Near geospatial fusion domain identification method and system considering spatial overlay analysis

Abstract

The invention discloses a near geospatial fusion domain identification method and system considering spatial overlay analysis, and the method comprises the steps: constructing a geospatial definition model, obtaining an attribute data set sample of a near geospatial region, training the geospatial definition model through a training set, and calibrating the weight parameters of variables; a research area range is determined on a GIS platform, and variables are mapped to a research area according to longitude and latitude; dividing a research area into regular grid units, defining an overlapping area of the two intersecting grid units as a candidate fusion band when the two intersecting grid units are judged to be in different categories, and obtaining a candidate fusion band set T through grid search; and analyzing the regional attribute stability of each fusion band in the screened preliminary adjacent geographic space fusion band set, further screening out fusion bands with unstable regional attributes, and obtaining a final adjacent geographic space fusion band set. And finally determining an adjacent geographic space fusion domain through clustering.

Description

Technical Field

[0001] This invention relates to the fields of geographic information systems (GIS) and spatial identification technology, and in particular to a method and system for identifying neighboring geospatial fusion domains that takes into account spatial overlay analysis. Background Technology

[0002] With rapid economic and social development and accelerated regional integration, interactions between previously relatively independent geographical spaces are becoming increasingly frequent and in-depth, gradually exhibiting a trend of functional interweaving and blurred boundaries. This phenomenon is particularly prominent in the border areas of adjacent geographical spaces. Adjacent geographical spaces refer to different types of areas that are geographically adjacent and functionally connected (such as cities and rural areas, port areas and urban areas, comprehensive transportation hubs and surrounding development zones, etc.). These adjacent geographical spaces often have a complex relationship of mutual support and coordinated evolution, and their development profoundly influences and shapes the spatial form and functional structure of the intersecting and integrated areas.

[0003] Taking port areas and urban areas as examples, ports are the engine of urban development, while cities are the driving force behind port development. Under the broader context of port-city integration, the boundaries between port areas and urban areas are becoming increasingly blurred. As a highly complex, multifunctional, and dynamically evolving specific geographical unit formed by the deep interweaving and symbiotic interaction of port and urban functions, the port-city integration area faces a series of problems and challenges related to planning and layout, hub construction, industrial agglomeration, open development, policy innovation, and environmental protection. Therefore, defining the port-city integration area can provide a reliable scientific basis for policy-making regarding port-city development, promote efficient, coordinated, and integrated port-city development, resolve port-city conflicts, and create a positive layout.

[0004] However, current methods for identifying and defining adjacent geographic spatial boundaries largely rely on traditional, static boundary delineation based on single attributes (such as administrative boundaries, land port boundaries, and hub walls). These methods struggle to adapt to the dynamic evolution of functional integration and often lag behind actual development. Furthermore, most studies focus on indirectly inferring spatial relationships by monitoring changes in land cover types using remote sensing imagery data (such as CN202410255559.2 and CN202110750300.1), failing to deeply characterize key integration drivers within the region, such as functional connections, industrial linkages, traffic flows, and pedestrian flows. Therefore, this invention aims to scientifically and dynamically identify adjacent geographic spatial integration domains based on multi-dimensional data including land use, transportation systems, and industrial layout, combined with spatial overlay analysis and other technologies. This will not only effectively guide the subsequent collaborative planning and construction of various adjacent geographic spaces, such as port cities and hub areas, resolving potential conflicts and optimizing spatial layout, but also provide crucial scientific evidence and decision support for promoting sustainable and coordinated regional development.

[0005] In scenarios such as urban renewal, port-city integration, and mixed-use development, the boundaries of different functional land uses often exhibit a strip-like transition. A common approach is to delineate these boundaries using expert-weighted criteria combined with kernel density or general spatial clustering. However, this approach has several drawbacks: Inaccurate boundary placement: density peaks / thermal zones may not correspond to the actual transition zone; Parameter sensitive and noise amplified: one-time thresholding is prone to producing pseudobands; Poor reproducibility: Subjective weighting and empirical thresholds are difficult to transfer to new regions.

[0006] Therefore, there is an urgent need for a reproducible, noise-resistant, and engineering-usable fusion domain identification scheme. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for identifying adjacent geographic spatial fusion domains that considers spatial overlay analysis, so as to achieve scientific identification and delineation of adjacent geographic spaces.

[0008] The objective of this invention is achieved through the following technical solution: A method for identifying neighboring geospatial fusion domains considering spatial overlay analysis includes: S1. Standardize the multidimensional variables used to distinguish between two types of adjacent geospatial areas, construct a geospatial delimitation model, obtain attribute dataset samples of adjacent geospatial areas, and divide the samples into training set and test set. Train the geospatial delimitation model and calibrate the weight parameters of the variables through the training set. Validate the geospatial delimitation model through the test set. Proceed to the next step when the confidence level of the test is true reaches the preset threshold β0. S2. Define the study area on the GIS platform and map the variables to the study area according to latitude and longitude; S3. Set a movable grid cell within the study area, control the grid cell to move at a set step size and traverse all study areas, and evaluate and record the attribute information of the corresponding area of ​​each grid cell through the geospatial delimitation model; when two intersecting grid cells are determined to be of different categories, define the overlapping area of ​​the two intersecting grid cells as a candidate fusion zone, and finally obtain the candidate fusion zone set T. S4. For each candidate fusion zone in the candidate fusion zone set T, the independent variables involved are randomly perturbed and re-judged D times within a preset range according to a specified distribution, where D≥100; the rate of change γ = C / D is calculated, where C is the number of times the initial regional attributes of the selected candidate fusion zone are different; when the rate of change γ≥threshold γ0, the candidate fusion zone is taken as the fusion zone and included in the final fusion zone set Q; S5. Spatial perspective on the final fusion zone set based on proximity distance Clustering is performed on each fusion zone to obtain the adjacent geospatial fusion partial domain. And integrate adjacent geographic spaces into the domain. The number of fusion zones was assessed to ultimately determine the overall adjacent geospatial fusion domain within the study area.

