-economic coordination. With the acceleration of urbanization, especially as land resources become increasingly scarce and the conflict between population and land becomes more prominent, the traditional incremental expansion model is no longer sufficient to meet the requirements of sustainable development. Instead, there is a growing emphasis on optimizing and renovating existing spaces. This necessitates a shift in planning methods from macro-strategic guidance to refined management, integrating digital technologies to enhance the scientific rigor and efficiency of decision-making.

[0003] In the field of urban renewal planning and redevelopment, the evaluation, identification, and spatial classification of potential renewal resources are crucial steps and essential prerequisites. Evaluation and identification aim to quantify the renewal potential of built-up areas through a multi-dimensional indicator system, including factors such as the building itself, the built environment, economic benefits, property rights, and policy impacts. Classification, based on the evaluation results, categorizes the renewal resources of built-up areas into different renewal types, ensuring the targeted nature and operability of renewal actions.

[0004] Currently, the evaluation, identification, classification, and spatial division of urban renewal resources mainly rely on traditional planning methods such as indicator scoring, expert consultation, and simple GIS overlay analysis. These methods have supported the renewal and transformation of existing urban spaces to a certain extent, but they have certain significant limitations: the evaluation indicator system is often singular or has insufficient indicator coverage, focusing on building age or economic indicators while ignoring multi-dimensional comprehensive evaluation, resulting in poor scientific validity of the identification results and difficulty in implementation.

[0005] Specifically, existing scoring methods typically select indicators such as building age, population density, and output per unit area. However, significant differences exist in data caliber, indicator standards, and scoring rules across different regions, making direct comparison of evaluation results between different towns difficult. Some models and methods can be used for spatial boundary delineation or resource identification, but they do not adequately consider factors such as population activity agglomeration, accessibility to public services, protection of historical and cultural resources, complexity of property rights, and the wishes of rights holders. While methods such as spatial autocorrelation and kernel density analysis have been applied in relevant research, they often lack a complete application path in actual planning, integrating them with indicator systems, renewal types, and implementation unit divisions. Furthermore, external conditions such as policy guidance, recent project plans, and the impact of major public welfare or regional projects are often weakened in existing methods, affecting the feasibility and operability of the resource identification results.

[0006] In addition, some related technical solutions utilize statistical data, remote sensing data, patent data, kernel density analysis, or spatial models for specific object identification. These methods provide some reference for urban renewal spatial identification and resource evaluation. However, their technical focus is mostly concentrated on single spatial objects, single evaluation targets, or specific data types, and they are not yet fully applicable to the comprehensive evaluation and identification of multiple types of objects in urban renewal resources, such as old residential land, inefficient industrial land, inefficient commercial land, abandoned public land, and other land uses.

[0007] In summary, existing technologies suffer from the following shortcomings: First, the evaluation index system has limited coverage, making it difficult to comprehensively reflect the multi-dimensional factors of urban renewal resources, including the building itself, built environment, economic benefits, property rights, and policy impacts. Second, the determination of weights and classification still relies heavily on manual experience, and the stability and repeatability of evaluation results need improvement. Third, data sources are relatively scattered, and the spatial placement and standardization of indicator data are insufficient, making it difficult to support refined grid-based evaluation. Fourth, there is insufficient connection between the identification of renewal resources, type classification, and spatial division, making it difficult to directly transform them into subsequent renewal planning and implementation units. Based on these problems, it is necessary to propose a method for the evaluation, identification, and spatial division of urban renewal resources that can integrate multi-source data, objective weighting, spatial statistics, and parametric division. Summary of the Invention

[0008] The purpose of this invention is to address the problems existing in the evaluation, identification, type classification, and spatial division of urban renewal resources in the prior art, such as incomplete indicator coverage, reliance on manual experience in weight determination, insufficient data spatialization, and imprecise spatial type classification. This invention provides a method for evaluating, identifying, and spatially dividing urban renewal resources. By constructing a five-dimensional indicator system, a hierarchical indicator system with objective weighting, and big data scoring, this invention achieves standardized identification, type classification, and reasonable spatial division of renewal resources.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A method for evaluating, identifying, and spatially partitioning urban renewal resources includes the following steps:

[0011] S1: Import the urban renewal research area scope into the geographic information system platform;

[0012] S2: Based on the study area's scope, built environment density, and evaluation accuracy requirements, establish a rule-based analysis spatial grid within the study area;

[0013] S3: Based on the types of existing resources involved in urban renewal, the renewal objects are divided into five categories: old residential land, inefficient industrial land, inefficient commercial land, abandoned public land and other land.

[0014] S4: Construct evaluation indicators based on the building itself, the built environment, economic benefits, property rights, and policy impacts;

[0015] S5: Obtain the original data corresponding to the indicators based on multi-source data, and calculate the weight of each indicator according to the information content, dispersion and differences between indicators;

[0016] S6: Based on the formal evaluation data corresponding to the indicators obtained from multi-source data, the data is spatialized into the established analysis space grid to form a grid scoring matrix for comprehensive update requirement calculation;

[0017] S7: Calculate the comprehensive renewal demand score for each spatial grid cell, and combine the renewal demand level, spatial contiguousness, and dominant driving factors to determine high-demand areas and their renewal types.

[0018] The urban renewal resource evaluation, identification, and spatial division method proposed in this invention addresses the needs of urban stock space renewal. It constructs a multi-dimensional evaluation system encompassing building structure, built environment, economic benefits, property rights, and policy impacts. Combining geographic information systems, multi-source data processing, objective weighting, and spatial statistical analysis methods, it achieves quantitative evaluation and identification of renewal resources, analysis of renewal demand intensity, type classification, and spatial unit division. This invention aims to improve the scientific rigor, traceability, and operability of urban renewal resource evaluation and identification, providing technical support for the identification, classification, and spatial division of different types of renewal resources, such as old residential land, inefficient industrial land, inefficient commercial land, abandoned public land, and other land uses. It also provides a basis for urban renewal planning, project reserves, and implementation management.

[0019] Compared with existing technologies, this invention introduces multi-source data collection, objective weighting, spatial clustering, and parameterized partitioning techniques in the process of evaluating and identifying urban renewal resources, classifying types, and dividing spatial units. To a certain extent, it improves the problems of strong subjectivity, rough identification process, and insufficient implementation of partitioning results in traditional methods. It has good social, economic, technical, and management application value and is ready to be promoted and applied in different types of urban renewal areas.

[0020] (1) Objectivity and scientific rigor: Traditional techniques typically rely on subjective weighting by expert scoring in AHP, fixed threshold judgment, and the "maximum membership degree" assignment method, which easily leads to problems such as the influence of human factors on weight setting and insufficient stability of identification results. This invention adopts a composite objective weighting method combining entropy weighting, coefficient of variation, and CRITIC methods, and combines multi-source heterogeneous data spatial placement, natural breakpoint classification, and dominant driving factor assignment methods, so that the calculation and update requirements of indicator weights are mainly based on objective data, reducing the impact of additional manual adjustments on the results. Compared with the traditional AHP method, this method does not rely on pairwise comparison matrices and consistency ratio (CR) tests by experts, which helps to improve the stability, repeatability, and traceability of the calculation process of indicator weights.

