A method for identifying the scope and types of urban-suburban transition areas

Abstract

This invention discloses a method for identifying the scope and type of suburban transition zones, relating to the field of urban demarcation technology. The method includes: collecting and preprocessing multi-source urban big data, and using this data to clearly characterize different expansion patterns; detecting and extracting different urban development boundaries using the Densi-Graph threshold extraction method; identifying the urban core zone (UCZ) and the suburban transition zone (USTZ) driven by different factors by superimposing and dislocating the detected urban development boundaries, and subdividing the USTZs into planning-driven and market-driven USTZs; and analyzing the spatial balance of suburban transition zones using raster data, analyzing their heterogeneity and spatial development balance, and clarifying the expansion pattern of metropolitan areas. This invention separates USTZ identification from urban-rural transition zone identification and, based on USTZ identification, integrates dynamic mechanism and spatial development balance analysis, addressing the shortcomings of existing technical solutions that can only analyze static spatial patterns but cannot dynamically analyze spatial formation and spatial evolution.

Description

Technical Field

[0001] The present invention relates to the technical field of urban demarcation, and in particular to a method for identifying the scope and type of an urban-suburban transition zone. Background Art

[0002] The urban-suburban transition zone (USTZ) is a product of spatial production. It not only reflects the interaction, exchange, and transformation between urban and non-urban spaces, but also establishes the organizational structure of newly expanded urban spaces, profoundly influencing the overall spatial structure and organizational model of the metropolis. The urban-suburban transition zone is not a single spatial transition, but rather a series of comprehensive transitions in spatial organization, industrial structure, and economic activity. It promotes the transformation of existing urban sub-centers into urban cores, while non-urban spaces become new urban sub-centers, thus constituting the succession of metropolitan expansion. Therefore, identifying the urban-suburban transition zone will establish the organizational structure of newly expanded urban spaces and explore the overall spatial structure and organizational model of the metropolis.

[0003] With the acceleration of urbanization, the boundaries between urban and suburban areas are blurring. The formation of urban transition zones (USTZs) poses new challenges for urban planning, resource management, and environmental protection. The urban-suburban transition zone (USTZ) is not only the physical boundary between urban and suburban areas but also the intersection of socioeconomic activities and the natural environment. This area exhibits diverse land use types, significant population density variations, and diverse building types and transportation patterns. Therefore, accurately identifying urban-suburban transition zones is crucial for optimizing urban spatial layout, improving resource utilization efficiency, and enhancing environmental quality.

[0004] From a technical perspective, identifying USTZs involves integrating and analyzing multi-source data, including remote sensing imagery, social media data, traffic flow data, and other urban crowdsourced data. This data integration and analysis requires efficient data processing capabilities and advanced algorithms. However, the lack of a unified research framework makes it difficult to integrate and apply this multi-source data to the identification of suburban-urban transition zones. Therefore, establishing a unified and compatible research framework is crucial for identifying suburban-urban transition zones.

[0005] The existing research has the following potential problems and shortcomings:

[0006] (1) Suburban and urban-rural transition areas are mixed and not distinguished

[0007] Existing technologies prioritize the identification of urban-rural transition zones, treating suburban-urban transition zones as merely a subset of urban-rural transition zones. This confuses the concepts and connotations of the two, and fails to fully consider the differences between the two transition zones, mistaking the characteristics of suburban-urban transition zones for those of urban-rural transition zones. Alternatively, data analysis and model construction fail to distinguish the unique characteristics of the two transition zones. This confusion not only leads to misinterpretations of transition zone characteristics but also potentially impacts urban planning and resource allocation decisions.

[0008] (2) The urban-suburban transition zone identified by single data lacks comparability

[0009] Existing research and identification methods often rely on single data sources, such as land use, transportation networks, or demographic data. While these data sources are effective in some respects, they cannot fully capture the complexity and dynamics of the urban-suburban transition zone. Because different single data sources have varying characteristics and limitations, identification results from these sources often differ. This variability prevents unified identification results from different data sources, hindering their consistency and comparability, and leading to poor compatibility and limited versatility in the technical framework.