[0009] Furthermore, step S1 includes: S101. Taking into account the differences in development patterns and construction status of adjacent geographical areas, select... Each represents two adjacent geographic spaces Variables of regional attributes , , as variables for determining the initial geospatial delimitation model; S102. Establish the initial geospatial delimitation model, as shown below: (1-1); in, These represent the weights of their respective variables; when the evaluation results... Less than At that time, the evaluation result was The region, otherwise the evaluation result is area; S103. Standardize the acquired variable data: (1-2); in, Indicates the first One variable; Indicates the first The first variable under the variable There are 1 data point, totaling 10 data points. One data point; Representing variables The The result after data standardization; The geospatial delineation model at this time is: (1-3); S104. Obtain Regional attribute dataset samples, randomly partitioned sample data % as training set data, (100- The test set data is used to train the geospatial delimitation model using the training set data, and the parameters are determined. The value; S105. Test the geospatial delimitation model using test set data, and determine the confidence level for a true result as follows: If the above is achieved, proceed to the next step; otherwise, return to step S104 to continue training.

[0010] Furthermore, step S2 includes: S201. Using the ArcGIS platform, the study area is defined as a rectangular region from a spatial geographic perspective. Z , Determine the study area Z Latitude and longitude coordinates in the upper left corner Study area Z The latitude and longitude coordinates in the lower right corner are ,in Indicates the longitude value. Represents latitude value, study area Z Represented as [ , ], which is the study area Z Spatial coordinates; S202. Obtain the study area Variables within range , The data, along with its corresponding latitude and longitude coordinates, are spatially mapped to the study area. superior.

[0011] Furthermore, step S3 includes: S301. Studying the region from a spatial geographic perspective Z A certain length within Width The rectangular region is treated as a movable grid unit. Its spatial coordinates are [ , ]; S302. When two mesh elements intersect, if the evaluation result of one of the mesh elements is... The evaluation result of another grid cell is If the overlapping region of the two intersecting grid cells is identified, it is considered a candidate fusion zone; using the grid cell as the basic unit, a grid search is performed on the study area. Screening was conducted to preliminarily determine the research area. Candidate fusion bands within; specifically including: S3021. Based on the definition in S301, in the study area Define an initial grid cell in the upper left corner. At the same time, obtain Spatial coordinates are [ , ],in = , = , i.e., grid cell The top left corner is the study area. The top left corner; S3022. Set the grid cell movement step size to Degree, as a unit of latitude and longitude, makes the grid cell For every eastward movement in longitude, the longitude values ​​at the top left and bottom right corners increase. After moving, a new mesh cell is obtained. Its spatial coordinates are [ , ]=[ , ]; grid cell Each time it moves southward, the latitude values ​​at its top left and bottom right corners decrease. After moving, a new mesh cell is obtained. Its spatial coordinates are [ , ]=[ , ]; S3023. Apply the geospatial delimitation model from step S1 to the grid cells. Define the grid cells. The evaluation result is When, record it in a set In the middle, when the grid cell The evaluation result is When, record it in a set middle; S3024. Order Obtain the mesh cells according to step S3022. Spatial coordinates [ , ], determine when If the condition is met, proceed to step 3025; otherwise, proceed to step S3023. S3025. Order Obtain the mesh cells according to step S3022. Spatial coordinates [ , ], determine when If so, proceed to step S3026; otherwise, set the mesh cells... Translate to [ , Proceed to step S3023; S3026. Determine the overlapping region of two grid cells through spatial identification, and make a judgment for each overlapping region. When the two grid cells corresponding to the overlapping region are respectively in the set and set When it appears in the set, add it to the set. In the end, the final set is obtained This refers to the set of candidate fusion zones determined based on grid search.

[0012] Furthermore, step S4 includes: S401. For the candidate fusion band set Each candidate fusion band The independent variable is varied within a set numerical range, and the candidate fusion band is adjusted accordingly. The attributes are evaluated; specifically, this includes: S4011. Order Proceed to step S4012; S4012. Select candidate fusion bands An independent variable Mutations are performed within a defined range, the range of which depends on the independent variable. Determined by practical significance, Mutation within its range of variation The mutation formula is as follows: = ; in, This represents the result of the k-th mutation of the j-th variable; The results are the standardized independent variables; For variables Within its range of variation Random numbers generated internally; S4013. Record the results after each change of the independent variable, and apply the geospatial delimitation model to the candidate fusion zone. Redefine the criteria and record the results of each assessment; S402. Calculate its rate of change γ = C / D, where C is the candidate fusion band. The number of times the initial region attributes differ; if the rate of change γ ≥ the threshold γ0, then this candidate fusion band is identified. The new fusion zone is denoted as And recorded in the final fusion zone set. In the middle, when the candidate fusion band set T When each candidate fusion band is determined to be complete, proceed to the next step; otherwise, proceed to step S4011.

