Method for generating regional physical space boundary based on multi-dimensional index analysis

Abstract

The application provides a regional physical space boundary generation method based on multi-dimensional index analysis, relates to the technical field of regional boundary generation, extracts urban correlation data from national space data, obtains initial urban space range and target regional polygon class vector data, judges whether the data meets preset urban mandatory class judgment rules and class combination compatibility verification and whether the data meets preset urban candidate class judgment rules, further verifies urban core function bearing, obtains a polygon set of a region range to be divided into an urban entity, performs multi-round iteration optimization update, performs iteration characteristic quantization analysis on the multi-round iteration optimization update data, triggers an iteration termination condition according to a range convergence characteristic analysis result, further performs boundary topology consistency verification and dynamic update on the urban entity region range after iteration update, and realizes the automation of the urban entity region range, so that the pertinence and accuracy of the division are improved.

Description

Technical Field

[0001] This invention proposes a method for generating regional physical spatial boundaries based on multi-dimensional index analysis, which relates to the field of regional boundary generation technology, specifically to the field of regional physical spatial boundary generation technology based on multi-dimensional index analysis. Background Technology

[0002] The current work on delineating urban spatial boundaries faces many prominent problems, which seriously restricts the progress and implementation of related work and makes it difficult to meet the needs of practical applications.

[0003] Traditional land data acquisition channels are relatively singular, relying heavily on interim survey data from single departments. This lack of cross-departmental and multi-dimensional data integration and verification results in generally poor data quality. On the one hand, some data suffers from insufficient accuracy and untimely updates, failing to accurately reflect the real-time status of land space. On the other hand, land spatial data has not undergone targeted standardization processing, making it difficult to effectively integrate data from different sources and formats, further increasing the difficulty of urban data screening. In practice, staff often spend a significant amount of time processing redundant and invalid data, yet still struggle to filter out core data that truly reflects urban development, thus failing to accurately define the initial boundaries of urban space.

[0004] The existing urban spatial scope assessment indicator system is not scientific and comprehensive enough, and suffers from the problem of selecting indicators that are too simplistic and one-sided. Currently, most delineation work relies on only a few simple indicators, the most common of which is to judge solely based on land use type, directly equating urban construction land with urban spatial scope. This approach ignores the actual functional needs of urban development, excluding many areas that bear core urban functions but whose land use type does not match. At the same time, it may also include some idle or inefficiently used construction land in the urban scope, resulting in a delineation result that is out of touch with the actual development of the city and fails to truly reflect the core characteristics of the city, such as population agglomeration, industrial distribution, and functional layout.

[0005] The process of delineating urban spatial boundaries lacks dynamism, often employing a one-time static delineation model, which is ill-suited to the rapid pace of urban development. A city is a dynamically evolving organism; factors such as population movement, industrial upgrading, and infrastructure construction constantly alter its spatial form and functional layout. Traditional static delineation methods, once completed, remain in use for extended periods, lacking effective dynamic verification and correction mechanisms. In the boundary determination stage, the lack of scientific verification standards and procedures leads to insufficient accuracy in boundary delineation, resulting in blurred, overlapping, or omitted boundaries in some areas, further impacting the scientific validity and practicality of the delineation results.

[0006] More critically, there is a lack of effective iterative update mechanisms for map features included within the city limits. Verification of updated urban boundaries is lax, and a regular, periodic update system has not been established. In some areas, after initial delineation, map features were not promptly supplemented, adjusted, or removed based on changes in urban construction and land use. Furthermore, there is a lack of professional verification procedures and standards for updated urban boundaries, making it difficult to ensure their accuracy and rationality. In addition, the absence of a regular update system means that urban spatial boundaries cannot keep pace with the latest urban developments, resulting in increasingly outdated delineation results that fail to meet the practical needs of urban management and land-use planning in the new era. Summary of the Invention

[0007] This invention provides a method for generating regional physical spatial boundaries based on multi-dimensional index analysis to solve the above-mentioned problems: A method for generating regional physical spatial boundaries based on multi-dimensional index analysis, the method comprising: S1. Extract urban area related data from the land and space data to obtain the initial urban spatial range. Based on the initial urban spatial range and a preset spatial buffer threshold, obtain the target area land parcel vector data. S2. Determine whether the land use vector data of the target area plots conforms to the preset rules for determining the mandatory land use in urban areas, then perform land use combination compatibility verification and determine whether they conform to the preset rules for determining candidate land use in urban areas, then perform urban core function carrying capacity verification, and obtain the set of plots to be included in the physical area of ​​the urban area. S3. Perform multiple rounds of iterative optimization and updating on the set of map features to be included in the urban area. Perform iterative feature quantification analysis on the data from the multiple rounds of iterative optimization and updating to obtain the results of the range convergence feature analysis. Trigger the iteration termination condition based on the results of the range convergence feature analysis, and then perform boundary topology consistency verification and dynamic updating on the urban area that has been iteratively updated to obtain the final updated range data.

[0008] Further, S1 includes: Acquire land and space data, perform urban spatial attribute association filtering on the land and space data, and obtain urban spatial filtering data; Obtain the spatial vector range data corresponding to the preset geographic attribute threshold of the urban spatial filtering data, and use it as the initial range of the urban space; Based on the initial urban spatial extent, target area land cover vector data are obtained within a preset spatial buffer distance threshold range.

[0009] Further, S2 includes: Obtain the pre-defined mandatory land categories for urban physical features and the candidate land categories for urban physical features; Determine whether the target area's land parcel vector data conforms to the preset urban mandatory land parcel determination rules, and obtain the first mandatory determination classification operation result; Based on the results of the first mandatory classification operation, a spatial barrier verification operation for land use categories is performed to obtain the spatial adaptation results of the second mandatory land use category. Based on the result of the first mandatory judgment classification operation, determine whether the target area map patch land class vector data matches the urban entity land feature candidate land class list, and obtain the first candidate land class matching result; Based on the matching result of the first candidate land type, a judgment operation is performed to determine whether it has the carrying capacity of the necessary urban functions, and a second candidate judgment result is obtained. Based on the spatial adaptation results of the second mandatory land type, the judgment results of the second candidate land type, and the matching results of the first candidate land type, the land type vector data of the target area are incorporated into the urban entity area, and a set of land patches to be included in the urban entity area is obtained.

