Method for optimizing land use in small and medium-sized cities

Abstract

The application discloses a method for optimizing land use in small and medium-sized cities, and relates to the technical field of urban planning and land use. The method first identifies the main center of the city according to the functional aggregation degree, determines the integration degree by combining the commuting contact and functional complementarity of adjacent urban areas, delimits the collaborative center, divides the multi-level gradient circle layer, and sets the corresponding distance attenuation coefficient. Then, the traffic accessibility of each land block is calculated by adopting the mode of differentiated configuration of non-motor and public transport sub-weights. The land use mixing degree is calculated by combining the internal function of the land block and the functional complementarity calculation between groups, and the optimal value under the corresponding accessibility level is matched and corrected. The gradient coupling coordination degree of each land block is calculated through the coupling coordination model, and the land block type is divided. Finally, the differentiated optimization measures are matched according to the circle layer and type. The method is suitable for the dual-integrated development characteristics of small and medium-sized cities, and improves the adaptability and optimization effect of land use evaluation.

Description

Technical Field

[0001] This application relates to the fields of urban planning and land use technology, and in particular to land use optimization methods in the context of integrated development of adjacent urban areas in small and medium-sized cities. Background Technology

[0002] As my country's urbanization shifts from scale expansion to quality improvement, land use optimization has become a crucial technical means to enhance urban spatial efficiency and promote regional coordinated development. In recent years, many small and medium-sized cities across China have gradually formed integrated development patterns among neighboring urban areas through administrative division adjustments, complementary industrial functions, and strengthened commuting connections. Typical examples include Hantai District and Nanzheng District in Hanzhong City, Shaanxi Province; Baota District and Ansai District in Yan'an City; and Qindu District and Weicheng District in Xianyang City. These urban areas are geographically adjacent, have close industrial and economic ties, and residents frequently commute across districts, possessing a natural foundation for coordinated functional development. However, due to the long-term influence of existing administrative boundaries, land use planning and transportation system construction in these two urban areas are usually independently developed and implemented. This results in a lack of overall coordination in cross-regional functional layout, mismatched transportation corridor capacity, and redundant public service facilities, hindering the improvement of overall urban development efficiency.

[0003] Most commonly used urban land use optimization technologies are designed for medium and large cities and are generally based on the assumption of homogeneous space and a public transportation-dominated travel system. When applied to small and medium-sized cities with integrated development trends, they are not well-suited to various aspects.

[0004] First, there is a lack of cross-regional coordination mechanisms. Existing methods typically use a single administrative region as the evaluation boundary for land use assessment and optimization, failing to conduct systematic analysis of adjacent urban areas with integrated functions as a cohesive whole. This results in poor connectivity of cross-regional transportation corridors, a lack of overall planning in the layout of public service facilities, and problems such as redundant construction or service blind spots, making it difficult to fully realize the complementary advantages of the two regions. For integrated urban areas separated by natural barriers such as rivers and mountains, the limited number of cross-regional passages and the lack of coordination in land use layout on both sides are particularly prominent issues.

[0005] Second, spatial adaptability is insufficient. Existing methods assume a homogeneous spatial distribution in cities and use uniform evaluation standards for comprehensive assessments. They fail to consider the strong unicentric polarization of small and medium-sized cities, neglecting the distance attenuation effect from the core area to the periphery and the functional degradation caused by natural barriers. This results in significant discrepancies between land use evaluation results and actual conditions in the periphery, making it impossible to accurately identify areas of mismatch between transportation and land use. This is especially true for small and medium-sized cities in a rapid expansion phase, where the development gap between the core and periphery is large, making it difficult for homogeneous evaluation standards to reflect the actual operational efficiency of different locations.

[0006] Third, the matching degree of travel structure is low. Existing transportation accessibility assessments generally use public transportation and motor vehicle road networks as the main measurement dimensions, with a high weighting, which is suitable for the diverse travel structures of medium and large cities. However, for small and medium-sized cities, especially in the outer areas of cities, residents' daily travel is mainly by walking, electric bicycles and other non-motorized modes, and the share of public transportation is generally low. The traditional assessment system deviates significantly from the actual travel structure, resulting in a disconnect between transportation accessibility assessment results and residents' actual travel experience, and a large deviation in identifying the problem of mismatch between transportation and land use.

[0007] Furthermore, existing methods for evaluating land use mix largely focus only on the functional composition within a plot, failing to consider the cross-cluster functional complementarity between peripheral residential clusters and core employment areas in the context of job-housing space separation in a single-center city. This can easily underestimate the actual functional mix level of peripheral residential areas, thereby affecting the rationality of optimization decisions. Summary of the Invention

[0008] This application provides a method for optimizing land use in small and medium-sized cities, which addresses the problems of insufficient cross-regional coordination, poor spatial evaluation adaptability, and low matching degree of travel structure in the existing technology.

[0009] This application provides a method for optimizing land use in small and medium-sized cities, including the following steps: The system collects basic spatial data of the city, divides it into several spatial units according to preset boundaries, and uses the closed area enclosed by roads at all levels in the city as the boundary. Within each spatial unit, it further divides it into several land parcel evaluation units. Based on each spatial unit, it calculates the degree of functional agglomeration and identifies the physical boundary of the city's main center. It calculates the commuting connection intensity and functional complementarity between adjacent urban areas. When both indicators reach the set threshold, it determines that the corresponding urban area has the foundation for integrated development and is included in the unified collaborative center. It calculates the functional center coordinates of the collaborative center, divides it into multiple gradient layers along the main expansion direction of the city with the functional center as the origin, and determines the gradient layer to which each land parcel evaluation unit belongs and the corresponding distance attenuation coefficient. For each land parcel evaluation unit, the accessibility of non-motorized travel and public transport is calculated separately. Based on the gradient layer to which the land parcel evaluation unit belongs, the corresponding sub-item weights are matched, and the traffic accessibility of each land parcel evaluation unit is calculated by weighting. For each land parcel evaluation unit, the degree of land use function mixing is calculated. For the residential land parcels in the outer ring, the functional complementarity effect between the clusters is supplemented to obtain the initial land use mixing degree. The optimal land use mixing degree corresponding to the traffic accessibility of each land parcel evaluation unit is matched, the matching coefficient is calculated and the initial land use mixing degree is corrected to obtain the effective land use mixing degree. For each land parcel evaluation unit, the coupling degree of traffic accessibility and effective land use mix and the overall development level are calculated. Combined with the distance attenuation coefficient of the gradient layer to which it belongs, the gradient coupling coordination degree is obtained. Based on the level of gradient coupling coordination degree, the land parcel type is classified by comparing the traffic accessibility and effective land use mix of each land parcel evaluation unit with the city average level. Optimization measures are formulated for different gradient layers and different types of land parcel evaluation units to generate land use optimization schemes.

[0010] The method for optimizing land use in small and medium-sized cities presented in this application has the following advantages: First, it achieves overall optimization of integrated urban areas. It breaks down the evaluation boundaries of administrative divisions, delineates collaborative centers based on the strength of functional connections, incorporates adjacent urban areas with integrated development into a unified evaluation system, coordinates functional layout and transportation corridor connections at the overall level, reduces redundant construction of public services and cross-regional commuting bottlenecks, fully leverages the synergistic effect of the integration of the two zones, and improves the overall spatial efficiency of the city.

[0011] Second, it improves the accuracy of evaluations for different locations. A single-center gradient concentric evaluation system was constructed, and a distance attenuation coefficient was introduced to differentiate the coupling results of different locations. This system aligns with the spatial structure characteristics of strong single-center polarization in small and medium-sized cities, improves the problem of evaluation distortion in peripheral areas caused by homogeneous evaluation standards, and can accurately identify areas of traffic and land use mismatch in different locations.

[0012] Third, it adapts to the travel structure characteristics of small and medium-sized cities. The weighting logic of traffic accessibility has been reconstructed, with non-motorized travel accessibility as the main evaluation dimension. The weight ratio of non-motorized and public transportation is configured differently for different circles, matching the actual travel structure of small and medium-sized cities where non-motorized travel is the main mode. This makes the traffic accessibility evaluation more in line with residents' real travel experience and improves the accuracy of identifying traffic constraints.