[0010] (3) Unable to distinguish the types of urban-suburban transition areas and insufficient analysis of the dynamic mechanism

[0011] Existing technologies focus solely on the spatial pattern, distribution, and structure of the urban-suburban transition zone, failing to explore and explain the underlying dynamics or categorize the transition zone. The urban-suburban transition zone is not a single entity but rather encompasses multiple types, each with its own unique development characteristics and dynamics. Existing technologies often lack the ability to distinguish between these different types, resulting in an inadequate understanding of the transition zone's internal structure and functions. The development of the urban-suburban transition zone is influenced by a variety of factors, including economic, social, environmental, and policy factors. Existing technologies often lack depth in analyzing these dynamics, failing to accurately reveal the root causes and trends of the transition zone's development and changes.

[0012] Therefore, a new identification framework for urban-suburban transition zones is constructed and the driving mechanism behind it is explored, aiming to separate the research on urban-suburban transition zones from the urban-suburban-rural transition zones, build an independent, unified and inclusive research framework and algorithm, and provide new ideas and methods for the research on urban-suburban transition zones. Summary of the Invention

[0013] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for identifying the scope and type of urban-rural transition zones, separating the urban-rural transition zones from the urban-rural transition zones, and conducting targeted research on the urban-rural transition spaces in metropolitan areas, so as to make the technology for identifying urban-rural transition zones more compatible and universal.

[0014] The object of the present invention is achieved through the following technical solutions:

[0015] A method for identifying the scope and type of an urban-suburban transition zone comprises the following steps:

[0016] S1. Collect and preprocess multi-source urban big data, and use it to clarify the characterization of different expansion patterns: use network analysis to identify road intersections to characterize the expansion pattern of municipal planning, and use socioeconomic hotspot data to describe urban expansion patterns under market economic development;

[0017] S2, using the Densi-Graph threshold extraction method to detect and extract different urban development boundaries;

[0018] S3. By superimposing and dislocating the detected urban development boundaries, we identify the urban core zone (UCZ) and the urban-suburban transition zone (USTZ) driven by different factors, and further subdivide the USTZ into planning-driven USTZ and market-driven USTZ.

[0019] S4. Use raster data and socioeconomic hotspot data to analyze the spatial balance of urban-rural transition areas, analyze their heterogeneity and spatial development balance, and clarify the expansion pattern of metropolitan areas.

[0020] Furthermore, the multi-source urban big data includes municipal planning road network and intersection data, and socio-economic hotspot data;

[0021] The municipal planning road network is used to evaluate regional development, metropolitan planning and urban spatial structure, and the intersections are used to depict the spatial planning expansion pattern of the metropolis;

[0022] The socioeconomic hotspot data are used to represent the spatial distribution and clustering effects of socioeconomic hotspots.

[0023] Furthermore, the step S2 specifically involves extracting the urban development boundary from the municipal planning road network and intersection data and the socio-economic hotspot data using the Densi-Graph method, including:

[0024] S201, performing kernel density analysis on the point data and selecting the optimal bandwidth R threshold of the kernel function;

[0025] S202. Perform Densi-Graph analysis on the contour lines of the kernel density map, and extract urban development boundaries based on the critical value of the Densi-Graph.

[0026] Furthermore, the step S201 specifically includes:

[0027] The natural break method is used to segment the kernel density values ​​under different bandwidths, and it is assumed that when the number of levels is small enough, the highest density must be in the urban core area;

[0028] Calculate the density center ratio PDC in each segment. The PDC calculation formula is as follows:

[0029] PDC=Density first / Density total

[0030] Where, Density firstIt is the difference of the highest level of density value segment, Density total is the maximum value of the total density;

[0031] The bandwidth corresponding to when the PDC value remains stable is selected as the optimal bandwidth R threshold.

