Urban village property right safety and spatial reconstruction collaborative optimization method and device

Abstract

The invention discloses an in-city village property right safety and space reconstruction collaborative optimization method and device, and relates to the technical field of city updating and space governance. The method is used for solving the problems that property conflict overlying is complex, the extrusion risk of low-income tenants is high, and space schemes are difficult to plan as a whole. The method comprises the following steps: firstly, integrating cadastral data, a three-dimensional building model and income distribution information to generate a property right conflict distribution map; then, simulating a multi-class property right disposal path and spatial constraints thereof based on the conflict graph, calculating a demolition release space and plot ratio compensation demand of the illegal building, and outputting a spatial decoupling graph; further, inputting the decoupling graph into a risk model, predicting a tenant loss scale and a social network fracture risk, and generating a risk early warning graph; and finally, constructing a government, villager and developer three-party income distribution model, dynamically adjusting a guarantee house proportion, iteratively optimizing and updating a scheme, and forming a collaborative scheme package considering fairness and space efficiency.

Description

Technical Field

[0001] This invention relates to the field of urban renewal and spatial governance technology, specifically to a method and apparatus for the coordinated optimization of property rights security and spatial reconstruction in urban villages. Background Technology

[0002] As urban spatial renewal shifts from incremental expansion to stock optimization, the redevelopment of urban villages has become a key task in the context of high-quality urban development. Urban villages typically suffer from mixed property rights, irregular land use, and disordered spatial layout. They serve as important habitats for low-income urban residents and are frequently targeted by urban renewal initiatives. Especially in megacities, the contradiction between the dense residential function, flexible economic activities, and infrastructure supply in urban villages is becoming increasingly prominent, making systematic spatial restructuring of these areas a real need.

[0003] For example, Chinese invention patent CN117829666A discloses a spatial pattern reconstruction assessment method based on urban villages. Specifically, it relates to the field of spatial reconstruction technology. This method comprehensively considers building safety, infrastructure stability, and the compliance of public service facilities to quantitatively assess the overall condition of urban village areas, determining whether a comprehensive spatial pattern reconstruction is necessary and systematically evaluating the urgency of urban village redevelopment. By comprehensively considering the complexity of the property rights structure and residential density of urban village areas, it reflects the overall spatial pattern reconstruction difficulty, helping to predict the ease or difficulty of reconstruction in advance and providing a scientific basis for subsequent planning and decision-making. Based on the urgency and difficulty of spatial reconstruction in urban village areas, the method assesses the feasibility of spatial pattern reconstruction, facilitating scientific decision-making in practice, avoiding unnecessary redevelopment, and improving the efficiency of redevelopment work.

[0004] Currently, the technical approaches for urban village redevelopment mainly rely on decentralized land development, whole-village packaged renewal, or industry-led models. However, these approaches suffer from several substantial shortcomings in practice: First, most schemes only statically analyze ownership status in the early stages, failing to fully identify the spatial overlap between complex property rights types such as historical illegal constructions, shared ownership, and homestead use rights. This can easily lead to project delays or termination due to property disputes. Second, the spatial planning process is disconnected from ownership analysis, resulting in the failure to link the original land use restrictions with actual usage conditions, leading to a lack of rationality and continuity in the redevelopment plan. Third, some methods can easily lead to the concentrated relocation of low-income tenants and the breakdown of community relations, thereby triggering new governance risks. There is a lack of proactive intervention and dynamic revision mechanisms for potential negative effects. Therefore, there is an urgent need for an integrated optimization method that addresses the reconciliation of multiple stakeholders' interests, identification of property rights risks, spatial restructuring, and mitigation of social effects to support the redevelopment of complex urban village areas. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and apparatus for the collaborative optimization of property rights security and spatial reconstruction in urban villages, thus solving the problems mentioned in the background.

[0006] To achieve the above objectives, this invention provides the following technical solution: a collaborative optimization method for property rights security and spatial reconstruction in urban villages, comprising the following steps: S1. Integrating cadastral data, 3D building models, and tenant income distribution information to identify spatial overlap areas between disputed property rights plots and municipal planning facilities, simultaneously marking concentrated residential areas for low-income tenants, and generating a property rights spatial conflict distribution map including the coordinates of conflict points and the scope of impact; S2. Based on the property rights spatial conflict distribution map, simulating the constraint effects of different property rights disposal methods on spatial layout, calculating the boundaries of buildable areas released after the demolition of historical illegal buildings and the corresponding floor area ratio compensation requirements, and outputting the marked buildable areas. S3. Input the spatial decoupling map of the updated scope and compensation rules into the risk assessment model to predict the scale of low-income tenant displacement caused by spatial renovation, and analyze the degree of damage to residents' social networks caused by changes in public spaces, generating a risk warning map that marks high-risk areas for group loss and community relationship breakdowns; S4. Construct a tripartite benefit distribution model for the government, villagers, and developers, dynamically allocate land appreciation benefits according to the contribution of each party and ensure the basic income of villagers. When the risk of tenant loss in the risk warning map exceeds the set threshold, trigger the mechanism to increase the proportion of affordable housing construction, iteratively optimize the property rights disposal and spatial design schemes, and output a collaborative solution package.

[0007] Furthermore, the specific process of integrating cadastral data, 3D building models, and tenant income distribution information to identify spatial overlap areas between disputed land parcels and municipal planning facilities is as follows: extracting collective land ownership boundaries and historical unlicensed building ledgers to establish a property status classification coding system; performing spatial overlay analysis of cadastral boundaries and municipal facility 3D models to identify areas where building outlines encroach on road red lines or pipeline burial areas; classifying conflict levels based on the proportion of encroached area and the importance of facility functions, and generating a spatial coordinate set with hierarchical labels.

[0008] Furthermore, the specific process of simultaneously marking low-income tenant concentrated residential areas and generating a property space conflict distribution map containing the coordinates of conflict points and the scope of impact is as follows: overlay tenant income distribution data with the spatial location of building units, and screen dense residential units with rents significantly lower than the market level; dynamically calculate the radiation impact range according to the conflict level, with the property dispute coordinates or facility encroachment area as the center; integrate the property dispute coordinates, facility encroachment boundaries, tenant gathering area range and conflict impact domain into a unified spatial coordinate system to generate a vector conflict distribution map with hierarchical labels.

[0009] Furthermore, based on the property rights spatial conflict distribution map, the specific process of simulating the constraint effect of different property rights disposal methods on spatial layout is as follows: calling the property rights dispute coordinates and conflict level labels in the conflict distribution map, matching a preset disposal rule library for each type of property rights status; dynamically simulating the adaptation relationship between space and planning control lines in the case of collective land being converted to state-owned land, illegal construction being demolished for compensation, or ownership being exchanged through spatial topology inference algorithm; quantifying the spatial layout constraint effect caused by property rights disposal, and generating a set of constraint parameters including road network adjustment needs and building setback restrictions.

[0010] Furthermore, the specific process of calculating the boundaries of buildable areas released after the demolition of historical illegal buildings and the corresponding floor area ratio compensation requirements, and outputting a spatially decoupled map that marks the updatable range and compensation rules is as follows: Perform spatial geometric Boolean operations based on the constraint parameter set to calculate the net buildable area boundary formed after the demolition of illegal buildings; associate the location of the area with the urban planning intensity zoning, dynamically generate compensation values ​​through the floor area ratio balance model, bind the buildable area boundary with the compensation rules, and mark the updatable areas of different intensities through gradient color bands to form an analytical vector potential map.

[0011] Furthermore, the logical process of inputting the spatial decoupling map into the risk assessment model to predict the scale of low-income tenant displacement caused by spatial renovation is as follows: Identify the spatial overlap between the boundaries of renewable areas marked in the spatial decoupling map and the concentrated residential areas of low-income tenants; obtain the benchmark value of market-based rent in the same area by associating with the city's real estate transaction database, predict the rent increase of the renewal unit, calculate the deviation between the rent increase and the median income based on the tenant income stratification data, map it into a quantitative value of the tenant migration ratio, and generate a spatial marker layer for high-risk areas of group loss where the migration ratio exceeds the set threshold.