[0013] Furthermore, step S5 includes: S501. Spatial perspective on the final fusion zone set based on proximity distance Cluster the fusion bands in the data, and then combine the final fusion band set. The integration zone is formed into different adjacent geographic spatial integration domains; including: S5011. Determine the set through the ArcGIS platform. Each fusion zone The center point and its spatial coordinates are defined as the center point for each... The geometric center; S5012. Set the minimum proximity distance for the fusion zone. and the number of smallest neighboring units N min ， by With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with radius and determine the number of fusion zones that intersect with the circle. N i ; S5013. If Then it is considered that the fusion zone It is the core unit of integration and classify them as a core set of integration. Otherwise, it will be Considered as a fused discrete unit And classify it as a fused discrete set middle; S5014. Traversing a set All fusion zones within the set will yield a fusion core collection. and fusion discrete sets ; S5015. Randomly select a set of fusion cores. A core unit of integration With this core fusion unit With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with radius and classify the core units that intersect with the circle as adjacent geospatial fusion domains. and from the core set of integration Remove from the middle; continue with The newly added fusion core unit is centered on a circle with its geometric center. Fusion core units that intersect are grouped into... In the middle, the cycle continues until... No new fusion core units were added; S5016. Using integrated discrete units With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with radius , if the circle intersects with... If the core fusion units intersect, then they are classified as In the middle; traversing and fusing discrete sets SAll fused discrete units in the class are completed. The division, and the final merge into middle; S502. Repeat step S501 until the core set is merged. R Empty, integrate all fusion zones into different adjacent geospatial fusion domains. ; K m It refers to one of the merged domains of all adjacent geographic spaces; S503. Based on the spatial scale of the adjacent geospatial fusion domain, the adjacent geospatial fusion partial domain... The number of fusion bands is evaluated, and a discrimination threshold is set as follows. ,like The number of fusion zones is greater than Then The space occupied by the central fusion zone is considered as the adjacent geographic spatial fusion domain and recorded in the set. Otherwise Consider them as invalid adjacent geospatial fusion domains and remove them; S504. The final set The spatial area occupied by the central integration zone is the overall adjacent geographic spatial integration domain within the study area.

[0014] Furthermore, the multidimensional variables used to distinguish between the two types of adjacent geographic spaces include: residential land area, warehousing and logistics land area, population density, road network density, number of bus stops, and number of containers; the grid unit is a rectangular grid or a hexagonal grid.

[0015] Preferably, the present invention also provides a device for identifying a neighboring geospatial fusion domain considering spatial overlay analysis, comprising: The geospatial delimitation model construction module is used to standardize the multidimensional variables used to distinguish between two types of adjacent geospatial areas, construct the geospatial delimitation model, obtain attribute dataset samples of adjacent geospatial areas, and divide the samples into training set and test set. The geospatial delimitation model is trained using the training set and the weight parameters of the variables are calibrated. The geospatial delimitation model is validated using the test set. When the confidence level of the test is true reaches the preset threshold β0, the next step is performed. The mapping module is used to define the scope of the study area on the GIS platform and map variables to the study area according to latitude and longitude. The candidate fusion zone identification module is used to set a movable grid cell within the study area, control the grid cell to move at a set step size and traverse all study areas, and evaluate and record the attribute information of the corresponding area of ​​each grid cell through the geospatial delimitation model; when two intersecting grid cells are determined to be of different categories, the overlapping area of ​​the two intersecting grid cells is defined as a candidate fusion zone, and finally the candidate fusion zone set T is obtained. The final fusion zone determination module is used to sequentially perform D random perturbation re-judgments on the independent variables involved in the candidate fusion zones within the candidate fusion zone set T, according to a specified distribution within a preset range, where D≥100; calculate the rate of change γ = C / D, where C is the number of times the initial regional attributes of the selected candidate fusion zone are different; when the rate of change γ≥threshold γ0, the candidate fusion zone is taken as the fusion zone and included in the final fusion zone set Q; The neighborhood aggregation module is used to aggregate the final fusion band set from a spatial perspective based on proximity. Clustering is performed on each fusion zone to obtain the adjacent geospatial fusion partial domain. And integrate adjacent geographic spaces into the domain. The number of fusion zones was assessed to ultimately determine the overall adjacent geospatial fusion domain within the study area.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the nearest geospatial fusion domain identification method considering spatial overlay analysis.

[0017] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the nearby geospatial fusion domain identification method considering spatial overlay analysis.

[0018] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: 1. By constructing a geospatial delineation model and mapping variables to the study area; first, a judgment is made on a unified coordinate system using a unified model caliber, and subsequent steps have consistent input and judgment rules, which facilitates reproduction and auditing; this solves the problems of unstable and difficult-to-reproduce results when relying solely on experience thresholds or subjective judgment.

[0019] 2. Define grid cells within the study area, and define fusion zones using the overlapping regions of intersecting grid cells. Obtain a set T of candidate fusion zones through grid search. Using the simultaneous condition of "intersecting and having different attributes" (geometric + attribute), directly locate easily changing edge regions as fusion zones, making the location of the endpoints more precise. This solves the problem of inaccurate endpoints and lack of direct evidence of boundary conflicts when defining the range solely based on the "strength" of the region.

[0020] 3. Perform random mutation re-judgment on each fusion band in the candidate fusion band set T to screen to the final fusion band set Q; through multiple small changes and statistical threshold re-judgments, unstable or accidental objects are eliminated, and only fusion bands that still meet the judgment under the set conditions are retained, which significantly reduces false judgments.