[0032] Furthermore, the step S202 specifically includes: recording the enclosed area of ​​the contour line of the kernel density map as S d , and define the theoretical radius of the enclosed area of ​​the contour line as Plotting the theoretical radius The relationship diagram with the corresponding density value D is then fitted with a polynomial; the acceleration of the theoretical radius growth is expressed using the second derivative of the polynomial. The corresponding density value D0 when the acceleration of the theoretical radius growth reaches 0 is calculated as the critical value of Densi-Graph. The closed area formed by the contour line corresponding to the critical value is the urban core zone UCZ.

[0033] Furthermore, step S3 specifically includes: performing spatial overlay analysis on the two extracted urban development boundary results: defining the overlapping area of ​​the two urban development boundary results as the urban core zone UCZ, defining the non-overlapping area unique to the urban development zone extracted from the socioeconomic hotspot data as the market-led urban-suburban transition zone M-USTZ, and defining the non-overlapping area unique to the urban development zone extracted from the municipal planning road network and intersection data as the planning-led urban-suburban transition zone P-USTZ.

[0034] Furthermore, the step S4 specifically includes:

[0035] Preprocess the raster data, which includes population grid data and nighttime light data. The population grid data comes from the WorldPop dataset and is calibrated using census data with a spatial resolution of 100 meters. The nighttime light data comes from a long-term high-precision dataset with a spatial resolution of 500 meters, covering the years 2011 and 2021.

[0036] A grid method based on 500×500 m cells was used to compile nighttime lights and population. The Shannon diversity index of POIs in each grid was calculated based on the main categories of POI classification:

[0037] POI shdi =-∑P i lnP i

[0038] Where, POI shdi Indicates the degree of mixing of built environment functions within each grid, P irepresents the proportion of POI facilities of type i within the grid;

[0039] The three indicators of nighttime light intensity (NTL), population density (POP), and functional mixing degree of built environment (SHDI) were selected to examine the spatial development balance of metropolitan areas. The three indicators were converted into proportional formulas as follows:

[0040]

[0041] Among them, POP, NTL, and SHDI represent the proportion of population density, nighttime light intensity, and built environment functional mixing within the grid, respectively. SUM(pop category )、SUM(ntl category ) and SUM(shdi category ) represent the sum of population density, nighttime light intensity, and mixed degree of built environment functions in the three types of grids: UCZ, M-USTZ, and P-USTZ; pop, ntl, and shdi represent the values ​​of population density, nighttime light intensity, and mixed degree of built environment functions in a single grid, respectively;

[0042] The normalized values ​​of population density, nighttime light intensity, and built environment functional mix in each grid are visualized by constructing an equilateral triangle coordinate system with a side length of 1. Each side represents a normalized indicator. The method for establishing the three sides is as follows:

[0043]

[0044] Among them, V POP ,V NTL ,V SHDI are the normalized values ​​of population density, nighttime light intensity, and built environment functional mix in each grid, respectively.

[0045] Furthermore, the main categories of the POI classification include: daily life, catering, enterprise, medical, science and education, public facilities, finance, transportation, accommodation, shopping and government organizations, a total of 11 categories.

[0046] The beneficial effects of the present invention are:

[0047] 1) This paper cleverly integrates multi-source urban big data through a comprehensive analysis framework, overcoming the limitations of bias and incomparability brought about by the use of a single data source in the past, and constructing a more universal analysis framework.

[0048] 2) The fusion framework established by the present invention can also be compatible with other types and formats of urban crowdsourcing big data, thereby identifying more diverse and complex urban development dynamics mechanisms, thereby improving the compatibility of the urban development dynamics analysis framework.

[0049] 3) The present invention separates the identification of suburban transition areas from the identification of urban-rural transition areas, and constructs an analysis framework that is more scientific and more in line with the current urbanization process.

[0050] 4) The urban-suburban transition zone is divided into M-USTZ and P-USTZ, and classified according to the formation mechanism of the urban-suburban transition zone, which is more in line with the current rapid urbanization process and status quo, and makes up for the shortcoming that the current existing recognition technology does not classify the urban-suburban transition zone.