[0012] Furthermore, the specific process of simultaneously analyzing the degree of damage to residents' social networks caused by changes in public spaces and generating a risk warning map that marks high-risk areas for population loss and points of community relationship breakdown is as follows: extract the street corner squares, alleyway corridors, or community market public activity nodes that are to be demolished from the spatial decoupling map; construct a network of residents' high-frequency activity trajectories, analyze the shortest path increment caused by node demolition, quantify the accessibility attenuation of specific behavioral routes, including the daily social circles of the elderly and the pick-up and drop-off routes for children, and mark the key coordinates of path breaks and the gathering locations of affected people on the warning map.

[0013] Furthermore, a tripartite revenue distribution model is constructed for the government, villagers, and developers. The specific process of dynamically distributing land appreciation revenue based on the contributions of each party and ensuring the basic income of villagers is as follows: A land appreciation revenue pool is established, and the dynamic contribution weights of the government in terms of investment intensity in public facilities, villagers in terms of progress in clarifying land ownership, and developers in terms of improving space utilization efficiency are quantified. The proportion data of disputed land parcels in the property rights spatial conflict distribution map are used to calculate the adjustment coefficient of villagers' ownership contribution through a multi-subject collaborative allocation algorithm, and a baseline for guaranteeing villagers' income is set. When the output value of the allocation result is lower than the baseline, the transfer operation of government revenue weight to villagers' income weight is performed, and a tripartite revenue distribution list is output.

[0014] Furthermore, when the risk of tenant attrition in the risk warning map exceeds a set threshold, the mechanism for increasing the proportion of affordable housing is triggered, iteratively optimizing the property disposal and spatial design schemes, and outputting a collaborative solution package. The specific process is as follows: Extract the spatial coordinate set of high-risk areas for group attrition in the risk warning map, calculate the proportion of its area to the total renovation area, and when the proportion exceeds the critical value, calculate the increment of the affordable housing allocation ratio based on the positive correlation rule between rent increase and tenant income deviation; adjust the affordable housing allocation area in residential land according to the increment value, simultaneously reduce the commercial land area and redefine the land boundary coordinate set, input the updated land coordinate set into the tripartite revenue distribution model for recalculation, and repeat the process until the proportion of high-risk area area reaches the standard, and output the collaborative solution package.

[0015] The collaborative optimization device for property rights security and spatial restructuring in urban villages includes the following modules: a conflict fusion diagnosis module, a linkage decoupling analysis module, a risk coupling early warning module, and a dynamic equilibrium optimization module. The conflict fusion diagnosis module integrates cadastral data, 3D building models, and tenant income distribution information to identify spatial overlap areas between disputed property rights plots and municipal planning facilities, simultaneously marking concentrated residential areas for low-income tenants, and generating a property rights spatial conflict distribution map including conflict point coordinates and impact ranges. The linkage decoupling analysis module, based on the property rights spatial conflict distribution map, simulates the constraint effects of different property rights disposal methods on spatial layout, calculates the boundaries of buildable areas released after the demolition of historical illegal buildings, and the corresponding floor area ratio compensation requirements. The system outputs a spatial decoupling map with updated ranges and compensation rules. The risk coupling early warning module inputs the spatial decoupling map into the risk assessment model to predict the scale of low-income tenant displacement caused by spatial renovation. It also analyzes the degree of damage to residents' social networks caused by changes in public spaces and generates a risk early warning map that marks high-risk areas for group loss and community relationship breakdowns. The dynamic equilibrium optimization module constructs a tripartite benefit distribution model for the government, villagers, and developers. It dynamically distributes land appreciation benefits based on the contributions of each party and ensures the basic benefits for villagers. When the risk of tenant loss in the risk early warning map exceeds a set threshold, it triggers a mechanism to increase the proportion of affordable housing construction, iteratively optimizes the property rights disposal and spatial design schemes, and outputs a collaborative solution package.

[0016] The present invention has the following beneficial effects: (1) The method of coordinating the security of property rights and spatial reconstruction in urban villages integrates cadastral data, 3D building models and tenant income distribution information to accurately identify the spatial overlap between disputed property rights plots and municipal facility planning, and simultaneously marks the gathering areas of low-income tenants, effectively revealing the potential conflict relationship between property rights structure and spatial layout, and improving the accuracy and comprehensiveness of conflict identification. On this basis, by simulating the constraint effect of various property rights disposal paths on spatial layout, the release boundary and floor area ratio compensation demand of the buildable area after the demolition of historical illegal buildings are reasonably deduced, forming a spatial decoupling map, so that the property rights elements and spatial potential are structurally linked, providing dynamic support and underlying basis for the formulation of subsequent spatial adjustment strategies, and significantly improving the forward-looking judgment ability and adjustment flexibility in the early stage of renovation. The spatial decoupling map is input into the social risk assessment model to effectively identify the risk of low-income groups relocating due to spatial adjustment, and combined with social network analysis to provide early warning of the stability of community structure under changes in public space, realizing the forward response and precise intervention of the social impact of the renovation plan. Meanwhile, by constructing a tripartite benefit distribution model among the government, villagers, and developers, and introducing a dynamic adjustment mechanism to ensure the basic benefit level of villagers, the system automatically links the process of adjusting the proportion of affordable housing and optimizing the spatial layout in the event of a social risk warning. This achieves a synergistic balance between property rights disposal strategies, spatial reconstruction paths, and social compensation mechanisms, effectively enhancing the overall sustainability, social acceptance, and consensus among all parties involved in the scheme.

[0017] (2) The Urban Village Property Rights Security and Spatial Restructuring Collaborative Optimization Device, in the process of integrating spatial data processing, property rights information identification, and transformation potential analysis, achieves integrated processing of multi-source data and accurate extraction of property rights-space conflicts, improving the efficiency and depth of the front-end analysis stage in urban village renewal. Through the built-in simulation module and rule model, it can quickly output a spatial decoupling map that takes into account both practical feasibility and boundaries, providing technical support for optimization schemes. At the same time, the device's built-in risk assessment and early warning sub-module can dynamically identify and quantify the social disturbances caused by the transformation, and through linkage with the benefit distribution model, realize the immediate response of the guarantee mechanism and the automatic optimization and adjustment of the scheme, thereby ensuring the synergy and execution flexibility of the transformation process under multi-objective constraints, and improving the intelligent decision-making ability and dynamic response level of the renewal scheme.

[0018] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0019] Figure 1 This is a flowchart of the collaborative optimization method for property rights security and spatial reconstruction in urban villages according to the present invention.

[0020] Figure 2 This is a flowchart of the collaborative optimization device for property rights security and spatial reconstruction in urban villages according to the present invention. Detailed Implementation

[0021] This application's embodiments address the implementation obstacles, social conflicts, and imbalances in benefit distribution caused by the disconnect between property rights security and spatial restructuring in urban village redevelopment through a collaborative optimization method and apparatus. Specifically, these issues manifest as: property disputes leading to repeated planning revisions (e.g., fire lanes being obstructed by unlicensed housing); spatial design schemes detached from ownership realities (e.g., ineffective floor area ratio allocation); redevelopment triggering the migration of low-income groups and disrupting community relations; and static benefit distribution models causing project stagnation midway.

[0022] The overall concept of the solution in this application embodiment is as follows: First, by integrating cadastral data, architectural space models, and socioeconomic attribute data, the system identifies the property rights relationships and current space use status within urban villages, accurately marks areas of property rights conflict and low-income groups, and generates a property rights-space conflict distribution map, thus achieving spatial positioning and structural identification of core issues from the data side.

[0023] Secondly, based on the conflict map, the spatial release boundary and floor area ratio compensation requirements under different property rights disposal methods are simulated, and the spatial decoupling map is output to clarify the buildable areas and coordinateable resources, providing a quantitative basis for subsequent design.

[0024] Then, the spatial decoupling results are input into the social risk assessment model to predict the scale of tenant attrition and the degree of damage to social networks, forming a risk warning map to provide decision-making reference for setting up control mechanisms.

