Urban space form identification method, device and equipment and storage medium

Abstract

The application relates to the technical field of geographic information processing, and particularly provides a city space form identification method, device and equipment and a storage medium, aiming to solve the problem that general space form representation and classification cannot effectively formulate space planning design optimization measures. To this end, the city space form identification method comprises the following steps: acquiring a space form data set of a target region, dividing the target region into a plurality of road grid regions based on the space form data set, each road grid region comprising a plurality of first measurement indexes, calculating a variance inflation factor of each first measurement index of each road grid region based on the space form data set, determining a plurality of candidate space form types contained in the target region based on the variance inflation factors, and determining a target space form type of each road grid region based on the plurality of candidate space form types and the variance inflation factors. The application significantly improves the efficiency and stability of city space form identification.

Description

Technical Field

[0001] This application relates to the field of geographic information processing technology, specifically to a method, apparatus, device, and storage medium for urban spatial morphology recognition. Background Technology

[0002] Urban spatial morphology is a numerical representation that abstracts and simplifies specific urban details. Accurate and effective identification of urban spatial morphology types can provide a reference for urban planning and landscape design, including current status descriptions, comparisons, and future optimization adjustments.

[0003] Current urban spatial morphology identification technologies are still limited to relatively simple spatial data, such as constructing indicator systems based on data like area and floor area ratio (numerical type), land function and use (categorical type), and remote sensing imagery (spatial raster). While single or combined indicator systems constructed using such data can roughly classify spatial morphology at a relatively macro scale, they are insufficient for effectively distinguishing spatial morphology categories in the face of increasingly diverse, three-dimensional, and complex urban spatial morphologies. Overly general spatial morphology representations and classifications cannot be effectively used to formulate targeted spatial planning and design optimization measures. Summary of the Invention

[0004] This application addresses the shortcomings of the prior art by proposing a method, apparatus, device, and storage medium for urban spatial form recognition.

[0005] In a first aspect, embodiments of this application provide a method for identifying urban spatial morphology, comprising: acquiring a spatial morphology dataset of a target area; dividing the target area into multiple road grid areas based on the spatial morphology dataset, each road grid area including multiple first measurement indicators; calculating the variance inflation coefficient of each of the first measurement indicators for each of the road grid areas based on the spatial morphology dataset; determining multiple candidate spatial morphology types contained in the target area based on each variance inflation coefficient; and determining the target spatial morphology type of each of the road grid areas based on the multiple candidate spatial morphology types and each variance inflation coefficient.

[0006] Secondly, embodiments of this application provide an urban spatial morphology recognition device, comprising: an acquisition module for acquiring a spatial morphology dataset of a target area; a division module for dividing the target area into multiple road grid areas based on the spatial morphology dataset, each road grid area including multiple first measurement indicators; a calculation module for calculating the variance inflation coefficient of each of the first measurement indicators of each road grid area based on the spatial morphology dataset; a first determination module for determining multiple candidate spatial morphology types contained in the target area based on each variance inflation coefficient; and a second determination module for determining the target spatial morphology type of each road grid area based on the multiple candidate spatial morphology types and each variance inflation coefficient.

[0007] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method described in the first aspect above.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0009] The technical solutions provided in this application embodiment have at least the following technical effects or advantages:

[0010] The urban spatial morphology recognition method of this application acquires a spatial morphology dataset of a target area, divides the target area into multiple road grid areas based on the spatial morphology dataset, and each road grid area includes multiple first measurement indicators. Based on the spatial morphology dataset, the variance inflation coefficient of each first measurement indicator in each road grid area is calculated. Based on each variance inflation coefficient, multiple candidate spatial morphology types contained in the target area are determined. This method can effectively integrate spatial morphology data, perform comprehensive first measurement indicator calculations at a unified spatial scale, and thus accurately and efficiently identify urban spatial morphology.

