A method for measuring the functional mixing degree of high-density urban streets

Abstract

This invention proposes a method for measuring the functional mixing degree of streets in high-density urban areas, comprising the following steps: determining a spatial sample of the high-density urban area and collecting basic data within the sample area; preprocessing the urban street network data within the spatial sample to filter out valid street centerline objects; classifying the collected POI data by business type and assigning corresponding numbers; establishing street area based on the street centerline and adjacent block boundaries, and then establishing the correlation between land use and the street centerline; forming a correlation dataset between POI points and the street centerline based on the correlation; and calculating the street functional mixing degree for POI points associated with the street centerline. This invention employs a big data quantitative processing method, which can achieve accurate correlation between POI business types and street centerlines, providing technical and methodological support for scientifically and efficiently identifying and evaluating street functional mixing degree and discovering general patterns in high-density urban streets.

Description

Technical Field

[0001] This invention relates to the field of urban design analysis, and in particular to a method for measuring the functional mixing of streets in high-density urban areas. Background Technology

[0002] High-density urban areas are regions within urban construction land characterized by high construction intensity, concentrated building distribution, and dense population. As my country's urbanization process enters a high-quality development stage, high-density urban areas, as core areas with highly concentrated population, industry, and infrastructure, directly impact urban vitality, residents' convenience, and space utilization efficiency due to the functional complexity of their street spaces. Street function mixing, as a core indicator for quantitatively assessing the level of functional mixing in street spaces, is not only a crucial basis for optimizing spatial layout and improving public service facilities in urban planning, but also an important reference for judging the effectiveness of street renovation and guiding the rational allocation of business types in urban renewal projects. In measuring street function mixing, the spatial correlation between POI (Point of Interest) and the street is the core link connecting "business type data" and "street units," and its accuracy directly determines the reliability of subsequent mixing calculation results.

[0003] However, there is often a discrepancy between the actual location of POI and the street boundary, and the land use type is often based on the main function of the plot, making it difficult to accurately express the functional type distribution of the street.

[0004] For measuring the mixed-use of street functions in high-density urban areas, the most commonly used methods for associating Points of Interest (POIs) with streets are buffer zone analysis and simple distance matching. The former assigns all POIs within a fixed radius (e.g., a 20-meter buffer) around a street to that street; the latter extracts the coordinates of all street centerlines, calculates the straight-line distance between a single POI and each street centerline, and identifies the "nearest street" as the associated street for that POI. Both methods can measure the mixed-use of street functions in high-density urban areas to a certain extent, but they also have limitations. The former, due to the small distances between adjacent streets and continuous building facades in high-density urban areas, often uses a fixed buffer zone larger than the street spacing, which can easily lead to POIs being repeatedly associated with multiple streets, or POIs that are close to a street but actually belong to another street being incorrectly associated. The latter has no distance threshold limit, so even if a POI is extremely far from the nearest street, it will still be forcibly associated with that street, resulting in an inflated street function mixture calculation that fails to reflect the true street-facing function. This invention establishes a street surface domain to associate POIs with street centerlines, ensuring the accuracy of POI association. Summary of the Invention

[0005] To address the aforementioned issues, this invention discloses a method for measuring the functional mixing degree of streets in high-density urban areas. This method improves the accuracy of POI (Point of Interest) and street centerline attribution, enhances adaptability to the complex functions of high-density urban areas, and achieves accurate, systematic, and reusable measurement of the functional mixing degree of streets in high-density urban areas. It provides key technical support for urban function optimization and spatial quality improvement.