[0010] Further, the step of performing a land use spatial barrier verification operation based on the result of the first mandatory classification operation to obtain the second mandatory land use spatial adaptation result includes: When the result of the first mandatory classification operation is that it conforms to the mandatory type of physical land cover category, the spatial connectivity barrier between land cover categories is checked. When the result of the first mandatory classification operation is that it does not meet the mandatory type of entity feature category, it is determined whether the target area map feature vector data meets the candidate land type list of urban entity features.

[0011] Further, based on the matching results of the first candidate land type, an operation is performed to determine whether the land possesses the necessary urban functional carrying capacity, resulting in a second candidate judgment result, including: When the first candidate land type matching result is consistent with the candidate type of physical land feature category, the carrying capacity of the city's core functions is verified. When the first candidate land type matching result does not conform to the candidate type of entity land cover, the land type vector data of the target area will be determined not to be included in the urban entity area.

[0012] Further, based on the matching results of the first candidate land type, an operation is performed to determine whether the land possesses the necessary urban functional carrying capacity, resulting in a second candidate judgment result, including: When the second candidate judgment result is that it has the capacity to carry the necessary functions of the city, the spatial connectivity barrier between land categories is checked. When the second candidate judgment result is that it does not have the capacity to carry the necessary urban functions, the vector data of the land parcels in the target area will be determined not to be included in the urban entity area.

[0013] Furthermore, based on the spatial adaptation results of the second mandatory land type, the second candidate judgment results, and the matching results of the first candidate land type, the land type vector data of the target area are incorporated into the urban entity geographical area, obtaining a set of land patches to be included in the urban entity geographical area, including: When the spatial adaptation result of the second mandatory land category is a disconnection judgment, the second candidate judgment result is that it does not have the necessary urban function carrying capacity, and the first candidate land category matching result is that it does not conform to the list of candidate land categories for urban physical land features, the land category vector data of the corresponding target area will not be included in the urban physical area. When the spatial adaptation result of the second mandatory land category is "connected", the judgment result of the second candidate is "possibly capable of carrying the necessary urban functions", and the matching result of the first candidate land category is "compliant with the candidate land category list of urban physical features", the land category vector data of the corresponding target area map patch is included in the urban physical area.

[0014] Further, S3 includes: The set of map features to be included in the urban area is subject to multiple rounds of iterative optimization and update to determine the set of map features to be included in the urban area and the connection conditions. The data of the multiple rounds of iterative optimization and update are subjected to iterative feature quantification analysis to obtain the range convergence feature analysis results. The iteration termination condition is triggered based on the range convergence feature analysis results. After obtaining the urban entity geographical area obtained after the initial urban spatial range is iteratively updated, the boundary of the urban entity geographical area is checked to obtain boundary check information; Annual land change survey data is obtained according to a preset cycle, and the physical geographical area of ​​the urban area is updated to obtain the final updated geographical area data of the urban area.

[0015] Furthermore, the step of performing iterative feature quantization analysis on the multi-round iterative optimization update data to obtain range convergence feature analysis results, and triggering the iteration termination condition based on the range convergence feature analysis results, includes: After each iteration, key geographic feature points are identified for the urban entity geographical area, and multiple urban entity geographical feature range information with iterative feature points are obtained. Acquire spatial coordinate difference data between multiple iterative feature points of the geographical feature range information of each urban area; Calculate the feature point difference quantization coefficient based on the spatial coordinate difference data; Calculate the feature factor based on the feature point difference quantization coefficient; Determine the iteration convergence determination coefficient based on the aforementioned characteristic factors; The iteration termination condition is triggered based on the aforementioned convergence determination coefficient.

[0016] Furthermore, the system includes: The map patch acquisition module is used to extract urban area related data from land and space data, obtain the initial urban spatial range, and obtain the land type vector data of the target area map patches based on the initial urban spatial range and a preset spatial buffer threshold. The category judgment module is used to determine whether the land use vector data of the target area plots conforms to the preset rules for determining the mandatory land use in urban areas, and then to perform land use combination compatibility verification and whether it conforms to the preset rules for determining candidate land use in urban areas, and then to verify the carrying capacity of the core urban functions, so as to obtain a set of plots that are to be included in the physical area of ​​the urban area. The iterative update module is used to perform multiple rounds of iterative optimization and update on the set of map features to be included in the urban area. It performs iterative feature quantification analysis on the data from multiple rounds of iterative optimization and update to obtain the range convergence feature analysis results. Based on the range convergence feature analysis results, iterative termination conditions are triggered, and then the boundary topology consistency check and dynamic update are performed on the urban area of ​​the iteratively updated entity to obtain the final updated range data.

[0017] The beneficial effects of this invention are: improving the accuracy of results by filtering map features; efficient and intelligent judgment through data processing methods from data acquisition to scope determination, improving the efficiency and accuracy of information processing and judgment; and a multi-round iterative optimization and update mechanism that can promptly capture changes in land use and function during urban development, enabling the delineated urban area to dynamically adapt to changes in urban data and maintain data timeliness. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a method for generating regional physical spatial boundaries based on multi-dimensional index analysis. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] In one embodiment of the present invention, the proposed method for generating regional physical spatial boundaries based on multi-dimensional index analysis includes: S1. Extract urban area related data from the land and space data to obtain the initial urban spatial range. Based on the initial urban spatial range and a preset spatial buffer threshold, obtain the target area land parcel vector data. S2. Determine whether the land use vector data of the target area plots conforms to the preset rules for determining the mandatory land use in urban areas, then perform land use combination compatibility verification and determine whether they conform to the preset rules for determining candidate land use in urban areas, then perform urban core function carrying capacity verification, and obtain the set of plots to be included in the physical area of ​​the urban area. S3. The set of map features to be included in the proposed urban area is iteratively optimized and updated multiple times. Iterative feature quantification analysis is performed on the updated data to obtain the range convergence feature analysis results. Based on these results, the iteration termination condition is triggered. Then, the boundary topology consistency check and dynamic update of the updated urban area are performed to obtain the final updated range data. Figure 1 As shown.