[0021] (2) Regarding identification efficiency and result stability: Traditional resource identification methods often require extensive manual interpretation, on-site comparison, and repeated corrections, resulting in a long work cycle and potential discrepancies between different operators. This invention achieves continuous processing from indicator data input to the identification of areas with high update needs through steps such as gridded analysis, standardized scoring, automatic grading, and GIS spatial aggregation. It can quickly complete comprehensive scoring, grading, and spatial contiguous identification within the research scope. The use of the dominant driving factor method for type classification also helps reduce judgment conflicts between different types of update objects and improves the interpretability of the results.

[0022] (3) Social benefits: This invention incorporates livelihood-related indicators such as residential environment evaluation, property owners' willingness to renovate, and willingness to improve the public environment into the evaluation system, so that old residential land, areas with insufficient public service facilities, and areas with low public environmental quality can be fully reflected in the evaluation process. This method helps to avoid the problem of traditional renewal focusing on economic benefits and neglecting the actual needs of residents, and provides technical support for improving the living environment, responding to residents' demands, and improving the level of public services.

[0023] (4) Economic benefits: Resource allocation is more efficient and investment returns are more predictable. Through objective identification and parameterized unit division, this invention can assist planning departments in more accurately judging the renewal intensity, spatial scope, and implementation priority of different renewal resources, reducing blind delineation and extensive renewal. For inefficient industrial land, inefficient commercial land, and other types of land, inefficient spaces can be identified by combining indicators such as output per unit area, land use intensity, building utilization rate, and tax contribution, providing a basis for subsequent industrial adjustment, functional optimization, and resource allocation, thereby helping to improve land use efficiency and renewal implementation benefits.

[0024] (5) Technical benefits: A complete and interconnected process with parameterized adaptation capabilities is formed. This invention integrates the steps of "multi-source data acquisition - composite objective weighting - natural breakpoint classification - dominant driving attribution - parameterized spatial unit division - automatic output of results" into an interconnected technical process, forming a relatively complete method for evaluating and identifying urban renewal resources. The system can adjust the grid scale, contiguous area, distance tolerance, and type attribution rules according to different town sizes, area characteristics, and local guidelines, and has good parameter adaptation and extended application capabilities. It can be used for evaluation and identification work in urban renewal areas of different sizes and types.

[0025] (6) Institutional benefits: Supporting refined governance and the implementation of statutory planning. This invention can automatically generate heat maps of urban renewal needs, distribution maps of high-demand areas, type classification result tables, lists of leading driving indicators, and results of urban renewal unit division, providing data support for planning, project selection, implementation scheduling, and dynamic evaluation. The relevant results can be further organized into maps, tables, coordinates, and explanatory materials according to local urban renewal unit delineation guidelines, facilitating their inclusion in planning management and project implementation processes, and helping to improve the level of refined, standardized, and dynamic management of urban renewal work. Attached Figure Description

[0026] Figure 1 The flowchart shows the method for evaluating, identifying, and spatially dividing urban renewal resources, with steps S1 to S8 shown.

[0027] Figure 2 This is a map showing the classification of urban renewal resources.

[0028] Figure 3 A tree-structured indicator system for evaluating urban renewal resources is presented, displaying five dimensions.

[0029] Figure 4 A schematic diagram showing the residential land renewal needs and spatial unit division in the Hexia Ancient Town area, with a heat map displaying the fractional distribution. Detailed Implementation

[0030] Please see Figure 1As shown, this invention proposes a method for evaluating, identifying, and spatially dividing urban renewal resources by combining geographic information systems (GIS), a hierarchical indicator system, objective weighting methods, and multi-source data analysis. This method uses GIS as the basis for spatial analysis, a hierarchical indicator system as the evaluation framework, and combines objective weighting methods such as entropy weighting, coefficient of variation, and CRITIC methods, as well as multi-source data scoring methods, to comprehensively evaluate, classify, and spatially divide urban renewal resources in existing urban spaces.

[0031] This method comprises eight consecutive steps, S1-S8, which are sequentially linked to form a technical workflow from spatial data import, spatial grid establishment, object classification, indicator system construction, weight calculation, data scoring, comprehensive identification, to spatial unit division. This workflow can improve the stability, traceability, and operability of urban renewal resource identification results.

[0032] S1: Import the scope of urban renewal areas.

[0033] The urban renewal study area is imported into a geographic information system (GIS) platform, which can be ArcGIS, QGIS, or other software with spatial data processing capabilities. Imported data may include vector layers such as administrative boundaries, planning scope, renewal area boundaries, plot boundaries, road and water system data, and building outlines. Relevant data can originate from existing spatial data from departments such as natural resources, housing and urban-rural development, and planning management, or can be compiled by combining publicly available GIS platform data, planning maps, surveying and mapping data, and current status survey data.

[0034] In terms of static relationships, this step is used to establish a unified spatial boundary. The imported vector layers serve as a spatial reference framework for subsequent analysis, enabling overlay analysis of data such as grids, land use, buildings, public facilities, transportation, municipal affairs, and population under a unified coordinate system. This coordinate system can be the China Geodetic Coordinate System 2000 (CGCS2000) or other coordinate systems required by local planning management departments. In terms of dynamic relationships, coordinate transformation, projection unification, boundary verification, topology checks, and attribute field organization can be performed during data import to reduce problems such as layer misalignment, boundary overlap, spatial missing data, or attribute inconsistencies.

[0035] The effect of this step is to define a precise study area, prevent data misalignment, and facilitate seamless integration with external datasets. It helps reduce spatial data misalignment and manual demarcation errors, and improves the reliability of subsequent spatial overlay analysis.

[0036] S2: Establish the analysis space grid.

[0037] Based on the study area's scope, built-up environment density, and evaluation accuracy requirements, a rule-based spatial grid is established within the study area. For areas requiring refined identification, such as historical districts and old residential areas, a 200m×200m grid can be used as the basic analysis unit; in areas with concentrated local update needs or high data accuracy, it can be refined to a 100m×100m grid as needed. In terms of static relationships, each spatial grid unit serves as an independent spatial evaluation unit, forming a spatial correspondence with layers such as administrative boundaries, land parcels, buildings, public service facilities, and municipal transportation facilities. In terms of dynamic relationships, the system uses GIS grid generation, clipping, and spatial overlay tools to aggregate various indicator data into the corresponding grid.

[0038] S3: Determine the type of town renewal object.

[0039] Based on the types of existing resources involved in urban renewal, the renewal targets are divided into five categories: old residential land, inefficient industrial land, inefficient commercial land, abandoned public land, and other land. Figure 2 (As shown). Among them, old residential land includes old communities, shantytowns, urban villages, dilapidated houses and renovation and upgrading areas; inefficient industrial land includes various development zones, industrial zones, mining areas and warehousing and logistics areas; inefficient commercial land includes business offices, traditional commerce, professional markets, exhibition land, etc.; abandoned public land includes schools, hospitals, grain depots, cinemas, stadiums and administrative office land, etc.; other land includes transportation infrastructure land, old parks, squares, waterfront areas and other open spaces, as well as historical and cultural blocks, historical style areas, historical sites and other historical resource areas.

[0040] The above five categories together constitute the urban renewal resource classification system, which can be used to cover different types of existing spaces within the study area. During the classification process, based on information such as land use nature, building status, functional usage status, historical and cultural attributes, public service attributes, and spatial location, the system uses a combination of GIS attribute query, field assignment, spatial overlay, and manual verification to label various types of land use, building, or public space resources to corresponding grids, plots, or building objects, forming a "spatial unit-resource type" correspondence.