[0025] Finally, a tripartite benefit distribution model among the government, villagers, and developers is constructed, and the proportion of affordable housing and spatial design schemes are dynamically adjusted based on risk warning results. Through multiple iterations, the coordinated optimization of property rights disposal, spatial reconstruction, and social compensation is achieved, resulting in a set of coordinated solutions that are feasible and socially acceptable.

[0026] Through the above-mentioned scheme, this application has achieved integrated coordination of property rights risk identification, spatial potential release, social impact mitigation and benefit redistribution in the complex urban village renewal scenario, which has significantly improved the systematicness, stability and feasibility of urban village renewal projects.

[0027] Please see Figure 1This invention provides a technical solution: a collaborative optimization method for property rights security and spatial reconstruction in urban villages, comprising the following steps: S1. Integrating cadastral data, 3D building models, and tenant income distribution information to identify spatial overlap areas between disputed property rights plots and municipal planning facilities, simultaneously marking concentrated residential areas for low-income tenants, and generating a property rights spatial conflict distribution map including the coordinates of conflict points and the scope of impact; S2. Based on the property rights spatial conflict distribution map, simulating the constraint effects of different property rights disposal methods on spatial layout, calculating the boundaries of buildable areas released after the demolition of historical illegal buildings and the corresponding floor area ratio compensation requirements, and outputting the marked and updatable range. S3. Input the spatial decoupling map with the compensation rules into the risk assessment model to predict the scale of low-income tenant displacement caused by spatial transformation, and analyze the degree of damage to residents' social networks caused by changes in public spaces, generating a risk warning map that marks high-risk areas of group loss and community relationship breakpoints; S4. Construct a tripartite benefit distribution model for the government, villagers, and developers, dynamically allocate land appreciation benefits according to the contribution of each party and ensure the basic income of villagers. When the risk of tenant loss in the risk warning map exceeds the set threshold, trigger the mechanism to increase the proportion of affordable housing construction, iteratively optimize the property rights disposal and spatial design schemes, and output a collaborative solution package.

[0028] In this implementation plan, S1. Addressing the spatial conflict between disputed property areas and municipal facilities, preventing planning schemes from deviating from the actual ownership status. Spatial overlap area identification: Automatically detecting areas where buildings encroach on municipal control lines (e.g., buildings encroaching on fire lanes) by geometrically overlaying cadastral boundaries with 3D models of municipal facilities (roads / pipelines); Low-income residential area labeling: Linking tenant income data with building location to filter densely populated residential units with rents significantly lower than the market average; Conflict distribution map output: Integrating property dispute coordinates, municipal encroachment boundaries, and residential area boundaries to form a vector map with hierarchical labels. Spatial overlap area: Areas where building outlines geometrically overlap with municipal planning control lines (e.g., road red lines, pipeline burial areas). Impact range: The affected area radiating outwards from the conflict point according to conflict level (e.g., a 500-meter radius for a level 1 conflict). S2. Transforming property disposal methods into quantifiable spatial constraints, eliminating the defect of traditional methods where floor area ratio allocation is divorced from ownership reality. Property Disposal Simulation: For disputed plots in S1 (such as collectively owned land), the rule base is used to simulate the spatial impact of disposal methods (conversion to state ownership / demolition / replacement); Buildable Area Calculation: By deducting the municipal avoidance zone after the demolition of illegal buildings through Boolean operations, a net buildable boundary is generated; Compensation Rule Binding: Based on the location of the plot and the association with urban planning intensity zoning, a floor area ratio compensation value is dynamically generated (e.g., a compensation coefficient of 1.8 times for the central area). Spatial Decoupling Map: A machine-readable vector map marking the boundaries of updatable areas and the bound floor area ratio compensation rules, i.e., a spatial data layer marking the updatable boundaries and floor area ratio compensation rules. Constraint Effects: Spatial restrictions caused by property disposal (e.g., building setback requirements, road network offset values). S3. Quantifying the social risks caused by transformation, overcoming the potential for group conflicts caused by traditional qualitative assessments. Tenant Exodus Prediction: Identify the spatial overlap between the S2 renewable area and low-income residential areas, and calculate the migration rate based on rent increase / income deviation; Social Network Analysis: Extract public nodes (alleyway corridors, community markets) slated for demolition, and analyze the impact of their demolition on residents' high-frequency paths (elderly morning exercise circles, children's pick-up routes); Risk Visualization: Mark high-risk areas for population loss where migration rates exceed thresholds and community relationship breakpoints where path increases exceed limits. Accessibility Decline: The increase in path length / time for residents to reach frequently used places due to the demolition of public nodes. High-Risk Areas for Population Loss: Predict spatial clusters where tenant migration rates exceed critical values; Risk Warning Map: A visualization of the migration risk of low-income groups and social relationship breakpoints. S4. Through dynamic allocation and threshold triggering mechanisms, achieve the blocking of social risk sources and real-time balance of interests among multiple parties. Revenue Distribution Model: Establish a weight system based on parameters such as public investment (government), original land ownership (villagers), and funding and operational efficiency (developers) to reasonably allocate the land appreciation revenue after renovation, avoiding disputes caused by imbalance of interests. Iterative optimization: Recalculate the benefits and verify the risk indicators, and iterate until the targets are met before outputting the final solution package.The mechanism for increasing the proportion of affordable housing in urban renewal projects is a risk response mechanism: when the predicted risk of social displacement exceeds a preset standard, the supply of affordable housing is automatically increased to stabilize the resident structure and balance the pressure of urban renewal. The collaborative solution package is the final output, integrating property rights arrangements, spatial design, and social suggestions to support multi-objective, multi-stakeholder collaborative governance during urban renewal.

[0029] Specifically, the process of integrating cadastral data, 3D building models, and tenant income distribution information to identify areas where disputed land parcels and municipal planning facilities overlap is as follows: extracting the boundaries of collective land ownership and the ledger of historical unlicensed buildings to establish a classification and coding system for property status; performing spatial overlay analysis of cadastral boundaries and 3D models of municipal facilities to identify areas where building outlines encroach on road red lines or pipeline burial areas; classifying conflict levels based on the proportion of encroached area and the importance of facility functions, and generating a spatial coordinate set with hierarchical labels.

[0030] This implementation plan extracts the boundaries of collective land ownership and records of historically unlicensed buildings, establishing a property status classification and coding system: constructing a unified property status identification system to provide data basis and classification support for subsequent conflict identification. Processing steps: Collective land boundary extraction: Source: cadastral database, parcel map vector data; Content: boundary coordinates, plot number, registration status, etc. Unlicensed building record consolidation: Source: paper or electronic records provided by street offices, village committees, etc.; Content: building name, location, area, construction time, current user, etc.; Processing method: positioning and geometric reconstruction through real-scene imagery + on-site inspection (building outline reconstruction combined with UAV imagery when necessary). Property status classification and coding system: establishing the following multi-level classification system (example): Code corresponding to property status description: A1 legally registered building, A2 building with supplementary procedures, B1 historically unregistered building, B2 disputed plot or multiple ownership claims, C1 plot with incomplete data awaiting verification; each building or land unit is assigned a unique ID; each record is accompanied by "verification method" and "data source". Spatial overlay analysis of cadastral boundaries and municipal facilities 3D models to identify encroachment areas: Identifying spatial conflicts between buildings and municipal planning facilities, such as building outlines encroaching on road red lines or underground pipeline protection zones. Processing procedure: Unified data processing: Unifying the projection coordinate system of the building 3D model (BIM / CIM simplified form) and municipal facilities (red lines, pipelines); simplifying 3D facilities into 2D projection surfaces for initial overlay (retaining Z-dimensional judgment in complex cases, such as basements crossing pipeline protection layers). Overlay analysis logic: Determining whether the building projection outline intersects with the following facility areas: road red line range; pipeline buried protection zone; public safety control zones such as fire protection and water supply. Spatial intersection output: Outputting conflict geometric information (polygon intersection); labeling conflicting building IDs, conflicting facility types, and location information. Classifying conflict levels based on the proportion of encroached area and the importance of facility functions, generating spatial coordinate sets with hierarchical labels: Quantitatively assessing and classifying the identified encroachment situations to form a visualized, decision-supportable data product. Conflict levels are categorized based on the proportion of occupied area and the importance of facility functions, generating a spatial coordinate set with hierarchical labels: For identified occupied areas, conflicts are classified according to their severity and potential consequences, allowing for differentiated handling strategies for different levels. The proportion of occupied area is defined as the percentage of a building's encroached portion within the total area of ​​the building or plot, reflecting the scale of the conflict. An indicator of the importance of facility functions is determined, reflecting the importance of the encroached facilities to urban operation and security. A comprehensive conflict score is calculated by combining the proportion of occupied area and the importance of the facilities, enabling categorized labeling. The purpose of the comprehensive conflict score calculation is to comprehensively reflect the degree of conflict impact through a unified indicator. Formula: Parameter explanation: Conflict Assessment Score (for grading); : Occupancy ratio weighting coefficient (e.g., 0.6); : Percentage of occupied area; : Facility importance weighting coefficient (e.g., 0.4); Numerical indicators of the importance of facility functions. Note: The specific values ​​are set according to the actual needs of the project. The conflict level classification logic and grading method are based on scoring. Interval values ​​are divided into: high-conflict (e.g., ): This should be a priority, as it may affect public safety; conflicts (such as...) ): Demolition or relocation needs to be considered holistically; low conflict (e.g.) ): Tolerable and negotiable adjustments are possible.