[0021] The working principle and technical effect of the above technical solution are as follows: collect land space data and filter urban data to determine the initial urban space range, and obtain the target area land parcel vector data by combining the preset spatial buffer threshold.

[0022] Based on the preset mandatory category information, the vector data of land use types of the target area are screened to ensure that key land types are taken into consideration; it is determined whether they meet the candidate category information to broaden the source of potential urban area patches; finally, the patch set is selected to be included in the physical area of ​​the urban area after assessing whether the patches have the necessary urban function carrying capacity.

[0023] The map features to be included are iterated and updated multiple times to continuously correct and improve the combination of map features and the boundaries of the area. Then, the boundaries of the urban area are checked after the iteration to finally obtain the final updated range data and determine the urban area.

[0024] By filtering image patches, the accuracy of the results can be improved.

[0025] From data acquisition to range determination, efficient and intelligent judgment can be made through data processing methods, improving the efficiency and accuracy of information processing and judgment.

[0026] The multi-round iterative optimization and update mechanism can capture changes in land use and function in a timely manner during urban development, enabling the designated urban area to dynamically adapt to changes in urban data and maintain the timeliness of the data.

[0027] In one embodiment of the present invention, S1 includes: Acquire land and space data, perform urban spatial attribute association filtering on the land and space data, and obtain urban spatial filtering data; the urban spatial filtering data includes the town and village attribute codes of cities, the town and village attribute codes of counties, and the town and village attribute codes of counties that are contiguous with the city's districts and undertake the functions of the central urban area and are adjacent to the central urban area. Obtain the spatial vector range data corresponding to the preset geographic attribute threshold of the urban spatial filtering data, and use it as the initial range of the urban space; Based on the initial urban spatial extent, target area land cover vector data are obtained within a preset spatial buffer distance threshold range.

[0028] The process of determining the city's boundaries requires the support of basic data, which includes: Image data: The latest remote sensing images of the administrative region with a resolution of no less than 2m; Vector data: the latest administrative division vector boundary data, the latest annual land change survey data (mainly including land category patches, urban and rural land use, administrative districts, village-level survey areas, etc.), the latest urban minimum statistical unit jurisdiction data, municipal public facilities and public service facilities spatial data, POI data, etc. Other data: Statistical data from municipal departments, distribution data of municipal public utilities such as electricity, water supply and drainage, roads and traffic, fire protection, and environmental sanitation, as well as public service facilities such as culture, education, and health.

[0029] The working principle and technical effect of the above technical solution are as follows: For urban districts and cities without districts, the spatial range defined by the map patch data with town and village attribute codes 201 and 201A is selected as the initial range of the urban area. For counties, the spatial range defined by the map patch data with the town and village attribute codes 202 and 202A of the county's subdistricts or the town where the county seat is located is selected as the initial urban area. For towns within a county that are contiguous with the municipal districts and serve as the central urban area, and which border the central urban area, map data with town / village attribute codes 202 and 202A can be selected and included in the initial urban area scope. However, this portion of map data will not participate in the iteration. The above data is for illustrative purposes only.

[0030] If the initial scope of the urban area is based on the annual land change survey process data, it shall be used in its entirety without any selection; it shall be replaced and updated after the annual land change survey results data are released.

[0031] By filtering the land and space data, the city data of the land survey was accurately extracted, and the data was further refined to the attribute codes of specific towns and villages. A large amount of irrelevant information was removed, making the data more targeted and pure, and effectively reducing the impact of data noise on the results.

[0032] The inclusion of attribute code data for towns and villages that are contiguous with the city's districts and serve the functions of the central urban area strengthens the correlation between data from different areas of the city and can more accurately reflect the city's actual spatial structure and functional distribution.

[0033] The initial scope of urban space is determined by using preset geographical attribute thresholds for urban spatial screening data, and this data attribute-based definition method avoids subjective arbitrariness.

[0034] Based on the initial urban spatial boundaries, target area landform vector data are acquired outward within a preset buffer distance. The buffer range is set to reserve reasonable space for urban development.

[0035] The target area within the buffer zone is acquired using vector data of land parcels, which contains a rich variety of land type information, such as construction land, agricultural land, and unused land.

[0036] Including the area within the buffer zone surrounding the city in the analysis scope can fully take into account the collaborative information between the city and the surrounding areas.

[0037] In one embodiment of the present invention, S2 includes: Obtain the pre-defined mandatory land categories for urban physical features and the candidate land categories for urban physical features; Determine whether the target area's land parcel vector data conforms to the preset urban mandatory land parcel determination rules, and obtain the first mandatory determination classification operation result; Based on the results of the first mandatory classification operation, a spatial barrier verification operation for land use categories is performed to obtain the spatial adaptation results of the second mandatory land use category. Based on the result of the first mandatory judgment classification operation, determine whether the target area map patch land class vector data matches the urban entity land feature candidate land class list, and obtain the first candidate land class matching result; Based on the matching result of the first candidate land type, a judgment operation is performed to determine whether it has the carrying capacity of the necessary urban functions, and a second candidate judgment result is obtained. Based on the spatial adaptation results of the second mandatory land type, the judgment results of the second candidate land type, and the matching results of the first candidate land type, the land type vector data of the target area are incorporated into the urban entity area, and a set of land patches to be included in the urban entity area is obtained.

[0038] The working principle and technical effect of the above technical solution are as follows: if the required category is met, then the connection condition is judged; If a candidate category is met, the city's actual situation will be taken into account, and land features that have urban residential functions and undertake urban leisure and recreation, natural and historical and cultural protection, and other necessary urban functions (such as public management and public service functions, commercial service functions, transportation functions, municipal public functions, ecological greening functions, cultural display functions, and logistics warehousing functions) will be selected for connection condition judgment.

[0039] Pre-set the rules for determining the mandatory land categories for physical features in urban areas and the list of candidate land categories for physical features in urban areas.