[0013] Fourth, improve the targeting and rationality of land use optimization. By correcting the land use mix ratio, the problem of inflated evaluations due to high mix and low support is eliminated. By combining the results of gradient coupling coordination to classify land parcel types, differentiated optimization strategies are matched for different layers and types of land parcels, avoiding a one-size-fits-all extensive optimization model and improving the overall optimization effect of land use. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a method for optimizing land use in small and medium-sized cities, as provided in this application embodiment.

[0016] Figure 2 This is a schematic diagram showing the gradient coupling coordination degree before optimization, illustrating the optimization simulation results in the experiments of this application embodiment.

[0017] Figure 3 This is a schematic diagram illustrating the optimized gradient coupling coordination degree, showing the optimized simulation results in the experiments of this application embodiment.

[0018] Figure 4 This is a schematic diagram showing the accuracy of identifying mismatched plots before optimization, illustrating the results of the optimization simulation in the experiment of this application embodiment.

[0019] Figure 5 This is a schematic diagram illustrating the accuracy of identifying mismatched land parcels after optimization, as shown in the simulation results of the experiment in this embodiment of the application.

[0020] Figure 6 This diagram illustrates the correlation coefficient between the accessibility evaluation before optimization and residents' actual travel satisfaction, as shown in the simulation results of the experiment in this embodiment of the application.

[0021] Figure 7 This diagram illustrates the correlation coefficient between the optimized accessibility evaluation and residents' actual travel satisfaction, showcasing the optimized simulation results in an embodiment of this application.

[0022] Figure 8 This is a schematic diagram showing the functional connectivity of the Hantai-Nanzheng cross-river corridor before optimization, illustrating the optimized simulation results in the experiments of this application embodiment.

[0023] Figure 9 This is a schematic diagram illustrating the functional connectivity of the optimized Hantai-Nanzheng cross-river corridor, showcasing the optimized simulation results in an experiment according to an embodiment of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Figure 1 A flowchart illustrating a method for optimizing land use in small and medium-sized cities, provided as an embodiment of this application. This application provides a method for optimizing land use in small and medium-sized cities, including: S100 collects basic spatial data of the city, divides it into several spatial units according to preset boundaries, and uses the closed area enclosed by the city's roads at all levels as the boundary to divide several land parcel evaluation units within the spatial units; based on the functional agglomeration degree of each spatial unit, it identifies the physical boundary of the city's main center. The commuting connection intensity and functional complementarity between adjacent urban areas are calculated separately. When both indicators reach the set threshold, the corresponding urban area is determined to have the foundation for integrated development and is included in the scope of the unified collaborative center. Calculate the functional centroid coordinates of the collaborative center, divide the city into multiple gradient concentric circles along the main expansion direction with the functional centroid as the origin, and determine the gradient concentric circle to which each land parcel evaluation unit belongs and the corresponding distance attenuation coefficient.

[0026] For example, the basic spatial data includes data on road networks, public sites, land use classifications, and points of interest. After acquiring this data, it first undergoes standardized processing to eliminate calculation biases caused by differences in data format, coordinate system, and precision. Specific processing includes: Coordinate System 1. The four types of spatial data—road network, land use classification, bus stops, and points of interest—are uniformly transformed to the urban local coordinate system, using the Gauss-Kruger projection to ensure consistent spatial reference for all spatial data and to prevent offset during overlay analysis.

[0027] Topology checking and correction. A topology check is performed on the road network data to correct issues such as suspended nodes, duplicate road segments, and road network breaks, constructing a complete network topology to provide a foundation for subsequent network analysis. Boundary topology checks are performed on the land use classification data to eliminate errors such as overlaps, gaps, and self-intersections, ensuring that each plot is a closed polygon with complete attributes.

[0028] Attribute standardization processing. Land use classification data is categorized and merged according to the "Urban Land Use Classification and Planning Construction Land Standards", and uniformly divided into seven categories: residential land, commercial service facilities land, public management and public service land, industrial land, road and transportation facilities land, green space and square land, and other land. Point of interest data is deduplicated, classified and cleaned, and functional attributes are matched to the major application land categories. Bus stop data is supplemented with attribute fields such as route number, departure frequency, and stop level.

[0029] Initial division of evaluation units. Based on the closed area enclosed by urban arterial roads, secondary arterial roads, and branch roads, land parcel evaluation units are generated; small and fragmented land parcels with an area of ​​less than 0.5 hectares are merged, and super-large land parcels with an area of ​​more than 10 hectares are divided according to internal branch roads, ensuring that the area scale of all land parcel evaluation units is within the range of 0.5 to 10 hectares, balancing evaluation accuracy and computational efficiency.

[0030] Next, a multi-index comprehensive evaluation method is used to calculate the functional agglomeration degree of each spatial unit and identify the physical boundary of the city's main core. The specific calculation process is as follows: Three main indicators are selected to characterize the degree of functional agglomeration: first, the density of points of interest, i.e. the number of points of interest per unit area, reflecting the activity level of spatial functions; second, the density of employment positions, i.e. the number of employment positions per unit area, which is obtained by multiplying the building area of ​​employment-oriented land such as industrial, commercial, and public service land by the job conversion factor; and third, the level of public service facilities, which adopts the facility level assignment method, assigning values ​​to public service facilities such as education, medical care, and culture at the municipal, district, street, and community levels respectively, and calculating the sum of the levels of all facilities within the unit.

[0031] The three indicators were standardized to map their values ​​to the range of 0 to 100, eliminating dimensional differences. A weighted summation of the functional clustering scores for each spatial unit was used to calculate the overall score. Then, the natural discontinuity method was employed to classify the functional clustering scores of all spatial units into five levels. The highest-level contiguous area was identified as the city's main core and served as the origin benchmark for subsequent gradient zoning. The natural discontinuity method iteratively divides the data into several groups, minimizing the within-group variance and maximizing the between-group variance, thereby identifying natural discontinuities in the data distribution and corresponding abrupt boundaries of functional clustering. In practice, this can be achieved using the natural discontinuity classification tool within a GIS (Geographic Information System) spatial analysis tool, with an initial iteration count of 100 and a convergence threshold set to within-group variance change rate less than 0.1%.

[0032] For two adjacent urban districts, the degree of integration is assessed based on two dimensions: commuting connectivity and functional complementarity, to determine whether they should be included in the unified coordination center. The specific methods are as follows: Commuter connection intensity calculation. Based on resident commuting survey data or mobile phone signaling data, calculate the proportion of two-way commuting between the two urban areas to the total commuting volume of each urban area, and take the average of the two as the commuter connection intensity. The calculation formula is:

[0033] In the formula, The commuter connection intensity between the two urban areas is represented by a value ranging from 0 to 1. The number of commuters traveling from District A to District B; This represents the number of commuters traveling between urban area B and urban area A. This represents the total number of commuters in District A. This represents the total number of commuters in District B.

[0034] Functional complementarity assessment. The reciprocal of the industrial structure similarity coefficient is used to represent the degree of functional complementarity. The proportion of various types of jobs in the two urban areas is calculated separately, and the structural similarity is calculated using cosine similarity, thus yielding the functional complementarity. The calculation formula is:

[0035] In the formula, The functional complementarity between the two urban areas is represented by a value ranging from 0 to 1. This represents the percentage of job positions of type k in District A. denoted as k, representing the percentage of job positions of type k in District B; n represents the total number of job types.

[0036] Collaborative Center Determination and Boundary Delineation. An integration threshold is set: when both commuting connection intensity and functional complementarity reach the corresponding thresholds, the two urban districts are deemed to have the foundation for integrated development and are included in the unified collaborative center evaluation scope. This threshold is based on statistical calibration of cross-district connection survey data from multiple small and medium-sized cities in Northwest China. When the commuting connection intensity between the two urban districts exceeds the corresponding threshold (0.7), the cross-district commuting ratio has reached the level of city integration; when the functional complementarity exceeds the corresponding threshold (0.6), the characteristics of differentiated industrial function development and synergistic complementarity are significant, meeting the core characteristics of integrated development. The functionally contiguous and closely connected construction areas within the two urban districts are collectively designated as collaborative centers, serving as the core research scope for subsequent optimization. Connecting areas such as ecological barriers and transportation corridors between the two urban districts are included within the corridor control scope of the collaborative centers.

