Method for identifying range and type of suburban transition area

Abstract

The invention discloses a suburban transition area range and type identification method, and relates to the technical field of city attribution, and the method comprises the steps: collecting and preprocessing multi-source city big data, and determining the characterization of different expansion modes through the multi-source city big data; the method comprises the following steps of: detecting and extracting different urban development boundaries by using a Densis-Graph threshold extraction method; identifying a city core area UCZ and a suburban transition area USTZ driven by different factors through a city development boundary detected by superposition and dislocation, and subdividing the USTZ into a planning-dominant USTZ and a market-dominant USTZ; the spatial balance of suburban transition areas is analyzed by utilizing raster data, the heterogeneity and the spatial development balance of the suburban transition areas are analyzed, and the expansion mode of metropolitan areas is defined. According to the method, USTZ recognition is independent from urban and rural transition area recognition, and on the basis of USTZ recognition, a power mechanism and spatial development balance analysis are fused, so that the defect that the existing technical scheme can only analyze a static spatial pattern but cannot dynamically analyze spatial formation and spatial evolution is overcome.

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 conversion between urban space and non-urban space, but also establishes the organizational structure of the newly expanded urban space, profoundly affecting the overall spatial structure and organizational model of the metropolis. The urban-suburban transition zone is not a single spatial transition, but a series of all-round transitions in spatial organization, industrial structure, economic activities, etc., which promotes the transformation of the original urban sub-center into the urban core area, while the non-urban space becomes the new urban sub-center, thus constituting the succession of metropolitan expansion. Therefore, the identification of the urban-suburban transition zone will establish the organizational structure of the newly expanded urban space and explore the overall spatial structure and organizational model of the metropolis.

[0003] With the acceleration of urbanization, the boundaries between cities and suburbs are gradually blurred. The formation of USTZ has brought new challenges to urban planning, resource management, environmental protection and other aspects. The urban-suburban transition zone is not only the physical boundary between cities and suburbs, but also the intersection of social and economic activities and the natural environment. The land use types in this area are diverse, the population density changes significantly, and the building types and transportation modes also show diverse characteristics. Therefore, accurately identifying the urban-suburban transition zone is of great significance for optimizing urban spatial layout, improving resource utilization efficiency, and improving environmental quality.

[0004] From a technical perspective, the identification of USTZs involves the integration and analysis of multi-source data, including remote sensing images, social media data, traffic flow data and other urban crowdsourced data. The integration and analysis of these data requires efficient data processing capabilities and advanced algorithm support. However, the lack of a unified research framework makes it difficult to integrate and apply multi-source data to the identification of suburban transition zones. Therefore, building a unified and compatible research framework is the key to the identification of suburban transition zones.

[0005] In existing research, there are the following potential problems and shortcomings:

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

[0007] Existing technologies pay more attention to the identification of urban-rural transition zones, and only regard the identification of suburban transition zones as part of urban-rural transition zones, confusing the concepts and connotations between the two. At the same time, they fail to fully consider the differences between the two transition zones, mistaking the characteristics of suburban transition zones for those of urban-rural transition zones. Or, in data analysis and model construction, they fail to distinguish the uniqueness of the two transition zones. This confusion will not only lead to a misinterpretation of the characteristics of the transition zone, but may also affect decisions on urban planning and resource allocation.

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

[0009] Existing research and identification methods mostly rely on a single data source, such as land use, transportation network or demographic data. Although a single data source has certain effectiveness in some aspects, it cannot fully reflect the complexity and dynamics of the urban-rural transition zone. Since different single data sources have different characteristics and limitations, the identification results of a single data source often differ. This difference makes it impossible to unify the identification results of different data sources, affecting the consistency and comparability of the identification results, resulting in poor compatibility and insufficient versatility of the technical framework.

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

[0011] Existing technologies only focus on the spatial pattern, distribution and structure of the urban-suburban transition zone, and are unable to explore and explain the driving mechanisms behind it and classify the types of transition zones. The urban-suburban transition zone is not a single entity, but contains multiple types, each of which has its own unique development characteristics and driving mechanisms. Existing technologies often lack the ability to distinguish these different types, resulting in an insufficient understanding of the internal structure and function of the transition zone. The development of the urban-suburban transition zone is affected by many factors, including economic, social, environmental and policy. Existing technologies often lack depth in analyzing these driving mechanisms and are unable to accurately reveal the root causes and trends of the development and changes in the transition zone.