[0051] 5) Based on USTZ identification, the present invention also integrates dynamic mechanism and spatial development balance analysis, which makes up for the deficiency of existing technical solutions that can only analyze static spatial patterns but cannot dynamically analyze spatial formation and spatial evolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The technical route and calculation process of the method for identifying the scope and type of urban-suburban transition areas provided by the present invention;

[0053] Figure 2 This is the multi-source data fusion framework and method flow based on Densi-Graph in the present invention;

[0054] Figure 3 This is a schematic diagram of the density contour lines of Densi-Graph in the present invention;

[0055] Figure 4 This is a diagram showing the UCZ and USTZ identification results for Chengdu urban area in an embodiment provided by the present invention;

[0056] Figure 5 A diagram showing the results of a spatial development balance analysis of the USTZ in an embodiment of the present invention;

[0057] Figure 6 This is a graph of the spatial balance analysis results of P-USTZ in different direction areas in 2021 in an embodiment provided by the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0059] See Figures 1-6 , the present invention provides a technical solution:

[0060] A method for identifying the scope and type of urban-suburban transition areas, the technical route is as follows Figure 1 , including the following steps:

[0061] S1. Collect and preprocess multi-source urban big data, and use it to clarify the characterization of different expansion patterns: use network analysis to identify road intersections to characterize the expansion pattern of municipal planning, and use socioeconomic hotspot data to describe urban expansion patterns under market economic development;

[0062] Multi-source urban big data includes municipal planning road network and intersection data, and socioeconomic hotspot data;

[0063] The municipal planning road network is used to evaluate regional development, metropolitan planning and urban spatial structure, and the intersections are used to depict the spatial planning expansion pattern of the metropolis;

[0064] In a specific embodiment, the present invention selects a road network consisting of highways, expressways, first-class highways, second-class highways and third-class highways from the vector road network data set of Amap according to the road grade classification specified in the "Interim Provisions on Urban Planning Quota Indicators" and the "Highway Engineering Technical Standards" to extract road intersections. These intersections are used to depict the spatial planning expansion pattern of the metropolis. However, in reality, it is often necessary to adopt a multi-level road network within a certain metropolitan area, or when extending to a satellite city over a long distance, it is necessary to build roads in suburban and mountainous areas, thereby forming a winding road network. Therefore, the present invention adopts a clustering algorithm based on DBSCAN to detect outlier road intersections and winding road networks, aiming to identify road intersection outliers while retaining the spatial structure of the road layout and selecting road intersections with better connectivity.

[0065] The socioeconomic hotspot data are used to represent the spatial distribution and clustering effects of socioeconomic hotspots.

[0066] In a specific embodiment, the present invention uses POI data from Amap (https: / / lbs.amap.com) from 2011 (199,446 points) and 2021 (923,902 points). Spatial clustering of socioeconomic hotspot (POI) data can accurately reflect market economic development trends and the functional extension of urban core zones (UCZs), thereby effectively dividing urban and suburban areas.

[0067] S2, using the Densi-Graph threshold extraction method to detect and extract different urban development boundaries;

[0068] Furthermore, the step S2 specifically involves extracting the urban development boundary from the municipal planning road network and intersection data and the socio-economic hotspot data using the Densi-Graph method, including:

[0069] S201, performing kernel density analysis on the point data and selecting the optimal bandwidth R threshold of the kernel function;

[0070] The core of kernel density calculation based on point data is to calculate the density of point data within the bandwidth, and the bandwidth is to determine the range of density calculation.

[0071] Step S201 specifically includes:

[0072] The natural break method is used to segment the kernel density values ​​under different bandwidths, and it is assumed that when the number of levels is small enough, the highest density must be in the urban core area;

[0073] Calculate the density center ratio PDC in each segment. The PDC calculation formula is as follows:

[0074] PDC=Density first / Density total

[0075] Where, Density first It is the difference of the highest level of density value segment, Density total The maximum value of the total density. Based on the natural fracture method, the density value is segmented into different density value intervals, such as 100-200, 200-300, 300-500, etc. The difference between the highest level of the density value segment is the difference between the highest level in the segment (300-500), that is, 500-300=200.

[0076] The bandwidth corresponding to when the PDC value remains stable is selected as the optimal bandwidth R threshold.