[0021] 4. Based on proximity distance and the number of least neighboring units, a fused partial domain is formed and its spatial scale is assessed. By considering proximity distance, the number of least neighboring units, and scale conditions, discrete objects are merged into a continuous and usable spatial domain, and fragmented segments are eliminated, resulting in a more suitable outcome for engineering applications. This addresses the problem that scattered individual objects can significantly interfere with regional assessments and cannot directly support governance or construction-level decisions.

[0022] 5. The example variables clearly specify residential land area, warehousing and logistics land area, population density, road network density, number of bus stops, number of containers, etc., and all include latitude and longitude for mapping; the steps are completed on a unified platform; the entire process is based on a unified model and unified coordinate mapping, with clear parameters and definitions, facilitating verification and migration.

[0023] 6. This invention first uses intersecting and different attributes to lock candidate fusion bands, and then uses multiple variations to test whether they can withstand disturbances. The two steps are combined to achieve the process of finding the band first and then finding it accurately.

[0024] 7. This invention, through spatial overlay analysis, comprehensively considers multi-dimensional and dynamically changing current data on land use (warehousing, residential), transportation systems (port collection and distribution networks, urban road networks), etc., enabling a more scientific, precise, and dynamic identification of the actual "integration domain" where adjacent geographical functions are deeply intertwined and interact symbiotically, rather than simply administrative or planning boundaries. This provides a real and objective spatial basis for subsequent planning.

[0025] 8. This invention transforms complex neighboring geospatial interactions into visualized and measurable geospatial information. While enhancing the scientific rigor and accuracy of neighboring geospatial fusion domain research, it also provides technical tools for practical operations by planning and management departments.

[0026] 9. The overlay analysis method based on multi-factor dynamic data makes the identified fusion areas more adaptable and updatable. The system can re-analyze periodically or based on new data to track the evolution trend of fusion areas. This helps to formulate more forward-looking planning strategies, reserve development space, optimize infrastructure layout, and ensure that adjacent geographical spaces can adapt to each other and evolve collaboratively in the long term, avoiding future conflicts caused by rigid boundaries. Attached Figure Description

[0027] Figure 1This is a flowchart illustrating the proximity geospatial fusion domain identification method of the present invention.

[0028] Figure 2 This is a flowchart of the proximity geospatial fusion zone identification algorithm of the present invention.

[0029] Figure 3 This is a schematic diagram illustrating the concepts of mesh cells and fusion bands in this invention.

[0030] Figure 4 This is a flowchart of the neighboring geospatial fusion domain determination algorithm based on proximity distance clustering and scale discrimination of the present invention. Detailed Implementation

[0031] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0032] This embodiment provides a method and system for identifying port-city fusion domains considering spatial overlay analysis. See Figure 1 Specifically, it includes the following steps: S1. Taking into account the differences in development models and construction status between port and urban areas, variables that can characterize the attributes of port and urban areas are selected to construct a port-city area delineation model, and the parameters of the model are calibrated. S101. Selecting Model Variables. Port areas are primarily focused on logistics, warehousing, and industry, with lower population density; urban areas, on the other hand, are typically dominated by residential, commercial, and public service functions, with higher population density and more developed transportation networks. Based on the differences in development patterns and construction status between port and urban areas, quantifiable, easily obtainable variables that cover multiple dimensions such as land use, population, transportation, and port activities are selected as model variables. Therefore, residential land area is selected. Warehousing and logistics land area Population density Road network density Number of bus stops Container quantity , as variables in the port city area delineation model; S102. Establish a regional boundary model for the port city, as shown below: (1-1); in, These represent the weights of their respective variables; when the evaluation results... If the value is less than 0.5, the assessment result is for an urban area; otherwise, the assessment result is for a port area. S103. Data Standardization. Because the units of the independent variables are inconsistent, the acquired data is standardized for easier observation: (1-2); in, Indicates the first One variable; Indicates the first The first variable under the variable There are 1 data point, totaling 10 data points. One data point; Representing variables The The result after data standardization; At this time, the model for defining the port city area is: (1-3); S104. Obtain a dataset sample of the urban and port areas to be studied. Randomly divide the sample data into 70% as the training set and 30% as the test set. Use the training set data to train the model and determine the parameters. The value; S105. Test the model using the test set data, and set the confidence level for the test result to be true at [value missing]. If the value is above 0.8, proceed to step S201; otherwise, return to step S104 to continue training.

[0033] S2. Select a research area that includes both the port area and the urban area. Z Statistics on residential land area Warehousing and logistics land area Population density Road network density Number of bus stops Container quantity The values ​​and distribution across the entire study area are determined, and this information is mapped to the area under study. Z superior; S201. Using the ArcGIS platform, the study area is defined as a rectangular region from a spatial geographic perspective. Z , Determine the study area Z Latitude and longitude coordinates in the upper left corner Study area Z The latitude and longitude coordinates in the lower right corner are ,in Indicates the longitude value. Represents latitude value, study area Z Represented as [ , ], which is the study area Z Spatial coordinates; S202. Obtain the study area Z Residential land area within the scope Warehousing and logistics land area Population density Road network density Number of bus stops Container quantity And its corresponding latitude and longitude coordinates, which are then spatially mapped to the region. Z superior.

[0034] S3. See Figure 2 and Figure 3 A movable grid cell is set within the study area. The grid cell is controlled to move at a set step size and traverse all study areas. During this process, the attribute information of each grid cell within its corresponding area is evaluated and recorded using the geospatial delimitation model. When two intersecting grid cells are determined to be of different categories, the overlapping area of ​​the two intersecting grid cells is defined as a candidate fusion zone, and finally, a set of candidate fusion zones T is obtained. S301. Define grid cells. This defines the study area from a spatial geographic perspective. Z A rectangular region within the grid is treated as a movable grid cell. Its spatial coordinates are [ , ].