[0031] Specifically, the process of simultaneously marking low-income tenant concentrated residential areas and generating a property space conflict distribution map containing the coordinates of conflict points and the scope of impact is as follows: overlay tenant income distribution data with the spatial location of building units, and screen dense residential units with rents significantly lower than the market level; dynamically calculate the radiation impact range according to the conflict level, with the property dispute coordinates or facility encroachment area as the center; integrate the property dispute coordinates, facility encroachment boundaries, tenant gathering area range and conflict impact domain into a unified spatial coordinate system to generate a vector conflict distribution map with hierarchical labels.

[0032] The purpose of screening low-income tenant clusters in this implementation plan is to identify areas with concentrated low-income tenant populations, providing foundational data for subsequent social risk assessments. Processing method: Data overlay: Spatially linking tenant income data (e.g., from census, street registration, etc.) with the spatial location of building units; establishing a mapping relationship between tenant attributes within building units. Low rent determination: Benchmark indicator: Regional market average rent. The selection criteria combine valuation factors such as building area, building age, and location. ;in: : No. Average rent per building unit; Average rent in the area; The rent deviation threshold coefficient is recommended to be between 0.6 and 0.7. Spatial proximity analysis (DBSCAN clustering algorithm) is used to determine whether a spatially continuous low-rent area has formed; only areas exhibiting "area-like clustering" characteristics are retained, while scattered individual buildings are removed. The purpose of dynamically calculating the conflict impact range is to construct an "impact domain" based on the potential impact intensity of conflict points at different levels, for analyzing their spillover effects. Formula: Parameter explanation: :No. Radius of influence of each conflict point; The radius coefficient of conflict impact can be adjusted according to local conditions based on urban density. :No. The conflict level score for each conflict point is derived from the formula result in the previous step, with a value range of [0,1]. Higher-level conflict points have a larger impact radius, potentially affecting a wider range of residents and spatial functions; a buffer circle or equipotential circle centered on the point is formed for subsequent spatial linkage analysis. A unified spatial conflict distribution map is constructed: Property dispute points, facility encroachment boundaries, low-income residential areas, and conflict impact domains are integrated into the same spatial reference system, generating a visual data product with semantic labels. Spatial reference unification: All elements (building vectors, facility red lines, tenant labels, impact domain circles, etc.) are uniformly projected onto a unified urban coordinate system (such as CGCS2000 / Gauss-Krüger); ensuring layer consistency and supporting spatial overlay calculations and analysis. Element integration and field binding: Each spatial element (building / point / area) has attribute fields, including: element type (property point / encroachment boundary / low-income area / conflict impact domain); relevant conflict level; plot number, tenant density, etc.; the output format is GeoJSON or vector Shapefile, facilitating subsequent integration into the urban information platform or BIM system. Layer generation: The layer content includes: point layer: coordinates of property disputes; area layer: facilities encroachment area; region layer: low-income residential area; buffer layer: conflict impact domain; finally, it is synthesized into a comprehensive "property rights-social conflict distribution map", which can support derived display methods such as heat map, risk level map, evolution map, etc.

[0033] Specifically, based on the property rights spatial conflict distribution map, the specific process of simulating the constraint effect of different property rights disposal methods on spatial layout is as follows: calling the property rights dispute coordinates and conflict level labels in the conflict distribution map, and matching the preset disposal rule library for each type of property rights status; dynamically simulating the adaptation relationship between space and planning control lines in the case of collective land being converted to state-owned land, illegal construction being demolished for compensation, or ownership being exchanged through spatial topology inference algorithm; quantifying the spatial layout constraint effect caused by property rights disposal, and generating a set of constraint parameters including road network adjustment needs and building setback restrictions.

[0034] In this implementation plan, the coordinates of the property disputes and the conflict level labels from the conflict distribution map are called and matched against the handling rule base. This step extracts the spatial coordinate set of each disputed plot based on the generated property spatial conflict distribution map. and corresponding conflict level tag set The spatial indexing mechanism is used to associate the processing rule base. Each rule Includes attribute fields ,in: : The corresponding applicable conflict level; Suggested disposal methods (such as expropriation, replacement, or retention by agreement); This rule influences the weighting of spatial structure. The system is based on... Filter for compatible First, assign a disposal logic label to each plot of land. Second, based on a spatial topology deduction algorithm, dynamically simulate the adaptation relationship between the spatial release boundary and the planning control line after the disposal of property rights. Let the spatial set released after the disposal of collective land be denoted as . Its form in the simulation needs to be consistent with the functional boundaries of the existing regulatory detailed planning. Maintain structural fit. The goal of topology matching can be expressed as maximizing spatial coverage and boundary overlap, and its fitness objective function is: ;in: Planning adaptability index; : Weighting of overlapping area ratio; Boundary fit weight; : The first day after the treatment One release space unit; : Control line units planned and matched accordingly; : Represents the area operator; : Represents the boundary of a spatial unit; : indicates a space overlap operation. By adjusting... The process involves a trade-off between proportional control and boundary alignment, dynamically outputting the spatial release form. It quantifies the spatial layout constraints caused by property rights disposal, generating a parameter set that includes road network adjustment needs and building setback restrictions. Based on the established spatial adaptation form, combined with urban design parameters and building control rules, it calculates the interference range of new land parcels on surrounding roads and building layouts. A comprehensive set of layout constraint indicators is then established. ,in: : This represents the adjustment radius of the road system, calculated using the following formula: The parameters mean: Overall road network disturbance radius; : The geometric offset of processing unit k relative to the nearest existing road centerline; The weight of traffic restructuring caused by the handling method; Total number of processing units; Road interference coefficient. The building setback limit width is determined by a combination of control conditions, including the proposed building's relationship with the urban red line, land parcel boundaries, and sight distance to adjacent land parcels. A rule engine is used to determine and generate an executable setback control range layer. Ultimately, this forms a set of spatial constraint parameters for subsequent floor area ratio compensation simulations and land use adjustments, providing the basic input for generating spatial decoupling maps.

[0035] Specifically, the process of calculating the boundaries of buildable areas released after the demolition of historical illegal buildings and the corresponding floor area ratio compensation requirements, and outputting a spatially decoupled map that marks the updatable range and compensation rules is as follows: Perform spatial geometric Boolean operations based on the constraint parameter set to calculate the net buildable area boundary formed after the demolition of illegal buildings; associate the location of the area with the urban planning intensity zoning, dynamically generate compensation values ​​through the floor area ratio balance model, bind the buildable area boundary with the compensation rules, and mark the updatable areas of different intensities with gradient color bands to form an analytical vector potential map.