[0077] In a specific embodiment, the present invention generates a kernel density sequence of 400 to 4800 meters at equal intervals of 400 meters, such as Figure 2 (a) to (c) are shown to determine the optimal bandwidth. Theoretically, the smaller the number of segments in the natural fracture method, the better. In this case, the highest density must be in the urban core area. However, in actual testing, the present invention found that the extracted PDC curve is relatively stable when the number of segments is 5 to 13. When the number of segments is 9, it can meet the analysis requirements.

[0078] When the PDC value remains stable, it indicates that the range of UCZ is no longer affected by the bandwidth, that is, it has reached a balanced state, such as Figure 2 As shown in (d), the bandwidth corresponding to when the PDC value remains stable is used as the optimal bandwidth R threshold. Then, after determining the optimal bandwidth, a determined kernel density value (raster data) is generated according to the optimal bandwidth, which is beneficial to the subsequent critical value extraction of Densi-Graph.

[0079] S202, perform Densi-Graph analysis on the contour lines of the kernel density map, and extract the urban development boundary based on the critical value of the Densi-Graph. Specifically, the enclosed area of ​​the contour lines of the kernel density map is recorded as S d , and define the theoretical radius of the enclosed area of ​​the contour line as Plotting the theoretical radius The relationship diagram with the corresponding density value D is then fitted with a polynomial; the acceleration of the theoretical radius growth is expressed using the second derivative of the polynomial. The corresponding density value D0 when the acceleration of the theoretical radius growth reaches 0 is calculated as the critical value of Densi-Graph. The closed area formed by the contour line corresponding to the critical value is the urban core area UCZ.

[0080] The kernel density is generated according to the optimal bandwidth threshold, the kernel density layer is divided according to the kernel density critical value, and the critical value contour lines in the kernel density are extracted. The closed range boundary formed is the urban development boundary.

[0081] The principle of extracting UCZ based on Densi-Graph density contour is as follows: Figure 3 As shown in (a), when the acceleration of the theoretical radius growth reaches 0 and remains at this state, it indicates that the density contour has reached the boundary of urban development. The D0 value at this time is the critical value of Densi-Graph. However, real urban development cannot show a completely consistent form ( Figure 3 (b)), but due to the similarity of the density contours in the urban area, the acceleration of the theoretical radius growth will fluctuate around 0, which is significantly different from the suburban-rural area. That is, the D0 value can still be used as the critical value of Densi-Graph ( Figure 2 e). At the same time, the Densi-Graph method for extracting UDB has been proven to be applicable to cities with different spatial structures, such as single-center, dual-center, and multi-center.

[0082] S3. By superimposing and dislocating the detected urban development boundaries, we identify the urban core zone (UCZ) and the urban-suburban transition zone (USTZ) driven by different factors, and further subdivide the USTZ into planning-driven USTZ and market-driven USTZ.

[0083] Step S3 specifically includes: performing spatial overlay analysis on the two extracted urban development boundary results: Figure 2 (g) The overlapping area of ​​the two urban development boundary results is defined as the urban core zone (UCZ), the non-overlapping area unique to the urban development zone extracted from the socioeconomic hotspot data is defined as the market-oriented urban-suburban transition zone (M-USTZ), and the non-overlapping area unique to the urban development zone extracted from the municipal planning road network and intersection data is defined as the planning-oriented urban-suburban transition zone (P-USTZ).

[0084] In a specific embodiment, the method for identifying the scope and type of the urban-suburban transition zone of the present invention is applied to the specific Chengdu metropolitan area to identify the urban core zone (UCZ) and the urban-suburban transition zone (USTZ) of the Chengdu metropolitan area. The first threshold for detecting mutations can be determined by calculating the second-order derivative of the kernel density curve by Densi-Graph. The results show that in 2011, the thresholds for road intersections and POIs were 20 and 100, respectively. In 2021, the thresholds for road intersections and POIs were 40 and 280, respectively. Based on these mutation values, UDBs of different dimensions are extracted, and the UDBs of the same year are superimposed. The obtained identification results of the determined areas of UCZ and USTZ are as follows: Figure 4 As shown;