[0035] S302. Define the "Port-City Integration Zone". When two intersecting grid cells intersect, if one of the grid cells is evaluated as a port area... Another grid unit assessment result is for the urban area. If the overlapping area of ​​the two grid units is identified, then the "port-city integration zone" is defined. Using the grid unit as the basic unit, the study area is analyzed using a grid search method. Z Candidate port-city integration zones within the initial screening scope have been identified.

[0036] Furthermore, step S302 includes the following steps: S3021. Based on the definition in the region of S301 Z Define a length of [missing information] in the upper left corner. Width Rectangles as initial grid units At the same time, obtain Spatial coordinates are [ , ],in = , = , i.e., grid cell The top left corner is the study area. Z The top left corner; S3022. Set the grid cell movement step size to 0.001 degrees (latitude and longitude units), and let the grid cell... For every eastward movement in longitude, the longitude values ​​at the top left and bottom right corners increase. After moving, a new mesh cell is obtained. Its spatial coordinates are [ , ]=[ , ]; grid cell Each southward movement in latitude reduces the latitude values ​​of the top-left and bottom-right corners by 0.001, resulting in a new grid cell. Its spatial coordinates are [ , ]=[ , ]; S3023. Apply the model from step S1 to the mesh elements. Conduct an evaluation when the grid cell When the assessment result is for an urban area, record it in the set. In the middle, when the grid cell When the assessment result is a port area, it is recorded in the set. middle; S3024. Order Obtain the mesh cells according to step S3022. Spatial coordinates [ , ], determine when If the condition is met, proceed to step 3025; otherwise, proceed to step S3023. S3025. Order Obtain the mesh cells according to step S3022. Spatial coordinates [ , ], determine when If so, proceed to step S3026; otherwise, set the mesh cells... Translate to [ , Proceed to step S3023; S3026. Determine the overlapping region of two grid cells through spatial identification, and make a judgment for each overlapping region. When the two grid cells corresponding to the overlapping region are respectively in the set and set When it appears in the set, add it to the set. In the end, the final set is obtained That is, the set of candidate port city integration zones determined based on the grid search method. T .

[0037] S4. The selected candidate port city integration zones are set. The stability of regional attributes of each integration zone within the area was analyzed, and integration zones with unstable regional attributes were further screened out to obtain the final set of port city integration zones. .

[0038] S401. For sets Each candidate port city integration zone The variables are varied within a certain numerical range, and the resulting candidate port city integration zone... The attributes are evaluated. This includes the following steps: S4011. Order Proceed to step S4012; S4012. Select candidate port-city integration zone An independent variable Mutations occur within a certain range, the range of which depends on the independent variable. Determined by practical significance. For example, when the independent variable... When considering the area of ​​land for warehousing and logistics, how much can the area of ​​warehousing land increase or decrease in the next few years? Within its range of variation, the mutation occurs D=100 times, and the mutation formula is as follows: = ; in, For the first The first variable The result of the mutation; The results are the standardized independent variables; For variables Within its range of variation Random numbers generated internally. For example, for the variable of warehousing and logistics land area, its value ranges from approximately 20,000 to 200,000 square meters.

[0039] S4013. Record the results after each change of the independent variable, and apply the model from step S1 to the candidate port-city integration zone. Conduct a reassessment and record the results of each assessment; S402. When the candidate port city integration zone The rate of change γ of the evaluation results is above the threshold γ0 = 0.3, and the rate of change γ = C / D, where C is the candidate fusion band. If the number of times the initial regional attributes differ from the initial attributes is more than 30, then this candidate port city integration zone is identified. As a new port city integration zone, it is designated as And recorded in the final collection of port city integration zones. In the middle, when the set TWhen each port city integration zone is determined to be completed, proceed to the next step; otherwise, proceed to step S4011. S5. Spatial perspective on the final port city integration zone based on proximity. The various integration zones within the area are clustered and integrated into a single integrated zone for the port city. And for some areas of the port city integration zone The spatial scale was assessed to ultimately determine the overall port-city integration domain within the study area. Figure 4 .

[0040] S501. Spatial perspective on the final port city integration zone based on proximity distance Clustering is performed on the various fusion zones in the set, and the set is then... The integration zone will be divided into different integrated areas of the port city. Details are as follows: S5011. Determine the set through the ArcGIS platform. Each port city integration zone The center point and its spatial coordinates are defined as the center point for each... The geometric center; S5012. Considering the spatial continuity of the port-city integration domain, the discrete port-city integration zones have limited impact on regional development. To avoid the influence of discrete port-city integration zones on the determination of the port-city integration domain, a minimum proximity distance for port-city integration zones is set. and the number of smallest neighboring units N min ， by With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with radius and determine the number of port-city integration zones that intersect with the circle. N i ; S5013. If Then it is believed that the port city integration zone It is the core unit of port-city integration And classify it as the core collection of port city integration. Otherwise, it will be Considered as a discrete unit of port city integration And classify it as a port city integrated discrete set. middle.

[0041] S5014. Traversing a set All port-city integration zones within the region will obtain the core collection of port-city integration. The integration of discrete sets with port city .

[0042] S5015. Randomly select the core set of port city integration. A core unit for port-city integration With this port city integration core unit With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with a radius equal to the port city integration area, and classify the core units of the port city integration that intersect with the circle as the port city integration sub-domain. and from the core set of integration Remove from the middle. Continue with The newly added port-city integration core units are categorized into circles with their geometric centers as the center. Intersecting port-city integration core units are grouped into... In the middle, the cycle continues until... No new core units for port-city integration were added.