[0040] The vector data of land parcels in the target area are evaluated to determine whether they conform to the preset mandatory land parcel classification rules for urban areas, resulting in the first mandatory classification operation result. This clarifies whether a land parcel belongs to a category that must be included within the urban area's physical geographical scope.

[0041] Based on the results of the first mandatory classification operation, a spatial barrier verification operation for land categories is performed. Considering that some map features that meet the mandatory categories may not be actually included in the urban area due to barrier factors, a second mandatory land category spatial adaptation result is obtained to further refine the selection of mandatory categories.

[0042] Based on the results of the first mandatory classification operation, it is determined whether the vector data of land cover features in the target area match the candidate land cover category list for urban areas, thus obtaining the first candidate land cover category matching result. Candidate categories supplement the mandatory categories and are used to include land cover features that, while not mandatory, meet certain conditions for inclusion within the urban area.

[0043] Next, based on the matching results of the first candidate land type, an operation is performed to determine whether it has the capacity to carry out the necessary urban functions. Even if a land parcel matches the candidate category information, it is still necessary to determine whether it has the capacity to carry out the necessary urban functions (such as public services, transportation hubs, etc.) in order to determine whether it truly has the value of being included in the physical area of ​​the urban area, and thus obtain the second candidate judgment result.

[0044] By combining the spatial adaptation results of the second mandatory land category, the judgment results of the second candidate land category, and the judgment information on whether it conforms to the candidate land category list of urban physical features, the land category vector data of the target area that meets the conditions are included in the urban physical area, and finally the set of land parcels to be included in the urban physical area is obtained.

[0045] Through multiple rounds of meticulous judgment, the map patches were screened from two dimensions: mandatory categories and candidate categories, taking into account factors such as whether they are obstructive and whether they have the capacity to carry the necessary urban functions. This process avoided the one-sidedness that may be caused by a single judgment criterion, and made the final map patches to be included more in line with the actual requirements of the urban area, greatly improving the accuracy of the delineation.

[0046] First, clarify the judgment criteria, then judge the mandatory and candidate categories in turn, and set up further detailed judgment steps in each category judgment.

[0047] The system employs a step-by-step judgment approach, with each step having a clear judgment objective and output result, facilitating computer program processing and optimization. This structured processing workflow improves data processing efficiency, enabling rapid filtering and analysis of large amounts of target area land parcel vector data, saving time and computational resources.

[0048] The preset mandatory and candidate category information can be flexibly adjusted according to the characteristics and development needs of different cities. At the same time, the verification of the carrying capacity of urban core functions also takes into account the dynamic changes of urban functions.

[0049] In one embodiment of the present invention, the step of performing a land use spatial barrier verification operation based on the result of the first mandatory classification operation to obtain a second mandatory land use spatial adaptation result includes: When the result of the first mandatory classification operation is that it conforms to the mandatory type of physical land cover category, the spatial connectivity barrier between land cover categories is checked. When the result of the first mandatory classification operation is that it does not meet the mandatory type of entity feature category, it is determined whether the target area map feature vector data meets the candidate land type list of urban entity features.

[0050] Based on the matching results of the first candidate land type, an operation is performed to determine whether the land possesses the necessary urban functional carrying capacity, resulting in a second candidate judgment result, including: When the first candidate land type matching result is consistent with the candidate type of physical land feature category, the carrying capacity of the city's core functions is verified. When the first candidate land type matching result does not conform to the candidate type of entity land cover, the land type vector data of the target area will be determined not to be included in the urban entity area.

[0051] Based on the matching results of the first candidate land type, an operation is performed to determine whether the land possesses the necessary urban functional carrying capacity, resulting in a second candidate judgment result, including: When the second candidate judgment result is that it has the capacity to carry the necessary functions of the city, the spatial connectivity barrier between land categories is checked. When the second candidate judgment result is that it does not have the capacity to carry the necessary urban functions, the vector data of the land parcels in the target area will be determined not to be included in the urban entity area.

[0052] The working principle and technical effect of the above technical solution are as follows: A first mandatory classification operation is performed on the land cover vector data of the target area to determine whether it conforms to the mandatory type of physical feature category. If the result is yes, it indicates that the land cover has the potential to be included in the urban physical area at the basic category level. At this point, the spatial connectivity barrier verification step between land cover categories is initiated. The barrier judgment is used to examine whether there are factors around the land cover that hinder its effective integration into the urban area. If there are no barriers, the land cover can proceed to the subsequent comprehensive consideration stage; if barriers exist, it will not be considered for inclusion in the urban physical area at this time.

[0053] If the result of the first mandatory classification operation is that it does not meet the mandatory type of entity feature category, it means that the map patch does not meet the requirements for direct inclusion in the urban area in terms of basic category. At this time, the operation will switch to judging whether it meets the candidate land category list of entity features in the urban area.

[0054] When a map patch does not meet the mandatory category requirements and enters the candidate category judgment, the first candidate judgment is performed first. If the result is that it meets the candidate type of entity feature category, it means that although the map patch is not mandatory, it has a certain possibility of being included in the urban area. Then, the carrying capacity of the urban core functions is verified. If it has the carrying capacity of the necessary urban functions, it enters the spatial connectivity barrier verification between land categories to further examine its feasibility of integration into the urban area; if it does not have the carrying capacity of the necessary urban functions, it is directly determined that the map patch is not included in the physical area of ​​the urban area.

[0055] If the first candidate land type matching result does not meet the candidate type of entity land cover category, it means that the map patch does not meet the conditions in terms of candidate category, and the land type vector data of the target area map patch is directly determined not to be included in the urban entity area.

[0056] Through a dual screening mechanism of mandatory and candidate categories, combined with assessments of barriers and urban functions, map patches are meticulously evaluated from multiple dimensions. This approach accurately identifies map patches that truly meet the requirements for the physical boundaries of urban areas, avoiding the inclusion of patches that do not meet the criteria or present integration obstacles. Consequently, it significantly improves the accuracy of urban area delineation, making the delineated urban boundaries more closely aligned with actual development needs.