[0011] By determining the target spatial morphology type of each road grid area based on multiple candidate spatial morphology types and various variance inflation coefficients, the efficiency and accuracy of urban spatial morphology recognition are greatly improved.

[0012] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0013] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0014] Figure 1 A flowchart of an urban spatial morphology recognition method provided in an embodiment of this application is shown;

[0015] Figure 2 This illustration shows a structural schematic diagram of an urban spatial form recognition device provided in an embodiment of this application;

[0016] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0017] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0018] Current urban spatial morphology identification technologies are still limited to relatively simple spatial data, such as constructing indicator systems based on data like area and floor area ratio (numerical type), land function and use (categorical type), and remote sensing imagery (spatial raster). While single or combined indicator systems constructed using such data can roughly classify spatial morphology at a relatively macro scale, they are insufficient for effectively distinguishing spatial morphology categories in the face of increasingly diverse, three-dimensional, and complex urban spatial morphologies. Overly general spatial morphology representations and classifications cannot be effectively used to formulate targeted spatial planning and design optimization measures.

[0019] Based on this, embodiments of this application provide a method for urban spatial morphology recognition. The following detailed description of the embodiments of this application is provided in conjunction with the accompanying drawings.

[0020] See Figure 1 The flowchart shown is a method for identifying urban spatial forms. The method specifically includes the following steps:

[0021] Step 101: Obtain the spatial morphology dataset of the target region.

[0022] In this embodiment, the spatial morphology dataset can refer to a multi-source, multi-modal spatial morphology dataset, that is, a data set integrating multiple sources and multiple modalities. The spatial morphology dataset may include building 3D data, traffic network data, point-of-interest data, street view image data, active population data, vegetation cover data retrieved from satellite images, land parcel boundary data, etc., but this embodiment does not impose specific limitations.

[0023] In one implementation, spatial morphological data of the target area can be obtained from different sources such as satellite remote sensing, drone aerial photography, and ground observation stations as the spatial morphological dataset of the target area.

[0024] Step 102: Based on the spatial morphology dataset, the target area is divided into multiple road grid areas, and each road grid area includes multiple primary metrics.

[0025] In one implementation, traffic network data and land parcel boundary data can be input into ArcGIS (Geographic Information System), and then the target area can be segmented to obtain multiple road grid areas, each of which includes multiple first-order metrics.

[0026] Furthermore, the first metric may include one or more of the following: building height density, average building height, average building base perimeter, average building area, floor area ratio, street height-to-width ratio, population, perceived value, style, and accessibility.

[0027] Step 103: Based on the spatial morphology dataset, calculate the variance inflation coefficient of each first metric for each road grid region.

[0028] Since the calculation method of variance inflation coefficient is a conventional technique in this field, it will not be repeated in the embodiments of this application.

[0029] Step 104: Based on each variance inflation coefficient, determine the multiple candidate spatial morphology types contained in the target region.

[0030] In one implementation, based on the variance inflation coefficient of each first metric of each road grid region, each second metric that meets the preset conditions can be obtained for each first metric of each road grid region. Then, based on each second metric of each road grid region, multiple candidate spatial morphology types contained in the target region can be determined.

[0031] Furthermore, if the variance inflation coefficient of each first measure is less than the preset variance inflation coefficient threshold, it is determined that the first measure corresponding to the variance inflation coefficient meets the preset conditions; otherwise, it is determined that the first measure corresponding to the variance inflation coefficient does not meet the preset conditions.

[0032] It should be noted that the preset variance inflation coefficient threshold can be a coefficient threshold preset by those skilled in the art according to actual needs, or it can be a coefficient threshold obtained by those skilled in the art after adjusting a preset coefficient threshold according to actual needs. This application does not impose specific limitations; for example, the preset variance inflation coefficient threshold can be set to 10.

[0033] Based on the above implementation method, in some modified implementation methods, multiple candidate spatial morphology types contained in the target area are determined based on each second metric of each road grid area. Specifically, this may include: calculating multiple candidate spatial morphology types contained in the target area using a clustering algorithm based on each second metric.