[0043] S5016. Port-city integrated discrete unit With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with radius , if the circle intersects with... If the core units of the port-city integration intersect, then they are classified as In the middle. Traversing the discrete set of port cities. S All port city integrated discrete units, completing the class The division, and the final merge into middle.

[0044] S502. Repeat step S501 until the core set of port city integration is completed. R Empty, integrate all port city integration zones into different port city integration sub-domains. ，K m It refers to one of the many adjacent geographic spatial regions that are integrated.

[0045] S503. Considering the spatial scale of the port-city integration area, the port-city integration area is divided into several parts. The number of fusion bands is evaluated, and a discrimination threshold is set as follows. ,like The number of integrated development zones of China-Hong Kong City is greater than or equal to Then The space occupied by the China-Hong Kong City integration zone is considered the Hong Kong-City integration domain and is recorded in the collection. Otherwise The port city integration part was deemed invalid and removed.

[0046] S504. The final set The spatial area occupied by the central integration zone is the overall port-city integration domain within the study area.

[0047] Example 2 Based on the same inventive concept, embodiments of this application also provide a device for identifying neighboring geospatial fusion domains considering spatial overlay analysis, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of the device for identifying neighboring geospatial fusion domains considering spatial overlay analysis is similar to that of the method for identifying neighboring geospatial fusion domains considering spatial overlay analysis, the implementation of the device for identifying neighboring geospatial fusion domains considering spatial overlay analysis can refer to the implementation of the method for identifying neighboring geospatial fusion domains considering spatial overlay analysis; repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0048] The embodiments of the present invention provide a specific implementation of a proximity geospatial fusion domain identification device that considers spatial overlay analysis and is capable of implementing a proximity geospatial fusion domain identification method that takes spatial overlay analysis into account. The specific implementation includes the following: A device for identifying neighboring geospatial fusion domains considering spatial overlay analysis, comprising: The geospatial delimitation model construction module is used to standardize the multidimensional variables used to distinguish between two types of adjacent geospatial areas, construct the geospatial delimitation model, obtain attribute dataset samples of adjacent geospatial areas, divide the samples into training set and test set, train the geospatial delimitation model and calibrate the weight parameters of the variables through the training set, and validate the geospatial delimitation model through the test set. When the confidence level of the test is true reaches the preset threshold β0, the mapping module is executed. The mapping module is used to define the scope of the study area on the GIS platform and map variables to the study area according to latitude and longitude. The candidate fusion zone identification module is used to set a movable grid cell within the study area, control the grid cell to move at a set step size and traverse all study areas, and evaluate and record the attribute information of the corresponding area of ​​each grid cell through the geospatial delimitation model; when two intersecting grid cells are determined to be of different categories, the overlapping area of ​​the two intersecting grid cells is defined as a candidate fusion zone, and finally the candidate fusion zone set T is obtained. The final fusion zone determination module is used to sequentially perform D random perturbation re-judgments on the independent variables involved in the candidate fusion zones within the candidate fusion zone set T, according to a specified distribution within a preset range, where D≥100; calculate the rate of change γ=C / D, where C is the number of times the initial regional attributes of the selected candidate fusion zone are different; when the rate of change γ≥threshold γ0, the candidate fusion zone is taken as the fusion zone and included in the final fusion zone set Q; The neighborhood aggregation module is used to aggregate the final fusion band set from a spatial perspective based on proximity. Clustering is performed on each fusion zone to obtain the adjacent geospatial fusion partial domain. And integrate adjacent geographic spaces into the domain. The number of fusion zones was assessed to ultimately determine the overall adjacent geospatial fusion domain within the study area.

[0049] Preferably, embodiments of this application also provide a specific implementation of an electronic device capable of implementing all steps in the near geospatial fusion domain identification method considering spatial overlay analysis in the above embodiments. The electronic device specifically includes the following: Processor, memory, communications interface, and bus; The processor, memory, and communication interface communicate with each other via a bus; the communication interface is used to realize information transmission between server-side devices, metering devices, and user-side devices.

[0050] The processor is used to call the computer program in memory. When the processor executes the computer program, it implements all the steps in the near geospatial fusion domain identification method that takes into account spatial overlay analysis in the above embodiments.

[0051] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the near geospatial fusion domain identification method considering spatial overlay analysis in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the near geospatial fusion domain identification method considering spatial overlay analysis in the above embodiments.

[0052] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0053] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0054] While this application provides method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the method can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0055] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0058] This invention is not limited to the embodiments described above. The above description of specific embodiments is intended to illustrate and explain the technical solutions of this invention. The specific embodiments described above are merely illustrative and not restrictive. Without departing from the spirit and scope of the claims, those skilled in the art can make many specific modifications based on the teachings of this invention, and these modifications all fall within the scope of protection of this invention.