[0057] This method offers high flexibility, allowing the selection of mandatory and candidate categories to be adjusted based on the characteristics, development stage, and planning objectives of different cities. Whether a city is large or small, in a period of rapid or stable development, it can effectively delineate the physical boundaries of urban areas by rationally setting category standards and judgment conditions, thus enhancing the method's versatility and applicability.

[0058] During the selection process, the crucial factor of whether the map features possess the necessary urban functional carrying capacity was fully considered. Ensuring that map features included within the urban area's physical boundaries improves the accuracy of data analysis. Simultaneously, the barrier assessment avoids issues arising from geographical or human-induced functional segmentation or developmental obstacles within the urban area.

[0059] By adopting a step-by-step, condition-based judgment process, the data processing becomes clearer and more efficient. Computers can quickly process and analyze large amounts of map data according to a predetermined logical sequence, reducing unnecessary calculations and judgments, improving data utilization efficiency, and enabling the delineation of urban physical boundaries in a shorter time.

[0060] In one embodiment of the present invention, the land use vector data of the target area patches are incorporated into the urban entity area based on the spatial adaptation result of the second mandatory land use type, the second candidate judgment result, and the matching result of the first candidate land use type, thereby obtaining a set of patches to be included in the urban entity area, including: When the spatial adaptation result of the second mandatory land category is a disconnection judgment, the second candidate judgment result is that it does not have the necessary urban function carrying capacity, and the first candidate land category matching result is that it does not conform to the list of candidate land categories for urban physical land features, the land category vector data of the corresponding target area will not be included in the urban physical area. When the spatial adaptation result of the second mandatory land category is "connected", the judgment result of the second candidate is "possibly capable of carrying the necessary urban functions", and the matching result of the first candidate land category is "compliant with the candidate land category list of urban physical features", the land category vector data of the corresponding target area map patch is included in the urban physical area.

[0061] The working principle and technical effect of the above technical solution are as follows: For the set of map patches to be included in the urban area, according to preset conditions, the connection status of each patch with the current urban area physical area composed of the initial urban area and the map patches already included in the urban area physical area is determined one by one, and those that meet the conditions are included in the urban area physical area.

[0062] To determine whether the land use vector data of target area patches can be included within the urban entity geographical area, three key judgment dimensions were set. The second mandatory land use spatial adaptation result reflects the basic connection status between the patch and the urban entity, divided into non-connection judgment and connected judgment; the second candidate judgment result reflects whether the patch has the capacity to carry the necessary urban functions, divided into non-capacity and capacity to carry the necessary urban functions; the judgment information clarifies whether the patch conforms to the candidate land use category list of urban entity features, i.e., does not conform to the candidate land use category list of urban entity features and conforms to the candidate land use category list of urban entity features.

[0063] The judgment results of the three dimensions are comprehensively considered using a logical combination approach. When the spatial adaptation result of the second mandatory land category is disconnection, the judgment result of the second candidate is lack of urban necessary functional carrying capacity, and the judgment information is not in line with the candidate land category list of urban physical features, all three unfavorable conditions are met simultaneously. This indicates that the map patch does not meet the requirements for inclusion in the urban physical area in terms of connectivity, functionality, and category conformity. Therefore, it is not included.

[0064] When the spatial adaptation result of the second mandatory land type is "connected", the judgment result of the second candidate is "possibly capable of carrying the necessary urban functions", and the judgment information is "in line with the candidate land type list of urban entity features", these three favorable conditions are met simultaneously, indicating that the map patch meets the requirements in terms of connecting urban entities, possessing urban functions, and conforming to the preset category, so it is included in the urban entity area.

[0065] According to the preset conditions, the land use vector data of each target area patch is evaluated in sequence according to the three dimensions mentioned above. First, the connectivity between the patch and the current urban entity area (consisting of the initial urban area area and patches already included in the urban entity area) is determined. Then, it is determined whether the patch has the capacity to carry out necessary urban functions. Finally, it is checked whether the patch conforms to the candidate land use category list for urban entity features. Based on the combination of these three evaluation results, a decision is made on whether to include the patch in the urban entity area.

[0066] By employing multi-dimensional and comprehensive judgment criteria, the biases that may arise from judging based on a single factor are avoided. A comprehensive evaluation of map features based on connectivity, functionality, and category conformity allows for more accurate selection of map features that truly meet the requirements of the urban area's physical boundaries, resulting in more precise delineation of urban boundaries and reducing misjudgments and omissions.

[0067] The emphasis is on whether a map patch possesses the capacity to carry the necessary urban functions. Data analysis can be used to maintain the integrity and coordination of urban functions, avoiding problems such as missing or fragmented urban functions.

[0068] Based on a systematic judgment process defined by pre-defined conditions, the delineation of the entire urban area has clear norms and standards. Each map patch is judged and processed according to uniform rules, ensuring the consistency and reliability of the delineation results. At the same time, it has strong versatility and operability.

[0069] By employing a step-by-step judgment approach combined with clear judgment logic, the computer program can efficiently process large amounts of map data. While ensuring the quality of the delineation, unnecessary calculations and judgments are reduced, improving data processing efficiency and saving computing resources and time costs.

[0070] The preset conditions include distance judgment. Measure the shortest distance between the feature patch and the current urban area (initially the initial urban area). If the distance is less than or equal to 100m, proceed to the next step of barrier judgment.

[0071] Blockage judgment Obstacles include three types: rivers, highways, and railways. Determine whether a feature is connected to the current urban area using the following steps: a) If there are no obstructing elements between the land feature and the current urban area, it is determined that the land feature is connected; b) If there is any obstruction between the feature and the current urban area, determine whether there are bridges, culverts, tunnels, or ferries on these three types of obstructions that connect the feature to the current urban area (taking bridges as an example; the situation for culverts and tunnels is similar): 1) If there is a bridge, culvert, or tunnel on the blocking element, and the sum of the shortest distances from both ends of the bridge, culvert, or tunnel to the ground features on both sides is less than or equal to 100m, then it is determined that it is connected. 2) If the feature and the current urban area are located on opposite sides of a river, and there is no bridge across the river or the sum of the shortest distances from both ends of the bridge to the features on both sides is greater than 100m, and there is a ferry connecting the two banks, such that the distance from the current urban area to the boundary of the construction land where the ferry (or dock) is located on the same side is less than or equal to 100m, and the distance from the feature to the boundary of the construction land where the ferry (or dock) is located on the same side is less than or equal to 100m, then it is determined that the feature is connected.