[0041] This step identifies several potential urban renewal resource units and categorizes them based on land use, current function, and spatial characteristics, providing a data foundation for subsequent comprehensive evaluation, dominant type attribution, and spatial unit division. By identifying these categories, a single evaluation method can be avoided for different urban renewal resources, allowing old residential land, inefficient industrial land, inefficient commercial land, abandoned public land, and other land uses to be evaluated according to their respective indicators and spatial analysis processes.

[0042] S4: Construct an evaluation index system.

[0043] Five dimensions, 13 element layers, and 40 indicator layers were constructed, as detailed below:

[0044] Five dimensions: building itself, built environment, economic benefits, property rights and policy impact ( Figure 3 (As shown).

[0045] Thirteen element layers: design service life, building quality, building structure, living standards, historical and cultural resources, public service facilities, municipal transportation facilities, ecological environment, industrial development, economic output, ownership status, property rights holders' willingness to renew, local government and other factors.

[0046] 40 indicator layers: building usage time, building quality, architectural style, structural evaluation level, per capita residential building area, housing unit rate, resident population density, resources of historical and cultural blocks and historical districts, protection and utilization of historical and cultural blocks and historical districts, protection of historical buildings, supporting facilities within a 5-minute living circle, supporting facilities within a 10-minute living circle, supporting facilities within a 15-minute living circle, land use of schools / hospitals / grain depots and administrative offices, water / electricity / gas / heating facilities, implementation of rainwater and sewage separation and undergrounding of utility poles and cables, distance to public transportation stations, parking facilities, road system construction, and green space and landscape construction. The following factors are considered: the current situation, the development of waterfront spaces, the construction of supporting facilities for plazas and parks, the safety and environmental compliance of industrial types, the matching degree between industrial types and the market, the advancement of technological processes, the output per unit area of ​​industrial land, the intensity of industrial land use, the utilization rate of commercial / business buildings, the tax contribution of commercial / business land, the retail sales of consumer goods, the contract value of service trade, the clarity of property rights, the evaluation of satisfaction with the living environment, the willingness to renew industrial land, the willingness to improve the public environment, recent project plans, financial or policy support, the latest planning guidance, the policy guidance of higher-level governments, and the impact of major public welfare and regional projects.

[0047] In the building itself dimension, the main assessments include the building's service life, quality, appearance, and structural condition; in the built environment dimension, the assessments focus on living standards, historical and cultural resources, public service facilities, municipal transportation facilities, and ecological environment quality; in the economic benefits dimension, the assessments evaluate industrial development and economic output, including industry type, market matching degree, and economic benefits of industrial and commercial land use; in the property rights dimension, the assessments focus on the clarity of property rights and the willingness of property owners to improve the living environment, industrial land renewal, and public environment; and in the policy impact dimension, the assessments consider the policy guidance of the local and higher-level governments, financial and policy support, the latest planning guidance, and the impact of major public welfare or regional projects.

[0048] The indicator system can be appropriately adjusted based on data availability, local guidelines, and the actual conditions of each area. For example, in areas rich in historical and cultural resources, indicators such as the protection and utilization of historical and cultural resources, the preservation of historical buildings, and the traditional street and alley layout can be strengthened; in areas with a high concentration of inefficient industrial land, indicators such as output per unit area, land use intensity, and the suitability of industrial types can be strengthened. This system helps to incorporate multi-dimensional factors such as architecture, environment, economy, property rights, and policy into a unified evaluation framework.

[0049] S5: Calculate the weight of the indicator.

[0050] This invention uses a hierarchical index system as the weight calculation framework, and on this basis, employs objective weighting methods to calculate index weights. The objective weighting methods mainly include the entropy weight method, the coefficient of variation method, and the CRITIC method.

[0051] Specifically, the raw data of 40 indicator layers are subjected to forward / reverse standardization, and then the weight of each indicator is calculated based on the information content, dispersion, and differences between indicators.

[0052] First, for continuous quantitative indicators, the system uses either forward standardization or reverse standardization methods to transform them based on the relationship between the original value of the indicator and the update requirements.

[0053] If a larger original value for a certain indicator indicates a stronger need for updates, then a positive standardization formula is used:

[0054]

[0055] If a smaller original value for a certain indicator indicates a stronger need for updates, then the inverse standardization formula is used:

[0056]

[0057] in, This represents the original value of the i-th spatial grid cell on the j-th index. and These represent the maximum and minimum values ​​of the j-th index in all spatial grid cells, respectively. This represents the standardized value of the i-th spatial grid cell on the j-th index.

[0058] After completing the forward / reverse standardization of the indicators, a standardized sample matrix is ​​formed. :

[0059]

[0060] in, The number of sample space units involved in the weight calculation. This represents the number of indicators. This matrix is ​​used to calculate indicator weights and is not directly used as the scoring matrix for subsequent comprehensive update requirements.

[0061] Then, calculate the first one respectively. Entropy weights of each indicator Coefficient of variation weight and CRITIC weight Then, the final weights are obtained by using a linear combination method. The specific steps are as follows:

[0062] (1) Entropy weight calculate:

[0063] For the The first indicator is calculated. The proportion of each spatial grid cell under this index:

[0064]

[0065] when When necessary, the indicator can be corrected to a zero value, or the indicator weight can be reset to 0 and then renormalized.

[0066] Then calculate the first... Information entropy of each indicator:

[0067]

[0068] in:

[0069] when season .

[0070] Further calculation of the first Coefficient of variation for each indicator:

[0071]

[0072] Then the first Entropy weights of each indicator for:

[0073]

[0074] (2) Coefficient of variation weight calculate:

[0075] For the Each indicator is used to calculate its mean. and standard deviation And obtain the coefficient of variation:

[0076]

[0077] when When necessary, a very small positive number can be used for correction, or the coefficient of variation of the index can be set to 0 before being included in normalization.

[0078] Then the first The coefficient of variation weight of each indicator for:

[0079]

[0080] (3) CRITIC weight calculate:

[0081] The CRITIC method considers both the volatility of the indicator itself and the conflict between indicators. For the first... Let there be _ indicators, and their standard deviation be _. , No. The first indicator and the first The correlation coefficient between the indicators is Then the first The information content of each indicator is:

[0082]

[0083] No. CRITIC weights of each indicator for:

[0084]

[0085] (4) Final combination weights calculate:

[0086] After obtaining the entropy weights respectively Coefficient of variation weight and CRITIC weight Then, the first step is obtained by using a linear combination method. The final weight of each indicator :

[0087]

[0088] in, For the first The final weight of each indicator, , , Let be the combination coefficients, and satisfy:

[0089]

[0090] The output of this step is the final weights corresponding to the 40 indicators. Weights are calculated using a standardized sample matrix. It is used only as an intermediate variable in the weight calculation process and is not used as a direct input for subsequent comprehensive evaluation.

[0091] S6: Obtain indicator data and score it based on big data.

[0092] After calculating the indicator weights in S5, this step focuses on the specific study area and the specific analysis space grid. Based on multi-source data, it acquires the formal evaluation data corresponding to 40 indicators and spatializes the data into the analysis space grid established in S2, forming a grid scoring matrix for the comprehensive update requirement calculation in S7. .