[0006] The technical solution provided by this invention is: a method for measuring the functional mixing degree of streets in high-density urban areas, the method comprising:

[0007] A method for measuring the functional mixing of streets in high-density urban areas includes:

[0008] Step 1: Determine the spatial sample of high-density urban areas and collect basic data within the sample area;

[0009] Step 2: Preprocess the urban street network data within the spatial sample to filter out valid street centerline objects;

[0010] Step 3: Classify the collected POI data by business type and assign corresponding numbers;

[0011] Step 4: Establish a street area based on the street centerline and the boundaries of adjacent blocks, and then establish the relationship between the land use and the street centerline;

[0012] Step 5: Generate a dataset relating POIs to the street centerline based on the association relationships;

[0013] Step 6: Calculate the street function mixing degree for POI points associated with the street centerline.

[0014] As a further improvement of the present invention, the specific steps in step 1 of determining high-density urban spatial samples and collecting basic data within the sample area include:

[0015] Step 11 divides the target high-density urban area into The spatial grid is used to calculate the ratio of the total street length to the grid area in each grid as the street density. A density threshold is set to filter out high-density areas as spatial samples.

[0016] The road network data mentioned in step 12 comes from the road network data of the Open Street Map (OSM) database, which includes road centerline vectors and road network topology; the POI data comes from the Gaode Map Open Platform, which includes at least POI name, POI type, POI type code and address; the land use division data comes from the national land spatial planning database, which includes land parcel boundary vector data and land use status classification information.

[0017] As a further improvement of the present invention, the specific steps in step 2 of preprocessing and filtering the urban street network data within the spatial sample to identify valid street centerline objects include:

[0018] 2-1 Preprocess the road network data to ensure the integrity of the road network topology, identify nodes in the road network based on the road network topology relationships, and filter the node degrees. The nodes or nodes located at the intersection of street registration points are used as intersection points, and a continuous polyline between two adjacent intersection points is used as a street object. ,

[0019] 2-2 Set a length threshold to exclude micro-segments that lack functional representativeness. In high-density urban areas, the threshold is preferably 50m, retaining only the length. The street objects are designated as "valid street centerlines"; each street centerline is assigned a unique code. Ensure the code is unique within the spatial sample range; register the coordinates of the two endpoints and record the street centerline. Any one of the endpoints is The coordinates are Let the other endpoint be . The coordinates are Arrange all endpoints in descending order of their y-coordinates. If the y-values ​​are the same, sort them in ascending order of their x-coordinates. Assign each endpoint a consecutive natural number (e.g., 1, 2, 3, ..., N) to form a street centerline dataset.

[0020] As a further improvement of the present invention, in step 3, the collected POI data is classified by business type and assigned a corresponding number. The specific steps are as follows:

[0021] 3-1 Based on the POI types in Gaode Maps, POIs are reclassified into 6 major categories and coded, establishing a three-level mapping system of "major category - subcategory - original POI type", coded as follows: 1-Commercial Services, 2-Residential Support, 3-Office & R&D, 4-Transportation Facilities, 5-Public Services, 6-Culture & Leisure. Each major category is further subdivided into subcategories based on the Gaode Maps POI type (e.g., "Chinese Restaurant" and "Western Restaurant" under "Catering Services"). Each subcategory corresponds to a set of original POI types with similar functional attributes.

[0022] 3-2 For the functional identifiers contained in the POI name and type description, establish a "keyword-subclass code" correspondence table to assist in the rapid classification of the original POI type. Establish a one-to-one mapping relationship between the original type code of Gaode Map POI and the three-level classification code of this invention to form Gaode POI code-custom three-level code mapping table.

[0023] As a further improvement of the present invention, in step 4, a street area is established based on the street centerline and the boundary of the adjacent block, thereby establishing the relationship between the land use and the street centerline. Specific steps include:

[0024] 4-1 pairs of street center lines endpoints and endpoints Identify the center line of the street directly connected to it.

[0025] 4-2 Taking the endpoints as vertices, if the endpoints form a cross, the center lines of any two adjacent streets form four interior angles. Calculate the angle bisector of each interior angle; if the endpoints intersect in a T-shape, forming 3 interior angles... If one of the angles is 180° (extended by a straight line), then only the other two non-flat angles are bisectors; if the endpoint is the end point, the angle bisectors of the interior angles are drawn on both sides of the direction of street extension; if the endpoint is the end point and there is no adjacent street forming an angle, perpendicular line segments are drawn on both sides perpendicular to the current street centerline.