[0036] In this implementation plan, spatial geometric Boolean operations are performed based on the constraint parameter set to calculate the boundary of the net buildable area formed after the demolition of illegal structures. The spatial constraint parameter set generated in the previous stage is then invoked. Historical illegal construction layers Perform a spatial Boolean clipping operation to exclude restrictions such as planned roads, building setbacks, and sight corridors, generating a set of net buildable areas. Boolean operations are expressed as follows: ;in: Net buildable area set; : Boundary layer of land for demolition of historical illegal buildings; Theoretically, there is room for construction after the treatment; Undevelopable boundaries in urban control planning; Road setback control buffer zone, by generate; Building setback control buffer zone, by generate; : These represent the intersection and difference operations, respectively. The output... This is an updatable map layer constrained by planning conditions, serving as the base area for floor area ratio (FAR) compensation analysis. It correlates the region's location with urban planning intensity zoning, dynamically generating compensation values ​​through a FAR balance model. Each plot of land is associated with its urban planning intensity level zone Φ(z)={z1,z2,…}, and the floor area ratio benchmark value is called accordingly. Combining the original floor area ratio of historical illegal constructions Development intensity control indicators We then perform compensation value derivation. The floor area ratio compensation demand function for plot u is defined as: The parameters are explained below: : Floor area ratio compensation requirement for plot u; Wη: Historical floor area ratio weighting coefficient, used to balance the intensity of past illegal development; μ: Planning and regulation flexibility coefficient, used to reflect the flexibility of local management; : Historical actual floor area ratio of plot u; : Corresponding to the planned benchmark plot ratio; : The maximum planned floor area ratio; The area of ​​the land corresponding to the actual building area of ​​the historical illegal construction; Net buildable area (i.e., a subset of Ψ(n)); all variables and coefficients are extracted or fitted synchronously during the data access and regional attribute analysis stages. The numerical values ​​are used to guide whether the new planned floor area ratio needs to be increased, or whether other economic compensation methods should be adopted to achieve a balance. The boundaries of buildable areas are bound to compensation rules, and different intensities of updatable areas are marked with gradient color bands to form a resolvable vector potential map. The aforementioned generated... The boundary coordinates of each plot of land and their corresponding Establish binding relationships to form layer pairs. , System basis The distribution characteristics of the values ​​define the color band gradient levels and divide the update intensity level intervals, such as: extremely high potential zone: >M1 High Potential Zone: M2< ≤M1 Medium Potential Zone: M3< ≤M2 Weak Potential Zone: Δρu≤M3, where M1, M2, and M3 are threshold levels set by the system. The final output "Spatial Decoupling Map" is encoded in vector map format and has attribute fields (plot number, boundary coordinates, compensation rule type, and update intensity level), which can be used for downstream risk analysis and benefit allocation model linkage.

[0037] Specifically, the logical process of inputting the spatial decoupling map into the risk assessment model to predict the scale of low-income tenant displacement caused by spatial renovation is as follows: identify the spatial overlap between the boundaries of renewable areas marked in the spatial decoupling map and the concentrated residential areas of low-income tenants; obtain the benchmark value of market-based rent in the same area by associating with the city's real estate transaction database, predict the rent increase of the renewal unit, calculate the deviation between the rent increase and the median income based on the tenant income stratification data, map it into a quantitative value of the tenant migration ratio, and generate a spatial marker layer for high-risk areas of group loss where the migration ratio exceeds the set threshold.

[0038] In this implementation plan, overlapping areas are identified. Through spatial overlay operations, the boundaries of updatable areas marked in the spatial decoupling map are intersected with the layer of low-income tenant concentrated residential areas. The spatially overlapping portions are filtered out, and overlapping polygonal features are output with attached attribute fields (such as plot identifier, residential area identifier, and overlapping area). This operation relies on the spatial connection or overlay query function of GIS to clarify the spatial basis for subsequent rent and migration predictions. To obtain the market-based rent benchmark and predicted rent increase, firstly, the city's real estate transaction database is linked, and the market rent benchmark for the same or similar areas is extracted based on the location of the overlapping areas, denoted by the letter E. Then, the updated rent is estimated based on the renovation premium or market trend, denoted by the letter F. The predicted rent F is estimated based on the benchmark rent E and the renovation premium factor. After obtaining the benchmark rent E and the predicted rent F, the deviation between the rent increase and the median income is calculated using a custom deviation formula to quantify the impact of rent increases on the affordability of low-income groups. ;in: Rental deviation; : Predict the updated rental level; : Affordable rent level corresponding to the median income of low-income tenants in this area; Explanation of parameters: Updated rent based on estimates; : Rent affordability corresponding to the median income of the low-income group extracted from income stratification data; through calculation This yields the difference in the proportion of rent increases relative to income capacity. A positive and relatively large value indicates that rent increases may exceed the affordability of low-income tenants. This is mapped to a quantitative value of the tenant migration rate based on rent deviation. A more complex mapping formula is used to convert the deviation into a quantitative value of the tenant migration ratio. A logistic mapping can be used to represent the non-linear relationship between rental pressure and migration intention: ;in: : Quantitative value of predicted migration ratio; Sensitivity coefficient; : The rental deviation calculated in the previous step; : The set deviation threshold indicates that migration intention increases significantly when this level is exceeded; Explanation of each parameter: This reflects the sensitivity of migration intentions to changes in rental pressure; the higher the value, the steeper the change in migration rate with deviation. Rental deviation; : Rent deviation threshold, indicating that migration intentions increase significantly when this level is exceeded; the formula outputs The value is between 0 and 1, when far below hour, Near 0; when Much higher hour, The algorithm is closer to 1, better reflecting the non-linear characteristics of rental pressure and migration behavior. It generates a spatial marker layer for high-risk areas of population loss, comparing the migration ratio of each overlapping area with a preset threshold to filter out areas exceeding the threshold and mark them as high-risk areas. Simultaneously, it can buffer and expand the area around the high-risk area as needed to account for spillover effects, forming the final risk warning layer. This operation is an attribute filtering and spatial generation process, relying solely on threshold judgment and layer generation logic, outputting a vector layer with high-risk markers for subsequent decision-making.

[0039] Specifically, the process of simultaneously analyzing the degree of damage to residents' social networks caused by changes in public spaces and generating a risk warning map that marks high-risk areas for population loss and points of community relationship breakdown is as follows: extract the street corner squares, alleyway corridors, or community market public activity nodes that are to be demolished from the spatial decoupling map; construct a network of residents' high-frequency activity trajectories, analyze the shortest path increment caused by node demolition, quantify the accessibility attenuation of specific behavioral routes, including the daily social circles of the elderly and the pick-up and drop-off routes for children, and mark the key coordinates of path breaks and the locations where affected groups gather on the warning map.

