Spatial special planning review analysis method

Abstract

The invention belongs to the technical field of urban and rural planning, and provides a spatial special planning review analysis method, which comprises the following steps: acquiring urban population and position distribution data, dividing urban unit areas, and calculating a population aggregation index and a job-to-housing ratio; based on the classified summary, a regional imbalance group is determined, and a current pendulum type traffic judgment value is calculated to judge whether pendulum type traffic occurs or not; if not, combining a population gathering trend, and using an ARIMA model to predict an imbalance value and moment; analyzing the regional imbalance group, identifying and predicting an imbalance aggravation region, judging whether an imbalance chain reaction exists or not, and calculating an adjustment priority value if the imbalance chain reaction exists; and sequentially optimizing the planning according to the priority values, adding employment posts in the first imbalance area, adding residential lands in the second imbalance area, and optimizing the planning of the traffic network. According to the method, unbalance can be accurately pre-judged, a basis is provided for planning optimization, and the resource use efficiency is improved.

Description

Technical Field

[0001] This invention belongs to the field of urban and rural planning technology, specifically a method for reviewing and analyzing spatial special plans. Background Technology

[0002] In the field of urban and rural planning technology, with the development of urban traffic planning and management, avoiding pendulum-style traffic has become an important direction for improving urban operational efficiency. By scientifically planning the layout of work and residence spaces and balancing the number of jobs and residents within a region, it is possible to effectively reduce tidal commuting, alleviate traffic pressure, and improve the quality of life for residents. However, current technology has significant limitations in addressing pendulum-style traffic patterns. Because the planning of the new area failed to adequately consider the dynamic matching of the job-housing ratio with traffic capacity, focusing solely on residential land planning while neglecting employment opportunities, a severe job-housing imbalance has emerged after residents moved in. This means a large number of residents need to commute across areas, exacerbating congestion on commuting routes. This commuting congestion caused by the imbalance in job-housing planning disrupts the normal operation of urban transportation, increases residents' commuting costs, and has become a key bottleneck restricting the sustainable development of the new area.

[0003] Therefore, this invention provides a method for reviewing and analyzing spatial special plans. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0005] The technical solution adopted by this invention to solve its technical problem is: a spatial special planning review and analysis method comprising: The city's two-dimensional plan is evenly divided into several urban unit areas. The population, number of jobs, and area of ​​each urban unit area are counted. The current population concentration index and current job-housing ratio of the urban unit area are calculated. Based on the current population agglomeration index and the current job-housing ratio, urban unit areas are classified and summarized to identify regional imbalance groups, calculate the current pendulum traffic judgment value for each regional imbalance group, and determine whether pendulum traffic phenomenon is currently occurring. If it is determined that pendulum-style traffic is not currently occurring, analyze the population agglomeration trend and job-housing ratio change trend of urban unit areas, and calculate the predicted imbalance value and predicted imbalance time of each regional imbalance group. An imbalance aggravation analysis is performed on each regional imbalance group to identify regions predicted to aggravate imbalance. A spatial syntactic model is used to determine whether there is an imbalance chain reaction. If so, the adjustment priority value of the predicted imbalance aggravation region is calculated. Based on the adjustment priority value of the predicted areas of increased imbalance, spatial special planning optimization is carried out for the regional imbalance groups in turn.

[0006] The beneficial effects of this invention are as follows: This invention incorporates pendulum traffic assessment values ​​into the review of spatial special planning, and combines the predicted imbalance values, imbalance times, and spatial correlations of regional imbalance groups to construct an evaluation model to quantify the impact of planning on pendulum traffic. Simultaneously, it innovatively establishes a review priority mechanism based on adjustment priority values ​​(combining the chain reaction sequence, predicted imbalance values, and population agglomeration index). Specifically, based on the degree of impact of each planning scheme on the pendulum traffic assessment value, and combined with the adjustment priority value ranking, necessary plans that can reduce the pendulum traffic assessment value (such as creating new jobs in the first imbalance area and increasing residential land in the second imbalance area) are reviewed and approved first, while unnecessary plans that exacerbate the imbalance are rejected. This optimizes the technical bottleneck of spatial special planning reviews not being linked to the impact of pendulum traffic, being unable to control unnecessary plans, and lacking mitigation basis, achieving precise alignment between planning review and pendulum traffic mitigation. Attached Figure Description

[0007] The invention will now be further described with reference to the accompanying drawings.

[0008] Figure 1 This is a flowchart illustrating the steps of a spatial special planning review and analysis method according to an embodiment of the present invention.