[0026] 4-3 Based on the street centerline Take the axis as its starting point ,end ,and The endpoints of the angle bisector (or perpendicular line) generated by the point ,as well as The endpoints of the angle bisector (or perpendicular line) generated by the point Together, they serve as vertices of the region. (According to "...") Connect the vertices in the order of “” (clockwise or counterclockwise to ensure polygon closure) to form a street surface region. Assign a unique code to each street surface region and establish a street surface region-street centerline association dataset.

[0027] 4-4 The base performs spatial overlay analysis on land use and street area. If a land use intersects with only one street area, it is directly associated with the center line of the street corresponding to that area; if a land use intersects with multiple street areas, the multiple street areas are used as associated objects.

[0028] As a further improvement of the present invention, in step 5, the specific steps for forming the association dataset between the POI and the street centerline based on the association relationship are as follows: For POIs falling within the street area, they are associated with the street centerline through the street area-street centerline association dataset. For POIs located within the land use area, if the land use is associated with only one street centerline, the POI is directly associated with that street centerline; if the land use is associated with multiple street centerlines, the straight-line distance between the POI and the associated street centerline is calculated and denoted as... As an auxiliary indicator of association reliability, POIs are associated with the street centerline with the smallest d-value based on the shortest distance. The two types of association results are then integrated to establish a POI-street centerline association dataset.

[0029] As a further improvement of the present invention, in step 6, the street function mixing degree is calculated for the POI points associated with the street centerline, and the specific steps are as follows:

[0030] 6-1 Using the centerline of each street as the street function evaluation unit, extract all POIs associated with that street to form the POI set for that street. ; Statistical set The number of business categories (1-6) involved reflects the richness of functions; statistics on the first... Class Function In the set Number of POIs in the data;

[0031] 6-2 Statistical Sets Its functional components mainly include: Number of POI functional categories included , Inner Class Function Number of POIs ;calculate Total number of POIs for all functions within ; Calculate the first POI-like functions in percentage within

[0032] calculate The total number of all functional POIs within the scope is calculated using the following formula: ;

[0033] Calculate the first POI-like functions in The percentage within is calculated using the following formula:

[0034]

[0035] 6-3 Street functional mixing degree calculation, using the modified Shannon diversity index. As the core computational model, it is supplemented by a functional hybrid intensity index to assist in verification and reflect its richness. With balance The calculation formula is:

[0036]

[0037] in, This is a richness correction factor, with a value range of: , This indicates only one type of function. This indicates that all six functionalities are perfectly balanced.

[0038] The mixture results of 6-4 were standardized, and the modified Shannon index was used. Mapping to the [0,1] interval, the calculation formula is:

[0039]

[0040] Among them, when And the proportion of each function (In perfect equilibrium) (Theoretical maximum value); when (Single function) (Theoretical minimum value).

[0041] The standardization of the functional mixing index in section 6-5, taking into account the characteristics of "compact functions and cross-type" in high-density urban areas, will standardize the values... Divided into 5 mixing levels:

[0042] [0.8, 1.0] represents "extremely high", and its characteristic description is: Functional type [0.6, 0.8) is "higher", characterized by 4-5 functional types with relatively balanced distribution; [0.4, 0.6) is "medium", characterized by 3-4 functional types with 1-2 dominant functions; [0.2, 0.4) is "lower", characterized by 2-3 functional types with 1 functional type accounting for a certain percentage. [0, 0.2) represents "extremely low", and its characteristic description is: functional type Category 2, Category 1 functional proportions .