[0085] Compared with official statistics on urban built-up areas, the area of ​​urban built-up areas calculated by this invention is often underestimated based on POIs, while the area of ​​urban built-up areas delineated by road intersections is easily overestimated. However, these differences remain within a 10% range, demonstrating the strong adaptability of the proposed identification framework. However, it is worth noting that the built-up areas reported by official data (primarily derived from municipal construction statistics) may not accurately reflect the spatial agglomeration and spread of socioeconomic hotspots. Instead, they often include transition zones connecting the city center with surrounding areas. Therefore, considering these complexities, this invention selects the overlapping areas of multidimensional UDBs to define UCZs, while the dislocated areas are identified as USTZs. Based on different spatial expansion patterns, USTZs exclusively occupied by POI-based UDBs are further divided into M-USTZs. Similarly, USTZs that fall entirely within intersection-based UDBs are identified as P-USTZs, while the remaining areas are designated as suburban (rural) zones (SRZs).

[0086] S4. Use raster data to analyze the spatial balance of urban-suburban transition areas, analyze their heterogeneity and spatial development balance, and clarify the expansion pattern of metropolitan areas.

[0087] Furthermore, the step S4 specifically includes steps S401 to S404:

[0088] S401. Preprocess the raster data, which includes population grid data and night light data. The population grid data comes from the WorldPop dataset and is precision-corrected using census data with a spatial resolution of 100 meters. The night light data comes from a long-term high-precision dataset with a spatial resolution of 500 meters, covering the years 2011 and 2021. This raster data has strong temporal continuity, which helps to accurately reflect the urban spatial evolution over the past decade.

[0089] S402. Use a grid method based on 500×500 m cells to compile nighttime lights and population. Calculate the Shannon diversity index of POIs in each grid based on the main categories of POI classification:

[0090] POI shdi =-∑P i lnP i

[0091] Where, POI shdi Indicates the degree of mixing of building environment functions within each grid, P i represents the proportion of POI facilities of type i within the grid;

[0092] Since the maximum resolution of the raster data used is 500 meters, a grid method based on 500×500 meter cells is used to compile night lights and population. This scale is consistent with the resolution of the night lights data and does not violate the division units of the UCZ and USTZ.

[0093] The main categories of POI classification include: daily life, catering, enterprise, medical, science and education, public facilities, finance, transportation, accommodation, shopping and government organizations. The POI category screening and proportion distribution data for 2011 and 2021 are shown in Table 1:

[0094] Table 1. POI category screening and proportion distribution

[0095]

[0096] S403: Select three indicators: nighttime light intensity (NTL), population density (POP), and functional mix of building environments (SHDI) to examine the spatial development balance of metropolitan areas, and convert the three indicators into a proportional formula as follows:

[0097]

[0098] Among them, POP, NTL, and SHDI represent the proportion of population density, nighttime light intensity, and built environment functional mixing within the grid, respectively. SUM(pop category )、SUM(ntl category ) and SUM(shdi category ) represent the sum of population density, nighttime light intensity, and mixed degree of built environment functions in the three types of grids: UCZ, M-USTZ, and P-USTZ; pop, ntl, and shdi represent the values ​​of population density, nighttime light intensity, and mixed degree of built environment functions in a single grid, respectively;

[0099] The trinity of urban construction, population concentration, and built environment is an integral component of metropolitan expansion, but due to the different sizes and units of these indicators, they cannot be directly used to assess spatial balance and need to be converted into proportions.

[0100] S404: Visualize and analyze the normalized values ​​of population density, nighttime light intensity, and built environment functional mix in each grid by constructing an equilateral triangle coordinate system with a side length of 1, where each side represents a normalized indicator. The method for establishing the three sides is as follows:

[0101]

[0102] Among them, V POP ,V NTL ,V SHDI are the normalized values ​​of population density, nighttime light intensity, and built environment functional mix in each grid. Establishing an equilateral triangle coordinate system can intuitively display the spatial balance relationship between the three indicators.