[0009] Figure 2 This is a schematic diagram of the logical judgment of a spatial special planning review and analysis method according to an embodiment of the present invention. Detailed Implementation

[0010] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0011] Please see Figure 1 - Figure 2 As shown in Embodiment 1 of the present invention, a spatial special planning review and analysis method includes the following steps: This scheme provides a quantitative basis for the review of spatial planning by calculating the pendulum traffic judgment value of regional imbalance groups, predicting imbalance trends, and adjustment priority values. During the review, the core criterion is whether it can alleviate pendulum traffic. Plans that can reduce the judgment value are given priority in the adjustment priority ranking and are approved first. Irrelevant plans that aggravate the imbalance are rejected, so that the review of spatial planning directly serves the goal of alleviating pendulum traffic.

[0012] It should be noted that the following study does not consider the case of autonomous expansion of urban area; Step 1: Divide the city's two-dimensional map evenly into several city unit areas, count the population, number of jobs, and area of ​​each city unit area, and calculate the current population concentration index and current job-housing ratio of each city unit area; In step one, the process of obtaining current city population distribution data and current city job distribution data, and dividing the city's two-dimensional map into several unit areas, is as follows: The urban population distribution data refers to the number of residents in different areas of the city (such as a street, a community, or a divided urban unit area); including but not limited to the number of residents in each community in the city that can be obtained through regular sampling surveys by government statistical departments, i.e., the current urban population distribution data. Urban job distribution data refers to the number of jobs in different industries and types within a city's various regions (the number of jobs provided in different locations such as office buildings in commercial areas, factories in industrial areas, and service establishments in the city); including but not limited to the total number of jobs in each region of the city that can be obtained through data published by government human resources departments, enterprise registration information, etc. Obtain current city population distribution data and current city job distribution data; Obtain the city's web map package, and use ArcGIS software to interface the current city's population distribution data and job distribution data with the geographic data in the web map package to output a two-dimensional city map. The city's two-dimensional plan is divided into several city unit areas with the same area; In step one, the process of calculating the population agglomeration index for each urban unit area is as follows: For any given urban unit area, calculate the current population of that urban unit area. The ratio of the current population of the urban unit area to the current total urban population is used as the current population percentage of the unit. The area of ​​this urban unit is calculated as the ratio of the total urban area to the unit area percentage. The urban population density is obtained by dividing the current total urban population by the total urban area. The current population density of the urban unit area is obtained by comparing the current population density with the area of ​​the urban unit area. The relative density index of the urban unit area is then obtained by comparing the relative population density with the urban population density. The ratio of the current population proportion to the area proportion of the urban unit area is calculated, and then multiplied with the current relative density index to obtain the current population agglomeration index of the urban unit area. It should be noted that the physical significance of obtaining the current population agglomeration index of an urban unit area lies in: The current population agglomeration index of an urban unit area is obtained by analyzing three factors: unit population ratio, unit area ratio, and relative density index. Among them, the unit population ratio reflects the proportion of the population of each urban unit area in the total population of the city, the unit area ratio reflects the proportion of the area of ​​each urban unit area in the total area of ​​the city (if the proportions are the same), and the relative density index reflects the population density of the urban unit area. The larger the unit population ratio and the larger the relative density index, the larger the population agglomeration index, indicating that the population of the urban unit area is more concentrated.

[0013] In step one, the process of calculating the current job-housing ratio of the urban unit area is as follows: Based on a two-dimensional city map, count the number of jobs in each city unit area; For any city unit area, the current number of positions in that city unit area is recorded as the current number of positions in the unit. The current job-housing ratio of a unit is obtained by comparing the current number of jobs in the unit with the current population of the unit. The beneficial effects of obtaining the current job-housing ratio for urban unit areas are: Effect 1: Classify urban unit areas by dividing them into high and low job-housing ratio urban unit areas based on the current job-housing ratio. Combine the overlapping areas of high and low job-housing ratio urban unit areas to achieve the classification of urban unit areas. Effect 2: Provides a method for calculating the job-housing ratio to predict imbalance values ​​and the timing of imbalances. By repeatedly calculating the job-housing ratio of historical urban unit areas, the development trend of the job-housing ratio is analyzed, and combined with the corresponding population agglomeration trend, the imbalance value and the timing of imbalance are predicted.