[0037] Using the functional center of gravity of the collaborative center as the origin, gradient concentric circles are divided and the corresponding distance attenuation coefficients are calibrated to construct a heterogeneous evaluation basis. The specific process is as follows: Determine the coordinates of the functional centroid. The functional centroid of the synergy center is calculated using a weighted average method. The weighted average coordinates of the geometric centers of all spatial units are calculated using the functional clustering scores of each spatial unit as weights, and this average is used as the functional centroid of the entire synergy center.

[0038] Divide the city into tiered concentric circles. Using the functional center as the origin, and along the main expansion direction of the city, typically the main corridors connecting different districts, divide the city into three tiered concentric circles based on spatial distance and functional connectivity: The central circle, located 0 to 3 kilometers from the functional center, is the main area of ​​urban function concentration, characterized by high functional concentration, ample transportation supply, and high land use mix; the middle circle, located 3 to 6 kilometers from the functional center, is the main area for recent urban expansion, primarily residential, with gradually improving supporting facilities and close commuting connections to the central circle; and the outer circle, located more than 6 kilometers from the functional center, is the area for long-term urban expansion, with relatively singular functions, weak transportation supply, and gradually weakening connections to the central circle.

[0039] A distance attenuation coefficient was calibrated. Based on the spatial characteristics of single-center polarization in small and medium-sized cities, the coupling effect of transportation and land use weakens with distance from the center. Therefore, a distance attenuation coefficient was introduced to correct the location of subsequent coupling results. Through statistical analysis of samples from multiple small and medium-sized cities, and combined with the distance attenuation law of functional connection strength, the attenuation coefficients for the three concentric circles were calibrated as follows: 1.0 for the central circle, meaning the coupling effect in the central area is fully realized with no attenuation; 0.85 for the middle circle, meaning the coupling effect is affected by distance, and the actual degree of realization of the coupling effect is 85% of that in the central area; and 0.65 for the outer circle, meaning the distance is relatively far, and the actual degree of realization of the coupling effect is 65% of that in the central area. This coefficient system fully considers the characteristics of small and medium-sized cities, such as small size, strong single-center polarization effect, and rapid functional attenuation. Compared with the unified evaluation standard for homogeneous spaces, it is more in line with the actual spatial operation law.

[0040] Step S200: Calculate the accessibility of non-motorized travel and public transport for each land parcel evaluation unit. Match the corresponding sub-item weights according to the gradient layer to which the land parcel evaluation unit belongs, and calculate the traffic accessibility of each land parcel evaluation unit by weighting.

[0041] For example, traditional urban accessibility assessments are generally designed for medium and large cities, with public transportation and motor vehicle road networks as the main components and their weightings being relatively high. However, for small and medium-sized cities, especially in peripheral areas, residents mainly travel by walking and electric bicycles, and the share of public transportation is generally low. Therefore, the traditional assessment system deviates significantly from the actual travel structure.

[0042] This application reconstructs the accessibility evaluation system, making non-motorized travel accessibility the primary component and conventional public transport accessibility a supplementary component. Furthermore, based on the differences in travel structures across different zones, the weights of each component are differentiated: in the central zone, where public transport supply is relatively abundant and travel modes are diverse, the weights for non-motorized and public transport are configured at 6:4; in the middle and outer zones, where public transport coverage is weak and non-motorized travel is dominant, the weights are configured at 8:2. This tiered weighting ensures that the accessibility evaluation results better match the actual travel experiences of residents in each zone.

[0043] Non-motorized travel accessibility encompasses two sub-items: walking accessibility and electric bicycle accessibility. It is calculated using the modified network buffer method, with a road network connectivity reduction coefficient introduced for correction. The specific calculation process is as follows: Effective radius setting. Based on the time consumption characteristics of non-motorized travel for residents of small and medium-sized cities, the effective travel radius for walking is set at 500 meters, corresponding to a travel time of approximately 8 minutes; the effective travel radius for electric bicycles is set at 1500 meters, corresponding to a travel time of approximately 8 minutes. Both are within the acceptable time consumption range for residents' daily travel.

[0044] Network buffer generation. Based on road network topology data, road segment impedances are constructed at walking speeds of 4 km / h and electric bicycle speeds of 20 km / h. A shortest path algorithm is used to calculate the furthest distance reachable along the road network from each plot's entrance / exit, generating network buffers of 500 meters for walking and 1500 meters for electric bicycles. This can be implemented using GIS network analysis tools. First, a road network dataset is constructed and connectivity rules are set. Then, equidistant service areas are generated starting from the plot centroid or entrance / exit. Compared to traditional straight-line buffers, network buffers are generated along actual road paths, considering road connectivity and detour distances, better reflecting real-world travel scenarios, and providing more accurate calculation results.

[0045] Coverage area ratio calculation. The percentage of the overlapping area between the pedestrian buffer zone, the e-bike buffer zone and the plot is calculated as the ratio of the total plot area. This ratio serves as the base score for pedestrian accessibility and e-bike accessibility, with a value ranging from 0 to 100.

[0046] Road network connectivity reduction correction. A road network connectivity reduction coefficient is introduced to correct for areas with incomplete branch road networks and many dead-end roads, reflecting actual traffic efficiency. The formula for calculating the reduction coefficient is:

[0047] In the formula, The road network connectivity reduction factor for the i-th land parcel evaluation unit, with a value ranging from 0 to 1; Let be the branch road network density of the i-th land parcel evaluation unit, in kilometers per square kilometer; The baseline value for urban branch road network density is 8 kilometers per square kilometer. When the branch road network density of a plot evaluation unit reaches or exceeds the baseline value, the reduction factor is 1.0, and there is no reduction; when it is lower than the baseline value, it is reduced proportionally.

[0048] The overall score for non-motorized accessibility is calculated by weighting pedestrian accessibility and e-bike accessibility at a ratio of 4:6 and summing the results. E-bikes have a higher weighting because they have a higher share of daily commutes in small and medium-sized cities and a wider coverage area.

[0049] Conventional public transport accessibility is comprehensively calculated using three sub-indicators: station coverage, route density, and time travel to the center. The specific method is as follows: Bus stop coverage. A straight buffer zone with a radius of 300 meters is generated centered on the bus stop. The ratio of the overlap area between the buffer zone and the land parcel evaluation unit to the total area of ​​the land parcel evaluation unit is calculated as the bus stop coverage score, reflecting the physical coverage level of the bus stops.

[0050] Bus route density. The ratio of the total length of bus routes within the evaluation unit of a land parcel to the area of ​​the land parcel is calculated, and after standardization, a route density score is obtained, which reflects the supply intensity of public transportation services.

[0051] Public transport center reachability time. Calculate the shortest travel time from the site evaluation unit to the city's main core functional area by taking regular public transport, including walking time, waiting time, and travel time. After standardization, a center reachability time score is obtained. The shorter the time, the higher the score, reflecting the efficiency of public transport for long-distance travel.

[0052] The entropy weight method is used to objectively determine the weights of the three sub-indicators, avoiding the bias of subjective weighting. The execution process of the entropy weight method is as follows: First, an evaluation matrix containing multiple land parcel evaluation units and three sub-indicators is constructed. The sub-indicators are positively standardized and uniformly mapped to the interval of 0 to 1. Then, the proportion of each land parcel evaluation unit under each sub-indicator is calculated, and then the information entropy of each sub-indicator is calculated. The smaller the entropy value, the greater the dispersion of the sub-indicator, the more information it provides, and the higher its weight. Finally, the weight coefficient of each sub-indicator is calculated based on the information entropy, and the sum of the weights is 1.

[0053] Using the entropy weight method, the weights of the three sub-indicators are: station coverage 0.5, route density 0.3, and center reachability time 0.2. The weighted summation yields the public transport accessibility sub-value.