Claims

1. A method for identifying neighboring geospatial fusion domains considering spatial overlay analysis, characterized in that, include: S1. Standardize the multidimensional variables used to distinguish between two types of adjacent geospatial areas, construct a geospatial delimitation model, obtain attribute dataset samples of adjacent geospatial areas, and divide the samples into training set and test set. Train the geospatial delimitation model and calibrate the weight parameters of the variables through the training set. Validate the geospatial delimitation model through the test set. Proceed to the next step when the confidence level of the test is true reaches the preset threshold β0. S2. Define the study area on the GIS platform and map the variables to the study area according to latitude and longitude; S3. Set a movable grid cell within the study area, control the grid cell to move at a set step size and traverse all study areas, and evaluate and record the attribute information of the corresponding area of ​​each grid cell through the geospatial delimitation model; when two intersecting grid cells are determined to be of different categories, define the overlapping area of ​​the two intersecting grid cells as a candidate fusion zone, and finally obtain the candidate fusion zone set T. S4. For each candidate fusion zone in the candidate fusion zone set T, the independent variables involved are randomly perturbed and re-judged D times within a preset range according to a specified distribution, where D≥100; the rate of change γ=C / D is calculated, where C is the number of times the initial regional attributes of the selected candidate fusion zone are different; when the rate of change γ≥threshold γ0, the candidate fusion zone is taken as a new fusion zone and included in the final fusion zone set Q; S5. Spatial perspective on the final fusion zone set based on proximity distance Clustering is performed on each fusion zone to obtain the adjacent geospatial fusion partial domain. And integrate adjacent geographic spaces into the domain. The number of fusion zones was assessed to ultimately determine the overall adjacent geospatial fusion domain within the study area.

2. The method for identifying neighboring geospatial fusion domains considering spatial overlay analysis according to claim 1, characterized in that, Step S1 includes: S101. Taking into account the differences in development patterns and construction status of adjacent geographical areas, select... Each represents two adjacent geographic spaces and Variables of regional attributes , , as variables for determining the initial geospatial delineation model; S102. Establish the initial geospatial delineation model, as shown below: (1-1)； in, These represent the weights of their respective variables; when the evaluation results... Less than At that time, the evaluation result was The region, otherwise the evaluation result is area; To distinguish Region and The critical threshold of the region; S103. Standardize the acquired variable data: (1-2)； in, Indicates the first One variable; Indicates the first The first variable under the variable There are 1 data point, totaling 10 data points. One data point; Representing variables The The result after standardizing the data; taking variables The mean of all data is denoted as = j=1,2,3,…,n; The geospatial delineation model at this time is: (1-3)； S104. Obtain Regional attribute dataset samples, randomly partitioned sample data % as training set data, (100- The test set data is used to train the geospatial delimitation model using the training set data, and the parameters are determined. The value; S105. Test the geospatial delimitation model using test set data, and determine the confidence level for a true result as follows: If the above is achieved, proceed to the next step; otherwise, return to step S104 to continue training.

3. The method for identifying neighboring geospatial fusion domains considering spatial overlay analysis according to claim 1, characterized in that, Step S2 includes: S201. Using the ArcGIS platform, the study area is defined as a rectangular region from a spatial geographic perspective. Z , Determine the study area Z Latitude and longitude coordinates in the upper left corner Study area Z The latitude and longitude coordinates in the lower right corner are ,in Indicates the longitude value. Represents latitude value, study area Z Represented as [ , ], which is the study area Z Spatial coordinates; S202. Obtain the study area Variables within range , The data, along with its corresponding latitude and longitude coordinates, are spatially mapped to the study area. superior.

4. The method for identifying neighboring geospatial fusion domains considering spatial overlay analysis according to claim 1, characterized in that, Step S3 includes: S301. Studying the region from a spatial geographic perspective Z A certain length within Width The rectangular region is treated as a movable grid unit. Its spatial coordinates are [ , ]; S302. When two mesh elements intersect, if the evaluation result of one of the mesh elements is... The evaluation result of another grid cell is If the overlapping region of the two intersecting grid cells is identified, it is considered a candidate fusion zone; using the grid cell as the basic unit, a grid search is performed on the study area. Screening was conducted to preliminarily determine the research area. Candidate fusion bands within; specifically including: S3021. Based on the definition in S301, in the study area Define an initial grid cell in the upper left corner. At the same time, obtain Spatial coordinates are [ , ],in = , = , i.e., grid cell The top left corner is the study area. The top left corner; S3022. Set the grid cell movement step size to Degree, as a unit of latitude and longitude, makes the grid cell For every eastward movement in longitude, the longitude values ​​at the top left and bottom right corners increase. After moving, a new mesh cell is obtained. Its spatial coordinates are [ , ]=[ , ]; grid cell Each time it moves southward, the latitude values ​​at its top left and bottom right corners decrease. After moving, a new mesh cell is obtained. Its spatial coordinates are [ , ]=[ , ]; S3023. Apply the geospatial delimitation model from step S1 to the grid cells. Define the grid cells. The evaluation result is When, record it in a set In the middle, when the grid cell The evaluation result is When, record it in a set middle; S3024. Order Obtain the mesh cells according to step S3022. Spatial coordinates [ , ], determine when If the condition is met, proceed to step 3025; otherwise, proceed to step S3023. S3025. Order Obtain the mesh cells according to step S3022. Spatial coordinates [ , ], determine when If so, proceed to step S3026; otherwise, set the mesh cells... Translate to [ , Proceed to step S3023; S3026. Determine the overlapping region of two grid cells through spatial identification, and make a judgment for each overlapping region. When the two grid cells corresponding to the overlapping region are respectively in the set and set When it appears in the set, add it to the set. In the end, the final set is obtained This refers to the set of candidate fusion zones determined based on grid search.