[0014] Step 2: Based on the current population agglomeration index and current job-housing ratio of urban unit areas, classify and summarize each urban unit area, identify the regional imbalance group, and calculate the current pendulum traffic judgment value of each regional imbalance group to determine whether pendulum traffic phenomenon has occurred. In step two, the process of classifying and summarizing the various city unit areas is as follows: Compare the current job-housing ratio of all urban unit areas with the job-housing ratio threshold: If the current job-housing ratio of a city unit area is greater than or equal to the job-housing ratio threshold, it is determined to be a city unit area with a high job-housing ratio. If the current job-housing ratio of an urban unit area is less than the job-housing ratio threshold, it is determined to be an urban unit area with a low job-housing ratio. Compare the current population agglomeration index of each urban unit area with the population agglomeration index threshold: If the current population agglomeration index of a city unit area is greater than or equal to the population agglomeration index threshold, it is determined to be a highly agglomerated city unit area. If the current population agglomeration index of a city unit area is less than the population agglomeration index threshold, it is determined to be a low-agglomeration city unit area. Among them, the job-housing ratio threshold and the population agglomeration index threshold are set by technical personnel in relevant fields based on research on actual scenarios; By merging adjacent urban unit areas of the same category, we can obtain areas with high job-housing ratio, low job-housing ratio, high aggregation, and low aggregation (for example, merging two adjacent urban unit areas with high job-housing ratio results in a high job-housing ratio area). The intersection of high-aggregation areas and low-job-housing-ratio areas is defined as the first imbalance area. The intersection of low-aggregation areas and high-job-housing-ratio areas is defined as the second imbalance area. The intersection of high-aggregation areas and high-job-housing-ratio areas is defined as a job-housing balance area. The intersection of low-aggregation areas and low-job-housing-ratio areas is defined as a sparse, balanced area. In step two, the process of calculating the current pendulum traffic judgment value for each area imbalance group is as follows: Based on any first imbalance region, select the second imbalance region with the smallest spatial distance (the spatial distance between the spatial center of the first imbalance region and the spatial center of the second imbalance region), integrate the two into a region imbalance group, and summarize all region imbalance groups into a region imbalance set. Among them, the first imbalanced area in the regional imbalance group is: the current population of this area is relatively large and the number of jobs is relatively small (i.e., the commuting departure area), and the second imbalanced area is: the current population of this area is relatively small and the number of jobs is relatively large (i.e., the commuting destination area). At the same time, the closer the spatial distance between the two types of areas, the lower their actual commuting cost, which can reflect the commuting supply and demand relationship between the two types of areas to a certain extent. Based on any regional imbalance group, the difference between the mean of the current population agglomeration index in the first imbalance region and the mean of the current population agglomeration index in the second imbalance region is processed and the absolute value is taken to obtain the current population agglomeration index difference. It should be noted that since both the first and second imbalanced regions contain multiple urban unit areas, it is necessary to calculate the average of the current population agglomeration index of the urban unit areas as the current population agglomeration index of the first or second imbalanced region. The difference between the mean current job-housing ratio in the first imbalanced region and the mean current job-housing ratio in the second imbalanced region is taken as the absolute value to obtain the current job-housing ratio difference. The current pendulum traffic judgment value is obtained by multiplying the spatial distance of the class area, the difference in the current population agglomeration index, and the difference in the current job-housing ratio. It should be noted that the reason for selecting the first and second imbalance zones to calculate the current pendulum traffic judgment value is as follows: The essence of pendulum traffic is a large-scale, one-way, tidal spatial separation between residences and workplaces. That is, a large number of people live in one type of area but must go to another type of area for employment, forming a regular flow of morning and evening peaks. Therefore, to determine whether it occurs, both spatial separation and scale matching must be satisfied. The work-residence balance type and the sparse balance type meet the conditions and pendulum traffic will not occur. The first imbalance type and the second imbalance type do not meet the conditions. Therefore, the first imbalance type and the second imbalance type are selected to calculate the current pendulum traffic judgment value.