[0054] Based on the gradient layer of the land parcel evaluation unit, corresponding sub-weights are matched, and a weighted composite traffic accessibility index is generated. Details are as follows: The weighting rules are as follows: for the central circle, the non-motorized travel accessibility weight is 0.6 and the public transport accessibility weight is 0.4; for the middle circle, the non-motorized travel accessibility weight is 0.8 and the public transport accessibility weight is 0.2; and for the outer circle, the non-motorized travel accessibility weight is 0.8 and the public transport accessibility weight is 0.2.

[0055] The comprehensive formula for calculating accessibility is as follows:

[0056] In the formula, The value represents the traffic accessibility of the i-th land parcel evaluation unit, ranging from 0 to 100. The larger the value, the better the traffic accessibility. This is a weighting coefficient for the accessibility of non-motorized travel; Let be the weighting coefficient for public transport accessibility, and satisfy . ; For the non-motorized accessibility component of the i-th land parcel evaluation unit; Let be the public transport accessibility component value for the i-th land parcel evaluation unit.

[0057] After calculating the accessibility index of all land parcel evaluation units, the accessibility was divided into three levels: high, medium, and low using the natural breakpoint method, with corresponding score ranges of 70 to 100, 40 to 70, and 0 to 40, respectively.

[0058] The spatial distribution of accessibility is verified to validate the rationality of the evaluation results: First, the single-center gradient characteristic is verified; high accessibility values ​​should be concentrated in the central area and gradually decrease towards the periphery, consistent with the traffic supply pattern of a single-center city. Second, the corridor orientation characteristic is verified; along major traffic arteries and cross-river bridges, accessibility should be significantly higher than that of the areas on either side, consistent with the radiation effect of traffic corridors. Third, the correlation with actual travel data is verified; resident travel satisfaction survey data from some communities are extracted, and the correlation coefficient between accessibility scores and satisfaction is calculated. The correlation coefficient should reach above 0.75 to prove the authenticity of the evaluation results. If the verification fails, the process is returned to adjust the sub-item weights and effective radius parameters, and recalculated.

[0059] Step S300: Calculate the internal land use function mixing degree for each land parcel evaluation unit, supplement the functional complementarity effect between the residential land parcels in the outer ring, and obtain the initial land use mixing degree; match the optimal land use mixing degree corresponding to the traffic accessibility of each land parcel evaluation unit, calculate the matching coefficient and correct the initial land use mixing degree to obtain the effective land use mixing degree.

[0060] For example, the information entropy-equilibrium degree model is used to calculate the degree of land use function mixing within a land parcel evaluation unit, and the functional activity is corrected by combining the density of points of interest to obtain an initial mixing degree benchmark value. The specific calculation process is as follows: Land use function information entropy calculation. Information entropy reflects the richness of land use function types. The more land use types there are and the more balanced the area of ​​each type, the higher the information entropy and the better the mixing. The calculation formula is:

[0061] In the formula, Let be the land use function information entropy of the i-th land parcel evaluation unit; For the i-th land parcel evaluation unit, the th The area proportion of land use type; The total number of land use types is 7.

[0062] Land use functional balance calculation. The magnitude of information entropy is affected by the number of land use types. To facilitate horizontal comparison between different plots, the information entropy is standardized into a balance index, ranging from 0 to 1. The closer to 1, the more balanced the area of ​​various land uses. The calculation formula is:

[0063] In the formula, The land use function balance of the i-th land parcel evaluation unit is defined as the degree of land use function mixing. The maximum possible information entropy is obtained when the areas of all land use types are equal. .

[0064] Functional Activity Correction. Land use mix only reflects the structural proportions of spatial functions, failing to reflect the actual level of functional activity. An interest point density correction coefficient is introduced, using the number of interest points per unit area to characterize functional activity and correct for imbalance. The correction formula is:

[0065] In the formula, This is the functional activity correction coefficient for the i-th land parcel evaluation unit; Let be the point of interest density of the i-th land parcel evaluation unit, in units of points per square kilometer; The baseline value for the density of points of interest is set at 1000 per square kilometer. The upper limit of the correction factor is set to 1.2 to avoid over-correction.

[0066] Initial land use mix degree calculation. Multiply the balance degree by the functional activity correction factor, and then standardize to the range of 0 to 100 to obtain the initial mix degree benchmark value within the land parcel evaluation unit.

[0067] In response to the characteristics of single-center polarization and cross-district layout of work and residence in small and medium-sized cities, although the internal mixing of peripheral residential clusters is low, they have close commuting connections with the core employment service area, forming functional complementarity across clusters. Traditional methods that only measure the internal mixing of land parcel evaluation units underestimate the actual functional mixing level of such areas; therefore, a correction term for inter-cluster functional complementarity is introduced. The specific construction method is as follows: Identify cross-regional complementary land parcels. The criteria are as follows: if the land parcel evaluation unit is located in the outer ring, the land use type is mainly residential, the proportion of residential land is not less than 60%, and the commuting connection intensity with the central ring is not less than 0.3, then this type of land parcel evaluation unit belongs to the cross-regional complementary work-residence type and needs to be adjusted for external mixing.

[0068] Calculate the functional complementarity correction value. Using the commuting connection strength between the land parcel evaluation unit and the central area as weights, and combining this with the average land use mix of the central area, the functional complementarity correction value is calculated. The calculation formula is:

[0069] In the formula, This is the inter-cluster functional complementarity correction value for the i-th land parcel evaluation unit; The commuting connection strength between the i-th land parcel evaluation unit and the central ring is represented by a value ranging from 0 to 1. The average land use mix of the central area is represented by the coefficient 0.4, which is the conversion factor for external complementarity, reflecting the contribution of external functional complementarity to the land use mix, and is based on the statistical calibration of the sample cities.

[0070] Initial land use mix designation. For eligible land parcels, the initial mix designation baseline value is added to the complementary correction value to obtain the initial land use mix designation; for ineligible parcels, the initial mix designation baseline value is used directly as the initial land use mix designation. An upper limit is set after designation to prevent the corrected value from exceeding the specified range.

[0071] By adjusting the functional complementarity between clusters, we can more accurately reflect the actual functional mixing level under the separation of work and residence spaces in a single-center city, and avoid underestimating the mixing degree of the outer residential clusters.

[0072] Land use mix is ​​not necessarily better the higher it is; its appropriate level needs to be commensurate with the capacity of traffic accessibility. Highly accessible areas can support higher mix levels, while excessive mix levels in low-accessibility areas can exacerbate traffic congestion and reduce operational efficiency. Therefore, it is necessary to define the optimal land use mix levels for different accessibility tiers as a basis for matching adjustments. The specific definition process is as follows: Sample data preparation. Sample data from multiple similar small and medium-sized cities were selected. Each sample includes the traffic accessibility value, land use mix, and corresponding land use efficiency data for each land parcel evaluation unit. This land use efficiency is characterized by neighborhood vitality and resident travel satisfaction. The formula for calculating land use efficiency is as follows:

[0073] In the formula, Let be the land use efficiency value of the i-th land parcel evaluation unit, with a value range of 0 to 100; The standardized value of the street vitality of the i-th plot evaluation unit is obtained by standardizing the number of POIs (Points of Interest) per unit area and the richness of business formats in a 6:4 weighted ratio, and reflects the activity level of the spatial function. , is the standardized value of residents' travel satisfaction for the i-th land parcel evaluation unit, which comes from the satisfaction score of the residents' commuting travel survey and reflects the residents' actual feelings about the degree of matching between transportation and land use; , The weighting coefficients for the two corresponding items are set to 0.55 and 0.45 respectively, with their sum being 1. The total sample size is no less than 300, covering different layers and types of land parcel evaluation units.

[0074] Model Training and Fitting. A quantile regression model is used, with normalized traffic accessibility as the independent variable and the optimal land use mix as the dependent variable, to fit the curve of optimal land use mix as a function of accessibility. The model adopts a quadratic polynomial form to capture the nonlinear relationship between traffic accessibility and optimal land use mix. In this embodiment, 75th quantile regression is used to fit the curve of optimal land use mix as a function of traffic accessibility, with the quadratic polynomial form as follows:

[0075] In the formula, The optimal land use mix is ​​the land use mix value that corresponds to the highest land use efficiency under the level of transportation accessibility. This represents the normalized traffic accessibility, with a value ranging from 0 to 1. This is the regression constant term, with a value of 0.21. The regression coefficient for the first-order term has a value of 0.78. The regression coefficient is a quadratic term with a value of -0.32. The model aims to maximize land use efficiency, employing gradient descent to iteratively solve for the coefficients. The loss function uses mean squared error, and 10-fold cross-validation is used to ensure fitting accuracy. The 75th quantile was chosen because the land use efficiency corresponding to this quantile is at a relatively high level, which can characterize the optimal land use mix under current accessibility.