[0040] In this implementation plan, public activity nodes slated for demolition are extracted and mapped to social network impact units. Urban micro-public spaces marked as "to be updated" or "functionally replaced" are selected from the spatial decoupling map, including street corner plazas, alleyway corridors, and open community markets. Their spatial geometric center points are used as the node set Ω to construct an initial set of social activity sites. Simultaneously, high-frequency social activity trajectory paths of residents are extracted based on mobile phone signaling data or trajectory collection data, constructing a social path network graph G=(U,E) based on spatial topology, where U represents activity nodes and E represents edges connecting trajectories. The impact of node removal on specific behavioral paths is simulated, and the shortest path increment is calculated. For each type of specific behavioral path (such as daily neighborhood travel for the elderly, child pick-up and drop-off routes, etc.), the original shortest path length is set to... The shortest path length after removing node ω∈Ω is The path breakage increment coefficient Θ is defined as: ;in: : Social accessibility decay coefficient, the larger the value, the more severe the impact of path breakage; Z: Total number of samples of the specific behavioral path being analyzed; : The shortest path distance of the z-th path before the node is removed; : The shortest path distance of the z-th path after the node is removed; : The average daily contact frequency of the target group corresponding to this path (e.g., the number of daily activities of the elderly); : The typical shortest activity period for this route service (e.g., the length of the travel window during morning and evening peak hours for picking up and dropping off children); ln(1+ This factor adjusts for the intensity of behavioral activities over time, reflecting the greater sensitivity of high-frequency, short-duration activities to pathway disruptions. It not only quantifies the increased cost of pathway disruptions but also incorporates factors such as behavioral frequency and timeliness, enhancing the ability to characterize the impact of behavioral pathway disruptions on vulnerable populations. It identifies key coordinates of pathway disruptions and marks disruption risk points, adjusting the attenuation coefficient accordingly. Location paths exceeding a threshold (e.g., 0.25) are filtered, and the coordinates of their breakpoints are traced back to mark them as "high-risk points for social disruption." The center points of high-frequency behavioral clusters covered by these paths are extracted and superimposed as "core areas of community disruption." This process uses spatial path analysis to trace the start and end points of the break before and after the shortest path change, constructing a path breakage layer to provide a foundation for the scope of the breakage's impact and precise population location. An early warning layer is output and integrated with social risk level classifications. Finally, "high-risk points for social disruption" and "core areas of community disruption" are represented by different risk level layers, with path breakage attenuation coefficients marked by color bands. The classification range (e.g., 0-0.1 is green, 0.1-0.25 is orange, and above 0.25 is red) is used, and linked with high-risk areas of group migration in the original spatial decoupling map, to generate a joint early warning map for the dual risks of "physical space change + social relationship structure disruption". This step is the symbolization and visualization of spatial layers.

[0041] Specifically, the process of constructing a tripartite revenue distribution model among the government, villagers, and developers, and dynamically allocating land appreciation revenue based on the contributions of each party while ensuring the basic income of villagers, is as follows: A land appreciation revenue pool is established, and the dynamic contribution weights of the government's investment intensity in public facilities, villagers' progress in clarifying land ownership, and developers' improvement in space utilization efficiency are quantified. The proportion of disputed land parcels in the property rights spatial conflict distribution map is used to calculate the villager ownership contribution adjustment coefficient through a multi-party collaborative allocation algorithm. A baseline for guaranteeing villager income is set. When the output value of the allocation result is lower than the baseline, a transfer operation from the government's revenue weight to the villager's revenue weight is executed, and a tripartite revenue distribution list is output.

[0042] In this implementation plan, the dynamic contribution weights of the three parties are quantified starting from the land appreciation revenue pool, and the contribution indicators of the government, villagers, and developers in the project are calculated separately: The government's investment in public facilities is denoted as φ, where φ is comprehensively evaluated based on the scale of investment funds, the scope of facility impact, and the overall improvement effect of the project; the progress of clarifying villagers' land ownership is denoted as qψ, calculated by the proportion of the area with completed land ownership confirmation to the disputed area and the efficiency of dispute resolution; the developer's improvement in space utilization efficiency is denoted as χ, obtained by evaluating the ratio of newly added buildable area to the original area and the technical level of the construction plan. Based on these three contribution indicators, a custom weighted model is used to generate initial dynamic weights: ;in: Initial government revenue weighting; Initial income weighting for villagers; : Developer's initial revenue weight; φ: Government's investment in public facilities; qψ: Progress of clarifying villagers' land ownership; χ: Developer's improvement in space utilization efficiency; α: Government investment sensitivity index, used to adjust the intensity of the government's contribution in the weight; β: Villagers' land ownership confirmation progress sensitivity index; γ: Developer's efficiency sensitivity index. Through this formula, the contributions of each party are amplified or compressed by an index before being normalized in the denominator to obtain the initial allocation weight, ensuring that the proportion of high-contributing parties in the revenue pool increases accordingly. The villager ownership contribution adjustment coefficient is calculated by calling the proportion data of disputed plots in the property rights spatial conflict distribution map to obtain the proportion ρ of disputed plots under villagers' names in the total disputed area, used to measure the risks and contributions borne by villagers in the land clarification process. Based on this proportion, the villager contribution adjustment coefficient Θ is defined: Where: Θ: Villager contribution adjustment coefficient; λ: Risk sensitivity coefficient, used to adjust the strength of the bonus on the villager's benefit weight by the proportion of disputes; ρ: Proportion of disputed land plots under the villager's name, i.e., the area of ​​disputed land under the villager's name that needs to be dealt with / the total area of ​​disputes; δ: Contribution nonlinear mapping index, used to characterize the bonus or diminishing marginal effect on the adjustment coefficient when the proportion of disputes is high. When ρ is large, Θ is amplified through index mapping and sensitivity coefficient, reflecting the high contribution of villagers in risk resolution, thereby increasing the villager's weight in the next allocation. Multi-subject collaborative allocation is implemented and the villager's guarantee benchmark is applied based on the initial weight. With adjustment coefficient Adjust the initial weights of the villagers: Let the adjusted villager weights be... The weights of the three parties were then re-evaluated. ;in: : Adjusted government weighting result; : Adjusted villager weight normalization result; The adjusted developer weighting result; next, a villager income guarantee baseline Bq is set, representing the minimum income proportion that should be given to villagers after the project renovation, based on the calculated villager distribution proportion. When the value is below Bq, the mechanism for transferring government revenue weights to villager revenue weights is triggered: Calculate the weight Δ to be transferred. ;in: η: Weight transfer amount, used to allocate weights from the government's weight pool to villagers; η: Transfer efficiency coefficient, used to control the magnitude of each adjustment to avoid drastic fluctuations; then update the weights: The system then normalizes the weights again to ensure the sum of the weights of all three parties is 1. This process guarantees that the villagers' share will at least reach the guaranteed standard, while the government's weight will decrease accordingly, and the developer's weight will remain unchanged. The system then outputs a list of the three parties' revenue distribution based on the final normalized weights. Based on the total amount Z of the land appreciation revenue pool, a revenue distribution list is generated: the amount allocated to the government is... The amount distributed to the villagers was The developer received the following amount: The list also includes explanations of the weight calculation process, the basis for each parameter value, and adjustment records, ensuring that the allocation process is transparent and traceable. If further adjustments are needed, parameters such as φ, qΨ, χ, and ρ can be recalculated based on new data during implementation, and the above process can be repeated to achieve dynamic iterative allocation.

[0043] Specifically, when the risk of tenant attrition in the risk warning map exceeds a set threshold, the mechanism for increasing the proportion of affordable housing is triggered. The process of iteratively optimizing the property disposal and spatial design schemes and outputting a collaborative solution package is as follows: Extract the spatial coordinate set of high-risk areas for group attrition in the risk warning map, calculate the proportion of its area to the total renovation area, and when the proportion exceeds the critical value, calculate the increment of the affordable housing allocation ratio based on the positive correlation rule between rent increase and tenant income deviation; adjust the affordable housing allocation area in residential land according to the increment value, simultaneously reduce the commercial land area and redefine the land boundary coordinate set, input the updated land coordinate set into the tripartite benefit distribution model for recalculation, and repeat the process until the proportion of high-risk area area reaches the standard, and output the collaborative solution package.