[0072] In one embodiment of the present invention, S3 includes: The set of map features to be included in the urban area is subject to multiple rounds of iterative optimization and update to determine the set of map features to be included in the urban area and the connection conditions. The data of the multiple rounds of iterative optimization and update are subjected to iterative feature quantification analysis to obtain the range convergence feature analysis results. The iteration termination condition is triggered based on the range convergence feature analysis results. After obtaining the urban entity geographical area obtained after the initial urban spatial range is iteratively updated, the boundary of the urban entity geographical area is checked to obtain boundary check information; Annual land change survey data is obtained according to a preset cycle, and the physical geographical area of ​​the urban area is updated to obtain the final updated geographical area data of the urban area.

[0073] The working principle and technical effects of the above-mentioned technical solution are as follows: Initially, there is a set of map features intended to be included in the urban area. For these map features, it is necessary to determine which features can ultimately be included, and also to assess the connection conditions between them. Connection conditions involve the spatial adjacency and functional relevance of the map features.

[0074] In each iteration, based on the current judgment criteria, map features that meet the connection conditions and inclusion requirements are included in the urban entity geographical area. Then, based on the updated urban entity geographical area, the remaining map features to be included are re-acquired, and the operation of determining map features and connection conditions is performed again. This process is repeated, continuously incorporating new map features that meet the conditions. Iterative feature quantification analysis is performed on the data updated through multiple iterations to obtain the results of the range convergence feature analysis. Based on the results of the range convergence feature analysis, the iteration termination condition is triggered. After the initial iterative update of the urban spatial range is completed, a preliminary urban entity geographical area is obtained. To ensure the accuracy and rationality of this range, boundary verification is required. Boundary verification may include comparing it with relevant information such as actual geographical features, administrative divisions, and urban planning to check for any unreasonable aspects of the boundary, such as the boundary crossing buildings or not conforming to the natural terrain. Through boundary verification, information on the rationality of the boundaries of the urban entity geographical area is obtained, i.e., boundary verification information.

[0075] Cities are constantly evolving. To ensure that the physical boundaries of urban areas accurately reflect the city's actual conditions, annual land use change survey data is collected at predetermined intervals (usually one year). This data contains the latest information on urban land use, changes in land features, and other related aspects.

[0076] Using the acquired annual land use change survey data, the existing urban area boundaries are updated. The update process may involve identifying newly appearing map features and adjusting existing ones, ultimately obtaining updated data reflecting the latest urban area boundaries.

[0077] The multi-round iterative optimization and update mechanism can fully consider various relationships and conditions between map features, and through multiple screenings and judgments, gradually approximate the urban area boundary that best reflects the actual situation. Compared with a one-time judgment, this iterative approach can more comprehensively consider various factors, reduce omissions and errors, and greatly improve the accuracy of urban area delineation.

[0078] The boundary verification process rigorously checks the updated urban area boundaries to ensure they conform to actual geographical, administrative, and planning requirements. This helps avoid urban management problems caused by unreasonable boundaries, such as uneven resource allocation and planning conflicts.

[0079] Annual land use change survey data is acquired and the physical boundaries of urban areas are updated according to a preset cycle, ensuring that the urban area boundaries can reflect urban development and changes in a timely manner. As the city expands, renovates, and adjusts its functions, the physical boundaries of the urban area can be dynamically adjusted, enhancing the timeliness and adaptability of urban management.

[0080] The entire process is based on unified standards and procedures. From iterative updates of the initial scope to boundary checks and annual updates, each step is interconnected, ensuring data consistency and continuity.

[0081] In one embodiment of the present invention, the step of performing iterative feature quantization analysis on the multi-round iterative optimization update data to obtain range convergence feature analysis results, and triggering the iteration termination condition based on the range convergence feature analysis results, includes: After each iteration, key geographic feature points are identified for the urban entity geographical area, and multiple urban entity geographical feature range information with iterative feature points are obtained. Acquire spatial coordinate difference data between multiple iterative feature points of the geographical feature range information of each urban area; Calculate the feature point difference quantization coefficient for each iterative feature point based on the spatial coordinate difference data; The formula for calculating the feature point difference quantization coefficient is as follows: Where tx is the feature point difference quantization coefficient, P i For the information of the i-th iterative feature point, P u is the median of the information from multiple iterative feature points, and e is a minimal constant; Calculate the feature factor based on the feature point difference quantization coefficient; The formula for calculating the feature factor is as follows: Where ty is the characteristic factor, tx i For the i-th iterative feature point, tx i-1 For the (i-1)th iteration feature factor, a and b are two different constants, and tt is a preset difference threshold; a can also include multiple different constants to achieve further refinement.

[0082] Determine the iteration convergence determination coefficient based on the aforementioned characteristic factors; The formula for calculating the iterative convergence determination coefficient is as follows: Obtain the number of iterative feature points whose difference quantization coefficient is the same feature factor (a or b) for all feature points; Then, the number of iterative feature points with the same feature factor 'a' for all feature point difference quantization coefficients and the number of iterative feature points with the same feature factor 'b' for all feature point difference quantization coefficients are obtained. Calculate their ratio to obtain the iteration convergence determination coefficient; The iteration termination condition is triggered based on the aforementioned convergence determination coefficient.

[0083] The iteration convergence determination coefficient is compared with the preset iteration coefficient threshold. When the iteration convergence determination coefficient is greater than the preset iteration threshold, the iteration termination condition is triggered. The maximum number of iterations is 5.

[0084] The working principle and technical effect of the above solution are as follows: After each iterative update of the urban area's physical geographical scope, the system initiates a key geographic feature point calibration process. Based on the specific attributes and analytical needs of the urban area's physical geographical scope, representative range feature points are accurately located and labeled, typically the same type of indicator data from different target area land parcel vector data types. Through this operation, multiple urban area feature range information with iterative feature points are ultimately obtained, reflecting the key feature status of the urban area's physical geographical scope after each iteration.