[0076] During model training, the goal is to maximize land use efficiency. The loss function is the mean squared error, and the regression coefficients are solved through gradient descent iterative optimization. Ten-fold cross-validation is used to verify model accuracy during training. The initial learning rate is set to 0.01, and the iteration termination condition is that the decrease in the loss function is less than 1e-5, ensuring that the model's goodness of fit meets the requirements.

[0077] Optimal value calibration at each level. Based on the fitted curves, the optimal land use mix is ​​calibrated for the high, medium, and low accessibility levels. This benchmark system reflects the logic that accessibility supports land use mix: the stronger the transportation support capacity, the higher the optimal land use mix; when transportation support is insufficient, moderately reducing the mix is ​​more conducive to improving overall efficiency.

[0078] Based on the optimal land use mix, the matching coefficient of each land parcel evaluation unit is calculated, and the initial land use mix is ​​corrected to obtain the effective land use mix, eliminating inflated evaluations. The specific calculation process is as follows: Matching coefficient calculation. The matching coefficient reflects the degree of matching between the actual land use mix of the land parcel evaluation unit and the optimal land use mix of the corresponding accessibility level. The closer to the optimal value, the higher the matching coefficient, and the closer the corrected effective land use mix is ​​to the initial value; the farther away from the optimal value, the lower the matching coefficient, and the greater the correction magnitude. The calculation formula is:

[0079] In the formula, is the matching coefficient for the i-th land parcel evaluation unit, with a value ranging from 0 to 1; Let be the initial land use mix degree of the i-th land parcel evaluation unit; This represents the optimal land use mix for the accessibility level corresponding to the i-th land parcel evaluation unit.

[0080] When the actual mixing degree equals the optimal value, the matching coefficient is 1, with no correction. When the actual mixing degree deviates from the optimal value, the matching coefficient is less than 1, and the larger the deviation, the smaller the coefficient. For plots with insufficient mixing degree, the matching coefficient is also less than 1, reflecting the efficiency loss caused by insufficient mixing degree.

[0081] Effective land use mix degree calculation. Multiply the initial land use mix degree by the matching coefficient to obtain the corrected effective land use mix degree.

[0082] Through matching correction, the evaluation results of two typical land parcels were adjusted: one type consisted of parcels with low accessibility but high surface mixing, whose actual efficiency was not high due to insufficient transportation support; after correction, the effective land use mixing was significantly reduced, eliminating the inflated evaluation. The other type consisted of parcels with good accessibility but low mixing; after correction, the effective land use mixing was also reduced, reflecting the efficiency loss caused by insufficient functionality. The corrected effective land use mixing more closely reflects the actual operational efficiency of land use, providing more accurate input for subsequent coupling analysis.

[0083] Step S400: For each land parcel evaluation unit, calculate the coupling degree of traffic accessibility and effective land use mix and the overall development level, and obtain the gradient coupling coordination degree by combining the distance attenuation coefficient of the gradient layer to which it belongs; according to the level of gradient coupling coordination degree, classify the land parcel type by combining the comparison relationship between the traffic accessibility and effective land use mix of each land parcel evaluation unit and the city average level.

[0084] For example, traffic accessibility and effective land use mix are two interacting and mutually restrictive subsystems in the urban land use system: traffic accessibility is a supporting condition for land use mix, determining the service radius and operational efficiency of mixed functions; effective land use mix is ​​the source of demand for the transportation system, the more mixed the functions, the more diverse the transportation demand, and the higher the requirements for the transportation system. The degree of coordination between the two directly determines the overall efficiency of land use.

[0085] This application's method constructs a two-level coupling coordination model: the first level calculates the coupling degree, reflecting the interaction strength between the two subsystems; the second level calculates the comprehensive development level, reflecting the overall development level of the two subsystems; finally, the coupling coordination degree is synthesized through geometric mean, comprehensively reflecting the interaction strength and the overall level. Compared with simple weighted summation, the coupling coordination model can more profoundly reveal the synergistic relationship between the two systems and identify different types of imbalance problems such as high development with low coupling and high coupling with low development.

[0086] The coupling degree is calculated using a harmonic mean model, reflecting the interaction strength between the two subsystems of traffic accessibility and effective land use mix. To ensure that the coupling degree value falls within the range of 0 to 1, the traffic accessibility index and the effective land use mix index are normalized and mapped to the range of 0 to 1 before being substituted into the coupling degree formula.

[0087] The specific formula for calculating the degree of coupling is as follows:

[0088] In the formula, The coupling degree of the i-th land parcel evaluation unit, with a value ranging from 0 to 1; The normalized traffic accessibility of the i-th land parcel evaluation unit; Let be the normalized effective land use mix degree of the i-th land parcel evaluation unit. When both indicators are at their maximum value of 1, the coupling degree is taken as the maximum value of 1; when either indicator is 0, the coupling degree is 0, which conforms to the short-board effect characteristics of the harmonic average.

[0089] The numerical meaning of the coupling degree is as follows: the closer the value is to 1, the stronger the interaction between the two subsystems and the closer the connection; the closer the value is to 0, the more independent the two subsystems are and the less interaction they have.

[0090] Based on the coupling degree, the interaction intensity is divided into three levels: high-intensity interaction (coupling degree ≥ 0.6), where transportation and land use are closely connected and have significant mutual influence; medium-intensity interaction (coupling degree < 0.6), where transportation and land use have some interaction but limited impact; and low-intensity interaction (coupling degree < 0.3), where transportation and land use are weakly connected and have very little mutual influence.

[0091] The comprehensive development level reflects the overall development level of the two subsystems: accessibility and effective land use mix. It is calculated using a linear weighted model, with the weighting coefficients determined through the analytic hierarchy process (AHP). The specific process is as follows: A hierarchical model was constructed. The target layer represents the comprehensive development level of the land use system, while the criteria layer comprises two subsystems: transportation accessibility and effective land use mix. A judgment matrix was constructed using expert scoring.

[0092] A judgment matrix was constructed. Several experts in urban planning and transportation engineering were invited to score the relative importance of the two subsystems, and a judgment matrix was constructed using a scale of 1 to 9. For small and medium-sized cities in a period of rapid expansion, the rationality of land use allocation has a slightly greater impact on system development than transportation supply. Based on the expert scores, the average relative importance of effective land use mix and transportation accessibility was 1.2.

[0093] Consistency check and weight calculation. Calculate the largest eigenvalue and eigenvector of the judgment matrix, and perform a consistency check. If the consistency ratio is less than 0.1, the judgment matrix has satisfactory consistency. The normalized eigenvectors are then used as weight coefficients.

[0094] The weights for traffic accessibility and effective land use mix were calculated to be 0.45 and 0.55, respectively, with a sum of 1.

[0095] The formula for calculating the overall development level is:

[0096] In the formula, The comprehensive development level of the i-th land parcel evaluation unit, with a value ranging from 0 to 100; α The weighting factor for traffic accessibility is 0.45. β The weighting coefficient for effective land use mix is ​​0.55. This represents the effective land use mix after matching correction.

[0097] The higher the overall development level, the better the overall development level of the two subsystems; conversely, the lower the overall level, the lower the overall level and the existence of shortcomings.