[0044] In this implementation plan, the spatial coordinates of high-risk areas are extracted and the area proportions are calculated. This step extracts the spatial coordinate set of high-risk areas for population migration from the risk warning map and calculates their total area under a unified coordinate system. With the total area of ​​the entire renovation area proportion .in, This represents the total area after merging all high-risk elements (polygons or patches) in the risk warning map; This represents the total area of ​​the entire renovation project; this proportion This is used to determine whether the proportion of high-risk areas exceeds a preset threshold. It relies solely on GIS area calculation and ratio judgment logic. When the proportion of high-risk areas exceeds the limit, the increase in the proportion of affordable housing is calculated based on the deviation between rent increases and income. When the threshold is exceeded, the increase in the proportion of affordable housing should be determined based on the pressure of rising rents and the degree of income deviation. Define the rent increase rate as... (The ratio of the difference between the updated rent and the benchmark rent to the benchmark rent), the degree of income deviation is... (Rental deviation), the proportion of high-risk area is recorded as . formula: Explanation of each parameter: The increase in the proportion of affordable housing to be built is used to calculate the new proportion based on the existing base of affordable housing. : Scale factor, used to scale up or down the incremental benchmark; The rent increase ratio indicates the relative increase in projected rent compared to the market benchmark rent. The rent impact index depicts the non-linear amplification effect of rent increases on incremental rents. Income deviation weighting coefficient, used to control the strength of the effect of income deviation in the exponential function; : Income deviation, representing the percentage of rent increases that deviate from the affordable rent for low-income groups; Normalization constant, used to prevent the denominator from being too small. Too large; High-risk area sensitivity coefficient, used to adjust the inhibitory or balancing effect of the proportion of high-risk areas on incremental growth; The proportion of high-risk areas reflects the percentage of the affected space in the overall renovation. The high-risk area proportion index is used to characterize the marginal impact on incremental housing supply when the proportion of high-risk areas is large. It comprehensively considers the impact of rising rent pressure, income affordability, and the size of high-risk areas on the demand for affordable housing, and outputs... This will then be used for the next step of land use adjustment. The area of ​​affordable housing and commercial land will be adjusted, and the land boundary coordinate set will be redefined based on the incremental ratio. Calculating the newly added affordable housing land area within the residential land area: The original affordable housing land area... Add to To balance the total land area, a corresponding area will be deducted from the commercial land area simultaneously. The updated boundary coordinate sets for residential and commercial land are redefined in the GIS. Geometric resegmentation or boundary offset is performed on the adjusted plots to ensure a reasonable spatial connection between the newly added affordable housing land and the reduced commercial land, meeting planning control lines and infrastructure requirements. This process primarily relies on spatial geometric segmentation and boundary update logic. The updated land coordinate set is input into the revenue distribution model for recalculation and iterative processing until the proportion of high-risk areas meets the standard. The generated new land boundary coordinate set and its corresponding affordable housing and commercial area attributes are then input into the tripartite revenue distribution model to recalculate the revenue distribution ratio and amount for the government, villagers, and developers. Simultaneously, the impact on low-income tenants can be reassessed, and the proportion of high-risk areas in the risk warning map can be updated. If new Still exceeding the critical value, recalculated based on the latest rent and income deviation data. And continue iterating; if If the criteria are met, the loop terminates, and the final collaborative solution package is output, including the updated land use boundaries, affordable housing construction plan, commercial adjustment plan, and revenue distribution list. This loop process is highly logical, but its core is attribute updating and model iterative calculation, until the proportion of high-risk areas meets the preset standards, at which point the final solution is output.

[0045] Please see Figure 2 The collaborative optimization device for property rights security and spatial reconstruction in urban villages includes the following modules: a conflict fusion diagnosis module, a linkage decoupling analysis module, a risk coupling early warning module, and a dynamic equilibrium optimization module. The conflict fusion diagnosis module integrates cadastral data, 3D building models, and tenant income distribution information to identify spatial overlap areas between disputed property rights plots and municipal planning facilities, simultaneously marking concentrated residential areas for low-income tenants, and generating a property rights spatial conflict distribution map containing the coordinates of conflict points and their impact range. The linkage decoupling analysis module, based on the property rights spatial conflict distribution map, simulates the constraint effects of different property rights disposal methods on spatial layout, calculates the boundaries of buildable areas released after the demolition of historical illegal buildings, and the corresponding floor area ratio compensation required. The system aims to: output a spatial decoupling map with updated range and compensation rules; input the spatial decoupling map into a risk assessment model to predict the scale of low-income tenant displacement caused by spatial renovation, analyze the degree of damage to residents' social networks caused by changes in public spaces, and generate a risk warning map that marks high-risk areas for group loss and community relationship breakdowns; and construct a tripartite benefit distribution model for the government, villagers, and developers, dynamically allocate land appreciation benefits based on the contributions of each party, ensure the basic income of villagers, and trigger an upward adjustment mechanism for the proportion of affordable housing when the risk of tenant loss in the risk warning map exceeds a set threshold, iteratively optimize the property disposal and spatial design schemes, and output a collaborative solution package.

[0046] In this implementation plan, the conflict fusion diagnosis module introduces a multi-source data fusion mechanism combining cadastral maps, 3D building models, and tenant income distribution for the first time. Through spatial overlay analysis, it accurately identifies the spatial overlap between disputed land parcels and municipal planning facilities, and simultaneously spatially labels low-income tenant concentrated residential areas, outputting a hierarchical property conflict map. This process breaks away from the traditional approach of analyzing only "unit legality," proposing a comprehensive diagnostic model for the dual conflict of "property space-population vulnerability," providing a foundation for subsequent coordinated optimization. The coordinated decoupling analysis module proposes to simulate the spatial release effects under various disposal paths (such as the conversion of collective land to state-owned land, demolition of illegal buildings, etc.) starting from the property conflict point, and simulates its coupling impact on control lines, setbacks, and road network structures based on topological evolution algorithms. It establishes a logical mapping chain between "property status—disposal rules—spatial feedback"; dynamically calculates the spatial compensation demand brought about by the demolition of illegal buildings; and outputs a spatial decoupling map of "updatable space + compensation rules," realizing a quantitative expression of "conflict to potential transformation." The risk coupling early warning module constructs a tenant squeeze-out model by predicting the deviation between rent increases and the median income of tenants, quantifies the migration ratio, and generates a spatial layer of group loss, realizing the transformation of "social carrying capacity pressure" from qualitative to quantitative. Structurally oriented: Combining trajectory network analysis, it calculates changes in path accessibility before and after the demolition of key public spaces, identifies the impact on groups such as the elderly and children who rely on path nodes, and marks social network breakpoints. Ultimately, it forms a spatial risk layer that takes into account both individual migration and community network disruption, filling the gap in traditional renovations that lack "spatial expression of social vulnerability," and enhancing the human-centered attributes and resilient planning capabilities of spatial design. The Dynamic Equilibrium Optimization Module proposes a tripartite benefit distribution mechanism based on dual control of contribution and risk warning. It constructs a tripartite benefit model involving the government, villagers, and developers, quantifying three types of contributions: public investment, ownership clarity, and development intensity. The module dynamically adjusts the distribution ratio to enhance the scientific rigor and fairness of the negotiation model. It introduces a benchmark for villager benefit protection and a compensation adjustment mechanism to ensure the security of their basic benefits. It links risk warning results with an automatic adjustment mechanism for the proportion of affordable housing construction, forming a closed-loop control process of affordable housing area, land boundary, and benefit redistribution. Finally, it outputs a unified and balanced collaborative solution package of "spatial configuration + construction ratio + benefit distribution," providing a real-time iterative technical closed loop.

[0047] In summary, this application has at least the following effects: A collaborative optimization method and device for property rights security and spatial reconstruction in urban villages, by integrating cadastral data, 3D building models, and tenant income distribution information, can accurately identify the overlapping relationship between property rights disputes and municipal facilities. Simultaneously, it extracts low-income tenant clusters, achieving simultaneous coordination of property rights security identification and social impact, thus solving the problem of single-dimensional identification of property rights spatial conflicts in existing methods. By constructing a spatial adaptation simulation mechanism under multiple disposal scenarios, it dynamically calculates the constraining impact of collective land conversion, illegal construction demolition, and ownership swaps on urban spatial structure, forming a labeled spatial decoupling map, effectively supporting the flexible adaptation and risk control of urban renewal strategies. Using the spatial decoupling map, a rent-income deviation analysis model and a social path disruption assessment mechanism are constructed, accurately marking high-risk areas of population loss and social breakpoints, significantly improving the predictability of the population impact of spatial transformation and providing a basis for formulating precise intervention measures. By introducing a tripartite benefit distribution model among the government, villagers, and developers, and dynamically adjusting the distribution weights based on contribution and property rights clarity, while setting a baseline for villager benefit protection and an automatic adjustment mechanism for affordable housing allocation, dynamic linkage and continuous iteration of property rights disposal, spatial design, and social equity are achieved, thereby improving the feasibility and synergy of the renewal plan.