[0043] Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0044] 1. This invention uses the road center point as the basic unit and constructs three indicators—accessibility density, diversity index, and connectivity efficiency index—combined into a comprehensive model to quantify the functional mixing level of each street. It overcomes the shortcomings of traditional methods, such as subjective determination of POI and street affiliation and difficulty in resolving boundary conflicts, enhancing adaptability to complex scenarios such as narrow street networks and mixed functions at street corners in high-density urban areas. Through a three-level POI classification system and the introduction of a richness correction coefficient, it solves the problems of inconsistent traditional classification standards and the difficulty of a single index in simultaneously considering functional richness and balance, achieving refined quantification of mixing degree. The five-level mixing degree grading standard optimized based on the characteristics of high-density urban areas has greater practical guiding value compared to traditional general grading methods.

[0045] 2. In summary, this invention achieves accurate, systematic, and reusable measurement of the functional mixing degree of streets in high-density urban areas. It selects the road center point as the micro-unit for urban function analysis and constructs a composite calculation model integrating accessibility density, diversity index, and connectivity efficiency index. This method combines the dynamic urban traffic element (road) with the static functional element (POI), achieving effective quantification of functional mixing degree at the linear scale of streets. Attached Figure Description

[0046] Figure 1 This is a schematic diagram illustrating selected areas, land use, and streets according to the present invention;

[0047] Figure 2 This is a schematic diagram of the method for calculating angle bisectors according to the present invention;

[0048] Figure 3 This is a schematic diagram of establishing a street surface region according to the method of the present invention;

[0049] Figure 4 This is a schematic diagram of street sampling according to the present invention.

[0050] Figure 5 This is a schematic diagram of land sampling for the present invention.

[0051] Figure 6 This is a schematic diagram illustrating the association between a Point of Interest (POI) falling within the street area and the street centerline in this invention.

[0052] Figure 7 This is a schematic diagram illustrating the association between a Point of Interest (POI) located within the land use area and the center line of a street in this invention.

[0053] Figure 8 This is a flowchart of a specific embodiment of the present invention. Detailed Implementation

[0054] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, and the terms "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0055] Example 1:

[0056] like Figure 1-8 As shown in this embodiment, a method for measuring the functional mixing of streets in a high-density urban area includes the following steps:

[0057] Step 1: Determine the spatial sample of high-density urban areas and collect basic data within the sample area;

[0058] Step 2: Preprocess the urban street network data within the spatial sample to filter out valid street centerline objects;

[0059] Step 3: Classify the collected POI data by business type and assign corresponding numbers;

[0060] Step 4: Establish a street area based on the street centerline and the boundaries of adjacent blocks, and then establish the relationship between the land use and the street centerline;

[0061] Step 5: Generate a dataset linking the POI to the street centerline based on the association relationship;

[0062] Step 6: Calculate the street function mix degree for POIs associated with the street centerline.

[0063] Specifically, step 1, which involves determining high-density urban spatial samples and collecting basic data within the sample area, includes the following steps:

[0064] Step 1-1: Divide the target high-density urban area into... The spatial grid, in this example, is divided into 120 grids (12 columns × 10 rows). The street density is calculated using ArcGIS as the ratio of the total street length to the grid area within each grid. A density threshold is set at... 89 valid grids were selected as spatial samples.

[0065] The road network data mentioned in steps 1-2 comes from the road network data of the Open Street Map (OSM) database, including road centerline vectors (polylines) and road network topology; the POI data comes from the sample range of POIs collected by the Gaode Map Open Platform API, and the fields include name, type code, address, latitude and longitude, etc.; the land use division data comes from the national land spatial planning database, including land parcel boundary vector data and land use types, such as commercial land, residential land, and public management land.