5. The method for identifying neighboring geospatial fusion domains considering spatial overlay analysis according to claim 1, characterized in that, Step S4 includes: S401. For the candidate fusion band set Each candidate fusion band The independent variable is varied within a set numerical range, and the candidate fusion band is adjusted accordingly. The attributes are evaluated; specifically, this includes: S4011. Order Proceed to step S4012; S4012. Select candidate fusion bands An independent variable Mutations are performed within a defined range, the range of which depends on the independent variable. Determined by practical significance, Mutation within its range of variation The mutation formula is as follows: = ； in, This represents the result of the k-th mutation of the j-th variable; The results are the standardized independent variables; For variables Within its range of variation Random numbers generated internally; S4013. Record the results after each change of the independent variable, and apply the geospatial delimitation model to the candidate fusion zone. Redefine the criteria and record the results of each assessment; S402. Calculate its rate of change γ = C / D, where C is the candidate fusion band. The number of times the initial region attributes differ; when the rate of change γ ≥ the threshold γ0, the candidate fusion band is identified. The new fusion zone is denoted as And recorded in the final fusion zone set. In the middle, when the candidate fusion band set T When each candidate fusion band is determined to be complete, proceed to the next step; otherwise, proceed to step S4011.

6. The method for identifying neighboring geospatial fusion domains considering spatial overlay analysis according to claim 1, characterized in that, Step S5 includes: S501. Spatial perspective on the final fusion zone set based on proximity distance Cluster the fusion bands in the data, and then combine the final fusion band set. The fusion zone within the region is integrated into different adjacent geographic spatial fusion domains; including: S5011. Determine the set through the ArcGIS platform. Each fusion zone The center point and its spatial coordinates are defined as the center point for each... The geometric center; S5012. Set the minimum proximity distance for the fusion zone. and the number of smallest neighboring units N min ， by With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with radius and determine the number of fusion zones that intersect with the circle. N i ; S5013. If Then it is considered that the fusion zone It is the core unit of integration and classify them as a core set of integration. Otherwise, it will be Considered as a fused discrete unit And classify it as a fused discrete set middle; S5014. Traversing a set All fusion zones within the set will yield a fusion core collection. and fusion discrete sets ; S5015. Randomly select a set of fusion cores. A core unit of integration With this core fusion unit With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with radius and classify the core units that intersect with the circle as adjacent geospatial fusion domains. and from the core set of integration Remove from the middle; continue with The newly added fusion core unit is centered on a circle with its geometric center. Fusion core units that intersect are grouped into... In the middle, the cycle continues until... No new fusion core units were added; S5016. Using integrated discrete units With the geometric center as the center, and the minimum nearest neighbor distance Draw a circle with radius , if the circle intersects with... If the core fusion units intersect, then they are classified as In the middle; traversing and fusing discrete sets S All fused discrete units in the class are completed. The division, and the final merge into middle; S502. Repeat step S501 until the core set is merged. R Empty, integrate all fusion zones into different adjacent geospatial fusion domains. ; K m It refers to one of the merged domains of all adjacent geographic spaces; S503. Based on the spatial scale of the adjacent geospatial fusion domain, the adjacent geospatial fusion partial domain... The number of fusion bands is evaluated, and a discrimination threshold is set as follows. ,like The number of fusion zones is greater than or equal to Then The space occupied by the central fusion zone is considered as the adjacent geographic spatial fusion domain and recorded in the set. Otherwise Consider them as invalid adjacent geospatial fusion domains and remove them; S504. The final set The spatial area occupied by the central integration zone is the overall adjacent geographic spatial integration domain within the study area.

7. The method for identifying neighboring geospatial fusion domains considering spatial overlay analysis according to claim 1, characterized in that, The multidimensional variables used to distinguish between the two types of adjacent geographic spaces include: residential land area, warehousing and logistics land area, population density, road network density, number of bus stops, and number of containers; the grid unit is a rectangular grid or a hexagonal grid.

8. A device for identifying a neighboring geospatial fusion domain considering spatial overlay analysis, characterized in that, include: The geospatial delimitation model construction module is used to standardize the multidimensional variables used to distinguish between two types of adjacent geospatial areas, construct the geospatial delimitation model, obtain attribute dataset samples of adjacent geospatial areas, and divide the samples into training set and test set. The geospatial delimitation model is trained using the training set and the weight parameters of the variables are calibrated. The geospatial delimitation model is validated using the test set. When the confidence level of the test is true reaches the preset threshold β0, the next step is performed. The mapping module is used to define the scope of the study area on the GIS platform and map variables to the study area according to latitude and longitude. The candidate fusion zone identification module is used to set a movable grid cell within the study area, control the grid cell to move at a set step size and traverse all study areas, and evaluate and record the attribute information of the corresponding area of ​​each grid cell through the geospatial delimitation model; when two intersecting grid cells are determined to be of different categories, the overlapping area of ​​the two intersecting grid cells is defined as a candidate fusion zone, and finally the candidate fusion zone set T is obtained. The final fusion zone determination module is used to sequentially perform D random perturbation re-judgments on the independent variables involved in the candidate fusion zones within the candidate fusion zone set T, according to a specified distribution within a preset range, where D≥100; calculate the rate of change γ=C / D, where C is the number of times the initial regional attributes of the selected candidate fusion zone are different; when the rate of change γ≥threshold γ0, the candidate fusion zone is taken as the fusion zone and included in the final fusion zone set Q; The neighborhood aggregation module is used to aggregate the final fusion band set from a spatial perspective based on proximity. Clustering is performed on each fusion zone to obtain the adjacent geospatial fusion partial domain. And integrate adjacent geographic spaces into the domain. The number of fusion zones was assessed to ultimately determine the overall adjacent geospatial fusion domain within the study area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the nearest geospatial fusion domain identification method considering spatial overlay analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the nearest geospatial fusion domain identification method considering spatial overlay analysis as described in any one of claims 1 to 7.

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