[0093] in, This represents the standardized score (0-10) of the i-th spatial grid cell on the j-th index. This matrix is ​​used in the weight calculation in S5 with the standardized sample matrix Z. They have the same structure but different uses: Used to calculate indicator weights, Used to calculate the comprehensive update requirement score for a specific grid.

[0094] Data for 40 indicators were acquired from multiple sources and spatialized into an analysis spatial grid established by S2. Data sources may include statistical yearbooks, government thematic databases, planning documents, current status surveys, remote sensing imagery, POI data, public service facility data, transportation facility data, hotline work orders, online and offline questionnaires, etc. Different regions can select appropriate data sources based on their data foundation and management conditions.

[0095] The data acquisition and processing process includes data collection, data cleaning, field matching, spatial mapping, missing value handling, and standardized scoring. For departments or platforms with data interfaces, data can be acquired through Application Programming Interfaces (APIs) or scripts; for data without interfaces, it can be supplemented through table import, manual verification, or current status surveys. This establishes the correspondence between indicator data and spatial units.

[0096] The scoring uses a standardized 0-10 scale. It should be noted that the 0-10 scale in this invention does not directly represent the quality of the indicator's original state, but rather indicates the strength of its contribution to urban renewal needs, the urgency of renovation, or the necessity of improvement. In other words, after standardization, each indicator is uniformly converted into a "renewal demand-oriented" score. A higher score indicates a stronger renewal demand reflected by the indicator; a lower score indicates a weaker renewal demand reflected by the indicator. The standardization assignment rules for the 40 indicators are shown in Table 1.

[0097] Table 1: Standardized Assignment Rules for Urban Renewal Resource Evaluation Indicators (0-10 points)