[0085] For each obtained urban entity's geographical feature range information, in-depth analysis will be conducted on the characteristic information between multiple iterative feature points. Spatial coordinate difference data will be acquired. This difference data is an important basis for measuring the changes in each iteration, and can intuitively reflect the specific changes in the urban entity's geographical area at different iteration stages.

[0086] Based on the acquired spatial coordinate difference data, the feature point difference quantification coefficient is calculated. Using this coefficient as a basis, feature factors are further calculated. This allows for a more accurate description of the impact of each iteration on the urban entity's geographical features. The iteration convergence determination coefficient is then determined based on the feature factors, comprehensively reflecting the trend and extent of feature changes during the iteration process.

[0087] A threshold range or judgment condition for the iteration convergence determination coefficient is preset. The calculated iteration convergence determination coefficient is compared and analyzed with the preset condition. When the iteration convergence determination coefficient meets the condition for stopping iteration, the system automatically triggers the iteration termination condition. This process ensures that unnecessary iterative calculations can be stopped in a timely manner when the iteration reaches the expected effect or the change tends to stabilize, avoiding waste of resources.

[0088] By meticulously labeling feature points and conducting in-depth feature difference analysis on the urban area, the changes in the urban area during each iteration can be accurately captured. This refined analysis method avoids data omissions or errors that may occur in traditional methods, thereby greatly improving the accuracy of data processing.

[0089] Feature iteration can dynamically adjust the iteration strategy based on actual feature changes, stopping iteration in a timely manner when the iteration effect is not obvious or the expected goal is reached, thus avoiding the waste of computing resources and time caused by excessive iteration. At the same time, it can also ensure that further iterations are carried out as needed, guaranteeing the sufficiency and effectiveness of the iteration process.

[0090] Based on accurate feature analysis and reasonable iterative control, the final range convergence feature analysis results have higher reliability. These results can truly reflect the feature change patterns and development trends of urban physical areas during the iterative update process.

[0091] In one embodiment of the present invention, the system includes: The map patch acquisition module is used to extract urban area related data from land and space data, obtain the initial urban spatial range, and obtain the land type vector data of the target area map patches based on the initial urban spatial range and a preset spatial buffer threshold. The category judgment module is used to determine whether the land use vector data of the target area plots conforms to the preset rules for determining the mandatory land use in urban areas, and then to perform land use combination compatibility verification and whether it conforms to the preset rules for determining candidate land use in urban areas, and then to verify the carrying capacity of the core urban functions, so as to obtain a set of plots that are to be included in the physical area of ​​the urban area. The iterative update module is used to perform multiple rounds of iterative optimization and update on the set of map features to be included in the urban area. It performs iterative feature quantification analysis on the data from multiple rounds of iterative optimization and update to obtain the range convergence feature analysis results. Based on the range convergence feature analysis results, iterative termination conditions are triggered, and then the boundary topology consistency check and dynamic update are performed on the urban area of ​​the iteratively updated entity to obtain the final updated range data.

[0092] The working principle and technical effect of the above technical solution are as follows: collect land space data and filter urban data to determine the initial urban space range, and obtain the target area land parcel vector data by combining the preset spatial buffer threshold.

[0093] Based on the preset mandatory category information, the vector data of land use types of the target area are screened to ensure that key land types are taken into consideration; it is determined whether they meet the candidate category information to broaden the source of potential urban area patches; finally, the patch set is selected to be included in the physical area of ​​the urban area after assessing whether the patches have the necessary urban function carrying capacity.

[0094] The map features to be included are iterated and updated multiple times to continuously correct and improve the combination of map features and the boundaries of the area. Then, the boundaries of the urban area are checked after the iteration to finally obtain the final updated range data and determine the urban area.

[0095] By filtering image patches, the accuracy of the results can be improved.

[0096] From data acquisition to range determination, efficient and intelligent judgment can be made through data processing methods, improving the efficiency and accuracy of information processing and judgment.

[0097] The multi-round iterative optimization and update mechanism can capture changes in land use and function in a timely manner during urban development, enabling the designated urban area to dynamically adapt to changes in urban data and maintain the timeliness of the data.

[0098] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for generating regional physical spatial boundaries based on multi-dimensional index analysis, characterized in that, The method includes: S1. Extract urban area related data from the land and space data to obtain the initial urban spatial range. Based on the initial urban spatial range and a preset spatial buffer threshold, obtain the target area land parcel vector data. S2. Determine whether the land use vector data of the target area plots conforms to the preset rules for determining the mandatory land use in urban areas, then perform land use combination compatibility verification and determine whether they conform to the preset rules for determining candidate land use in urban areas, then perform urban core function carrying capacity verification, and obtain the set of plots to be included in the physical area of ​​the urban area. S3. Perform multiple rounds of iterative optimization and updating on the set of map features to be included in the urban area. Perform iterative feature quantification analysis on the data from the multiple rounds of iterative optimization and updating to obtain the results of the range convergence feature analysis. Trigger the iteration termination condition based on the results of the range convergence feature analysis, and then perform boundary topology consistency verification and dynamic updating on the urban area that has been iteratively updated to obtain the final updated range data.

2. The method for generating regional physical spatial boundaries based on multi-dimensional index analysis according to claim 1, characterized in that, S1 includes: Acquire land and space data, perform urban spatial attribute association filtering on the land and space data, and obtain urban spatial filtering data; Obtain the spatial vector range data corresponding to the preset geographic attribute threshold of the urban spatial filtering data, and use it as the initial range of the urban space; Based on the initial urban spatial extent, target area land cover vector data are obtained within a preset spatial buffer distance threshold range.