[0098] The coupling coordination degree is the geometric mean of the coupling degree and the overall development level, which can comprehensively reflect the interaction strength and the overall development level. Based on this, the gradient coupling coordination degree is obtained by superimposing the layer distance attenuation coefficient, reflecting the actual coupling effectiveness in a heterogeneous space. The specific calculation process is as follows: Basic Coupling Coordination Degree Calculation. The basic coupling coordination degree is obtained by combining the coupling degree and the normalized comprehensive development level using the geometric mean method. The comprehensive development degree ranges from 0 to 100, and the normalized value is the original value divided by 100, ranging from 0 to 1, consistent with the dimensions of the coupling degree. The calculation formula is:

[0099] In the formula, The basic coupling coordination degree of the i-th land parcel evaluation unit, with a value ranging from 0 to 1; Coupling degree; This represents the normalized overall development level. The geometric mean, rather than the arithmetic mean, is used to reflect the weakest link effect; that is, if either system's level is too low, it will significantly reduce the coupling coordination, thus avoiding an artificially inflated result when one is high and the other low.

[0100] Layer attenuation correction. The distance attenuation coefficient of the layer in which the land parcel evaluation unit is located is superimposed to perform locational correction on the basic coupling coordination degree, resulting in the final gradient coupling coordination degree. The calculation formula is:

[0101] In the formula, Let be the gradient coupling coordination degree of the i-th land parcel evaluation unit, with a value ranging from 0 to 1; The land parcel evaluation unit is located in the first Distance attenuation coefficient of the concentric circles.

[0102] Through attenuation correction, the coupling coordination degree of different concentric circles becomes horizontally comparable, enabling a fairer evaluation of land use efficiency in different locations and avoiding evaluation bias caused by naturally high values ​​in the core area and naturally low values ​​in the peripheral area.

[0103] Based on the degree of gradient coupling coordination and the relative comparison between traffic accessibility and effective land use mix, all land parcel evaluation units are divided into four types, corresponding to different optimization directions. The specific division rules are as follows: First, determine the two-dimensional averages. Calculate the average of the traffic accessibility and the average of the effective land use mix for all land parcel evaluation units, using these as a reference benchmark for type classification.

[0104] Then, combining the gradient coupling coordination degree value with the two-dimensional comparison relationship, four types of land parcels were divided: The first category is the highly efficient and collaborative type, judged by a gradient coupling coordination degree ≥ 0.6, and both traffic accessibility and effective land use mix not lower than the corresponding average values. These plots have good traffic accessibility, reasonable land use mix, and high synergy between the two, making them highly efficient areas for urban land use.

[0105] The second category is traffic-constrained, defined as follows: 0.4 ≤ gradient coupling coordination degree < 0.6, with traffic accessibility below average and effective land use mix not below average. These plots meet the land use mix standard, but their insufficient traffic accessibility and inadequate traffic support capacity to meet functional needs are the main bottlenecks hindering efficiency improvement.

[0106] The third category is functionally constrained, defined by a gradient coupling coordination degree of 0.4 ≤ gradient coupling coordination degree < 0.6, and a traffic accessibility not lower than the average, while an effective land use mix is ​​lower than the average. These plots have good traffic conditions, but insufficient land use mix, a single function, and their potential for transportation supply is not fully realized.

[0107] The fourth category is the inefficient and unbalanced type, characterized by a gradient coupling coordination degree of <0.4 and both traffic accessibility and effective land use mix below the average. These plots exhibit low levels of both traffic and land use factors, weak interaction between the two, and overall low system efficiency, requiring focused intervention.

[0108] By classifying land into four categories, we can accurately identify the efficiency level and type of shortcomings of each plot, providing a basis for subsequent differentiated optimization and avoiding a one-size-fits-all, extensive optimization model.

[0109] Step S500: Develop optimization measures for different gradient layers and different types of land parcel evaluation units, and generate land use optimization schemes.

[0110] For example, differentiated overall optimization principles are formulated for the different development stages and characteristics of the three gradient layers, clarifying the optimization focus and direction for each layer: Central Area: Improving Quality and Efficiency, Maintaining Stable Scale. The central area has a relatively good transportation and land use foundation. Optimization focuses on enhancing the quality of mixed-use functions, strengthening the synergy between public transportation and land use, maintaining the basic stability of existing land parcel scale, and avoiding large-scale demolition and construction. Through micro-updates of functions and minor traffic modifications, the level of coupling and coordination will be further improved, leveraging the core area's demonstrative and radiating influence.

[0111] Middle Ring: Addressing Weaknesses and Strengthening Corridors. The immediate periphery is a key area for recent urban expansion, characterized by a clear separation of work and residential functions and significant transportation shortcomings. Optimization efforts should focus on opening up non-motorized transportation corridors, supplementing public transport micro-circulation routes, simultaneously improving supporting public services and commercial facilities, enhancing the effective land use mix, and strengthening transportation connections and functional integration with the city center.

[0112] Outer Ring: Optimize Structure, Focus on Core Areas. The outer areas are still underdeveloped, with limited functionality and weak transportation infrastructure. Optimization should focus on appropriately integrating fragmented plots, expanding unit scale, and centrally allocating core public service facilities to avoid excessive traffic congestion caused by over-mixing; prioritizing basic functions while gradually improving the level of integration.

[0113] Targeted optimization measures were matched to address the different shortcomings of the four types of land parcels, achieving differentiated policy implementation: Highly efficient and collaborative plots: Maintain and enhance the existing plot size and functional structure, and regularly assess their operational status; encourage the introduction of higher-level functional formats to improve spatial quality; strengthen the connection between pedestrian and cycling systems and public transport stops to further consolidate the level of collaboration. As efficient spatial nodes in the city, leverage their radiating and driving effect on the surrounding areas.

[0114] For land parcels with transportation constraints: a transportation priority strategy. A tiered approach is implemented based on the degree of constraint: For parcels with insufficient pedestrian accessibility, improve the secondary road network, connect dead-end roads, increase pedestrian access routes, and enhance pedestrian network connectivity; for parcels with insufficient accessibility for electric bicycles, plan and construct dedicated electric bicycle corridors, optimize pedestrian crossing facilities, and improve the efficiency of long-distance non-motorized travel; for parcels with insufficient public transportation coverage, add micro-circulation bus routes, increase stop density, and optimize departure frequency. Through tiered measures, transportation accessibility is improved, supporting the full realization of land use functions.

[0115] Functionally constrained land parcels: Functional optimization strategies. Adjust the land use functional structure and increase the proportion of mixed functions: For residential-dominated land parcels, supplement community commerce, convenience services, basic education and other facilities according to service radius requirements to improve the mixed living functions; for industry-dominated land parcels, build supporting facilities such as employee dormitories, canteens, and commercial services to promote a balance between work and residence. At the same time, optimize the internal functional layout of the land parcels, enhance functional activity, and fully leverage the supporting potential of transportation supply.

[0116] Inefficient and unbalanced land parcels: Comprehensive intervention strategy. Systematic transformation should be carried out from both transportation and functional dimensions: In terms of transportation, key roads should be opened up, the secondary road network improved, and public transport micro-circulation introduced to comprehensively enhance accessibility; in terms of function, land use designation should be adjusted, functional types increased, functional ratios optimized, and mixed-use development improved. For extremely inefficient land parcels, overall transformation can be carried out in conjunction with urban renewal to reconstruct spatial functions and transportation systems.

[0117] In situations of limited resources, to maximize input and output, this application employs a gradient priority principle to determine the optimization priority of inefficient land parcels, prioritizing intervention in areas with the largest gradient values. The specific method is as follows: Identify high-value anchor points. Highly efficient and collaborative land parcels are used as high-value anchor points for optimization and serve as the source points for gradient radiation.

[0118] Calculate the spatial gradient value. For each inefficient plot, including those constrained by traffic, constrained by function, and inefficiently unbalanced, firstly, use Euclidean distance to initially screen high-efficiency, collaborative plots within a 3-kilometer radius as candidate anchor points. Then, calculate the actual path distance along the road network and select the shortest path distance as the nearest high-value anchor point for that inefficient plot. Based on this, calculate the gradient value between the two, reflecting the degree of difference in spatial quality. The calculation formula is:

[0119] In the formula, The gradient value from inefficient plot i to high-value anchor point j; The gradient coupling coordination degree of the high-value anchor point j; The gradient coupling coordination degree of inefficient plot i; This represents the spatial distance between two plots of land, expressed in kilometers.

[0120] The larger the gradient value, the greater the quality difference between the two plots, the higher the marginal improvement benefit that can be obtained by investing resources in that location, and the stronger the urgency of optimization.