[0098] Indicator Number Indicator Name Original data or judgment criteria Assignment direction Recommended assignment rules C1 Building usage time Building construction year, completion year, property registration information The longer you use it, the higher the rating. The building can be categorized or standardized based on its age. For example, 0-10 years can be 0-2 points, 10-20 years 2-4 points, 20-30 years 4-6 points, 30-40 years 6-8 points, and over 40 years 8-10 points. C2 Building quality Building safety inspection, current condition survey, and building integrity level assessment The worse the building quality, the higher the score. The scores can be assigned as "intact, mostly intact, moderately damaged, severely damaged, and dangerous", corresponding to 0-2 points, 2-4 points, 4-6 points, 6-8 points, and 8-10 points respectively. C3 Architectural style Architectural facade style, harmony with historical environment, and current status survey The more discordant the appearance, the higher the score. 0-2 points for being in harmony with the overall style of the area and well preserved; 2-4 points for being partially incompatible; 4-6 points for being generally incompatible; 6-8 points for being obviously incompatible or damaging the streetscape; and 8-10 points for seriously affecting the historical environment or overall style. C4 Structural evaluation level Building structural safety assessment and dangerous building inspection data The higher the structural risk, the higher the score. Structural safety levels can be categorized as follows: 0-2 points for structural safety and no reinforcement required; 2-4 points for minor hazards; 4-6 points for general hazards; 6-8 points for major hazards; and 8-10 points for dangerous hazards or those requiring immediate reinforcement or removal. C5 per capita residential building area Population data, residential building area, household survey The lower the area per capita, the higher the score. Reverse standardization is adopted. The lower the per capita residential building area, the higher the degree of living overcrowding, and the higher the score; alternatively, it can be divided according to the local average level, with 0-2 points for significantly above the average, 4-6 points for close to the average, and 8-10 points for significantly below the average. C6 Housing completeness rate Housing survey, real estate data, housing and construction information The lower the completion rate, the higher the score. Reverse standardization is adopted. High housing unit completion rate and complete kitchen and bathroom functions are scored 0-2 points; basic housing unit is scored 2-4 points; partially non-housing unit is scored 4-6 points; a large number of non-housing units are scored 6-8 points; and extremely low housing unit completion rate is scored 8-10 points. C7 Resident population density Resident population, residential land area, grid population data The higher the population density, the higher the score. Positive standardization was adopted. The higher the population density, the greater the spatial carrying capacity pressure and the stronger the demand for renewal; the population density quantile of the study area can be divided into 0-2 points, 2-4 points, 4-6 points, 6-8 points, and 8-10 points. C8 Historical and cultural blocks and historical districts resources Information on historical and cultural blocks, historical districts, historical sites, cultural relics protection units, traditional streets and alleys, etc. The stronger the need for resource protection and revitalization, the higher the score. 0-2 points for no relevant resources or no obvious need for protection and renewal; 2-4 points for a small amount of general historical resources; 4-6 points for some historical and cultural resources that need environmental improvement; 6-8 points for relatively concentrated historical resources that need protection and revitalization; 8-10 points for being located in an important historical and cultural block, historical district, or area with prominent protection and renewal tasks. C9 Protection and utilization of historical and cultural blocks and historical districts Protection planning, on-site survey, and historical resource utilization status The less protection and utilization, the higher the score. Good protection and utilization: 0-2 points; Basic protection but insufficient activation: 2-4 points; Partially idle or inefficiently utilized: 4-6 points; Insufficient protection and obvious damage: 6-8 points; Seriously insufficient protection and utilization or in urgent need of repair and activation: 8-10 points. C10 Historical building preservation status List of historical buildings, building preservation status, and repair records The worse the protection status, the higher the score. 0-2 points for well-preserved and normally maintained; 2-4 points for basically intact; 4-6 points for partial damage; 6-8 points for severely damaged or idle; 8-10 points for serious safety hazards or urgent need for repair. C11 5-minute living circle amenities Community facility coverage, service radius analysis, and POI data The less complete the supporting facilities, the higher the score. Reverse standardization is adopted. Adequate coverage of facilities within a 5-minute living circle is scored as follows: 0-2 points; basically satisfactory is scored as 2-4 points; some minor shortcomings are scored as 4-6 points; significant shortcomings are scored as 6-8 points; and serious deficiencies are scored as 8-10 points. C12 Amenities within a 10-minute radius Community facility coverage, service radius analysis, and POI data The less complete the supporting facilities, the higher the score. Reverse standardization is adopted. The more inadequate the public services within a 10-minute living circle, the higher the score; it can be divided into five levels based on facility coverage, number of facilities, or service accessibility. C13 Amenities within a 15-minute radius Community facility coverage, service radius analysis, and POI data The less complete the supporting facilities, the higher the score. Reverse standardization is adopted. The weaker the comprehensive service capability of the 15-minute living circle, the higher the score; high coverage is a low score, and low coverage or lack of facilities is a high score. C14 Land use status for schools / hospitals / grain depots and administrative offices Current status of public service facility land, vacancy rate, and building usage status The higher the score, the greater the need for idle or inefficient features or functional adjustments. Normal use and stable function: 0-2 points; local inefficiency: 2-4 points; average utilization efficiency or mismatched function: 4-6 points; significant inefficiency when idle: 6-8 points; long-term idleness, abandonment or function urgently needs adjustment: 8-10 points. C15 Water / electricity / gas / heating facilities Municipal pipeline data, facility coverage, and on-site investigation The less adequate the supporting facilities, the higher the score. 0-2 points for well-equipped and well-operated facilities; 2-4 points for basically complete facilities; 4-6 points for facilities with some shortcomings; 6-8 points for facilities that are aging or have insufficient coverage; and 8-10 points for facilities that are seriously lacking or have prominent operational risks. C16 Status of implementation of rainwater and sewage separation and undergrounding of utility poles and cables Municipal renovation data, pipeline survey, on-site investigation The lower the implementation level, the higher the score. Completed and running well: 0-2 points; basically completed: 2-4 points; partially completed: 4-6 points; insufficient implementation: 6-8 points; basically not implemented or with prominent problems: 8-10 points. C17 Distance from public transportation station Bus stops, subway stations, walking network distance The farther the distance, the higher the score. Positive standardization is adopted. The farther away from a public transportation stop, the worse the accessibility and the higher the score; alternatively, it can be categorized by walking distance, with closer distances receiving lower scores and farther distances receiving higher scores. C18 Parking facilities Survey on the number of parking spaces, parking demand, and parking conflicts The less adequate the supporting facilities, the higher the score. Parking supply and demand are basically balanced, scoring 0-2 points; slightly insufficient, scoring 2-4 points; generally insufficient, scoring 4-6 points; obviously insufficient, scoring 6-8 points; and parking conflicts are prominent and seriously affect the operation of the area, scoring 8-10 points. C19 Road system construction status Road density, road network connectivity, road width, and dead-end roads The less developed the road system, the higher the score. A well-developed road network with smooth traffic is rated 0-2 points; a basically well-developed road network is rated 2-4 points; a road network with local bottlenecks is rated 4-6 points; a road network that is discontinuous or has insufficient capacity is rated 6-8 points; and a road system with significant defects is rated 8-10 points. C20 Green space landscape construction status Green space ratio, service area of ​​park green space, and landscape quality survey The less green space there is, the higher the score. Sufficient green space and good landscape quality: 0-2 points; basically satisfactory: 2-4 points; some shortcomings: 4-6 points; insufficient green space or poor quality: 6-8 points; severe lack of green space and low environmental quality: 8-10 points. C21 Waterfront Space Development Waterfront space openness, accessibility, and landscape continuity The less developed the product, the higher the score. Waterfront spaces that are open, continuous, and of good quality score 0-2 points; those that are basically accessible score 2-4 points; those that are partially discontinuous or of average quality score 4-6 points; those that are poorly accessible or underutilized score 6-8 points; and those that are enclosed, dilapidated, or in dire need of improvement score 8-10 points. C22 Construction status of supporting facilities for plazas and parks Number and service scope of squares, parks, and small public spaces The less complete the supporting facilities, the higher the score. Sufficient public open space facilities are rated 0-2 points; basically satisfactory are rated 2-4 points; generally insufficient are rated 4-6 points; obviously insufficient are rated 6-8 points; and seriously lacking are rated 8-10 points. C23 Industry type safety and environmental compliance Environmental protection, safety, industry access, negative list and other materials The higher the degree of non-compliance, the higher the score. Fully compliant with safety and environmental protection requirements: 0-2 points; basically compliant: 2-4 points; generally incompatible: 4-6 points; relatively obvious safety and environmental protection issues: 6-8 points; seriously non-compliant or requiring withdrawal or transformation: 8-10 points. C24 Industry type and market matching degree Industry status, market demand, investment promotion and operation, and industry planning The lower the match, the higher the score. High matching degree and stable development are scored 0-2 points; basic matching degree is scored 2-4 points; general matching degree is scored 4-6 points; low matching degree is scored 6-8 points; obvious mismatch or urgent need for transformation is scored 8-10 points. C25 Advanced process flow Enterprise technology, equipment level, production efficiency, and environmental protection level The more backward it is, the higher the score. Advanced technology scores 0-2 points; basically applicable scores 2-4 points; partially outdated scores 4-6 points; relatively outdated scores 6-8 points; severely outdated, inefficient, or with significant pollution risks scores 8-10 points. C26 Industrial output per unit area Industrial output, tax revenue, and land area The lower the output, the higher the score. Reverse standardization is adopted. High output per unit area is assigned a low score, and low output per unit area is assigned a high score; the classification can be based on the average level of industrial land use in the study area or local area. C27 Industrial land use intensity Floor area ratio, building density, input-output per acre, land use efficiency The lower the intensity of use, the higher the score. Reverse standardization is adopted. High land use intensity and full utilization are scored low; low use intensity and significant inefficiency and idleness are scored high. C28 Commercial / business building occupancy rate Building occupancy rate, vacancy rate, and utilization of operating area The lower the usage rate, the higher the rating. Reverse standardization is used. High building occupancy rates result in low scores; high vacancy rates and underutilization result in high scores. C29 Tax contribution of commercial / business land Tax data, operating revenue, land area The lower the tax contribution, the higher the score. Reverse standardization is used. High tax contribution results in a low score; low tax contribution and weak economic benefits result in a high score. C30 Retail sales of consumer goods Business operation data, statistical data, and consumer activity data The lower the retail sales, the higher the rating. Reverse standardization is used. High retail sales and strong business activity are given low scores; low retail sales and weak business activity are given high scores. C31 Service trade contract value Service trade statistics, business data, platform data The lower the contract amount, the higher the score. Reverse standardization is adopted. A high service trade contract value results in a low score; a low contract value and weak service economy vitality result in a high score. C32 Clarity of property rights Real estate registration, ownership documents, number of rights holders, and dispute status. The more complex the ownership structure, the higher the score. Clear ownership and single subject: 0-2 points; basically clear: 2-4 points; generally complex ownership relationship: 4-6 points; multiple rights subjects and greater difficulty in coordination: 6-8 points; prominent ownership disputes or serious impact on implementation: 8-10 points. C33 Satisfaction with living environment Resident questionnaires, interviews, and complaint data The lower the satisfaction level, the higher the rating. The system uses a reverse conversion method. High satisfaction is 0-2 points; basically satisfied is 2-4 points; average is 4-6 points; dissatisfied is 6-8 points; and very dissatisfied or with a strong desire for improvement is 8-10 points. C34 Information on willingness to renovate industrial land Questionnaires or interviews with businesses, property owners, and rights holders The stronger the willingness to update, the higher the rating. No willingness to update is 0-2 points; weak willingness is 2-4 points; average is 4-6 points; strong willingness is 6-8 points; strong willingness and the ability to cooperate is 8-10 points. C35 Willingness to improve the public environment Questionnaires or interviews with residents, businesses, and public entities The stronger the desire to improve, the higher the score. No significant willingness to improve is scored as 0-2 points; weak willingness is scored as 2-4 points; average willingness is scored as 4-6 points; strong willingness is scored as 6-8 points; strong willingness and focused demands are scored as 8-10 points. C36 Recent Project Plans Recent construction plans, annual project database, urban renewal project database The higher the urgency of inclusion in the plan and its implementation, the higher the score. 0-2 points for not included in the plan; 2-4 points for having preliminary intentions; 4-6 points for being included in the reserve project; 6-8 points for being included in the near-term plan; 8-10 points for being included in key near-term projects or urgently needed for implementation. C37 Financial or policy support Fiscal funds, special funds, policy pilots, and incentive policies The stronger the support, the higher the score. No obvious support: 0-2 points; general policy support: 2-4 points; special policy or funding leads: 4-6 points; relatively clear support: 6-8 points; clear funding or policy support and mature implementation conditions: 8-10 points. C38 Latest planning guidelines Territorial spatial planning, detailed planning, special planning, and urban renewal planning The closer it aligns with the planning guidelines, the higher the score. A score of 0-2 indicates a weak connection to the planning guidance; a score of 2-4 indicates a moderate connection; a score of 4-6 indicates a certain degree of renewal guidance; a score of 6-8 indicates that the planning explicitly proposes renewal and upgrading requirements; and a score of 8-10 indicates that the area belongs to the key areas for renewal, protection, upgrading, or functional optimization in the planning. C39 Policy guidance from higher-level governments National, provincial, and municipal policies, action plans, and special tasks The stronger the policy impetus, the higher the score. No obvious policy guidance: 0-2 points; general policy connection: 2-4 points; some policy support or constraints: 4-6 points; relatively clear policy orientation: 6-8 points; belongs to the key support, key rectification or key promotion targets of higher-level policies: 8-10 points. C40 Impact of major public welfare and regional projects Major transportation, municipal, public service, ecological, and cultural tourism projects The stronger the project's driving force or influence, the higher the score. 0-2 points for virtually no impact; 2-4 points for a weak impact; 4-6 points for a general connection; 6-8 points for a significant impact; 8-10 points for being directly driven, constrained, or urgently requiring collaborative updates by major projects.