3. The method for generating regional physical spatial boundaries based on multi-dimensional index analysis according to claim 1, characterized in that, S2 includes: Obtain the pre-defined mandatory land categories for urban physical features and the candidate land categories for urban physical features; Determine whether the target area's land parcel vector data conforms to the preset urban mandatory land parcel determination rules, and obtain the first mandatory determination classification operation result; Based on the results of the first mandatory classification operation, a spatial barrier verification operation for land use categories is performed to obtain the spatial adaptation results of the second mandatory land use category. Based on the result of the first mandatory judgment classification operation, determine whether the target area map patch land class vector data matches the urban entity land feature candidate land class list, and obtain the first candidate land class matching result; Based on the matching result of the first candidate land type, a judgment operation is performed to determine whether it has the carrying capacity of the necessary urban functions, and a second candidate judgment result is obtained. Based on the spatial adaptation results of the second mandatory land type, the judgment results of the second candidate land type, and the matching results of the first candidate land type, the land type vector data of the target area are incorporated into the urban entity area, and a set of land patches to be included in the urban entity area is obtained.

4. The method for generating regional physical spatial boundaries based on multi-dimensional index analysis according to claim 3, characterized in that, The step of performing a land use spatial barrier verification operation based on the first mandatory classification operation result to obtain the second mandatory land use spatial adaptation result includes: When the result of the first mandatory classification operation is that it conforms to the mandatory type of physical land cover category, the spatial connectivity barrier between land cover categories is checked. When the result of the first mandatory classification operation is that it does not meet the mandatory type of entity feature category, it is determined whether the target area map feature vector data meets the candidate land type list of urban entity features.

5. The method for generating regional physical spatial boundaries based on multi-dimensional index analysis according to claim 3, characterized in that, Based on the matching results of the first candidate land type, an operation is performed to determine whether the land possesses the necessary urban functional carrying capacity, resulting in a second candidate judgment result, including: When the first candidate land type matching result is consistent with the candidate type of physical land feature category, the carrying capacity of the city's core functions is verified. When the first candidate land type matching result does not conform to the candidate type of entity land cover, the land type vector data of the target area will be determined not to be included in the urban entity area.

6. The method for generating regional physical spatial boundaries based on multi-dimensional index analysis according to claim 3, characterized in that, Based on the matching results of the first candidate land type, an operation is performed to determine whether the land possesses the necessary urban functional carrying capacity, resulting in a second candidate judgment result, including: When the second candidate judgment result is that it has the capacity to carry the necessary functions of the city, the spatial connectivity barrier between land categories is checked. When the second candidate judgment result is that it does not have the capacity to carry the necessary urban functions, the vector data of the land parcels in the target area will be determined not to be included in the urban entity area.

7. The method for generating regional physical spatial boundaries based on multi-dimensional index analysis according to claim 3, characterized in that, Based on the spatial adaptation results of the second mandatory land use type, the judgment results of the second candidate land use type, and the matching results of the first candidate land use type, the land use vector data of the target area patches are incorporated into the urban entity area, and a set of patches to be included in the urban entity area is obtained, including: When the spatial adaptation result of the second mandatory land category is a disconnection judgment, the second candidate judgment result is that it does not have the necessary urban function carrying capacity, and the first candidate land category matching result is that it does not conform to the list of candidate land categories for urban physical land features, the land category vector data of the corresponding target area will not be included in the urban physical area. When the spatial adaptation result of the second mandatory land category is "connected", the judgment result of the second candidate is "possibly capable of carrying the necessary urban functions", and the matching result of the first candidate land category is "compliant with the candidate land category list of urban physical features", the land category vector data of the corresponding target area map patch is included in the urban physical area.

8. The method for generating regional physical spatial boundaries based on multi-dimensional index analysis according to claim 3, characterized in that, S3 includes: The set of map features to be included in the urban area is subject to multiple rounds of iterative optimization and update to determine the set of map features to be included in the urban area and the connection conditions. The data of the multiple rounds of iterative optimization and update are subjected to iterative feature quantification analysis to obtain the range convergence feature analysis results. The iteration termination condition is triggered based on the range convergence feature analysis results. After obtaining the urban entity geographical area obtained after the initial urban spatial range is iteratively updated, the boundary of the urban entity geographical area is checked to obtain boundary check information; Annual land change survey data is obtained according to a preset cycle, and the physical geographical area of ​​the urban area is updated to obtain the final updated geographical area data of the urban area.

9. The method for generating regional physical spatial boundaries based on multi-dimensional index analysis according to claim 8, characterized in that, The iterative feature quantization analysis of the multi-round iterative optimization update data is performed to obtain the range convergence feature analysis result. The iteration termination condition is triggered based on the range convergence feature analysis result, including: After each iteration, key geographic feature points are identified for the urban entity geographical area, and multiple urban entity geographical feature range information with iterative feature points are obtained. Acquire spatial coordinate difference data between multiple iterative feature points of the geographical feature range information of each urban area; Calculate the feature point difference quantization coefficient based on the spatial coordinate difference data; Calculate the feature factor based on the feature point difference quantization coefficient; Determine the iteration convergence determination coefficient based on the aforementioned characteristic factors; The iteration termination condition is triggered based on the aforementioned convergence determination coefficient.

10. A system for generating regional physical spatial boundaries based on multi-dimensional index analysis, characterized in that, The system includes: The map patch acquisition module is used to extract urban area related data from land and space data, obtain the initial urban spatial range, and obtain the land type vector data of the target area map patches based on the initial urban spatial range and a preset spatial buffer threshold. The category judgment module is used to determine whether the land use vector data of the target area plots conforms to the preset rules for determining the mandatory land use in urban areas, and then to perform land use combination compatibility verification and whether it conforms to the preset rules for determining candidate land use in urban areas, and then to verify the carrying capacity of the core urban functions, so as to obtain a set of plots that are to be included in the physical area of ​​the urban area. The iterative update module is used to perform multiple rounds of iterative optimization and update on the set of map features to be included in the urban area. It performs iterative feature quantification analysis on the data from multiple rounds of iterative optimization and update to obtain the range convergence feature analysis results. Based on the range convergence feature analysis results, iterative termination conditions are triggered, and then the boundary topology consistency check and dynamic update are performed on the urban area of ​​the iteratively updated entity to obtain the final updated range data.

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