[0015] In step two, the process of determining whether a pendulum-style traffic phenomenon will occur is as follows: The current pendulum traffic judgment value of each regional imbalance group is compared with the pendulum traffic judgment threshold: If the current pendulum traffic judgment value is greater than or equal to the pendulum traffic judgment threshold, then the pendulum traffic phenomenon will occur in the current imbalance group of the region, and the imbalance group of the region will be optimized and adjusted. The optimization and adjustment methods include, but are not limited to: optimizing the public transportation network between the first and second imbalanced areas in the regional imbalance group; If the current pendulum traffic judgment value is less than the pendulum traffic judgment threshold, then the pendulum traffic phenomenon will not occur in the current imbalance group of the judgment area. Step 3: If it is determined that pendulum-style traffic is not currently occurring, analyze the population agglomeration trend and job-housing ratio change trend of urban unit areas, and calculate the predicted imbalance value and predicted imbalance time for each area imbalance group. Based on any regional imbalance group, we analyze the population agglomeration trend and job-housing ratio change trend in the first imbalance region, and the population agglomeration trend and job-housing ratio change trend in the second imbalance region. Taking the population agglomeration trend analysis of the first imbalanced region as an example: Obtain historical population distribution data of the first imbalanced region before the current time point, divide it according to the set time granularity (such as monthly, annual, etc.), calculate the population agglomeration index of the first imbalanced region at each time granularity (without considering changes in unit area and city area, only considering population flow), and obtain the time series sequence of historical population agglomeration index corresponding to the first imbalanced region. Based on the time series of population agglomeration, the population agglomeration trend of the first imbalance region is analyzed by using the autoregressive integral moving average model, and the set of predicted population agglomeration indices for the next h time granularities is obtained. Based on any time granularity: The difference between the predicted population agglomeration index of the first imbalanced region and the predicted population agglomeration index of the second imbalanced region is taken as the absolute value to obtain the difference in the predicted population agglomeration index. The difference between the predicted job-housing ratio of the first imbalanced region and the predicted job-housing ratio of the second imbalanced region is taken as the absolute value to obtain the difference in the predicted job-housing ratio. The spatial distance between the imbalanced areas, the difference in the predicted population agglomeration index, and the difference in the predicted job-housing ratio are multiplied to obtain the predicted pendulum traffic judgment value. The predicted pendulum traffic judgment values ​​are compared with the pendulum traffic judgment thresholds: If the predicted pendulum traffic judgment value is greater than or equal to the pendulum traffic judgment threshold, the regional imbalance group at that time granularity is determined to be imbalanced. The difference between the predicted pendulum traffic judgment value and the pendulum traffic judgment threshold is processed to obtain the predicted imbalance value. The starting time point of the time granularity corresponding to the predicted imbalance value is taken as the predicted imbalance time. If the predicted pendulum traffic judgment value is less than or equal to the pendulum traffic judgment threshold, the regional imbalance group at that time granularity is judged to be normal. It should be noted that the beneficial effects of calculating the predicted imbalance values ​​and predicted imbalance times for each regional imbalance group are as follows: Effect 1: It can estimate the specific time and degree of imbalance that triggers pendulum-like traffic in the regional imbalance group, providing data support for subsequent identification and prediction of areas where imbalance will worsen. Effect 2: Provides quantitative evidence for subsequent judgment of time series in imbalance chain reactions; Step 4: Perform imbalance aggravation analysis on each imbalance group, identify the areas where imbalance is predicted to worsen, and combine the predicted significant imbalance moment (i.e. the time point when the predicted imbalance value reaches its maximum value) to determine whether there is an imbalance chain reaction through a spatial syntactic model. If so, calculate the adjustment priority value of the areas where imbalance is predicted to worsen. In step four, the process of identifying the predicted areas of increased imbalance is as follows: For any imbalanced region group (consisting of the first imbalanced region A and the second imbalanced region B), among all urban unit regions between the adjacent geographical boundaries of A and B, extract the urban unit regions that border A and B (i.e., adjacent urban unit regions), and denote them as the connecting unit set. For example, the eastern boundary of A is adjacent to the western boundary of B, and the eastern boundary of A is adjacent to the western boundary of B. There are 20 urban unit areas between the two boundaries. Extract the 3 urban unit areas adjacent to the eastern boundary of A and the 2 urban unit areas of the western boundary of B, for a total of 5 urban unit areas as the connecting unit set. Based on any urban unit area in the connection unit set, obtain its predicted pendulum traffic judgment value at the next h time granularities; If the predicted pendulum traffic judgment value of the city unit area shows an upward trend in future time granularity (e.g., 120 in March 2024 → 150 in April 2024 → 180 in January 2025), then the city unit area is marked as an upward unit. If the predicted pendulum traffic judgment value of the urban unit area does not continuously increase in the future time granularity, it will not be marked. It should be noted that the process for determining the upward trend of the predicted pendulum traffic judgment value in the future time granularity for urban unit areas is as follows: Based on any urban unit area within the connecting unit set, the future h time granularities (e.g., from March 2024 to January 2025, a total of h=11 months) are converted into a numerical sequence, denoted as the time variable time series sequence T={1,2,3,4...t}. The predicted pendulum traffic judgment values ​​for each time granularity are then arranged in time series to obtain the predicted pendulum traffic judgment value time series sequence. ; Calculate the Pearson correlation coefficient of the time series of predicted pendulum traffic judgment values. If it is greater than or equal to the standard Pearson correlation coefficient, it is judged to be linearly correlated, and linear regression analysis is performed to calculate the linear regression slope b. The linear regression equation is as follows:

[0016] Calculate the mean of time variables in a time series. The mean of the predicted pendulum traffic decision values ​​in the time series of predicted pendulum traffic decision values. ,variance Covariance of time series of time variables and time series of predicted pendulum traffic decision values ; Calculate the linear regression slope b:

[0017] ; If b is positive, it indicates that the predicted pendulum traffic judgment value of the city unit area is on an upward trend in the future time granularity; if b is negative, it indicates that the predicted pendulum traffic judgment value of the city unit area is on a downward trend in the future time granularity; if b is 0, it indicates that the predicted pendulum traffic judgment value of the city unit area remains unchanged in the future time granularity. If the Pearson correlation coefficient of the predicted pendulum traffic judgment value time series is less than the standard Pearson correlation coefficient, it is judged to be nonlinear correlation. By fitting different nonlinear regression models, the parameters that can reflect the changing trend of each nonlinear regression model are analyzed to determine whether it is an upward trend. For example, the nonlinear regression equation is a quadratic curve regression model, which has the following form: ; Where t is the time variable within the time series, x is the predicted pendulum traffic decision value within the time series, and c is a constant term; If a>0 and d>0: the curve bends upward, and the predicted pendulum traffic judgment value increases rapidly with time (e.g., x=5 at time t=1, x=12 at time t=2, x=23 at time t=3), showing an upward trend; If a<0 but d>0, the slope of the tangent line of the curve is always positive (i.e., 2at+d>0): the growth rate of the pendulum traffic judgment value is slowing down but still continues to rise (e.g., x=20 at t=1, x=25 at t=2, x=28 at t=3), and it is still judged as an upward trend.

[0018] Calculate the proportion of rising units in the set of connecting units, and denot it as the rising proportion of connecting units; The total number of connecting unit sets refers to the total number of urban unit areas within the connecting unit set. If the proportion of the connecting units in a regional imbalance group increases to a preset threshold (e.g., 60%, set by technicians based on the density of urban units), then the regional imbalance group is determined to be a region where the imbalance is predicted to worsen.

[0019] For example: There are 5 connecting units in a certain imbalance group, of which 4 are rising units, accounting for 80% (≥60%), so it is marked as a region where the imbalance is predicted to worsen; It should be noted that the beneficial effects of identifying areas where the predicted imbalance is exacerbated are: Focusing on the connecting unit between the first and second imbalance areas, and through the upward trend of its predicted pendulum traffic judgment value, the specific area range of future imbalance deterioration is accurately delineated, providing a basis for subsequent judgment of spatial correlation and construction of continuous spatial chain; In step four, the process of determining whether a chain reaction exists is as follows: Sorting and Time Series Validation: Extract all regions where the predicted imbalance has worsened, calculate the maximum predicted imbalance value for each region, sort them in descending order, and then arrange the corresponding regions in the same order to obtain a sequence of regions where the predicted imbalance has worsened. : Set an allowable deviation ratio (e.g., ≤20%): If the number of regions that do not meet the time sequence requirement accounts for ≤20% of the total number of linked regions, it is considered an extremely rare deviation (e.g., only 1 out of 5 regions does not meet the requirement, the ratio is 20%). Extracting significant moments of predicted imbalance from the predicted imbalance aggravation region sequence (i.e., the starting time point of the time granularity corresponding to the maximum predicted imbalance value), and filter out those that meet the requirements. The longest continuous subsequence (i.e., the region of increased predicted imbalance corresponding to the largest predicted imbalance value appears earliest, and subsequent regions of increased predicted imbalance appear later) forms the core time chain, and will not satisfy the following conditions. The continuous subsequence is defined as the deviation region; If the core time chain contains an aggravated region of (1 - allowable deviation ratio) (e.g., when n=5, the core chain contains 4 or more aggravated regions of prediction imbalance), then the time chain condition is satisfied. For example: the sequence predicting the region of increased imbalance is ( The corresponding significant prediction imbalances are in January 2024, February 2024, April 2024 (deviation), March 2024, and May 2024; among which... (April 2024) Lagging behind (March 2024), then the core timeline is: ( The region contains 80% of the area, with a deviation rate of 20%, and is therefore considered to meet the time-chain condition.