[0066] In step 2, the specific steps for preprocessing and filtering out valid street centerline objects from the urban street network data within the spatial sample include:

[0067] Step 2-1 merges broken lines caused by acquisition errors into continuous line segments using ArcGIS's "Merge" tool, handling self-intersections and cleaning up duplicate segments. Based on the road network topology, nodes in the road network are identified, and node degrees are filtered. (T-shaped intersection) or node degree (Crossroads) nodes, and mark the node at the end of the street (degree = 1) as the end intersection. A street object is a continuous polyline between two adjacent intersections. A total of 48 street-level targets were obtained.

[0068] Step 2-2 sets the length threshold to 50m, eliminates micro-segments without functional representativeness, such as internal passages within residential communities, and retains 44 valid street centerlines. Sort the 88 endpoints of these 44 streets from largest to smallest according to their vertical coordinate (y-value) and assign them numbers. - Ensure the code is unique within the spatial sample range; register the coordinates of the two endpoints and record the street centerline. endpoints The coordinates are Let the other endpoint be . The coordinates are Arrange all endpoints in descending order of their vertical coordinates to form a street centerline dataset.

[0069] In step 3, the collected POI data is classified by business type and assigned a corresponding number. The specific steps are as follows:

[0070] Step 3-1: Based on the POI types in Gaode Maps, map the 862 POIs according to "Major Category - Subcategory - Original POI Type". For example, 1-Business Services (Major Category), 101-Retail (Subcategory), Supermarket 060100 (Example of Gaode's Original POI Type), 7-Eleven (Example of Sample Case POI), with a total of 82 (total for the sample area).

[0071] Step 3-2: Establish a "keyword-subclass code" mapping table for the functional identifiers contained in the POI name and type description. This table is used to assist in the rapid classification of the original POI type. For example, map the Gaode code "060400" (shopping mall) to the custom code "10102".

[0072] In step 4, a street area is established based on the street centerline and the boundaries of adjacent blocks, thereby establishing the relationship between the land use and the street centerline. Specific steps include:

[0073] Step 4-1: Align the street centerline endpoints The interior angles are 90° and 90° respectively, and 45° angle bisectors are drawn to both sides; endpoints For interior angles of 90° and 180°, draw angle bisectors of 45° and 90° respectively.

[0074] Step 4-2: Using the street centerline as the axis, and combining the intersection of the angle bisector endpoint and the block boundary, determine its starting point. ,end ,and The endpoints of the angle bisector (or perpendicular line) generated by the point ,as well as The endpoints of the angle bisector (or perpendicular line) generated by the point Together, they serve as vertices of the region. (According to "...") Connect the vertices in the order of “” to form a street area, and establish a street area-street centerline association dataset.

[0075] Step 4-3 uses spatial overlay analysis to calculate the area ratio of land covered by the area, establishes the association, and forms a "land use-street centerline association dataset" containing association information for 52 land parcels.

[0076] Specifically, in step 5, the steps for forming the association dataset between the POI and the street centerline based on the association relationship are as follows:

[0077] Step 5-1: There are 587 POIs within the street area. These are associated with the street centerlines using the street area-street centerline association dataset. There are 245 POIs within the land use area. If a land use is associated with only one street centerline, the POI is directly associated with that street centerline; if a land use is associated with multiple street centerlines, the straight-line distance between the POI and the associated street centerlines is calculated and denoted as... As an auxiliary indicator of association reliability, POIs are associated with the street centerline with the smallest d-value based on the shortest distance. Thirty isolated POIs are removed. The two types of association results are integrated to establish a POI-street centerline association dataset.

[0078] In step 6, the street function mixing degree is calculated for the POI points associated with the street centerline. The specific steps are as follows:

[0079] Step 6-1: Using the center line of each street as the street function evaluation unit, extract the POIs associated with each street and classify them according to the major business categories to form the POI set of that street. .

[0080] Step 6-1 Statistics gather The number of business categories (1-6) involved ( (Reaction function richness); Statistical analysis of the first Class functionality in collections The number of POIs in the data.

[0081] In this example, the POI set for street 1 Number of major functional categories Number of POIs for each business type .