[0103] In a specific embodiment, the results of spatial development balance analysis of different types of USTZs are as follows: Figure 5 As shown, from 2011 to 2021, the UCZ's disparity (the range of differences in NTL, POP, and SHDI values) decreased from 0.21 to 0.01. From 2011 to 2021, the M-USTZ's disparity decreased from 0.30 to 0.23. However, the P-USTZ's disparity increased from 0.21 to 0.36 and 0.12. Spatial balance also varied across regions, with the UCZ having the highest spatial development balance. In 2021, the centers of gravity of the NTL, POP, and SHDI almost coincided. In 2011, the M-USTZ's disparity was significantly higher than that of the P-USTZ (0.30 > 0.21), with the largest disparity within the NTL-SHDI.

[0104] In order to further reveal the reasons for the differences in spatial development trends within the P-USTZ region, this paper analyzes and visualizes the NTL, POP and SHDI values ​​in different directions of the P-USTZ in 2021, as shown in the figure below. Figure 6 . In the central sub-zone of the P-USTZ, the POP value greatly exceeded the NTL value and SHDI value. In the eastern sub-zone, the POP was still the highest, and the NTL and SHDI were relatively close. The POP and SHDI in the northern sub-zone were both higher than the NTL. At the same time, the new northern sub-zone and SHDI (0.46, 0.45>0.09) were significantly higher than the POP. Finally, in the western sub-zone, the POP was significantly higher than the NTL and SHDI.

[0105] The present invention cleverly integrates multi-source urban big data through a comprehensive analysis framework, overcoming the limitations of bias and incomparability brought about by the use of a single data source in the past, and constructing a more universal analysis framework; the established fusion framework can also be compatible with urban crowdsourced big data of other types and formats, thereby identifying more diverse and complex urban development dynamics, and improving the compatibility of the urban development dynamics analysis framework; the identification of suburban transition zones is separated from the identification of urban-rural transition zones, and a more scientific analysis framework that is more in line with the current urbanization process is constructed; the suburban transition zones are divided into M-USTZ and P-USTZ, and classified according to the formation mechanism of suburban transition zones, which is more in line with the current rapid urbanization process and status quo, and makes up for the shortcoming that current existing identification technologies do not classify suburban transition zones; on the basis of USTZ identification, the present invention also integrates dynamic mechanism and spatial development balance analysis, making up for the deficiency that existing technical solutions can only analyze static spatial patterns but cannot dynamically analyze spatial formation and spatial evolution.

[0106] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. A method for identifying the scope and type of urban-suburban transition areas, characterized by: The following steps are involved: S1. Collect and preprocess multi-source urban big data, and use it to clarify the characterization of different expansion patterns: use network analysis to identify road intersections to characterize the expansion pattern of municipal planning, and use socioeconomic hotspot data to describe urban expansion patterns under market economic development; S2, using the Densi-Graph threshold extraction method to detect and extract different urban development boundaries; S3. By superimposing and dislocating the detected urban development boundaries, we identify the urban core zone (UCZ) and the urban-suburban transition zone (USTZ) driven by different factors, and further subdivide the USTZ into planning-driven USTZ and market-driven USTZ. S4. Analyze the spatial balance of urban-suburban transition areas using raster data and socioeconomic hotspot data, analyze their heterogeneity and spatial development balance, and clarify the expansion pattern of metropolitan areas; The step S4 specifically includes: Preprocess the raster data, which includes population grid data and nighttime light data. The population grid data comes from the WorldPop dataset and is calibrated using census data with a spatial resolution of 100 meters. The nighttime light data comes from a long-term high-precision dataset with a spatial resolution of 500 meters, covering the years 2011 and 2021. A grid method based on 500×500 m cells was used to compile nighttime lights and population. The Shannon diversity index of POIs in each grid was calculated based on the main categories of POI classification: THEN shdi =-∑P i lnP i Where, POI shdi Indicates the degree of mixing of built environment functions within each grid, P i represents the proportion of POI facilities of type i within the grid; The three indicators of nighttime light intensity (NTL), population density (POP), and functional mixing degree of built environment (SHDI) were selected to examine the spatial development balance of metropolitan areas. The three indicators were converted into proportional formulas as follows: Among them, POP, NTL, and SHDI represent the proportion of population density, nighttime light intensity, and built environment functional mixing within the grid, respectively. SUM(pop category )、SUM(ntl category ) and SUM(shdi category ) represent the sum of population density, nighttime light intensity, and mixed degree of built environment functions in the three types of grids: UCZ, M-USTZ, and P-USTZ; pop, ntl, and shdi represent the values ​​of population density, nighttime light intensity, and mixed degree of built environment functions in a single grid, respectively; The normalized values ​​of population density, nighttime light intensity, and mixed degree of building environment functions in each grid are visualized by constructing an equilateral triangle coordinate system with a side length of 1. Each side represents a normalized indicator. The method of establishing the three sides is as follows: Among them, V POP ,V NTL ,V SHDI are the normalized values ​​of population density, nighttime light intensity, and mixed degree of built environment functions in each grid, respectively.