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

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

[0014] The objective 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 multi-source urban big data to clarify the representation of different expansion patterns: use network analysis to identify road intersections to represent the expansion pattern of municipal planning, and use social and economic hotspot data to describe the urban expansion pattern 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 area (UCZ) and the urban-suburban transition area (USTZ) driven by different factors, and 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 zones, 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 social and economic hotspot data;

[0021] The municipal planning road network is used to evaluate regional development, metropolitan planning and urban spatial structure, and the intersection is 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 effect of socioeconomic hotspots.

[0023] Furthermore, the step S2 specifically uses the Densi-Graph method to extract the urban development boundary from the municipal planning road network and intersection data and the social and economic hotspot data, including:

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

[0025] S202, performing Densi-Graph analysis on the contour lines of the kernel density map, and extracting the urban development boundary according to 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 classifications is small enough, the area with the highest density must be 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 / Densi tytotal

[0030] Where, Density first It is the difference of the highest level of density value segmentation, Densitytotal 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 plotted, and a polynomial is fitted; the acceleration of the theoretical radius growth is expressed by 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 lines corresponding to the critical value is the urban core area UCZ.

[0033] Furthermore, 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 socio-economic hotspot data as the market-driven 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-driven suburban transition zone P-USTZ.

[0034] Furthermore, the step S3 specifically includes:

[0035] Preprocess the raster data, which includes population grid data and night light data. The population grid data comes from the WorldPop dataset and is calibrated for accuracy 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 two periods of 2011 and 2021.

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

[0037] POI shdi =-∑P i ln i

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

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

[0040]

[0041] Among them, POP, NTL, and SHDI represent the proportion of population density, night light intensity, and mixed degree of built environment functions in the grid, respectively. SUM(pop category )、SUM(ntl category ) and SUM(shdi category ) represent the sum of population density, night 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, night 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, where 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 functional mix of the built environment 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 invention cleverly integrates multi-source urban big data through a comprehensive analysis framework, overcomes the limitations of bias and incomparability caused by the use of a single data source in the past, and constructs 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 zones from the identification of urban-rural transition zones, and constructs an analysis framework that is more scientific and more in line with the current urbanization process.

[0050] 4) The urban-rural transition zone is divided into M-USTZ and P-USTZ, and classified according to the formation mechanism of the urban-rural 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 identification technology does not classify the urban-rural transition zone.

[0051] 5) Based on USTZ identification, the present invention also integrates the dynamic mechanism and spatial development balance analysis, which makes up for the deficiency that the existing technical solutions can only analyze the static spatial pattern but cannot dynamically analyze the 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 the urban-suburban transition zone provided by the present invention;

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

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

[0055] Figure 4 A diagram of the UCZ and USTZ recognition results of 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 USTZ in an embodiment provided by the present invention;

[0057] Figure 6 A diagram of the spatial balance analysis results of P-USTZ in different directions in 2021 in an embodiment provided by the present invention. DETAILED DESCRIPTION

[0058] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1-Figure 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 multi-source urban big data to clarify the representation of different expansion patterns: use network analysis to identify road intersections to represent the expansion pattern of municipal planning, and use social and economic hotspot data to describe the urban expansion pattern under market economic development;

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

[0063] The municipal planning road network is used to evaluate regional development, metropolitan planning and urban spatial structure, and the intersection is 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 expressways, 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 division specified in the "Interim Provisions on Urban Planning Quota Indicators" and the "Technical Standards for Highway Engineering" to extract road intersections, and these intersections are used to depict the spatial planning expansion pattern of the metropolis. However, in fact, it is often necessary to adopt a multi-level road network in a certain metropolitan area, or when extending to a satellite city over a long distance, it is necessary to build roads in suburbs and mountainous areas to form a winding road network. Therefore, the present invention uses 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 effect of socioeconomic hotspots.