[0082] Step 6-2 Calculation The total number of POIs for all functions within ( The formula reflects the overall density of POIs on a street, and is as follows: , For the first The number of POIs for each class of functionality; calculate the number of POIs. POI-like functions in The proportion within the body, the balance of reaction functions, is calculated using the following formula:

[0083]

[0084] In this example, the total number of POIs Functional percentages: .

[0085] Step 6-3: Calculate the street function mix, selecting the modified Shannon diversity index. As the core computational model, it is supplemented by a functional hybrid intensity index to assist in verification and reflect its richness. With balance The calculation formula is:

[0086]

[0087] in, This is a richness correction factor, with a value range of: , This indicates only one type of function. This indicates that all six functionalities are perfectly balanced.

[0088] In this example, street 1, .

[0089] Step 6-4 standardizes the mixing results and uses the modified Shannon index. Mapping to the [0,1] interval, the calculation formula is:

[0090]

[0091] Among them, when And the proportion of each function (In perfect equilibrium) (Theoretical maximum value); when (Single function) (Theoretical minimum value).

[0092] After standardization, for street 1 in this example, .

[0093] Step 6-5 standardizes the functional mixing index, taking into account the characteristics of high-density urban areas that are "functionally compact and type-crossing," and standardizes the values... Divided into 5 mixing levels:

[0094] [0.8, 1.0] represents "extremely high", and its characteristic description is: Functional type [0.6, 0.8) is "higher", characterized by 4-5 functional types with relatively balanced distribution; [0.4, 0.6) is "medium", characterized by 3-4 functional types with 1-2 dominant functions; [0.2, 0.4) is "lower", characterized by 2-3 functional types with 1 functional type accounting for a certain percentage. [0, 0.2) represents "extremely low", and its characteristic description is: functional type Category 2, Category 1 functional proportions .

[0095] Street 1 in this example has a "low" level of functional mixing and three types of functions (commercial, public service, and cultural leisure). The commercial proportion is approximately 63.6% (the dominant function of type 1), which conforms to the characteristics of the interval [0.2, 0.4).

[0096] The technical means disclosed in this invention are not limited to those disclosed in the above embodiments, but also include technical solutions composed of any combination of the above technical features.

Claims

1. A method for measuring the functional mixing degree of high-density urban streets, characterized in that, The method includes the following steps: Step 1: Identify high-density urban spatial samples and collect basic data within the sample area; Step 2: Preprocess the urban street network data within the spatial sample to filter out valid street centerline objects; Step 3: Classify the collected POI data by business type and assign corresponding numbers; Step 4: Establish a street area based on the street centerline and the boundaries of adjacent blocks, and then establish the relationship between the land use and the street centerline; specific steps include: Step 41: Using each endpoint of the street centerline as a base point, draw the angle bisector of the angle formed by the two adjacent street centerlines to both sides; Step 42: For each street centerline endpoints and endpoints Identify the street centerline directly connected to it. If the endpoint is a cross or T-junction, use that endpoint as the vertex to calculate the interior angle between the adjacent street centerlines and draw the angle bisectors of the interior angles on both sides extending in the direction of the street. If the endpoint is the end point, use that endpoint as the starting point and draw perpendicular line segments on both sides perpendicular to the current street centerline to replace the angle bisectors. Step 43: Using the street centerline as the axis, perform spatial intersection calculations with the generated angle bisector and the block boundary, take the intersection point as the vertex of the area, enclose and form the street area, and establish a street area-street centerline related dataset; Step 44: Associate land use and street area based on intersection relationship, and then associate land use with street centerline to establish land use-street centerline associated dataset; Step 5: Generate a dataset linking POIs to the street centerline based on the association relationships; The specific steps are as follows: For POIs falling within the street area, they are associated with the street centerline through the street area-street centerline association dataset; for POIs located within the land use area, they are associated with the land use-street centerline association dataset and then associated with the street centerline according to the shortest distance; the two association results are integrated to establish the POI-street centerline association dataset. Step 6: Calculate the street function mixing degree for POI points associated with the street centerline.