[0020] Spatial correlation verification: Examine whether spatial connectivity exists in the regions where the predicted imbalance is exacerbated after sorting: and The set of geographically adjacent units is a non-empty set (geographically adjacent). and The set of regional connection units is non-empty, and so on, forming an undetermined continuous spatial chain; If the deviation region is geographically connected to both the preceding and following regions in the core time chain (e.g.) and , If all of them are adjacent, then they are included in the undetermined continuous space chain to obtain the final continuous space chain; If the deviation region is spatially isolated (e.g.) If a region is not connected to any region corresponding to the core time chain, it is removed from the undetermined continuous space chain, and only the undetermined continuous space chain corresponding to the core time chain is retained to obtain the final continuous space chain. If the sorting and time series verification and spatial correlation verification are satisfied at the same time, it is determined that there is an imbalance chain reaction, and the chain path is: the final continuous spatial chain; If the sorting and time series verification and spatial correlation verification are not satisfied at the same time, it is determined that there is no imbalance chain reaction, and spatial special planning optimization of the corresponding regional imbalance group is carried out in sequence according to the order of the predicted maximum imbalance value. It should be noted that the beneficial effect of determining whether an imbalance chain reaction exists is that: By verifying the temporal and spatial correlations, it is determined whether the imbalanced region forms a continuous spatiotemporal chain. The identified core temporal chain and final continuous spatial chain provide a direct basis for calculating the chain reaction sequence in the adjustment priority value. In step four, the process of calculating the adjustment priority value for the predicted area of ​​increased imbalance is as follows: Extract the regions where the predicted imbalance is aggravated, and calculate the proportion of indicators of different magnitudes for each region where the predicted imbalance is aggravated (the larger the sum of the proportions, the higher the priority of the adjustment of that indicator). Among them, the indicators of different magnitudes refer to: the predicted imbalance value, the predicted population agglomeration index, and the chain reaction order (the order in the final continuous spatial chain) of the region where the predicted imbalance is aggravated. Chain reaction sequence: The earlier a region appears in the final continuous spatial chain (e.g., the source region). The higher the percentage, the better; The total number of predicted regions of increased imbalance in the final continuous spatial chain is counted. ; Extract the chain reaction sequence of the region where the predicted imbalance worsens at the moment when the predicted imbalance becomes significant. (The sorting in the final contiguous chain, for example, sorting to 2, i.e.) =2); Therefore, the order of chain reactions is as follows: ; Predicted Imbalance Value: The total predicted imbalance value is obtained by summing the predicted imbalance values ​​of all regions where the predicted imbalance worsens at the moment of significant predicted imbalance. ; Extract the maximum prediction imbalance value in the region where the prediction imbalance worsens at the moment when the prediction imbalance becomes significant. ; The predicted proportion of imbalance values ​​is: ; Predicted Population Agglomeration Index: The predicted population agglomeration index of all regions with increased prediction imbalance at the moment of significant prediction imbalance is summed to obtain the total predicted population agglomeration index. ; Extract the predicted population clustering index of the region where the prediction imbalance worsens at the moment when the prediction imbalance is significant. ; The predicted population agglomeration index percentage is: ; The adjustment priority value for the region where the predicted imbalance is aggravated is obtained by summing the proportion of the chain reaction order, the proportion of the predicted imbalance value, and the proportion of the predicted population agglomeration index. It should be noted that the beneficial effect of calculating the adjustment priority value for regions where imbalances are exacerbated is that: By comprehensively predicting imbalance values, population agglomeration indices, and chain reaction sequences, the adjustment priority values ​​for each region are quantified, providing a clear standard for the priority order of planning optimization. This ensures that measures such as creating new jobs and increasing residential land can be accurately implemented in the areas that need them most, thereby improving the efficiency of planning resource utilization. Step 5: Based on the adjustment priority values ​​of the predicted areas of increased imbalance, spatial special planning optimization is carried out sequentially for the regional imbalance groups; In step five, the process of carrying out spatial-related special planning optimization for the regional imbalance group is as follows: Extract the adjustment priority value for each region where the predicted imbalance is exacerbated; The adjustment plan is as follows: Each predicted area of ​​increased imbalance will be adjusted according to its corresponding adjustment priority value, prioritizing the adjustment before the predicted pendulum traffic judgment value reaches the pendulum traffic judgment threshold (the higher the adjustment priority value, the higher the adjustment priority). For the first imbalance area in the predicted area of ​​increased imbalance, spatial special plans that create new jobs (such as converting idle commercial land into industrial parks and introducing service industry jobs that match the skills of the regional population) will be approved. For the second imbalanced area in the predicted area of ​​increased imbalance, the spatial special planning should be approved if it involves increasing the supply of residential land (such as building talent apartments and affordable housing), supporting education and medical facilities, and increasing the proportion of the resident population to the total number of jobs. Meanwhile, spatial special-purpose plans that optimize the transportation network between the first and second imbalanced areas (such as adding new subway branch lines and increasing the frequency of commuter buses) to shorten commuting time were approved. Spatial planning that does not fall under the aforementioned special spatial categories will be rejected upon review. The technical solution of this invention is as follows: Obtain current urban population distribution data and current urban job distribution data; uniformly divide the city's two-dimensional plan into several urban unit areas; calculate the current population agglomeration index and current job-housing ratio for each urban unit area; based on the current population agglomeration index and current job-housing ratio of each urban unit area, classify and summarize each urban unit area, identify regional imbalance groups, and calculate the current pendulum traffic judgment value for each regional imbalance group to determine whether pendulum traffic is currently occurring. If it is determined that pendulum traffic is not currently occurring, analyze the population agglomeration trend and job-housing ratio change trend of the urban unit areas; calculate the predicted imbalance value and predicted imbalance time for each regional imbalance group; conduct imbalance aggravation analysis on each regional imbalance group; identify areas where predicted imbalance aggravation is occurring; combine the predicted significant imbalance time (i.e., the time point when the predicted imbalance value reaches its maximum value); use a spatial syntax model to determine whether there is an imbalance chain reaction; if so, calculate the adjustment priority value for areas where predicted imbalance aggravation is occurring; based on the adjustment priority value for areas where predicted imbalance aggravation is occurring, sequentially carry out spatial special planning optimization for regional imbalance groups.