[0099] For continuous quantitative indicators, the system uses either forward standardization or reverse standardization methods to transform them based on the relationship between the original value of the indicator and the update requirements.

[0100] If a larger original value for a certain indicator indicates a stronger need for updates, then a positive standardization formula is used:

[0101]

[0102] If a smaller original value for a certain indicator indicates a stronger need for updates, then the inverse standardization formula is used:

[0103]

[0104] in, This represents the original value of the i-th spatial grid cell on the j-th index. and These represent the maximum and minimum values ​​of the j-th index across all spatial grid cells, respectively. This represents the 0-10 standardized score value of the i-th spatial grid cell on the j-th index.

[0105] The assignment rules in Table 1 represent one implementation method. Specific thresholds can be adjusted based on local technical guidelines, data distribution in the study area, planning and management requirements, and current status survey results. When a certain indicator has missing values, outliers, or extreme values, it can be processed using methods such as the average of neighboring grids, the average of similar plots, the current status survey verification value, reasonable threshold truncation, or unified missing value marking, and processing records should be retained.

[0106] When the maximum and minimum values ​​of a certain indicator are the same across all grids, that is... If the indicator lacks spatial variation within the current study area, a uniform score can be assigned based on the actual situation, or its influence can be reduced in the recalculation of weights, scenario verification, and comprehensive evaluation interpretation to avoid evaluation bias caused by indifferent indicators.

[0107] S7: Calculate the comprehensive update demand score, identify high-demand areas, and assign them spatial affiliations.

[0108] Final weights for 40 indicator layers were completed in S5. Calculate and use S6 to complete the standardized score of each spatial grid cell from 0 to 10. Next, this step calculates the comprehensive update requirement score for each spatial grid cell. The high-demand areas and their regeneration types are determined by combining the level of regeneration demand, spatial contiguousness, and dominant driving factors.

[0109] The specific calculation and recognition process is as follows:

[0110] (1) Calculation of comprehensive update requirement score: For the i-th spatial grid cell, its comprehensive update requirement score is calculated. Calculate using the following formula:

[0111]

[0112] in, For the first The final weight of each indicator, For the first The grid in the first Standardized scores on each indicator.

[0113] (2) Automatic classification of update demand intensity: complete the comprehensive update demand score for each grid. After calculation, the system performs calculations on all spatial grid cells within the study area. The values ​​are categorized into levels. The categorization uses Jenks' natural breakpoint method to divide the overall update demand score of all grids into five levels: extremely high update demand, relatively high update demand, medium update demand, relatively low update demand, and low update demand.

[0114] Jenks' natural breakpoint method aims to reduce intra-level differences and increase inter-level differences, automatically determining hierarchical breakpoints based on the actual distribution of the comprehensive update requirement scores for all grids. Let there be (m) spatial grid cells in the study area, with their comprehensive update requirement score set as follows:

[0115]

[0116] The system processes all data in ascending order. The values ​​are sorted, and four hierarchical breakpoints are determined using Jenks' natural breakpoint method, denoted as:

[0117]

[0118] in: ;

[0119] Meanwhile, the minimum and maximum scores for all comprehensive update requirements are recorded as follows: and ;

[0120] The criteria for determining the five levels of update demand are as follows:

[0121] Corresponding to low update requirement level; ;

[0122] Corresponds to a lower level of update requirement; ;

[0123] Corresponds to a medium level of update requirement; ;

[0124] Corresponds to a higher level of update requirements; ;

[0125] This corresponds to an extremely high level of update requirements. ;

[0126] Therefore, the boundaries of the five levels are not predetermined absolute values, but are automatically generated based on the actual distribution of the comprehensive update requirement scores of all grids within the current study area. For different towns, different areas, or different policy scenarios, due to differences in indicator scores, weights, and spatial samples, the Jenks natural breakpoint method generates... The threshold can be adjusted accordingly. This processing method avoids the grading bias caused by using a fixed threshold, making the grading results more consistent with the data distribution characteristics within the study area.

[0127] In practical applications, the system will use each spatial grid cell's... The value is compared with the aforementioned breakpoint interval, and the corresponding update requirement level field is automatically assigned. A grading result table containing the grid number, comprehensive update requirement score, level category, and spatial location is output. To ensure traceability, the system can simultaneously save the four grading breakpoints generated in this calculation. And the corresponding level range. If local technical guidelines or planning management requirements have clearly stipulated the threshold for the level of update demand, verification or parameter adjustment can also be made based on the Jenks natural breakpoint classification results.

[0128] (3) Spatial identification and contiguous processing of areas with high update demand. Grids with extremely high and relatively high demand levels are selected as candidate grids for high update demand and contiguous processing is performed using GIS spatial aggregation tools. The contiguous processing parameters are determined based on the grid scale and the size of the study area. Let the grid side length be... The minimum contiguous area can be taken as ,in This minimizes the number of continuous grid cells; this process reduces the interference of isolated grid cells on the identification results, forming high-update-demand areas with spatial continuity. If necessary, spatial autocorrelation methods such as global Moran's I can be used to verify or assist in the judgment of the clustering characteristics of high-demand areas.

[0129] (4) Automatic Assignment of Update Type (Dominant Driving Factor Method): For each high-demand grid or high-demand contiguous area, calculate the contribution value of each indicator. For a single grid, the contribution value is calculated as follows: Calculation: For high-demand contiguous areas, the contribution values ​​of indicators from each grid within the area can be summed, averaged, or area-weighted averaged to obtain the dominant driving indicator at the area scale. Based on the preset "indicator-renewal type" mapping relationship, the contribution ratio of the corresponding indicator for each renewal type is statistically analyzed. The type with the highest contribution ratio that meets the preset conditions is designated as the dominant renewal type for that grid or contiguous area. If the contributions of multiple types are close or do not form a clear dominant type, they are marked as comprehensive renewal type or composite renewal unit.

[0130] (5) Automatic output of results: comprehensive update demand heat map, distribution map of areas with high update demand (including boundary and type labeling of contiguous areas), grid-level score table, list of the top five leading indicators, which can support simulation of various policy weight scenarios, such as "people's livelihood first", "history first" and "efficiency first", and achieve automatic analysis under different strategies by dynamically adjusting the indicator weights.

[0131] By using the above-mentioned methods of score calculation, demand classification, contiguous aggregation, and attribution of dominant driving factors, a relatively stable identification of regions and types with high update demand can be achieved, and parameterized adaptation can be performed according to the spatial scale, data conditions, and management requirements of different regions.