2. The method for measuring the functional mixing degree of streets in high-density urban areas according to claim 1, characterized in that, The specific method for determining high-density urban spatial samples and collecting basic data within the sample area in step 1 is as follows: Step 11: Divide the target high-density urban area into spatial grids of a preset size, calculate the street density within each grid, and select grids with street densities greater than a preset density threshold as spatial samples. Step 12: The road network data comes from the OSM database, including road centerline vectors and road network topology; the POI data comes from the Gaode Map Open Platform, including at least the POI name, POI type, POI type code and address; the land use classification data comes from the national land spatial planning database, including land parcel boundary vector data and land use status classification information.

3. The method for measuring the functional mixing degree of streets in high-density urban areas according to claim 1, characterized in that, Step 2 involves preprocessing the urban street network data within the spatial sample to filter out valid street centerline objects. The specific method is as follows: Step 21: Preprocess the road network data to ensure the integrity of the road network topology, identify nodes in the road network based on the road network topology relationships, and filter the node degrees. The nodes or nodes located at the intersection of street registration points are used as intersection points, and a continuous polyline between two adjacent intersection points is used as a street object. , Step 22: Filter length greater than preset street objects As the center line of the street; assign a unique code to each street center line. Record the coordinates of the two endpoints; record the center line of the street. Any one of the endpoints is The coordinates are Let the other endpoint be . The coordinates are Arrange all endpoints in descending order of their vertical coordinates, and assign each endpoint a consecutive natural number to form a street centerline dataset.

4. The method for measuring the functional mixing degree of streets in high-density urban areas according to claim 1, characterized in that, Step 3 involves classifying the collected POI data by business type and assigning corresponding numbers. The specific steps are as follows: Step 31: Based on the POI types in Gaode Maps, reclassify the POIs into 6 major categories and encode them, establishing a three-level mapping system of "major category - subcategory - original POI type", with the following codes: 1-commercial services, 2-residential facilities, 3-office and R&D, 4-transportation facilities, 5-public services, and 6-culture and leisure. Under each major category, subcategories are further subdivided according to the specific POI classification in Gaode Maps, corresponding to the actual business type of the POI, and a unique business type code is assigned to each of the three-level classifications. Step 32: For each original POI type, extract feature keywords, establish a keyword-subclass code correspondence table, establish a one-to-one correspondence between the original type code of Gaode POI and the three-level classification code, form a code mapping table, and integrate the classification results to generate a POI business type dataset.

5. The method for measuring the functional mixing degree of streets in high-density urban areas according to claim 1, characterized in that, Step 6, which calculates the street function mixing degree for POIs associated with the street centerline, involves the following steps: Step 61: Using each street centerline as a street function evaluation unit, extract all POIs associated with that street centerline to form a POI set denoted as . ; Step 62: Statistical Set Its functional components mainly include: Number of POI functional categories included , Inner Number of POIs for Class Functionality , ;calculate Total number of POIs for all functions within ; Calculate the first POI-like functions in percentage within ; calculate The total number of all functional POIs within the scope is calculated using the following formula: ; Calculate the first POI-like functions in The percentage within is calculated using the following formula: ； Step 63: Calculate the street function mix, and select the modified Shannon diversity index. As the core computational model, it is supplemented by a functional hybrid intensity index to assist in verification and reflect its richness. With balance The calculation formula is: ； in, This is a richness correction factor, with a value range of: ; Step 64: Standardize the mixing results and apply the modified Shannon index. Mapping to the [0,1] interval, the calculation formula is: ； Step 65: Standardize the functional mixing index, taking into account the actual characteristics of high-density urban areas, and standardize the value. Divide the mixture into 5 levels and output the mixture results.

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