[0048] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0050] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0051] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0052] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0053] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for coordinating optimization of security of property rights and spatial reconstruction in urban villages, characterized in that, The method comprises the following steps: S1. Integrate cadastral data, building three-dimensional model and tenant income distribution information, identify the spatial overlap area of disputed land ownership and municipal planning facilities, simultaneously label the low-income tenant concentrated residential area, and generate the land ownership spatial conflict distribution map containing conflict point coordinates and influence range; S2. According to the land ownership spatial conflict distribution map, simulate the constraint effect of different land ownership disposal methods on spatial layout, calculate the released construction area boundary and corresponding volume rate compensation demand after the historical illegal building is demolished, and output the spatial decoupling atlas labeled with updateable range and compensation rules; S3. Input the spatial decoupling atlas into the risk assessment model, predict the low-income tenant displacement scale caused by spatial reconstruction, analyze the damage degree of public space change to residents' social network, and generate the risk warning map labeled with group loss high-risk area and community relationship breaking point; S4. Build a three-party income distribution model of government, villagers and developers, dynamically distribute the land value-added income according to the contribution of each party and ensure the basic income of villagers, when the tenant loss risk in the risk warning map exceeds the set threshold, trigger the mechanism of increasing the proportion of indemnificatory housing, iteratively optimize the land ownership disposal and spatial design scheme, and output the collaborative scheme package.

2. The method of claim 1, wherein: The specific process of integrating cadastral data, building three-dimensional model and tenant income distribution information, and identifying the spatial overlap area of disputed land ownership and municipal planning facilities is as follows: Extract the collective land ownership boundary and historical legacy undocumented building account, and establish a land ownership state classification coding system; Perform spatial overlap analysis on the cadastral boundary and municipal facility three-dimensional model, and identify the area where the building contour occupies the road red line or pipeline buried area; According to the conflict grade of the area proportion and facility function importance, a set of spatial coordinates with hierarchical labels is generated.

3. The method of claim 2, wherein: The specific process of synchronously labeling the low-income tenant concentrated residential area and generating the land ownership spatial conflict distribution map containing conflict point coordinates and influence range is as follows: Superimpose the tenant income distribution data and the spatial position of the building unit, and select the dense residential unit with rent significantly lower than the market level; According to the conflict grade, the influence range is dynamically calculated with the disputed land ownership coordinates or facility overlap area as the center; Integrate the disputed land ownership coordinates, facility overlap boundary, tenant aggregation area range and conflict influence domain into a unified spatial coordinate system, and generate a vector conflict distribution map with hierarchical labels.

4. The method of claim 3, wherein: The specific process of simulating the constraint effect of different land ownership disposal methods on spatial layout according to the land ownership spatial conflict distribution map is as follows: Call the land ownership dispute coordinates and conflict grade labels in the conflict distribution map, and match the preset disposal rule library for each type of land ownership state; Dynamically simulate the adaptive relationship between released space and planning control line under the conditions of collective land transfer to state-owned land, illegal building demolition or ownership replacement through spatial topology deduction algorithm; Quantify the spatial layout constraint effect caused by land ownership disposal, and generate a constraint parameter set containing road network adjustment demand and building setback restriction.

5. The method of claim 4, wherein: The specific process of calculating the released construction area boundary and corresponding volume rate compensation demand after the historical illegal building is demolished, and outputting the spatial decoupling atlas labeled with updateable range and compensation rules is as follows: According to the constraint parameter set, the space geometry Boolean operation is performed to calculate the boundary of the net developable area formed after the illegal buildings are demolished; The region position is associated with the urban planning intensity partition, and the compensation value is dynamically generated through the volume rate balance model. The developable area boundary is bound with the compensation rules, and the gradient color band is used to mark the different intensity of the updateable area to form an interpretable vector potential map.

6. The method of claim 5, wherein: The logical process of predicting the scale of low-income tenant displacement caused by space reconstruction by inputting the space decoupling map into the risk assessment model is as follows: Identify the spatial overlap range of the updateable area boundary marked in the space decoupling map and the low-income tenant concentrated residential area; Associate the urban real estate transaction database to obtain the marketization rent benchmark value of the same land section, predict the rent increase of the update unit, calculate the deviation degree of the rent increase and the median income according to the tenant income stratification data, and map it to the tenant migration proportion quantitative value to generate a spatial marker layer of the group loss high-risk area whose migration proportion exceeds the set threshold.

7. The method of claim 6, wherein: The specific process of analyzing the damage degree of public space changes to the residents' social network to generate a risk warning map marking the group loss high-risk area and the community relationship breaking point is as follows: Extract the corner squares, alley corridors, or community market public activity nodes to be demolished in the space decoupling map; Construct a resident high-frequency activity trajectory network, analyze the shortest path increment caused by the demolition of the node, quantify the accessibility attenuation amplitude of specific behavior routes including the daily social circle of the elderly group and the pickup path of children, and mark the path breaking key coordinates and the affected population gathering position in the warning map.

8. The method of claim 7, wherein: The specific process of building a government, villager, and developer three-party income distribution model and dynamically distributing land value-added income according to the contribution of each party and ensuring the basic income of the villagers is as follows: Establish a land value-added income pool and quantify the dynamic contribution weights of the government in public facility investment intensity, the villagers in land ownership clarification progress, and the developers in space utilization efficiency improvement; Call the proportion data of the property dispute land block in the property space conflict distribution map, calculate the villager ownership contribution adjustment coefficient through the multi-agent collaborative allocation algorithm, set the villager income guarantee baseline, and when the allocation result output value is lower than the baseline, perform the transfer operation of the government income weight to the villager income weight, and output the three-party income distribution list.

9. The method of claim 8, wherein: When the tenant loss risk in the risk warning map exceeds the set threshold, trigger the affordable housing allocation ratio adjustment mechanism, iteratively optimize the property disposal and space design scheme, and output the collaborative scheme package. The specific process is as follows: Extract the spatial coordinates of the group loss high-risk area in the risk warning map, calculate the proportion of its area in the total area of reconstruction, and when the proportion exceeds the critical value, calculate the affordable housing allocation ratio increment according to the positive correlation rule of rent increase and tenant income deviation; Adjust the affordable housing allocation area in residential land according to the increment value, simultaneously reduce the commercial land area and re-delineate the land boundary coordinate set, input the updated land coordinate set into the three-party income distribution model for recalculation, and execute the loop until the high-risk area area proportion meets the standard, and output the collaborative scheme package.

10. The device for optimizing the security of property rights and spatial reconstruction of urban villages, applied to the method for optimizing the security of property rights and spatial reconstruction of urban villages according to any one of claims 1-9, characterized in that, It includes the following modules: conflict fusion diagnosis module, linkage decoupling analysis module, risk coupling warning module, and dynamic balance optimization module; The conflict fusion diagnosis module is used for integrating cadastral data, building three-dimensional models and tenant income distribution information, identifying spatial overlapping areas of disputed land blocks and municipal planning facilities, synchronously labeling low-income tenant concentrated residential areas, and generating a property space conflict distribution map containing conflict point coordinates and influence range; The linkage decoupling analysis module is used for simulating constraint effects of different property disposal methods on spatial layout according to the property space conflict distribution map, calculating the boundary of the released constructible area after the historical illegal building is demolished and the corresponding volume rate compensation demand, and outputting a spatial decoupling atlas labeled with updateable range and compensation rules; The risk coupling early warning module is used for inputting the spatial decoupling atlas into a risk assessment model, predicting the low-income tenant displacement scale caused by spatial reconstruction, analyzing the damage degree of public space change to residents' social network, and generating a risk early warning map labeled with high-risk areas of group loss and community relationship breaking points; The dynamic balance optimization module is used for constructing a three-party income distribution model of government, villagers and developers, dynamically distributing land value-added income according to the contribution of each party and ensuring the basic income of villagers, triggering the mechanism of increasing the proportion of indemnificatory housing when the tenant loss risk in the risk early warning map exceeds the set threshold, iteratively optimizing the property disposal and spatial design scheme, and outputting a collaborative scheme package.

Images