2. The method for identifying the scope and type of urban-suburban transition areas according to claim 1, characterized in that: The multi-source urban big data includes municipal planning road network and intersection data, and social and economic hotspot data; The municipal planning road network is used to evaluate regional development, metropolitan planning and urban spatial structure, and the intersections are used to depict the spatial planning expansion pattern of the metropolis; The socioeconomic hotspot data are used to represent the spatial distribution and clustering effects of socioeconomic hotspots.

3. The method for identifying the scope and type of urban-suburban transition areas according to claim 2, characterized in that: The step S2 specifically involves extracting the urban development boundary from the municipal planning road network and intersection data and the socio-economic hotspot data using the Densi-Graph method, including: S201, performing kernel density analysis on the point data and selecting the optimal bandwidth R threshold of the kernel function; S202. Perform Densi-Graph analysis on the contour lines of the kernel density map, and extract urban development boundaries based on the critical value of the Densi-Graph.

4. The method for identifying the scope and type of urban-suburban transition areas according to claim 3, characterized in that: The step S201 specifically includes: The natural break method is used to segment the kernel density values ​​under different bandwidths, and it is assumed that when the number of levels is small enough, the highest density must be in the urban core area; Calculate the density center ratio PDC in each segment. The PDC calculation formula is as follows: PDC=Density first / Density total Where, Density first It is the difference of the highest level of density value segment, Density total is the maximum value of the total density; The bandwidth corresponding to when the PDC value remains stable is selected as the optimal bandwidth R threshold.

5. The method for identifying the scope and type of urban-suburban transition areas according to claim 3, characterized in that: The step S202 specifically includes: recording the enclosed area of ​​the contour line of the kernel density map as S d , and define the theoretical radius of the enclosed area of ​​the contour line as Plotting the theoretical radius The relationship diagram with the corresponding density value D is then fitted with a polynomial; the acceleration of the theoretical radius growth is expressed using the second derivative of the polynomial. The corresponding density value D0 when the acceleration of the theoretical radius growth reaches 0 is calculated as the critical value of Densi-Graph. The closed area formed by the contour line corresponding to the critical value is the urban core zone UCZ.

6. The method for identifying the scope and type of urban-suburban transition areas according to claim 3, characterized in that: The step S3 specifically includes: performing spatial overlay analysis on the two extracted urban development boundary results: defining the overlapping area of ​​the two urban development boundary results as the urban core zone (UCZ), defining the non-overlapping area unique to the urban development zone extracted from the socioeconomic hotspot data as the market-oriented urban-suburban transition zone (M-USTZ), and defining the non-overlapping area unique to the urban development zone extracted from the municipal planning road network and intersection data as the planning-oriented urban-suburban transition zone (P-USTZ).

7. The method for identifying the scope and type of urban-suburban transition areas according to claim 1, characterized in that: The main categories of the POI classification include: daily life, catering, enterprise, medical, science and education, public facilities, finance, transportation, accommodation, shopping and government organizations, a total of 11 categories.

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