[0121] Prioritization. All inefficient land parcels are sorted from highest to lowest gradient value. The top 30% of parcels by gradient value are prioritized for optimization, the middle 40% are second-priority, and the bottom 30% are for long-term optimization. This gradient-based prioritization allows for the fastest improvement in overall coordination and efficiency with limited public resources, achieving the highest return on investment.

[0122] After the optimized solution is generated, the improvement in coupling coordination after optimization is quantitatively calculated to pre-evaluate the solution's effectiveness and verify the optimization result. The specific evaluation method is as follows: Sub-indicator improvement calculations. For each optimized plot, the improvement in traffic accessibility and effective land use mix is ​​calculated based on the optimization measures adopted: the improvement in traffic measures is calculated based on changes in coverage and traffic efficiency after facility improvements; the improvement in functional measures is calculated based on changes in functional structure after land use adjustments.

[0123] Optimized coupling coordination degree calculation. Substitute the improved traffic accessibility and effective land use mix into the coupling coordination model to recalculate the gradient coupling coordination degree of each plot.

[0124] Overall effectiveness assessment. Calculate the average gradient coupling coordination degree within the study area after optimization, compare it with before optimization, and calculate the overall improvement; statistically analyze the changes in the number of various types of land parcels to assess the transformation effect of inefficient land parcels; calculate the improvement in the synergistic connection between the two urban districts to assess the degree of improvement in cross-regional synergy.

[0125] If the optimization effect does not meet the expected goal through pre-assessment, the optimization measures and priorities are adjusted and the plan is regenerated until the performance requirements are met.

[0126] To more clearly illustrate the implementation process and effect of the elimination method in this application, the following detailed explanation is based on the application example of the integrated area of ​​Hantai District and Nanzheng District in Hanzhong City, Shaanxi Province.

[0127] This case study uses the integrated area of ​​Hantai District and Nanzheng District in Hanzhong City, Shaanxi Province as the research object to verify the effectiveness and adaptability of the proposed method. Hanzhong City is a typical small-to-medium-sized city in eastern Northwest China. Hantai District is the traditional core of the main city, gathering the city's main commercial, administrative, medical, and educational resources. After Nanzheng District was changed from a county to a district in 2017, the Dahekan area is separated from the main city of Hantai by the Han River and closely connected by transportation corridors such as the Tianhan Bridge and Longgang Bridge. The proportion of cross-district commuting has continued to increase, showing a significant trend of integrated development. However, due to the long-term administrative division, the land use plans of the two places are independently prepared, the cross-river transportation corridors are not sufficiently connected, and the single-center polarization is prominent. The core of Hantai gathers more than 80% of the city's high-quality public service resources, while Dahekan in Nanzheng is mainly residential. The public transportation coverage in the outer areas is weak, and the proportion of walking and electric bicycle travel exceeds 70%. The mismatch between transportation and land use is prominent, making it a typical representative of the research.

[0128] The experimental area covers the core of Hantai's main urban area, Nanzheng Dahekan, and surrounding contiguous development areas, totaling approximately 98 square kilometers. Basic data includes: 1:2000 scale urban road network vector data, comprising arterial roads, secondary arterial roads, and local roads, with a total road length of approximately 380 kilometers; location and route data for 19 bus lines and 327 bus stops in Hanzhong's central urban area; vector data on urban land use classification from the Third National Land Survey, including 7 types of land use such as residential, commercial, industrial, and public services, divided into 2186 plots; point-of-interest (POI) data obtained from the Gaode Map open platform, covering 12 major categories such as commerce, catering, education, and healthcare, totaling over 12400 points; and 2023 resident commuting survey data, covering travel modes, travel time, and commuting destination characteristics of 126 communities. Initial evaluation units were divided based on the areas enclosed by urban arterial roads, secondary arterial roads, and local roads, resulting in 612 plots, which served as the basic analysis units for the experiment.

[0129] The first step was to delineate the collaborative center and tiered concentric circles. The density of points of interest, employment density, and public service capacity of each street unit were calculated. The core area along Tianhan Avenue in the old city of Hantai was identified as the main urban core, with the highest degree of functional agglomeration. Commuting data showed that the commuting connection intensity between the core area of ​​Nanzheng Dahekan and the main core of Hantai reached 0.79, and the functional complementarity reached 0.72, both meeting the integration threshold. Therefore, this area was included in the overall evaluation scope of the collaborative center. Taking Tianhan Square as the functional center of the collaborative center, three tiered concentric circles were divided along the main extension corridor of Tianhan Avenue-Nanzheng Avenue: the central circle, within 3 kilometers of the center, covering an area of ​​22 square kilometers, with a corresponding attenuation coefficient of 1.0; the middle circle, within 3 to 6 kilometers, covering an area of ​​41 square kilometers, with a corresponding attenuation coefficient of 0.85; and the outer circle, within 6 kilometers and above, covering an area of ​​35 square kilometers, with a corresponding attenuation coefficient of 0.65.

[0130] The second step is to calculate traffic accessibility. Weights are assigned to different concentric circles: 0.6 for non-motorized travel and 0.4 for public transport in the central circle; and 0.8 for non-motorized travel and 0.2 for public transport in the middle and outer circles. Non-motorized travel accessibility is calculated using a 500-meter walking radius and a 1500-meter electric bicycle radius, respectively. A shortest path algorithm is used to generate network buffer zones, and a road network connectivity reduction coefficient is applied. For areas with incomplete branch road networks, the reduction coefficient is set between 0.7 and 0.9. Public transport accessibility is calculated based on the percentage of the area covered by a 300-meter buffer zone around bus stops. The results show that the average traffic accessibility is 67.2 in the central circle, 52.8 in the middle circle, and 34.6 in the outer circle, exhibiting a significant single-center gradient decay characteristic, which highly matches the actual traffic supply pattern in Hanzhong City.

[0131] The third step is to calculate the effective land use mix. The initial land use mix of each plot is calculated using an information entropy model: the average is 0.72 for the central ring, 0.61 for the middle ring, and 0.58 for the outer ring. After introducing functional complementarity correction between clusters, the residential plots in the Dahekan area, due to their close commuting connections with the Hantai core area, have an average increase in mix of 9.2%, better reflecting the actual situation of cross-regional complementarity between work and residence. Then, the optimal land use mix corresponding to the accessibility of each ring is matched, and the matching coefficient is calculated to correct the effective mix: for plots in the outer area with high surface mix but insufficient transportation support, the effective mix is ​​reduced by an average of 17.3%, eliminating the problem of inflated evaluation and making the evaluation results more consistent with the actual operational efficiency of the land.

[0132] The fourth step is to calculate the gradient coupling coordination degree and land parcel classification. First, the accessibility of transportation and the effective land use mix are normalized to the range of 0 to 1. The coupling degree and comprehensive development degree of each parcel are calculated using the formula, and the final gradient coupling coordination degree is obtained by overlaying the concentric ring attenuation coefficient. Statistical results show: 130 highly efficient and coordinated parcels, accounting for 21.3%, are mainly concentrated in the core business district and administrative center of Hantai, with high matching degree between transportation and land use functions; 176 transportation-constrained parcels, accounting for 28.7%, are mostly distributed in residential areas of the near-edge expansion zone and the outer transition zone, with land use mix meeting standards but insufficient transportation support; 147 functionally constrained parcels, accounting for 24.1%, are mainly industrial areas and newly built communities on the periphery, with acceptable transportation conditions but single functional configuration; 159 inefficient and unbalanced parcels, accounting for 25.9%, are concentrated in the outer industrial clusters and remote residential areas, with both factors at a low level.

[0133] The fifth step involves generating and simulating the implementation of optimization plans. Differentiated optimization plans are developed based on the classification results: In the central ring, the land use function ratio of 12 plots is adjusted, increasing the proportion of mixed commercial and public service facilities to enhance functional integration; in the middle ring, 8 new micro-circulation bus routes are planned, and 11 dead-end roads are connected to form continuous non-motorized vehicle corridors, with simultaneous support for community commercial and convenience service facilities; in the outer ring, 27 fragmented plots are integrated, with a moderately expanded unit scale, focusing on supplementing core public service facilities such as community service centers and primary and secondary schools to avoid traffic congestion caused by excessive mixing. A gradient priority principle is adopted, prioritizing intervention for the top 30% of inefficient plots based on their gradient values.