[0066] In a specific embodiment, the present invention uses POI data from Amap (https: / / lbs.amap.com) in 2011 (199,446 points) and 2021 (923,902 points). The spatial clustering of socioeconomic hotspot (POI) data can accurately reflect the development trend of the market economy and the functional extension of the urban core zone (UCZ), 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 uses the Densi-Graph method to extract the urban development boundary from the municipal planning road network and intersection data and the social and economic hotspot data, 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 determines 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 classifications is small enough, the area with the highest density must be 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 segmentation, Density total It is the maximum value of the total density. Based on the natural fracture method, the density value is segmented to form different density value intervals, such as 100-200, 200-300, 300-500, etc. The difference of the highest level of the density value segment is the difference of the highest level (300-500) in the segment, 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 of the natural fracture method, the better. At this time, the highest density must be the urban core area. However, the present invention has found in actual tests that when the number of segments is 5 to 13 times, the extracted PDC curve is relatively stable. When the number of segments is 9, it can meet the analysis needs.

[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 taken as the optimal bandwidth R threshold. Then, after the optimal bandwidth is determined, 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 according to the critical value of 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 plotted, and a polynomial is fitted; the acceleration of the theoretical radius growth is expressed by 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 in 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, the real urban development cannot present 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 area (UCZ) and the urban-suburban transition area (USTZ) driven by different factors, and 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 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 suburban transition zone (P-USTZ).

[0084] In a specific embodiment, the method for identifying the scope and type of the suburban transition zone of the present invention is applied to the specific metropolitan area of ​​Chengdu to identify the urban core zone (UCZ) and the 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 determined area recognition results of UCZ and USTZ are obtained as shown in the figure. Figure 4 As shown;

[0085] Compared with official statistics of urban built-up areas, the area of ​​urban built-up areas determined by POIs is often underestimated, while the area of ​​urban built-up areas delineated by road intersections is easily overestimated. However, these differences are kept within the range of 10%, which shows that the identification framework proposed in the present invention has strong adaptability. However, it is worth noting that the built-up area reported by official data (mainly from municipal construction statistics) may not accurately reflect the spatial agglomeration and spread of socio-economic hotspots. Instead, they often include transition zones connecting the city center with surrounding areas. Therefore, considering these complexities, the present invention selects the overlapping area of ​​multi-dimensional UDBs to define UCZ, while the dislocation area is determined as USTZ. According to different spatial expansion patterns, the USTZ exclusively occupied by POI-based UDBs is further divided into M-USTZ. Similarly, the USTZ that belongs entirely to the UDB based on road intersections is determined as P-USTZ, and the remaining area is designated as suburban (rural) zone (SRZ).

[0086] S4. Use raster data to analyze the spatial balance of urban-suburban transition zones, 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, where the raster data includes population grid data and night light data. The population grid data comes from the WorldPop dataset, and the census data is used to calibrate its accuracy, 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 two periods of 2011 and 2021. The raster data has strong temporal continuity, which helps to accurately reflect the urban spatial evolution over the past decade.

[0089] S402. 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 according to the main categories of POI classification:

[0090] POI shdi =-ΣP i LqCy i

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

[0092] Since the maximum resolution of the raster data used is 500 meters, a grid method based on 500×500 meters cells is used to compile night lights and population. This scale is consistent with the resolution of the night light data and does not destroy the division units of 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 of night light intensity NTL, population density POP and mixed degree of building environment function SHDI to examine the spatial development balance of the metropolitan area, 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, night light intensity, and mixed degree of built environment functions in the grid, respectively. SUM(pop catgory )、SUM(ntl category ) and SUM(shdi category ) represent the sum of population density, night 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, night 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 part of metropolitan expansion, but due to the different sizes and units of these indicators, they cannot be directly used to assess spatial equilibrium 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 mixing degree in each grid by constructing an equilateral triangle coordinate system with a side length of 1, wherein each side represents a normalized indicator. The method of establishing 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 difference (the range of differences in NTL, POP and SHDI values) of the UCZ decreased from 0.21 to 0.01. From 2011 to 2021, the difference of the M-USTZ decreased from 0.30 to 0.23. However, the difference of the P-USTZ changed from 0.21 to 0.36 and 0.12. There are also differences in spatial balance between different regions, among which the UCZ area has the highest spatial development balance. In 2021, the centers of gravity of NTL, POP and SHDI almost coincided. In 2011, the difference of the M-USTZ was significantly higher than that of the P-USTZ (0.30>0.21), and the difference was the largest 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 Figure 2. Figure 6 . In the central sub-district of P-USTZ, the POP value greatly exceeds the NTL value and SHDI value. In the eastern sub-district, the POP is still the highest, and the NTL and SHDI are relatively close. The POP and SHDI in the northern sub-district are both higher than the NTL. At the same time, the new northern sub-district and SHDI (0.46, 0.45>0.09) in the southern sub-district are significantly higher than the POP. Finally, in the western sub-district, the POP is significantly higher than the NTL and SHDI.