[0034] Furthermore, based on each second metric, a clustering algorithm is used to calculate the multiple candidate spatial morphology types contained in the target region. Specifically, the silhouette coefficient is used to calculate the optimal number of classifications for spatial morphology type division based on each second metric, i.e., the number of candidate spatial morphology types. Then, the K-Means algorithm is used to calculate the number of candidate spatial morphology types based on each second metric.

[0035] For example, assuming that the second metrics include building height density, average building height, average building base perimeter, average building area, floor area ratio, and street height-to-width ratio, then the clustering algorithm can obtain 10 candidate spatial morphology types, namely, medium-sized medium-strength low-density low-rise, medium-sized low-strength high-density low-rise, medium-sized high-strength high-density mid-rise, medium-sized low-density low-rise, medium-sized medium-strength medium-low-density low-rise, medium-sized medium-strength medium-density low-rise, small-sized medium-strength high-density low-rise, mixed-type medium-strength high-density low-mid-rise, medium-sized high-strength medium-density high-rise, and large-sized high-density low-rise.

[0036] By using the variance inflation coefficient of each first metric for each road grid region, we can obtain each second metric that meets the preset conditions for each first metric for each road grid region. Then, based on each second metric for each road grid region, we can determine multiple candidate spatial morphology types contained in the target region. This can propose redundant first metric, simplify the indicator system for spatial morphology recognition, and thus significantly improve the accuracy of spatial morphology recognition.

[0037] Step 105: Based on multiple candidate spatial morphology types and various variance inflation coefficients, determine the target spatial morphology type for each road grid area.

[0038] In one implementation, a balance function in a pre-built decision tree model can be used to select multiple third metrics from various first metrics in a first road grid region, where the first road grid region is any one of the multiple road grid regions. Then, using the decision tree model, the predicted variance inflation coefficient range of each third metric belonging to each candidate spatial morphology type of the first road grid region can be calculated. Finally, based on the predicted variance inflation coefficient range of each third metric for each candidate spatial morphology type and the variance inflation coefficient of each third metric, the target spatial morphology type of the first road grid region can be determined.

[0039] Furthermore, based on the predicted variance inflation coefficient range of each third measure for each candidate spatial morphology type and the variance inflation coefficient of each third measure, the target spatial morphology type of the first road grid area is determined. Specifically, it can be determined whether the variance inflation coefficient of each third measure is within the predicted variance inflation coefficient range. If the variance inflation coefficient of each third measure is within the predicted variance inflation coefficient range, then the candidate spatial morphology type corresponding to the predicted variance inflation coefficient is the target spatial morphology type of the first road grid area.

[0040] Based on the above implementation method, in some modified implementation methods, a balance function in a pre-built decision tree model is used to select multiple third-level indicators from each first-level indicator in the first road grid region. Specifically, this may include: using the balance function in the pre-built decision tree model to determine the number of third-level indicators, then using the decision tree model to calculate the feature importance value of each first-level indicator, and then obtaining the number of third-level indicators with the highest feature importance value. The balance function includes:

[0041] Score=α×Accuracy-β×F_Number

[0042] Where Score represents the score of the decision tree model output, α represents the preset first adjustment coefficient threshold, β represents the preset second adjustment coefficient threshold, Accuracy represents the accuracy of the decision tree model, and F_Number represents the number of third metrics.

[0043] The embodiments of this application can effectively integrate multi-source and multi-modal data, reduce data redundancy, simplify the indicator system, and generate accurate prediction variance inflation coefficient ranges for each third measure of candidate spatial morphology types, thereby significantly improving the efficiency and stability of urban spatial morphology recognition and enhancing its scientificity and practicality.