[0134] Optimize simulation results as follows Figure 2-9 As shown, in Figure 2 and Figure 3 Within the study area, the average gradient coupling coordination degree improved from 0.48 to 0.59, an overall improvement of 22.9%; the number of traffic-constrained plots decreased by 32, the number of functionally constrained plots decreased by 28, and the proportion of inefficient and unbalanced plots decreased to 16.8%. Figure 4 and Figure 5 As shown, the traditional homogeneous evaluation method has an accuracy rate of 61% in identifying mismatched plots, while this method improves the accuracy rate to 95%, an increase of 34 percentage points. Figure 6 and Figure 7 As shown, the correlation coefficient between accessibility assessment and residents' actual travel satisfaction increased from 0.57 to 0.82. Meanwhile, as... Figure 8 and Figure 9 As shown, the functional connectivity of the Hantai-Nanzheng cross-river corridor improved from 0.75 to 0.96, an increase of 28%, significantly releasing the synergistic effect between the two zones. This verifies the applicability and effectiveness of this method in integrated small and medium-sized cities and can provide technical support for land use optimization in similar cities.

[0135] Although preferred embodiments of this application 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 the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0136] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing land use in small and medium-sized cities, characterized in that, include: The system collects basic spatial data of the city, divides it into several spatial units according to preset boundaries, and uses the closed area enclosed by roads at all levels in the city as the boundary. Within the spatial units, it further divides them into several land parcel evaluation units. Based on each spatial unit, it calculates the degree of functional agglomeration and identifies the physical boundary of the city's main center. It calculates the commuting connection intensity and functional complementarity between adjacent urban areas. When both indicators reach a set threshold, it determines that the corresponding urban area has the foundation for integrated development and is included in the unified collaborative center. It calculates the functional center coordinates of the collaborative center, divides it into multiple gradient layers along the main expansion direction of the city with the functional center as the origin, and determines the gradient layer to which each land parcel evaluation unit belongs and the corresponding distance attenuation coefficient. For each of the land parcel evaluation units, the accessibility of non-motorized travel and public transport is calculated separately. Based on the corresponding sub-item weights matched according to the gradient circle to which the land parcel evaluation unit belongs, the traffic accessibility of each land parcel evaluation unit is calculated by weighting. For each land parcel evaluation unit, the degree of land use function mixing is calculated. For the residential land parcels in the outer ring, the functional complementarity effect between the clusters is supplemented to obtain the initial land use mixing degree. The optimal land use mixing degree corresponding to the traffic accessibility of each land parcel evaluation unit is matched, the matching coefficient is calculated and the initial land use mixing degree is corrected to obtain the effective land use mixing degree. For each land parcel evaluation unit, the coupling degree of the traffic accessibility and the effective land use mix degree and the comprehensive development level are calculated. Combined with the distance attenuation coefficient of the gradient layer to which it belongs, the gradient coupling coordination degree is obtained. Based on the level of the gradient coupling coordination degree, the land parcel type is classified by combining the comparison relationship between the traffic accessibility and the effective land use mix degree of each land parcel evaluation unit and the city average level. Optimization measures are formulated for different gradient layers and different types of land parcel evaluation units to generate land use optimization schemes.

2. The method for optimizing land use in small and medium-sized cities according to claim 1, characterized in that, The degree of coupling between the traffic accessibility and the effective land use mix is ​​calculated using a harmonic mean model, and the calculation formula is as follows: In the formula, The degree of coupling of the i-th land parcel evaluation unit; The normalized traffic accessibility of the i-th land parcel evaluation unit; The effective land use mix degree is the normalized result for the i-th land parcel evaluation unit.

3. The method for optimizing land use in small and medium-sized cities according to claim 1, characterized in that, The gradient coupling coordination degree is obtained by combining the distance decay coefficient of the corresponding gradient layer, including: First, calculate the geometric mean of the coupling degree and the normalized comprehensive development level to obtain the basic coupling coordination degree. Then, multiply the basic coupling coordination degree by the distance attenuation coefficient corresponding to the gradient circle to which the land parcel evaluation unit belongs to obtain the final gradient coupling coordination degree.

4. The method for optimizing land use in small and medium-sized cities according to claim 1, characterized in that, When matching the corresponding sub-item weights according to the gradient circle to which the land parcel evaluation unit belongs, the land parcel evaluation unit located in the central circle has a weight of 0.6 for non-motorized travel accessibility and 0.4 for public transport accessibility, while the land parcel evaluation units located in the middle and outer circles have a weight of 0.8 for non-motorized travel accessibility and 0.2 for public transport accessibility, respectively.

5. The method for optimizing land use in small and medium-sized cities according to claim 1, characterized in that, Calculating the matching coefficient and correcting the initial land use mix includes: Using the optimal land use mix degree corresponding to the traffic accessibility of the land parcel evaluation unit as a benchmark, the proportion of the deviation between the initial land use mix degree and the optimal land use mix degree to the optimal land use mix degree is calculated to obtain the matching coefficient; the initial land use mix degree is multiplied by the matching coefficient to obtain the effective land use mix degree.

6. The method for optimizing land use in small and medium-sized cities according to claim 1, characterized in that, To enhance the complementary functions between residential clusters in the outer ring, including: Identify the land parcel evaluation units located in the outer ring and with residential land accounting for 60% or more. Using the commuting connection intensity between the land parcel evaluation unit and the central ring as a weight, and combining it with the average land use mix of the central ring, calculate the functional complementarity correction value. Add the initial mix of the land parcel evaluation unit's initial mix value, which is determined based on the degree of land use mix, to the functional complementarity correction value to obtain the initial land use mix.

7. The method for optimizing land use in small and medium-sized cities according to claim 1, characterized in that, Calculate the commuting connectivity and functional complementarity between adjacent urban areas, including: The proportion of two-way commuting volume between two adjacent urban areas to the total commuting volume of each urban area is calculated, and the average of the two proportions is taken to obtain the commuting connection intensity. Calculate the cosine similarity of the employment structure in two adjacent urban areas, and subtract the cosine similarity from 1 to obtain the degree of functional complementarity. When both the commuting connection strength and the functional complementarity are not lower than the corresponding set thresholds, it is determined that the two adjacent urban areas have the foundation for integrated development, wherein the thresholds for the commuting connection strength and the functional complementarity are 0.7 and 0.6, respectively.

8. The method for optimizing land use in small and medium-sized cities according to claim 1, characterized in that, The steps for classifying land parcel types are as follows: Calculate the average of the traffic accessibility and the average of the effective land use mix for all the aforementioned land parcel evaluation units; Based on the numerical range of the gradient coupling coordination degree, and the comparison between the traffic accessibility and the effective land use mix of each land parcel evaluation unit and the corresponding average value, the land parcel evaluation units are divided into four categories: high-efficiency coordination type, traffic-constrained type, functional-constrained type, and low-efficiency imbalance type.

9. A method for optimizing land use in small and medium-sized cities according to claim 8, characterized in that, Generate land use optimization plans, including: Using the highly efficient and collaborative land parcel evaluation unit as a high-value anchor point, the gradient value between other types of land parcel evaluation units and the high-value anchor point with the closest spatial distance is calculated. The gradient value is the ratio of the difference in gradient coupling coordination degree between two land parcel evaluation units to the spatial distance. The optimization priorities of the land parcel evaluation units, excluding the high-value anchor points, are sorted according to the gradient values ​​from largest to smallest.

10. A method for optimizing land use in small and medium-sized cities according to claim 1, characterized in that, Optimization measures are formulated for different gradient layers and different types of land parcel evaluation units, including: The central ring focuses on stabilizing land parcel scale and enhancing functional integration; the middle ring focuses on addressing shortcomings in transportation infrastructure and improving supporting service functions; and the outer ring focuses on integrating fragmented land parcels and concentrating on basic functional configurations. The different types of land parcel evaluation units correspond to optimization paths such as improving transportation infrastructure, adjusting land use functions, or multi-dimensional comprehensive intervention.

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