[0105] The present invention cleverly integrates multi-source urban big data through a comprehensive analysis framework, overcomes the limitations of deviation and incomparability caused by the use of a single data source in the past, and constructs a more universal analysis framework; the established fusion framework can also be compatible with other types and formats of urban crowdsourcing big data, thereby identifying more diverse and complex urban development dynamic mechanisms, and improving the compatibility of the urban development dynamic analysis framework; the identification of suburban transition zones is separated from the identification of urban and rural transition zones, and a more scientific and more in line with the current urbanization process analysis framework 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 the current existing identification technology does 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 the existing technical solutions can only analyze static spatial patterns but cannot dynamically analyze spatial formation and spatial evolution.

[0106] The above is only 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 regarded as excluding other embodiments, but 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 the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

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 multi-source urban big data to clarify the representation of different expansion patterns: use network analysis to identify road intersections to represent the expansion pattern of municipal planning, and use social and economic hotspot data to describe the urban expansion pattern 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 area (UCZ) and the urban-suburban transition area (USTZ) driven by different factors, and subdivide the USTZ into planning-driven USTZ and market-driven USTZ. S4. Use raster data and socioeconomic hotspot data to analyze the spatial balance of urban-rural transition zones, analyze their heterogeneity and spatial development balance, and clarify the expansion pattern of metropolitan areas.

2. The method for identifying the scope and type of the urban-suburban transition zone 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 intersection is used to depict the spatial planning expansion pattern of the metropolis; The socioeconomic hotspot data are used to represent the spatial distribution and clustering effect of socioeconomic hotspots.

3. The method for identifying the scope and type of the urban-suburban transition zone 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 social and 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, performing Densi-Graph analysis on the contour lines of the kernel density map, and extracting the urban development boundary according to the critical value of the Densi-Graph.

4. The method for identifying the scope and type of the urban-suburban transition zone according to claim 3 is characterized by: 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 classifications is small enough, the area with the highest density must be 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 segmentation, 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 the urban-suburban transition zone 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 plotted, and a polynomial is fitted; the acceleration of the theoretical radius growth is expressed by 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 lines corresponding to the critical value is the urban core area UCZ.

6. The method for identifying the scope and type of the urban-suburban transition zone 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 socio-economic hotspot data as the market-oriented 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 suburban transition zone P-USTZ.

7. The method for identifying the scope and type of urban-suburban transition zone according to claim 1, characterized in that: The step S4 specifically includes: Preprocess the raster data, which includes population grid data and night light data. The population grid data comes from the WorldPop dataset and is calibrated for accuracy 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 two periods of 2011 and 2021. A grid method based on 500×500 m cells was used to compile nighttime lights and population, and the Shannon diversity index of POIs in each grid was calculated according to the main categories of POI classification: POI shdi =-∑P i in P i Where POI shdi Indicates the degree of mixing of built environment functions within each grid, P i represents the proportion of the i-th type of POI facilities in the grid; The three indicators of night light intensity NTL, population density POP and built environment functional mixing degree SHDI are selected to examine the spatial development balance of metropolitan areas, and the three indicators are converted into proportional formulas as follows: Among them, POP, NTL, and SHDI represent the proportion of population density, night light intensity, and mixed degree of built environment functions in the grid, respectively. SUM(pOp category )、SUM(ntl category ) and SUM(shdi category ) represent the sum of population density, night 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, night 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, where each side represents a normalized indicator. The method for establishing three sides is as follows: Among them, V POP ,V NTL ,V SHDI are the normalized values ​​of population density, night light intensity, and mixed degree of built environment functions in each grid, respectively.

8. The method for identifying the scope and type of the urban-suburban transition zone according to claim 7, 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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