[0044] This application embodiment obtains a spatial morphology dataset of the target area, divides the target area into multiple road grid areas based on the spatial morphology dataset, and each road grid area includes multiple first measurement indicators. Based on the spatial morphology dataset, the variance inflation coefficient of each first measurement indicator in each road grid area is calculated. Based on each variance inflation coefficient, multiple candidate spatial morphology types contained in the target area are determined. This can effectively integrate spatial morphology data, perform comprehensive first measurement indicator calculations at a unified spatial scale, and thus enable accurate and efficient identification of urban spatial morphology.

[0045] By determining the target spatial morphology type of each road grid area based on multiple candidate spatial morphology types and various variance inflation coefficients, the efficiency and accuracy of urban spatial morphology recognition are greatly improved.

[0046] See Figure 2 This application also provides an urban spatial form recognition device, which is used to perform the urban spatial form recognition method described in the above embodiments. The device includes:

[0047] Module 201 is used to acquire the spatial morphology dataset of the target area;

[0048] The partitioning module 202 is used to divide the target area into multiple road grid areas based on the spatial morphology dataset, and each road grid area includes multiple first metric indicators.

[0049] Calculation module 203 is used to calculate the variance inflation coefficient of each first metric for each road grid area based on the spatial morphology dataset.

[0050] The first determining module 204 is used to determine multiple candidate spatial morphology types contained in the target region based on each variance inflation coefficient.

[0051] The second determining module 205 is used to determine the target spatial morphology type of each road grid area based on multiple candidate spatial morphology types and each variance expansion coefficient.

[0052] The urban spatial form recognition device provided in this application embodiment is based on the same inventive concept as the urban spatial form recognition method provided in the above embodiments, and has the same beneficial effects as the methods used, operated or implemented therein.

[0053] This application also provides an electronic device corresponding to the urban spatial morphology recognition method provided in the foregoing embodiments. Please refer to... Figure 3 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 3As shown, the electronic device 30 may include: a processor 300, a memory 301, a bus 302 and a communication interface 303. The processor 300, the communication interface 303 and the memory 301 are connected through the bus 302. The memory 301 stores a computer program that can run on the processor 300. When the processor 300 runs the computer program, it executes the urban spatial morphology recognition method provided in any of the foregoing embodiments of this application.

[0054] The memory 301 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one physical port 303 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0055] Bus 302 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs. After receiving an execution instruction, the processor 300 executes the program. The urban spatial morphology recognition method disclosed in any of the foregoing embodiments of this application can be applied to the processor 300, or implemented by the processor 300.

[0056] The processor 300 may be an integrated circuit with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 300 or by instructions in software form. The processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 301. The processor 300 reads the information in memory 301 and, in conjunction with its hardware, completes the steps of the above method.

[0057] The electronic device provided in this application embodiment and the urban spatial form recognition method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0058] This application also provides a computer-readable storage medium corresponding to the urban spatial form recognition method provided in the foregoing embodiments, wherein a computer program (i.e., a program product) is stored thereon, and the computer program, when run by a processor, executes the urban spatial form recognition method provided in any of the foregoing embodiments.

[0059] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0060] This application also provides a computer program product corresponding to the urban spatial form recognition method provided in the foregoing embodiments, including a computer program that is executed by a processor to implement the urban spatial form recognition method provided in the above embodiments.

[0061] The computer-readable storage medium and computer program product provided in the above embodiments of this application are based on the same inventive concept as the urban spatial form recognition method provided in the embodiments of this application, and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0062] It should be noted that:

[0063] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0064] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0065] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0066] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0067] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0068] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation apparatus according to embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0069] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0070] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying urban spatial morphology, characterized in that, include: Obtain the spatial morphology dataset of the target region; Based on the spatial morphology dataset, the target area is divided into multiple road grid areas, and each road grid area includes multiple first measurement indicators; Based on the spatial morphology dataset, the variance inflation coefficient of each of the first metrics for each of the road grid regions is calculated. Based on each of the variance inflation coefficients, the multiple candidate spatial morphology types contained in the target region are determined; Based on multiple candidate spatial morphology types and each variance inflation coefficient, the target spatial morphology type of each road grid region is determined, including: Using the balance function in a pre-built decision tree model, multiple third metrics are selected from each of the first metrics in the first road grid region, where the first road grid region is any one of the multiple road grid regions; Using the decision tree model, calculate the range of the predicted variance inflation coefficient for each of the third metrics for each of the candidate spatial morphology types in which the first road grid area belongs; Based on the predicted variance inflation coefficient range of each of the third metrics for each of the candidate spatial morphology types and the variance inflation coefficient of each of the third metrics, the target spatial morphology type of the first road grid area is determined.

2. The urban spatial morphology recognition method according to claim 1, characterized in that, The spatial morphology dataset includes traffic network data and land parcel boundary data. Based on the spatial morphology dataset, the target area is divided into multiple road grid areas, including: Based on the traffic network data and the land parcel boundary data, the target area is divided into multiple road grid areas.

3. The urban spatial morphology recognition method according to claim 1, characterized in that, The first metric includes one or more of the following: building height density, average building height, average building base perimeter, average building area, floor area ratio, street height-to-width ratio, population, perceived value, style, and accessibility.

4. The urban spatial morphology recognition method according to claim 1, characterized in that, The determination of multiple candidate spatial morphology types within the target region based on each of the variance inflation coefficients includes: Based on the variance inflation coefficient of each first metric of each road grid region, each second metric of each first metric of each road grid region that meets the preset conditions is obtained. Based on each of the second metrics for each of the road grid regions, the multiple candidate spatial morphology types contained in the target region are determined.

5. The urban spatial morphology recognition method according to claim 4, characterized in that, The step of determining the multiple candidate spatial morphology types contained in the target area based on each of the second metrics of each of the road grid areas includes: Based on each of the second metrics, a clustering algorithm is used to calculate the multiple candidate spatial morphology types contained in the target region.

6. The urban spatial morphology recognition method according to claim 1, characterized in that, The step of selecting multiple third metrics from various first metrics in the first road grid region using a pre-built balance function in a decision tree model includes: The number of the third metric is determined using the balance function in a pre-built decision tree model; Using the decision tree model, the feature importance values ​​of each of the first metrics are calculated; Obtain the number of the third metrics that have the highest feature importance values; The balance functions include: Score=α×Accuracy-β×F_Number Wherein, Score represents the score of the decision tree model output, α represents the preset first adjustment coefficient threshold, β represents the preset second adjustment coefficient threshold, Accuracy represents the accuracy of the decision tree model, and F_Number represents the number of the third measurement indicators.

7. A device for recognizing urban spatial forms, characterized in that, include: The acquisition module is used to acquire the spatial morphology dataset of the target area. The segmentation module is used to divide the target area into multiple road grid areas based on the spatial morphology dataset, and each road grid area includes multiple first measurement indicators; The calculation module is used to calculate the variance inflation coefficient of each of the first metrics for each of the road grid regions based on the spatial morphology dataset. The first determining module is used to determine multiple candidate spatial morphology types contained in the target region based on each of the variance inflation coefficients. The second determining module is used to determine the target spatial morphology type of each road grid region based on multiple candidate spatial morphology types and each variance inflation coefficient, including: Using the balance function in a pre-built decision tree model, multiple third metrics are selected from each of the first metrics in the first road grid region, where the first road grid region is any one of the multiple road grid regions; Using the decision tree model, calculate the range of the predicted variance inflation coefficient for each of the third metrics for each of the candidate spatial morphology types in which the first road grid area belongs; Based on the predicted variance inflation coefficient range of each of the third metrics for each of the candidate spatial morphology types and the variance inflation coefficient of each of the third metrics, the target spatial morphology type of the first road grid area is determined.

8. 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 method according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-6.

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