[0132] S8: Divide the space into units.

[0133] The system automatically divides urban renewal planning and implementation units based on comprehensive renewal demand scores and spatial contiguous characteristics. Building upon the classification of contiguous areas with high renewal demand and their types in S7, this step employs a parameterized and configurable five-dimensional rule system of "score-scale-contiguous-boundary-type" to achieve fully automated division of urban renewal planning units (planning level) and implementation units (operational level). This system is applicable to all types of towns and cities nationwide and outputs maps, tables, coordinates, and explanatory materials for planning, project preparation, and implementation management. Furthermore, it can be further refined into submission results based on local urban renewal unit delineation guidelines.

[0134] Based on the steps outlined above, this invention proposes a method for evaluating, identifying, and spatially dividing urban renewal resources. This method aims to achieve a comprehensive assessment of existing urban space, identification of renewal resources, and spatial unit division through a systematic approach. It uses a geographic information system (GIS) as the foundation for spatial analysis, a hierarchical indicator system and objective weighting methods for evaluation support, and combines multi-source data collection, gridded analysis, and spatial statistical methods to form a complete process from data import, grid establishment, type identification, indicator evaluation to spatial unit division.

[0135] Example 1

[0136] In this embodiment, steps S1-S8 are applied to the Hexia Ancient Town area in Huai'an City. This area is a pre-existing urban space characterized by a high concentration of historical and cultural resources, a mix of old residential and traditional commercial functions, and a need for both the renewal of public service facilities and the improvement of waterfront space quality.

[0137] During implementation, the research boundary can be imported based on the planning scope, the distribution range of historical and cultural resources, the protection and control scope of historical and cultural blocks or historical style areas, the current land use boundary, roads and water systems, building outlines and current survey data, and a spatial analysis basis can be established under a unified coordinate system.

[0138] In this embodiment, a 200m×200m analysis spatial grid can be established according to the area scale and refined identification requirements. Data such as buildings, land use, population, public service facilities, municipal transportation facilities, historical and cultural resources, industrial economy, property rights, property rights holders and related parties' wishes, and policy and project information are then assigned to the corresponding grids. For historical streets and alleys, old residential areas, traditional commercial districts, abandoned public land and public objects with inefficient, idle, or functional adjustment needs, waterfront spaces, and historical and cultural resource areas, they can be classified and labeled according to five types of urban renewal resources, forming a correspondence of "spatial grid unit - resource type - indicator data".

[0139] Specifically, within the Hexia Ancient Town area, old residential land can correspond to existing residential spaces such as urban villages, dilapidated houses, old communities, and renovation and upgrading areas; inefficient commercial land can correspond to commercial spaces in Guyi Street, Zhongjie Street, and some traditional streets and alleys that are underutilized, have declining business formats, or require functional adjustment; abandoned public land can correspond to public land such as grain depots, cinemas, primary schools, and administrative offices that are idle, underutilized, or require functional adjustment; other land can correspond to spaces with historical preservation, public opening, revitalization, or environmental improvement value such as ports, temples, former residences of celebrities, historical and cultural resource areas, waterfront spaces, and squares and parks. If there are existing industrial, warehousing, or logistics land within the study area or the evaluation-related area, it can also be identified as inefficient industrial land based on land use efficiency, industrial function, and current utilization; if such objects are not prominent within the area, they can be excluded from the evaluation of that area.

[0140] After object identification, an evaluation index system can be constructed based on five dimensions: building structure, built environment, economic benefits, property rights, and policy impact. Index weights are then calculated using sample data. Subsequently, standardized scoring from 0 to 10 points is applied to the formally collected data for the specific study area, forming a grid scoring matrix. The system further calculates the comprehensive renewal demand score for each grid, uses a natural breakpoint grading method to classify renewal demand levels, and spatially aggregates grids with extremely high and relatively high renewal demands to identify continuous high-renewal-demand areas.

[0141] like Figure 4 As shown, through the above steps, this method can systematically identify, classify, and divide spatial units of urban renewal resources in existing urban spaces such as the Hexia Ancient Town area. This embodiment demonstrates that this method can incorporate multiple dimensions such as building structure, built environment, economic benefits, property rights, and policy impacts into a unified evaluation framework, forming calculable, interpretable, and visualized results for identifying urban renewal resources, providing technical support for urban renewal planning, project selection, and implementation scheduling.

[0142] In summary, this invention addresses the shortcomings of existing technologies, such as strong subjectivity, insufficient identification accuracy, low efficiency, and limited applicability, through objective, automated, and parameterized technical means. It establishes a complete technical process from multi-source data collection, indicator weighting, and renewal demand identification to spatial unit division. This method helps improve the scientific rigor, stability, and operability of urban renewal resource evaluation and identification, and can provide technical support for the planning and implementation management of different types of urban renewal areas. It possesses significant novelty, inventiveness, and industrial application value.

[0143] Note: Explanation of abbreviations for English terms in the instruction manual:

[0144] GIS: Geographic Information System, is an information system used for spatial data acquisition, management, analysis, and visualization.

[0145] AHP: Analytic Hierarchy Process, is a hierarchical analysis method that decomposes complex evaluation problems into objective, criterion, and indicator layers. In this invention, it is primarily used to construct the hierarchical structure of evaluation indicators.

[0146] CRITIC, short for Criteria Importance Through Intercriteria Correlation, is an objective weighting method based on the information content of indicators and the conflict between indicators. In this invention, it can be used in combination with the entropy weight method and the coefficient of variation method to calculate indicator weights.

[0147] POI: Point of Interest, which usually refers to spatial objects such as facilities, shops, public service points, and transportation stations with clear location attributes on maps or spatial databases.

[0148] API: Application Programming Interface, is an interface for systems to call, transmit and interact with each other.

[0149] CGCS2000: China Geodetic Coordinate System 2000, is a commonly used national geodetic coordinate reference system in my country.

[0150] Jenks Natural Breaks Classification is a classification method that automatically determines classification breakpoints based on data distribution, used to reduce intra-class differences and increase inter-class differences.

[0151] Moran's I: Spatial Autocorrelation Index. The global Moran's I index described in this invention is used to determine whether spatial clustering characteristics exist in areas with high update demand.

[0152] ArcGIS: A commonly used geographic information system software that can be used for spatial data management, overlay analysis, grid generation, and thematic mapping.

[0153] QGIS: An open-source geographic information system software that can be used for spatial data processing, spatial analysis, and cartography.

Claims

1. A method for evaluating, identifying, and spatially partitioning urban renewal resources, characterized in that, The steps include the following: S1: Import the urban renewal research area scope into the geographic information system platform; S2: Based on the study area's scope, built environment density, and evaluation accuracy requirements, establish a rule-based analysis spatial grid within the study area; S3: Based on the types of existing resources involved in urban renewal, the renewal objects are divided into five categories: old residential land, inefficient industrial land, inefficient commercial land, abandoned public land and other land. S4: Construct evaluation indicators based on the building itself, the built environment, economic benefits, property rights, and policy impacts; S5: Obtain the original data corresponding to the indicators based on multi-source data, and calculate the weight of each indicator according to the information content, dispersion and differences between indicators; S6: Based on the formal evaluation data corresponding to the indicators obtained from multi-source data, the data is spatialized into the established analysis space grid to form a grid scoring matrix for comprehensive update requirement calculation; S7: Calculate the comprehensive renewal demand score for each spatial grid cell, and combine the renewal demand level, spatial contiguousness, and dominant driving factors to determine high-demand areas and their renewal types.