[0021] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A spatial special planning review analysis method, characterized in that: The application relates to a method for predicting and preventing pendulum traffic. The method comprises the following steps: Dividing a two-dimensional city plan into several city unit regions, counting the population number and the position number of each city unit region, calculating the current population aggregation index and the current job-housing ratio of the city unit region; Based on the current population aggregation index and the current job-housing ratio, the city unit region is classified and summarized to determine the regional imbalance group, calculate the current pendulum traffic judgment value of each regional imbalance group, and determine whether the pendulum traffic phenomenon occurs currently; If it is determined that the pendulum traffic phenomenon does not occur currently, the population aggregation trend and the job-housing ratio change trend of the city unit region are analyzed, the predicted imbalance value and the predicted imbalance time of each regional imbalance group are calculated; The imbalance intensification of each regional imbalance group is analyzed, the predicted imbalance intensification region is identified, and whether there is an imbalance chain reaction is determined through a space syntax model. If there is, the adjustment priority value of the predicted imbalance intensification region is calculated; 2. The method of claim 1, wherein: Based on the adjustment priority value of the predicted imbalance intensification region, the space special planning optimization of the regional imbalance group is carried out in sequence. The process of calculating the current population aggregation index and the current job-housing ratio of the city unit region comprises the following steps: For each city unit region; The proportion of the current population number of the unit in the total population number of the city is calculated, which is recorded as the current unit population proportion; The proportion of the unit area in the total area of the city is calculated, which is recorded as the unit area proportion; The total population number of the city is processed by ratio with the total area of the city to obtain the city population density; The current relative density index = the current population number of the unit / the unit area / the city population density; The current population aggregation index = the current unit population proportion * the current relative density index / the unit area proportion; 3. The method of claim 1, wherein: The current job-housing ratio = the current job number of the unit / the current population number of the unit. The process of determining the regional imbalance group comprises the following steps: If the current population aggregation index of the city unit region exceeds the standard, the city unit region is a high aggregation city unit region, and if the current population aggregation index of the city unit region does not exceed the standard, the city unit region is a low aggregation city unit region; If the current job-housing ratio of the city unit region exceeds the standard, the city unit region is a high job-housing ratio city unit region, and if the current job-housing ratio of the city unit region does not exceed the standard, the city unit region is a low job-housing ratio city unit region; Adjacent city unit regions of the same category are combined to obtain a high job-housing ratio region, a low job-housing ratio region, a high aggregation region and a low aggregation region; The intersection part of the high aggregation region and the low job-housing ratio region is defined as a first imbalance region; 4. The method of claim 3, wherein: The intersection part of the low aggregation region and the high job-housing ratio region is defined as a second imbalance region. The process of determining whether the pendulum traffic phenomenon occurs currently comprises the following steps: The spatial distance between the spatial center position of the first imbalance region and the spatial center position of the second imbalance region is defined as the class region spatial distance between the first imbalance region and the second imbalance region; Based on any first imbalance region, the second imbalance region with the minimum class region spatial distance is selected, the two regions are combined into a regional imbalance group, the current population aggregation index difference and the current job-housing ratio difference of the regional imbalance group are calculated, and the three are multiplied to obtain the current pendulum traffic judgment value; If the current pendulum traffic judgment value is less than the pendulum traffic judgment threshold value, it is determined that the pendulum traffic phenomenon does not occur currently in the regional imbalance group.