2. The method for evaluating, identifying, and spatially dividing urban renewal resources as described in claim 1, characterized in that, In step S4, the following aspects are assessed: In the building itself dimension, the building's service life, quality, appearance, and structural condition are evaluated; in the built environment dimension, the living standards, historical and cultural resources, public service facilities, municipal transportation facilities, and ecological environment quality are considered; in the economic benefits dimension, industrial development and economic output are assessed, including industry type, market matching degree, and economic benefits of industrial and commercial land use; in the property rights dimension, the clarity of property rights relationships and the willingness of property owners to improve the living environment, industrial land renewal, and public environment are considered; and in the policy impact dimension, the policy guidance of the local and higher-level governments, financial and policy support, the latest planning guidance, and the impact of major public welfare or regional projects are considered.

3. The method for evaluating, identifying, and spatially dividing urban renewal resources as described in claim 2, characterized in that, The specific evaluation indicators are as follows: building usage time, building quality, architectural style, structural evaluation grade, per capita residential building area, housing unit rate, resident population density, resources of historical and cultural blocks and historical districts, protection and utilization of historical and cultural blocks and historical districts, protection of historical buildings, supporting facilities within a 5-minute living circle, supporting facilities within a 10-minute living circle, supporting facilities within a 15-minute living circle, land use of schools / hospitals / grain depots and administrative offices, water / electricity / gas / heating facilities, implementation of rainwater and sewage separation and undergrounding of utility poles and cables, distance to public transportation stations, parking facilities, road system construction, and green space landscape. Construction status, waterfront space development, plaza and park supporting facilities construction status, safety and environmental compliance of industrial types, matching degree between industrial types and the market, technological advancement, output per unit area of ​​industrial land, industrial land use intensity, building utilization rate of commercial / business land, tax contribution of commercial / business land, retail sales of consumer goods, service trade contract value, clarity of property rights, satisfaction with the living environment, willingness to renew industrial land, willingness to improve the public environment, recent project plans, financial or policy support, latest planning guidance, policy guidance from higher-level governments, and impact of major public welfare and regional projects.

4. The method for evaluating, identifying, and spatially dividing urban renewal resources as described in claim 1, characterized in that, In step S5, firstly, for continuous quantitative indicators, the system uses either forward standardization or reverse standardization methods to transform them based on the relationship between the original value of the indicator and the update requirements. If a larger original value for a certain indicator indicates a stronger need for updates, then a positive standardization formula is used: If a smaller original value for a certain indicator indicates a stronger need for updates, then the inverse standardization formula is used: in, This represents the original value of the i-th spatial grid cell on the j-th index. and These represent the maximum and minimum values ​​of the j-th index in all spatial grid cells, respectively; This represents the standardized value of the i-th spatial grid cell on the j-th index; After completing the forward / reverse standardization of the indicators, a standardized sample matrix is ​​formed. : in, The number of sample space units participating in the weight calculation. For the number of indicators; Then, calculate the first one respectively. Entropy weights of each indicator Coefficient of variation weight and CRITIC weight Then, the final weights are obtained by using a linear combination method. The specific steps are as follows: (1) Entropy weight calculate: For the The first indicator is calculated. The proportion of each spatial grid cell under this index: when When necessary, the indicator can be corrected to a zero value, or the indicator weight can be reset to 0 and then renormalized. Then calculate the first... Information entropy of each indicator: in: when season ; Further calculation of the first Coefficient of variation for each indicator: Then the first Entropy weights of each indicator for: (2) Coefficient of variation weight calculate: For the Each indicator is used to calculate its mean. and standard deviation And obtain the coefficient of variation: when When this happens, a very small positive number can be used for correction, or the coefficient of variation of the index can be set to 0 before being normalized. Then the first The coefficient of variation weight of each indicator for: (3) CRITIC weight calculate: The CRITIC method considers both the volatility of the indicator itself and the conflict between indicators; for the first... Let there be 1 indicator, and its standard deviation be . , No. The first indicator and the first The correlation coefficient between the indicators is Then the first The information content of each indicator is: No. CRITIC weights of each indicator for: (4) Final combination weights calculate: After obtaining the entropy weights respectively Coefficient of variation weight and CRITIC weight Then, the first step is obtained by using a linear combination method. The final weight of each indicator : in, For the first The final weight of each indicator, , , Let be the combination coefficients, and satisfy: The output of this step is the final weight corresponding to the indicator. .

5. The method for evaluating, identifying, and spatially dividing urban renewal resources as described in claim 1, characterized in that, In step S6, The grid scoring matrix used for calculating comprehensive update requirements is as follows: in, This represents the standardized score of the i-th spatial grid cell on the j-th index, ranging from 0 to 10. For continuous quantitative indicators, the system uses either forward standardization or reverse standardization methods to transform them based on the relationship between the original value of the indicator and the update requirements. If a larger original value for a certain indicator indicates a stronger need for updates, then a positive standardization formula is used: If a smaller original value for a certain indicator indicates a stronger need for updates, then the inverse standardization formula is used: in, This represents the original value of the i-th spatial grid cell on the j-th index. and These represent the maximum and minimum values ​​of the j-th index across all spatial grid cells, respectively. This represents the 0-10 standardized score value of the i-th spatial grid cell on the j-th index.

6. The method for evaluating, identifying, and spatially partitioning urban renewal resources as described in claim 1, characterized in that, In step S7, the specific calculation and recognition process is as follows: (1) Calculation of comprehensive update requirement score: For the i-th spatial grid cell, its comprehensive update requirement score is calculated. Calculate using the following formula: in, For the first The final weight of each indicator, For the m grids, the first The grid in the first Standardized scores on each indicator; (2) Automatic classification of update demand intensity: complete the comprehensive update demand score for each grid. After calculation, the system performs calculations on all spatial grid cells within the study area. The values ​​are classified into five levels using Jenks' natural breakpoint method: extremely high update demand, relatively high update demand, medium update demand, relatively low update demand, and low update demand. (3) Spatial identification and contiguous processing of areas with high renewal demand; grids with extremely high and relatively high demand levels are used as candidate grids for high renewal demand, and contiguous processing is carried out in combination with GIS spatial aggregation tools; contiguous processing parameters are determined according to grid scale and study area size; (4) Automatic assignment of update type: For each high-demand grid or high-demand contiguous area, calculate the contribution value of each indicator; for a single grid, the contribution value is assigned according to... Calculation: For high-demand contiguous areas, the contribution values ​​of indicators of each grid within the area can be summed, averaged, or area-weighted averaged to obtain the dominant driving indicators at the area scale; Based on the preset "indicator-renewal type" mapping relationship, the contribution ratio of the corresponding indicators of each renewal type is calculated, and the type with the highest contribution ratio and meeting the preset conditions is taken as the dominant renewal type of the grid or contiguous area; If the contributions of multiple types are close or do not form a clear dominance, they are marked as comprehensive renewal type or composite renewal unit; (5) Automatic output of results: comprehensive update demand heat map, distribution map of areas with high update demand, grid-level score table, list of the top five leading indicators, and automatic analysis under different strategies by dynamically adjusting the indicator weights.

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