5. The method of claim 4, wherein: The process of calculating the current population aggregation index difference value and the current job-housing ratio difference value of the imbalance group of the calculation area is: calculating the absolute value of the difference between the current population aggregation index average in the first imbalance area and the second imbalance area, to obtain the current population aggregation index difference value; calculating the absolute value of the difference between the current job-housing ratio average in the first imbalance area and the second imbalance area, to obtain the current job-housing ratio difference value.

6. The method of claim 1, wherein: The process of calculating the predicted imbalance value and the predicted imbalance time of each imbalance group of the area is: obtaining the predicted population aggregation index and the predicted job-housing ratio of the future h time granularity through the autoregressive integrated moving average model analysis method; based on any time granularity, calculating the absolute value of the difference between the predicted population aggregation index of the first imbalance area and the second imbalance area, to obtain the predicted population aggregation index difference value; calculating the absolute value of the difference between the predicted job-housing ratio of the first imbalance area and the second imbalance area, to obtain the predicted job-housing ratio difference value; the spatial distance of the area imbalance group is unchanged, and the predicted pendulum traffic judgment value is calculated; if the predicted pendulum traffic judgment value is greater than or equal to the pendulum traffic judgment threshold value, it is judged that the area imbalance group at this time granularity is imbalanced, and the difference between the predicted pendulum traffic judgment value and the pendulum traffic judgment threshold value is processed to obtain the predicted imbalance value, and the time granularity starting time point corresponding to the predicted pendulum traffic judgment value is taken as the predicted imbalance time.

7. The method of claim 6, wherein: The process of imbalance intensification analysis of each area imbalance group and identifying the predicted imbalance intensification area is: for any area imbalance group, extract the city unit area adjacent to the geographical boundary of the area imbalance group, denoted as the connection unit set; based on any city unit area in the connection unit set, extract the predicted pendulum traffic judgment value of the future h time granularity; if the predicted pendulum traffic judgment value of the city unit area shows an upward trend in the future time granularity, the city unit area is marked as an upward unit; calculate the proportion of the upward unit in the connection unit set, denoted as the connection unit upward proportion; if the connection unit upward proportion of the area imbalance group exceeds the preset proportion, it is judged that the area imbalance group is a predicted imbalance intensification area.

8. The method of claim 7, wherein: The process of judging whether there is an imbalance chain reaction through the space syntax model is: extract all predicted imbalance intensification areas, calculate the maximum predicted imbalance value of each predicted imbalance intensification area, take the corresponding time granularity as the significant imbalance time, arrange the corresponding predicted imbalance intensification area and time granularity starting time point distribution in descending order of the maximum predicted imbalance value, to obtain the predicted imbalance intensification area sequence and the significant time sequence; check whether the significant time sequence corresponds to the predicted imbalance intensification area sequence one by one, if it corresponds one by one, it is judged that the sorting and time sequence verification is satisfied; if not one by one, calculate the corresponding proportion, if it meets the preset standard, it is judged that the sorting and time sequence verification is satisfied; check whether each predicted imbalance intensification area after sorting is geographically adjacent to each other, if it is geographically adjacent, it is judged that the spatial correlation verification is satisfied; if the sorting and time sequence verification and the spatial correlation verification are satisfied at the same time, it is judged that there is an imbalance chain reaction, and the longest continuous subsequence that satisfies at the same time is recorded as the final continuous spatial chain.

9. The method of claim 1, wherein: The process of calculating the adjustment priority of the prediction imbalance exacerbation region is: Extract all the corresponding prediction imbalance exacerbation regions in the final continuous spatial chain; Based on any prediction imbalance exacerbation region: Calculate the chain reaction sequence proportion, prediction imbalance value proportion and prediction population aggregation index proportion; Sum the chain reaction sequence proportion, prediction imbalance value proportion and prediction population aggregation index proportion of the prediction imbalance exacerbation region to obtain the adjustment priority of the prediction imbalance exacerbation region.

10. The method of claim 9, wherein: The process of calculating the chain reaction sequence proportion, prediction imbalance value proportion and prediction population aggregation index proportion is: counting the total number of the predicted imbalance aggravation regions in the final continuous spatial chain , extracting the chain reaction sequence of the predicted imbalance aggravation region at the moment when the predicted imbalance is significant ; Then: the proportion of the chain reaction sequence is: ; The prediction population aggregation index proportion is calculated in the same way as the prediction imbalance value proportion; Sum the prediction imbalance values of all prediction imbalance exacerbation regions at the prediction imbalance significant moment to obtain the total prediction imbalance value; Extract the maximum prediction imbalance value of the prediction imbalance exacerbation region at the prediction imbalance significant moment, and calculate its proportion in the total prediction imbalance value, which is the prediction imbalance value proportion.

Images